Projects
Awarded Projects by Year
Spring 2024
DEFECT PREDICTION OF STEEL SLABS WITH THE APPLICATION OF HPC AND AI
DOE Funding Office:
Energy Efficiency and Renewable Energy (EERE) Industrial Efficiency and Decarbonization Office (IEDO)
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Tathagata Bhattacharya | ArcelorMittal USA Research LLC
National Lab Partner:
Dr. Yeping Hu | Lawrence Livermore National Laboratory
Summary:
The iron and steel industry is the fourth largest energy-consuming industry in the U.S. The iron and steel industry consumed an estimated 6% (~1470 PJ) of the total energy consumed in the whole U.S. manufacturing sector. About 80% of the total energy consumed in the steel industry is used to produce steel slabs via the continuous casting route (96% of steel in the U.S. today is produced via this route). Therefore, being able to produce defect free slabs (by making it right, first time, every time) would lead to a huge benefit in terms of energy savings and reduction of CO2 emissions during the steelmaking process by minimizing wastes and increasing quality and yield. Therefore, as the most recycled material on earth (more than all other materials combined), a slight improvement to steel production would have a lasting positive impact on the environment, the current decarbonization efforts and on the U.S. energy landscape.
Spring 2024
DEVELOPMENT OF AN ULTRA LOW NOx INDUSTRIAL HYDROGEN BURNER MODEL FOR DECARBONIZATION
DOE Funding Office:
Energy Efficiency and Renewable Energy (EERE) Industrial Efficiency and Decarbonization Office (IEDO)
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Robert Geiger | ClearSign Technologies Corporation
National Lab Partner:
Dr. Marcus Day | National Renewable Energy Laboratory
Summary:
ClearSign Technologies Corporation, with the National Renewable Energy Laboratory (NREL) proposes a Virtual Test Furnace (VTF) using High-Performance Computing (HPC) to expand the application of 100% fueled hydrogen process burners. This project addresses retrofitting and optimizing high temperature furnaces with 100% hydrogen capable ultra low NOx burners. The focus will be on managing the high flame and furnace temperatures while controlling flame chemistry to minimize NOx emissions and ensure optimal heat flux control and operational flexibility. The VTF will simulate and optimize hydrogen burner performance, reduce development costs and time-to-market, aiding decarbonization of energy-intensive industries by nearly 276 million metric tons annually.
Spring 2024
ACCELERATED DESIGN AND DEVELOPMENT OF HIGH STRENGTH NICKEL ALLOYS RESISTANT TO HYDROGEN EMBRITTLEMENT
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Ramgopal Thodla | DNV
National Lab Partner:
Dr. Michael Gao, Dr. Martin Detrois, Dr. Wang Yi | National Energy Technology Laboratory
Summary:
High-strength precipitation hardened (PH) nickel-based superalloys are widely used for key components in extreme environments that require excellent mechanical properties and corrosion resistance. However, they are susceptible to hydrogen embrittlement (HE), as evidenced by significant decrease in fracture toughness and increase in crack growth rate (CGR). This project aims to design new cost-effective PH nickel-based alloys with improvement in fracture toughness by 25% and reduction in CGR by 5x compared to commercial IN725 in hydrogen. The team adopts an Integrated Computational Materials Engineering (ICME) approach by integrating high throughput (HT) CALPHAD and density functional theory (DFT) calculations with multi-objective machine learning to accelerate high-performance alloy design. Guided by high-performance computing (HPC), the best candidate alloy will be processed and it’s HE performance characterized. This research will greatly reduce the time for new alloy design with enhanced HE performance, enabling breakthroughs in materials for the hydrogen economy.
Spring 2024
MODELING OF A SUPERCRITICAL CO2 COMPRESSOR-AS-A-TURBINE (CaT) FOR ENERGY STORAGE SYSTEM
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Palash Panja | EarthEn Energy Inc.
National Lab Partner:
Nate See | Oak Ridge National Laboratory
Summary:
Current energy storage solutions rely on separate turbines and compressors, thereby entailing significant efficiency losses and high capital costs. To address these challenges, EarthEn proposes the development of a Compressor-as-a-Turbine (CaT) technology. This innovative system integrates the functionality of both a turbine and a compressor into a single machine with variable blade geometry, capable of operating efficiently with supercritical CO2 (sCO2) during disparate charge-discharge cycles. This technology promises to enhance the operational flexibility and cost-effectiveness of energy storage systems by combining the use of sCO2 as an efficient thermomechanical fluid, as well as enhancing the feasibility of long-duration energy storage systems. The CaT system will be designed computationally at Oak Ridge National Laboratory (ORNL), in collaboration with Concepts NREC, leveraging their experiences with advanced turbomachinery, modeling compressible flow, GPU-accelerated fluid dynamics simulations, and advanced manufacturing techniques. Successful CaT designs could promote rapid adoption in the sCO2-turbomachinery sector.
Spring 2024
IN SILICO SIMULATION OF CO2 UPTAKE IN CATION-EXCHANGED ZEOLITES IN THE PRESENCE OF POINT-SOURCE CAPTURE HUMIDITY LEVELS
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Matthew Rivera | First Light Energy Solutions
Dr. Ambar Kulkarni | UC Davis
National Lab Partner:
Dr. Sichi Li | Lawrence Livermore National Laboratory
Summary:
Energy generation accounts for approximately one-third of all greenhouse gas emissions, making point-source carbon capture essential to achieving the United States’ net-zero goals. However, existing carbon-capturing materials are characterized by high energy requirements and costs well above the Department of Energy’s targets. Zeolites are a class of carbon-capturing material characterized by low regeneration energies and costs, but which typically struggle to achieve significant CO2 capture levels in humid post-combustion streams. In this project, we aim to identify novel cation-exchanged zeolites that exhibit enhanced CO2 uptake under humid flue gas conditions via a phase transition. To do so, we will partner with LLNL and UC Davis to leverage high-performance computing to screen candidate structures using in silico methods and validate our simulations through industry-led laboratory synthesis and experimentation. Through this project, we will discover and manufacture carbon-capturing zeolites that deliver cost reductions and performance improvements for point-source capture.
Spring 2024
ENHANCING THE DURABILITY OF GAS TURBINE COMPONENTS AGAINST HYDROGEN EMBRITTLEMENT USING HIGH-PERFORMANCE COMPUTING (HPC)
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Indranil Roy | GE Vernova Advanced Research
National Lab Partner:
Dr. Dongwon Shin, Dr. Jiahao Cheng | Oak Ridge National Laboratory
Summary:
Identifying materials susceptible to hydrogen embrittlement (HE) is critical for efficiently delivering hydrogen-friendly gas turbines (GTs) to market. GE Vernova Advanced Research (GEVAR) and Oak Ridge National Laboratory (ORNL) will execute a high-performance computer project to develop a framework using high-throughput crystal plasticity (CP) coupled with machine learning (ML) models to address HE in GT components. A CP-generated large dataset will be analyzed to identify key features affecting HE and used to train ML models. GEVAR and GE Vernova Gas Power’s (GEV-GP) expertise and world-class materials dataset will be employed to validate and calibrate simulation results. From identified ML models, the team will succinctly explore the high-dimensional space of various alloys and conditions pertinent to the GT operating envelope.
Spring 2024
ENHANCING CO2 CAPTURE RATES USING MICRON-SCALE DROPLET SPRAY REACTORS
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Rawand Rasheed | Helix Earth Technologies, Inc
National Lab Partner:
Dr. Lorenzo Nocivelli | Argonne National Laboratory
Summary:
Absorber technologies are crucial for reducing CO2 emissions thereby mitigating climate change and are a pivotal element of the energy future of the US. Traditional CO2 capture methods using liquid sorbents are hindered by slow reaction kinetics resulting in large system footprints. This project proposes a novel CO2 absorber using micron-scale sprays of liquid amines, which significantly increase the liquid-gas surface area, resulting in order-of-magnitude increases in CO2 capture rates and increased system efficiency. Helix Earth Technologies has developed a patented, high-efficiency method for droplet capture that enables cost-effective development of these droplet absorbers. We aim to optimize spray dynamics and reaction kinetics to improve CO2 capture efficiency by leveraging advanced CFD modeling at Argonne National Laboratory, which will help accelerate the development of scalable, cost-effective CO2 capture systems that substantially reduce emissions, decreases system size, and enhance operational flexibility, helping transform CO2 capture processes and other liquid-gas chemical processes.
Spring 2024
REUSE OF WASTE HEAT IN INDUSTRIAL PROCESSES TO REDUCE CARBON EMISSIONS AND ENERGY USE
DOE Funding Office:
Energy Efficiency and Renewable Energy (EERE) Industrial Efficiency and Decarbonization Office (IEDO)
Federal Funding Amount:
$400,000
Principal Investigator:
Mr. Mark Schaefer, Mr. John Pressly | Nucor
National Lab Partner:
Dr. Jeff Haslam, Dr. Boyan Lazorav | Lawrence Livermore National Laboratory
Summary:
Nucor is seeking to increase energy efficiency of steel production by recycling the heat in exhaust gases from Electric Arc Furnaces (EAF). Waste heat from steel manufacturing accounts for 28-30 percent of energy inputs. The heat from the gases can be used for other energy intensive processes. However, the exhaust gases contain a large amount of particulates that must be cleaned before the gas can be used for other applications. The analysis and design of these processes is not well understood for high temperature applications. Nucor proposes an HPC4EI collaboration with Lawrence Livermore National Laboratory (LLNL) to conduct High Performance Computing analysis of these processes to better understand how their design and operation leads to efficient reductions in the particle load. Based on this, the project will develop engineering approaches to design cleaning systems to meet specific requirement of the processes that use the hot exhaust gas.
Spring 2024
FACTOR MANUFACTURING VARIABILITY IN THE DESIGN OF EFFICIENT FILM COOLING TECHNOLOGY (PHASE 2 PROJECT)
DOE Funding Office:
Advanced Materials and Manufacturing Technologies Office (AMMTO) & Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$200,000
Federal Funding Amount:
$200,000
Principal Investigator:
Dr. Michael Joly | RTX Technology Research Center
National Lab Partner:
Dr. Pinaki Pal, Dr. Muhsin Ameen | Argonne National Laboratory
Summary:
Reductions in cooling air flow can improve the thermal efficiency of gas turbine engines. Predictive models for near-wall cooling flow physics are, however, needed to optimize turbine cooling designs. Moreover, such models need to capture the effects of deviations from nominal design introduced by manufacturing processes. The primary objective of this proposal is to leverage advanced machine learning (ML) methods along with high-fidelity computational fluid dynamics (CFD) simulations and supercomputing to develop predictive but affordable data-driven models for near-wall mixing and heat transfer. Phase 1 developed a validated wall-resolved CFD simulation framework, an ensemble ML-augmented wall model, and proof-of-concept demonstration of a hybrid CFD-ML workflow for modeling near-wall mixing behavior in a nominal film cooling configuration. Phase 2 seeks to further advance the generalizability and broaden the applicability of the data-driven wall modeling approach by employing geometric deep learning techniques and extending it to practical scenarios incorporating manufacturing-induced variations.
Spring 2024
OPTIMAL DESIGN OF CARBON CAPTURE UNITS UNDER UNCERTAINTIES IN FEED COMPOSITIONS AND OPERATIONAL CONDITIONS
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Federal Funding Amount:
$400,000
Principal Investigator:
Dr. Rahul Gandhi |Shell
Dr. Sungho Shin |Massachusetts Institute of Technology
National Lab Partner:
Dr. Michel Schanen, Dr. Mihai Anitescu | Argonne National Laboratory
Summary:
Carbon capture, utilization, and storage (CCUS) technology offers an effective way to reduce carbon emissions for various industries lacking scalable decarbonization options, such as power generation, refining, and chemical production. Despite the successful application of optimization methods in carbon capture unit (CCU) design current deterministic, steady-state optimization approaches incur suboptimality when faced with uncertainties in feed conditions and/or dynamic operation conditions. This proposal aims to develop an HPC-enabled CCU design method that can consistently achieve a 95% carbon capture rate by performing design optimization with explicit considerations of uncertainties and dynamics. These problems exhibit high computational complexity, as they embed nonlinear physics, uncertainty scenarios, and discretized differential and algebraic equations (DAEs). We propose to address this challenge by leveraging (i) HPC-enabled nonlinear programming software, developed by the ANL and MIT team during the Exascale Computing Project, and (ii) HPC resources provided by the national laboratories.
Summer 2023
HIGH-FIDELITY COMPUTATIONAL MODELING OF ALTERNATE FUELS FOR THE ALLAM-FETVEDT CYCLE (PHASE 2 PROJECT)
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Hydrogen and Carbon Management
Principal Investigator:
Justin Miller | 8 Rivers, LLC
National Lab Partner:
Michael Martin, Shashank Yellapantula | National Renewable Energy Laboratory
Summary:
Biomass Carbon Removal and Storage (BiCRS) based on gasified biomass as part of electricity generation is a viable pathway to a carbon-negative energy system. Other uses of solid fuels, such as municipal solid waste, coupled with carbon capture enable a circular, sustainable economy with zero- emissions energy. One technology for enabling BiCRS is the Allam-Fetvedt Cycle (AFC), which is already being commercialized for use with natural gas. Challenges to the use of AFC with BiCRS include gaps in fundamental understandings around combustion characteristics of the synthesis gas generated for solid fuels, total plant costs, and combustor material sensitivities. These materials sensitivities increase when advanced manufacturing technologies are employed. This study focuses on the critical component of this system: the synthesis gas oxy-combustor. High-fidelity computational benchmarking of alternative fuels, with varied heating values and compositions, removes development obstacles from industry and accelerates the nation's timeline for achieving a truly clean electricity infrastructure.
Summer 2023
TOWARDS OPTIMAL DESIGN AND MANUFACTURING OF NEXT GENERATION ADVANCED HIGH STRENGH STEELS FROM ZERO-CARBON STEEL MAKING BY A MICRO⇔MACRO FAILURE MODELING APPROACH FOR STRUCTURAL LIGHT-WEIGHTING APPLICATION AND MATERIAL LIFE CYCLE ENERY SAVING
DOE Funding Office:
Energy Efficiency and Renewable Energy (EERE) Advanced Materials and Manufacturing Technologies Office (AMMTO) & Industrial Efficiency and Decarbonization Office (IEDO)
Principal Investigator:
Dr. Sriram Sadagopan, Dr. Narayan Pottore, Dr. Hong Zhu, Dr. Brian Lin | ArcelorMittal USA Research LLC
National Lab Partner:
Xiohua Hu | Oak Ridge National Laboratory
Summary:
ArcelorMittal and ORNL will collaborate to develop an advanced micro⇔macro finite element (FE) modeling framework to advance the state-of-the-art in multiscale modeling for predicting material microstructure-strength-failure relationship and accelerate the development of the next generation of zero carbon footprint Advanced High Strength Steels (AHSS). This will be based on a recently developed micro→macro advanced martensitic transformation FE model at Oak Ridge National Laboratory (ORNL) which involves advanced crystal plasticity and reduced order modeling for macro-property prediction in AHSS, which will be integrated into a macro→micro FE framework to correlate the component level loading condition to local material microstructure in failure prediction during forming or in service. The goal of this multiscale approach is to provide accurate prediction of materials failure in the next generation AHSS made from zero carbon steel making that has superior combined property of strength and failure resistance, ideal for structural lightweighting and lifecycle energy saving and carbon footprint reduction.
ELECTRIFICATION OF THERMAL MAGNESIUM METAL PRODUCTION VIA ALUMINOTHERMIC REDUCTION
DOE Funding Office:
EERE IEDO
Principal Investigator:
Boris Chubukov | Big Blue Technologies
National Lab Partner:
Marc Day | National Renewable Energy Laboratory
Summary:
Magnesium metal is a critical material that pervades a variety of industries including light-weighting vehicles, aluminum alloys, steel processing, and military munitions. Although magnesium ores are ubiquitous, no extraction technology exists that can be economically deployed in the western world; the US is currently fully dependent on imported sources which use archaic and polluting methods. Big Blue Technologies is scaling a commercially viable and clean process to produce magnesium metal. The core unit operation and largest energy-consuming step is the reduction furnace. Design and optimization of this step is critical to scaling. Computational fluid dynamic modeling of the multi-phase phenomena will guide the design and provide key insights to reducing energy consumption and ensuring high conversions. Key design criteria include modeling various reactor geometries to mitigate heat transfer limitations. Successful commercialization of this smelting technology will anchor domestic manufacturing and proliferate end-uses of the lightest structural metal.
Summer 2023
ICME-GUIDED LOW-COST PRODUCTION OF ULTRA LOW-OXYGEN REACTIVE METALS POWDERS THROUGH DEOXYGENATION
DOE Funding Offices:
Energy Efficiency and Renewable Energy (EERE) Advanced Materials and Manufacturing Technologies Office (AMMTO)
Principal Investigator:
Pei Sun | Blacksand Technology LLC & Z.Zak Fang | University of Utah
National Lab Partner:
Michael Gao, Shiqiang Hao | National Energy Technology Laboratory
Summary:
Reactive metals have a strong chemical affinity to oxygen, making it very challenging to produce high-purity metal powders of these elements with extremely low oxygen content. This project aims to optimize low-cost manufacturing routes to produce ultralow-oxygen reactive metal powders of M=Ti, Zr, and Nb using Mg or Ca as reduction agents. Density functional theory, grand canonical Monte Carlo simulations and CALPHAD calculations will be performed to predict the phase equilibria of metal-rich M-O-H systems and oxygen solubility in presence of Mg/Ca. Guided by validated HPC, key deoxygenation parameters will be optimized to achieve ultralow oxygen content ≤0.1 wt.%. Comprehensive microstructure characterization including x-ray diffraction and electron microscopy will be performed. This technology will greatly reduce the time and cost of processing high-purity reactive metals powders in ultralow oxygen content.
Summer 2023
ALTERNATIVE PATHS FOR CARBON DIOXIDE REMOVAL (CDR) THROUGH SALT WATER UTILIZATION AND VENTURI SCRUBBER CAPTURE SYSTEMS
DOE Funding Office:
Office of Fossil Energy and Carbon Management (FECM) - Carbon Dioxide Removal
Principal Investigator:
Alishan Salim, Rahul Surana | Capture6
National Lab Partner:
Nithin Panicker, Flavio Dal Forno Chuahy | Oak Ridge National Laboratory
Summary:
Capture6 proposes the use of a venturi scrubbing system to increase CO2 capture efficiency and reduce materials and maintenance cost, thus increasing commercial attractiveness of CDR. Capture6 will partner with ORNL to perform key simulations of carbon capture using venturi-scrubbers required to advance this technology for deployment in its pilot plant. This project will accelerate FECM’s mission through the investigation of system integration and design considerations on overall economics and energy requirements of CDR.
Summer 2023
COMPUTATIONAL DESIGN TO ADVANCE AM FABRICATION OF HIGH GAMMA PRIME ALLOYS FOR HOT GAS PATH COMPONENTS IN GAS TURBINE ENGINES: A PATHWAY TO ENHANCED GAS TURBINE EFFICIENCY AND ENERGY SAVING
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Mario Epler, Gian Colombo, Ning Zhou, Austin Dicus, Tao Wang, Stephane Forsik | Carpenter Technology Corp
National Lab Partner:
Jaihao Cheng, Patxi Fernandez-Zelaia | Oak Ridge National Laboratory
Summary:
Existing gas turbines operate at efficiencies of 28-40%. Increasing the turbine inlet temperature improves efficiency but increases the risk of creep failure in critical components. Additive manufacturing (AM), combined with novel, high-temperature alloys developed at Carpenter Technology Corporation (CTC), enables intricate air-cooling structures to be incorporated into the turbine tip shoe design to improve heat transfer with minimal pressure loss. This can increase the power output of the engine and improve efficiency. However, optimizing the AM build process and the post-build heat treatment to produce defect-free components remains a difficult challenge, especially with new alloy compositions and aggressive designs. This project aims to integrate ORNL's AM process modeling code (MUST-FE) and CTC's alloy-specific component design guidelines, to produce robust AM turbine components, thereby enhancing power output, reducing fuel consumption, and curbing CO2 emissions.
Summer 2023
HIGH PERFORMANCE OPTIMIZATION OF LIQUID PISTON COMPRESSION FOR LONG-DURATION ENERGY STORAGE
DOE Funding Offices:
EERE AMMTO
Principal Investigator:
Mark Weathers, Ben Robert | Energy Internet Corporation
National Lab Partner:
Thien Nguyen, Nithin Panicker | Oak Ridge National Laboratory
Summary:
Long Duration Energy Storage is essential for a cost-effective integration of variable renewable energy sources and being able to isothermally store energy is a key enabler because it could minimize the energy loss due to heat. Energy Internet Corporation (EIC) has developed a concept of Compressed Air Energy Storage (CAES) technology utilizing the liquid piston compression-based and successfully demonstrated an un-optimized 19% efficiency over conventional CAES. EIC is preparing an innovative high-pressure design for Isothermal-CAES (I-CAES) technology featuring a tube bundle module (TBM) design to push the efficiency advantage to 25% and facilitate energy storage in man-made at-surface vessels. I-CAES will enable 3600GJ national annual energy savings and energy storage for weeks to months, relative to conventional CAES. The proposed effort will advance HPC4Mfg’s mission applying to component improvements of advanced energy storage technologies for superior operational performance. EIC will partner with Oak Ridge National Laboratory (ORNL) to perform high-accuracy simulations coupling Large Eddy Simulation (LES) and Volume-of-Fluid (VOF) modeling approach to optimize the second-generation design and accelerate the commercial deployment for EIC’s pilot plant and pilot-dependent full-scale plants for renewable energy firming.
Summer 2023
MICROGRAVITY ENABLED GLASS SIMULATION (MEGS) FOR THE DEVELOPMENT OF HIGHLY-EFFICIENT OPTICAL CABLES WITH IMPACTFUL ENERGY SAVINGS CAPABILITIES (PHASE 2 PROJECT)
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Michael Vestel | Flawless Photonics
National Lab Partner:
Chris Walton | Lawrence Livermore National Laboratory
Summary:
Global demand for data rises annually, but data transmission comes at an unaffordable cost—excessive greenhouse emissions. Silica-based optical networks use 40Wh/Gb and 20+ hops for an average span of 800km, sending 1014 GB, annually, via undersea cables.
Innovators seek alternatives to silica, favoring heavy-metal fluoride glasses (HMFG) for their potential to reach “near-theoretical-absolute” transparency. ZBLAN, a well-characterized HMFG [1-5], offers superior mid-IR transparency and lower intrinsic loss with the potential for 20x less attenuation than silica, potentially saving ~0.5 quads annually with longer transmission distances and fewer amplifiers. However, challenges in unlocking ZBLAN’s potential lie in manufacturing. Flawless Photonics’ SpaceFiber, a next-gen optical fiber, was created to unlock ZBLAN’s potential. Its microgravity-based process promises highly-efficient, low-power data transmission. Phase I built a model that successfully predicted defects, but the model requires dedicated experiments. Phase II will test, refine, and apply the multiscale model to an advanced manufacturing approach.
Summer 2023
ATOMIC-SCALE KINETICS MODELING OF WET CO2 CHEMISORPTION IN METAL-ORGANIC FRAMEWORK (MOF) SORBENTS FOR DIRECT AIR CAPTURE (DAC)
DOE Funding Office:
FECM - Carbon Dioxide Removal
Principal Investigator:
Xiaolei Shi | GE Research
National Lab Partner:
Scott Bobbitt | Sandia National Laboratories
Summary:
GE Research (GE) and Sandia National Laboratory (SNL) propose a project to develop an atomic-scale simulation framework using high-performance computing (HPC). Kinetics-weighted capacity of CO2 adsorption, also known as productivity, is the most important determining factor for an economically-viable direct air capture (DAC) solution. Improving kinetics is crucial to reduce the energy cost and capital expenditures (CapEx) that govern the techno-economic viability. While the capacity of CO2 adsorption is mostly understood in state-of-the-art amine functionalized metal-organic framework (MOF) chemi-sorbents, kinetics remains a technical challenge that must be understood. In this project, GE and SNL will overcome this technical challenge by developing an understanding and prediction framework for kinetics of CO2 chemisorption.
Summer 2023
USING HPC FOR STUDYING GAS CHARGE TRANSPORT IN MEDIUM VOLTAGE SWITCHGEAR
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Mattewos Tefferi | G&W Electric
National Lab Partner:
Hans Johansen | Lawrence Berkeley Laboratory
Summary:
Demand for high and medium voltage gas-insulated switchgears and circuit breakers is high, as more renewable sources are added to the grid to promote decarbonization, while safely delivering electricity. The performance and reliability of these pervasive, utility-scale power system devices is critical, as failure can cause cascading effects and large blackouts resulting in huge economic loss. Recently, the switchgear industry has been challenged to eliminate sulfur hexafluoride (SF6) as the primary insulating gas; it has an extremely potent greenhouse effect, with a 100-year global atmospheric warming potential 23,500x that of CO2. To eliminate SF6, G&W Electric proposes to use HPC for “digital twin” simulations of pre-breakdown phenomenon (streamer inception and propagation), dielectric material design, and discharge physics, while meeting strict cost and space efficiency requirements. This will accelerate costly design-build-test-maintain product cycles, and enable high performance, eco-friendly devices.
Summer 2023
HIGH PERFORMANCE COMPUTING FOR DEVELOPMENT OF CRITICAL THERMODYNAMIC INPUTS FOR THE NEXT GENERATION THERMAL BARRIER COATING
DOE Funding Office:
EERE IEDO
Principal Investigator:
Elizabeth Monaghan, Paul Smith | General Electric Company
National Lab Partner:
Santanu Chaudhuri | Argonne National Laboratory
Summary:
GE and ANL propose to harness the power of High-Performance Computing (HPC) to perform complex thermodynamic calculations to develop novel Thermal Barrier Coatings (TBC) optimized for manufacturing energy consumption, lower thermal conductivity and survival in the harsh environment of hydrogen fueled turbines. The TBC thermal spray industry is critical to the United States' energy future due to its influence on turbine efficiency. Today, TBC development relies on costly, time-intensive experimental data with limited insight into phase behavior of the ceramic. Density Functional Theory (DFT) calculations will be used to predict the viability of synthesis and structure, then combined with machine learning to cover vast compositional spaces with millions of possible combinations. Predicted ternary oxide mixtures will be validated via synthesis and characterization. Multi-objective optimization of candidate compositions will include predictions of manufacturing energy usage. The framework and resulting database will be foundational for TBC development for decades to come.
Summer 2023
PHYSICS-BASED APPROACH FOR ACCURATELY MODELING CREEP IN HIGH TEMPERATURE TURBINE MATERIALS ENABLED BY HPC AND AUGMENTED WITH MACHINE LEARNING
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Dr. Austin Ross, Dr. Huajing Song | Pratt & Whitney
National Lab Partner:
Ricardo Lebensohn, Laurent Capolungo | Los Alamos National Laboratory
Summary:
Improving material temperature capability is vital for enabling efficiency-based emission reductions in aerospace turbines. The time and cost of evaluating new processes and materials could be improved with better predictive certainty for high temperature deformation mechanisms like creep. A model known as LApx (Los Alamos Polycrystal code) developed by Los Alamos National Laboratory (LANL) has shown promise in accurately predicting creep for select alloy systems using High Performance Computing (HPC) for calibration and optimization. This initiative proposes a collaboration between Pratt & Whitney (P&W) and LANL to extend the physical mechanisms in LApx to nickel (Ni) superalloys – alloys employed in the highest temperature regions of a turbine. A successful outcome of this collaboration would enable much improved material and process optimization of Ni superalloys for creep limited applications compared to the current state of the art. Furthermore, this collaboration will enable necessary infrastructure for connections between LApx and machine-learning tools.
Summer 2023
HYDROGEN ECONOMY: RELIABILITY QUANTIFICATION FOR FLEXIBLE AND EFFICIENT GAS TURBINES IN THE ENERGY TRANSITION WITH EXPERIMENTAL AND HIGH PERFORMANCE COMPUTING MODELING
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Kai Kadau, Thomas Bouchenot, Kevin Knipe | Siemens Energy
National Lab Partner:
Sarma Gorti, Balasubramaniam Radhakrishnan | Oak Ridge National Laboratory
Summary:
Gas Turbines (GT) will play an essential role in the transition to renewable energy sources such as wind and solar due to their reliability and operational flexibility, which are critical requirements for supplemental energy sources. GT’s additional ability to operate with hydrogen will be crucial for the hydrogen economy. Fast starts and load changes can significantly increase the cyclic damage seen in turbine blades. Large design margins are currently required to account for additional factors, such as casting porosity, without accurate models to predict its occurrence and impact. This proposal seeks to combine the extensive test data generated by Siemens Energy (SE) with HPC simulations of polycrystalline microstructures to facilitate the building of a reliable probabilistic fatigue engineering model. HPC simulations would be utilized for parametric studies to understand better the microstructure influence on material performance in the presence of porosity, allowing to optimize designs for this increased fatigue risk.
Fall 2022
SYNTHESIZING NOVEL H2 SENSORS FOR OPERATIONAL RESILIENCE IN PIPELINE INFRASTRUCTURE (SENSOR)
DOE Funding Office:
Fossil Energy and Carbon Management (FECM) - Hydrogen with Carbon Management
Principal Investigator:
Maruthi Devarakonda, David Madden | Baker Hughes Energy Transition LLC
National Lab Partner:
Wibe de Jong | Lawrence Berkeley National Laboratory
Summary:
Hydrogen (H2) transportation through dedicated pipelines or through existing natural gas infrastructure poses significant technical challenges in the form of H2 leakage and pipeline material embrittlement, as per a recent NREL report. In addition, increased blending of hydrogen with natural gas to power gas turbines and reciprocating engines presents engineering challenges, that need to be addressed before their wide-spread deployment. The proposed project aims to de-risk the potential corrosion and embrittlement challenges associated with hydrogen transportation and its blending with natural gas, through down-selection of a novel metal-organic framework (MOF) based hydrogen sensor using high performance computing (HPC). The technical approach outlined in this proposal relies on existing technologies and is technically feasible. High throughput computational screening (HTCS) of MOF materials will be performed using HPC to identify high potential candidate MOFs capable of high-resolution detection of H2 leakage in ppm concentrations. Machine learning (ML) will be used to identify critical component, structure, and performance relationships, which will significantly enhance the accuracy of HTCS studies.
Fall 2022
HPC SIMULATIONS TO ACCELERATE DESIGN AND MANUFACTURING OF IMPACT-RESISTANT COMPOSITE FUSELAGES FOR OPEN ROTOR ENGINES
DOE Funding Office:
Energy Efficiency and Renewable Energy (EERE) Advanced Materials and Manufacturing Technologies Office (AMMTO)
Principal Investigator:
Olaf Weckner | Boeing
National Lab Partner:
Pablo Seleson | Oak Ridge National Laboratory
Summary:
Boeing and Airbus are actively studying open rotor engine concepts. Eliminating fan ducts yields about 20% fuel savings compared to traditional turbofan engines. In addition to cost savings for airlines and passengers, the dependence on crude oil is reduced, as are CO2 and NOx emissions in turn reducing commercial aviation’s environmental impacts and contributions to climate change. A main challenge of using open rotor engines is the risk to the airplane and passengers in case of a fan blade out event. The Federal Aviation Administration requires test evidence that a severed blade must not penetrate the fuselage upon impact. However, it is infeasible to test every possible airplane configuration, as skin gages, materials, and stringer types are unique to each airplane. To accelerate design, testing, and certification, we propose to employ high-performance computing for simulation of impact-resistant composite fuselages for open rotor engines.
Fall 2022
BIOREACTOR OPTIMIZATION THROUGH MULTI-PHASE FLOW MODELS
DOE Funding Offices:
Energy Efficiency and Renewable Energy (EERE) Advanced Materials and Manufacturing Technologies Office (AMMTO) & Industrial Efficiency and Decarbonization Office (IEDO)
Principal Investigator:
Andrew Magyar, Elizabeth Onderko | Capra Biosciences, Inc.
National Lab Partner:
Ishan Srivastava | Lawrence Berkeley National Laboratory
Summary:
Chemical manufacturing uses 29% of energy in the United States and produces 925 million metric tons of CO2 annually. Bio-based chemicals can significantly reduce the carbon and energy impact of chemical manufacturing. However, their widespread adoption requires them to be cost-competitive with traditional petrochemicals. High-efficiency bioreactor technologies will play an important role in such a transition from a petroleum-based to a bio-based economy. These bioreactors, such as one developed by Capra Biosciences, involve multiphase flow of biofilm-coated solid support particles that are continuously circulated in a fluidized state within the reactor. Capra Biosciences and LBNL will develop a multiscale modeling framework within the current MFIX-Exa software to simulate bioreactor multiphase flows of solid particles in a liquid-gas bubble mixture. By leveraging HPC capabilities, this high-fidelity multiscale model will inform design decisions for bioreactor architecture that will make them cost-effective and carbon-efficient for widespread adoption in the industry.
Fall 2022
HYDROGEN COMBUSTION NOX CONTROL FOR C200 ENGINE
DOE Funding Office:
EERE IEDO
Principal Investigator:
Don Ayers | Capstone Green Energy
National Lab Partner:
Debolina Dasgupta | Argonne National Laboratory
Summary:
Combined Heat and Power (CHP) technologies with microturbines as the prime mover offer an attractive solution for distributed generation. With the push for decarbonization, such technologies are looking to transition to 100% hydrogen usage from natural gas, requiring redesign of the combustion and fuel injection systems. This redesign is essential to ensure reduced nitrogen oxide (NOx) emissions and a stable flame across the operability map of the system for different air and fuel flow rates. The aim of this project will be to perform design optimization of fuel flexible Capstone’s C200 microturbine system ranging from 70% natural gas/ 30% hydrogen blends to 100% hydrogen fuel operation using Computational Fluid Dynamics requiring high performance computing. Overall, the project will help solve critical technical challenges with high hydrogen operation by modifying fuel injection systems optimized for Capstone C200 microturbine system. The work will help accelerate transition to 100% hydrogen for the C200.
Fall 2022
MULTIPHYSICS CFD SIMULATIONS OF CO2 SOLIDIFICATION IN A TURBOEXPANDER UNIT-OPERATION FOR THE PURPOSE OF CARBON-CAPTURE AND SEQUESTRATION
DOE Funding Office:
FECM - Point Source Carbon Capture
Principal Investigator:
Jonathan Stickel | Carbon America
National Lab Partner:
Bruce Perry | National Renewable Energy Laboratory
Summary:
Carbon America has developed a cryogenic carbon-capture technology (FrostCCTM) that separates CO2 from point-source emissions by solidifying it at cold temperatures. FrostCC is projected to enable low-cost carbon-capture at less than $30 per tonne for coal-based power generation, in part due to low capital costs. Although energy efficiency is already a feature of FrostCC, efficiency may be improved by continuously separating solid CO2 from chilled exhaust gas as it is formed. Multiphysics computational fluid dynamics (CFD) models will be developed to simulate the formation of solid CO2 in subcooled exhaust flowing in turboexpander geometries. These simulations will require thousands of node hours on HPC resources to complete. The experimentally validated CFD models will be used to design processes that continuously and efficiently separate solid CO2 from exhaust gas streams in industry and power generation.
Fall 2022
HPC MODELING OF PHOTOPOLYMERIZATION OF POWDER SUSPENSIONS FOR ADDITIVE MANUFACTURING OF CERAMICS
DOE Funding Offices:
FECM - Hydrogen with Carbon Management & EERE AMMTO
Principal Investigator:
Suman Das | DDM Systems
National Lab Partner:
Adrian Sabau, Vimal Ramanuj | Oak Ridge National Laboratory
Summary:
DDM Systems has developed a novel additive manufacturing (AM) technology called Large Area Maskless Photopolymerization (LAMP) aimed at printing integrated cored ceramic shell molds for investment casting. LAMP 3D printers use patterned UV light to cure a ceramic slurry layer-by-layer to produce integrated-cored ceramic molds that are used in investment casting of components in diverse industrial sectors including power generation gas turbines, aerospace, automotive, oil and gas, healthcare, and many others. In pilot demonstrations, this new ceramic AM technology has drastically reduced the time and costs needed for the production of complex cast metal parts, while facilitating significant energy savings. In order to take full advantage of the new LAMP technology, we seek to use HPC simulations to elucidate the relationships between materials properties, process parameters and process variables. The modeling and simulation of the photochemical (including photoinitiation and photopolymerization) reactions under UV light will be critical to the control and optimization of the LAMP technology that will lead to widespread technology adoption.
Fall 2022
MODELING SOLID ELECTROLYTE INTERPHASE FORMATION AND GROWTH IN LI-ION BATTERIES USING REACTIVE MOLECULAR DYNAMICS SIMULATIONS
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Avinesh Ojha, Elham Honarvarfard, Ben Emley, Ann Straccia, Sabrina Peczonczyk, Mikhail Trought | Ford Motor Company
National Lab Partner:
Subramanian Sankaranarayanan | Argonne National Laboratory
Summary:
Formation of solid electrolyte interphase (SEI) is the most time-consuming and expensive step in the battery manufacturing process. Understanding SEI formation is therefore critical for designing high performance Li-ion batteries, and reducing material and energy costs. However, details of how the SEI builds up into a nanometer-thick layer from molecular-level reactions on the anode are still unclear. Using HPC, we propose to develop reactive molecular-dynamics simulations to track the evolution of the SEI composition and obtain key kinetic parameters governing SEI formation. These parameters will be input to an integrated atomistic-mesoscale model for SEI formation at experimentally relevant time and length scales. Our approach could accelerate discovery of new low-cost battery chemistries with reduced formation time. A 10% reduction in SEI formation time for 140 GWh annual battery capacity plant could potentially lead to 74 MWh of electrical energy savings and a reduction of 0.92 million MT CO2 emission annually.
Fall 2022
HPC SIMULATION OF MICROSTRUCTURE EVOLUTION OF BATTERY ELECTRODE DRYING PROCESS
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Wayne Cai | General Motors LLC
National Lab Partner:
Shenyang Hu | Pacific Northwest National Laboratory
Summary:
Electrode drying is one of the most time and energy consuming processes in Li-ion battery cell manufacturing. As an electric vehicle OEM and battery manufacturer, General Motors LLC (GM) seeks to enhance the understanding of the drying mechanisms towards producing high quality battery electrodes with reduced cost and energy usage. This project proposes to use high-performance computing (HPC) resources to simulate the microstructure evolution of electrode drying process. Taking advantage of the established expertise in phase field modeling at the Pacific Northwest National Lab (PNNL), an integrated Phase Field (PF) and Coarse-Grained Molecular Dynamics (CGMD) modeling framework will be developed to simulate the heat transfer and matter transport during the complex slurry drying process. Upon completion of the project, the microstructure of a dry electrode (including interfacial and particle debonding) will be predicted, providing critical electronic and ionic transport properties that ultimately impact the electrochemical performance of a battery cell.
Fall 2022
METADYNAMIC MODELING OF THE INTERACTION OF SODIUM AND METHYL LACTATE IN NA-FAU DURING DEHYDRATION TO ACRYLICS
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Christopher Nicholas | Lakril Technologies Corporation
National Lab Partner:
John Low | Argonne National Laboratory
Summary:
Acrylic acid and acrylate derivatives are the cornerstones of a $10B global industry supporting the paints, coatings, adhesives, and superabsorbent polymer (diapers) markets. However, over 16 million kgs of CO2 are generated yearly to produce these chemicals necessary for modern life. We will decarbonize this industry with a sustainable, bio-based solution by turning corn and other bio-derived sugar sources into paints and coatings. Our modified zeolite catalyst achieves the economic goal of >90% yield during the dehydration of lactic acid to acrylic acid. The key to improving catalyst performance is predicting the distribution of Na+ and interactions of reactants at reaction conditions within the confined pores of Na-FAU base zeolite. Cations move around the zeolite with temperature, conditions, etc. We propose to use metadynamics to predict reaction selectivity and rates within zeolite pores filled with water, sodium, methyl lactate, and an amine in motion at reaction conditions for improved catalysts.
Fall 2022
HIGH-PERFORMANCE COMPUTING MEETS HIGH-THROUGHPUT EXPERIMENTATION TO OPTIMIZE COMPOSITION AND CRYSTAL STRUCTURE IN CO2 REDUCTION ELECTROCATALYSTS
DOE Funding Office:
FECM - CO2 Removal and Conversion
Principal Investigator:
Jordan Swisher | Mattiq, Inc.
National Lab Partner:
Wissam Saidi | National Energy Technology Laboratory
Summary:
To decarbonize the chemicals and fuels industry, we require new and innovative technologies that can leverage renewable feedstocks including renewable electricity and renewable carbon sources, such as CO2. State-of-the-art (SOTA) CO2 utilization technologies that can produce renewable chemicals and fuels, such as the electrochemical reduction of CO2, are still significantly lacking in efficiency, and are therefore not cost-competitive with existing infrastructure. The key bottleneck to cost-competitive chemicals and fuels sourced from CO2 and renewable electricity is the lack of any efficient catalyst material that can effectively convert CO2 into products of interest. To overcome this, NETL will utilize high-performance computing to greatly reduce the number of potential materials to be investigated by Mattiq’s ultrahigh-throughput experimental catalyst discovery framework. This work is poised to rapidly accelerate the development of novel and efficient CO2 reduction electrocatalysts that can bring CO2 utilization closer to commercial viability.
Fall 2022
PHASE-FIELD PREDICTIONS OF THE INFLUENCE OF COOLING RATES DURING AM ON THE EVOLUTION OF MICROSTRUCTURES IN NICKEL-BASED SINGLE CRYSTAL SUPERALLOYS
DOE Funding Office:
EERE AMMTO
Principal Investigator:
Ayman Salem | MRL Materials Resources LLC
National Lab Partner:
Balasubramaniam Radhakrishnan | Oak Ridge National Laboratory
Summary:
Additive manufacturing (AM) of single crystals (SX) made of Ni-based superalloys offers major cost savings for gas turbine engines with the inclusion of internal cooling channels. However, the lack of understanding of the effect of transient thermal conditions on solidification grain structure during AM hinders the potential for process control to maintain the SX quality. The use of HPC high-fidelity simulations to predict the evolution of the solidification microstructure will enhance the abilities to tailor the microstructures through process optimization. Phase field (PF) simulations will be used to determine the effect local thermal conditions and defects on the stability of the solidification morphology, specifically with respect to the onset of columnar-to-equiaxed transition (CET) that results in the loss of the SX. The results are expected to be instrumental for developing future surrogate models to speed up the integration of design and manufacturing of turbine blades under the harsh in-service conditions.
Fall 2022
IMPLEMENTATION OF A LARGE-SCALE AND HIGH-FIDELITY REACTING FLOW SIMULATION METHODOLOGY WITH ARTIFICIAL INTELLIGENCE (AI) IN THE DEVELOPMENT CYCLE OF HIGH-HYDROGEN COMBUSTORS TO ELIMINATE CO2 EMISSIONS IN THE POWER SECTOR
DOE Funding Office:
FECM - Hydrogen with Carbon Management
Principal Investigator:
Raghu Kancherla, Gregory Vogel, | Power Systems Mfg., LLC, Veeraraghava Raju Hasti | North Carolina State University, Sonya Smith | Howard University
National Lab Partner:
Shashikant Aithal | Argonne National Laboratory
Summary:
The utilization of hydrogen (H2) for electrical power production can eliminate close to 31% CO2 emissions in the United States. However, the flashback tendency of H2 flame is a serious concern at this stage of H2 combustor technology development. Thus, Power Systems Mfg., LLC (PSM) would like to use this HPC4EI opportunity to utilize US national lab’s expertise and resources to develop an innovative flashback-predicting computational approach by integrating Artificial Intelligence (AI), Large-Eddy-Simulations (LES), and real experimental data, to develop highly reliable H2 capable combustion systems. Furthermore, the developed techniques and models will be implemented in the design and development process of high-H2 combustors to significantly reduce the development cycle time.
Fall 2022
HPC SIMULATIONS TO OPTIMIZE THE STORAGE OF FLAME-/CARBON-FREE HEAT AND THE DELIVERY OF THAT HEAT TO WARM MIX AND HOT MIX ASPHALT PLANTS
DOE Funding Office:
EERE IEDO
Principal Investigator:
Timothy Reed, James Wilson | CALMAT CO
National Lab Partner:
Thomas Buscheck, Boyan Lazarov | Lawrence Livermore National Laboratory
Summary:
CALMAT CO is the nation’s largest producer of construction aggregates—primarily crushed stone, sand and gravel—and a major producer of aggregates-based construction materials, including asphalt and ready-mixed concrete. CALMAT proposes an HPC4EI collaboration with Lawrence Livermore National Laboratory (LLNL) to conduct HPC simulations to optimize the storage and delivery of flame-/carbon-free heat to warm and hot mix asphalt (WMA and HMA) plants for sustainable operations, including increased use of reclaimed asphalt pavement (RAP). LLNL’s approach uses PV solar to power electrical furnaces to generate heat stored in pre-heated aggregate, RAP, and asphalt binder for mixing operations. This process lowers mixing temperatures, which reduces dust, aggregate breakage, volatizing asphalt binder constituents, and volatile emissions. HPC simulations optimize heat storage, balancing production rate against maintaining the integrity of pre-heated materials. Design objectives include increased production rate, extended pavement life, reduced greenhouse gas and noxious emissions, and reduced energy costs.
Spring 2022
HPC-ENABLED DIGITAL TWIN MANUFACTURING FOR SUSTAINABLE METALWORKING
Principal Investigator:
John Foltz | ATI
National Lab Partner:
Aaron Fisher, Vic Castillo | Lawrence Livermore National Laboratory
Summary:
Manufacturing of near-net shape mill-products used in aerospace, automotive, and other industries has a significant potential for reduction of both energy use and associated CO2 production. Up to 95% of metal used in manufacturing of aircraft is converted to machining scrap due to the complex shape of aerospace components. Production of near net-shape mill products (NNS-MP), such as through bar rolling, closed die forging, additive manufacturing (AM), or metamorphic manufacturing (MM) are limited in design and optimization by their complex processing path, computational resources required, and the lack of ad-hoc software. In this project, advanced HPC software will be used to simulate the multi-physics problem of multi-stand bar-shaped rolling and generate a machine learning (ML) model of the near net-shape process. This surrogate model will represent the digital object for a digital twin (DT) of the NNS-MP system.
Spring 2022
MULTI-PHYSICS SIMULATION OF MELTING BEHAVIOR OF LOW C DRI FOR GREEN STEEL
Principal Investigator:
Franco Gandin | Danieli USA
National Lab Partner:
Michael Martin, Marcus Day | National Renewable Energy Laboratory
Summary:
Steelmaking currently accounts for 8% of global carbon emissions. Moving to electric arc furnaces and replacing carbon with hydrogen in critical processes for the production of Direct Reduced Iron (DRI) will allow this amount to be reduced by more than 50 percent. However, the DRI produced by hydrogen, or H2DRI, has a different structure and chemical composition from current DRI. Danieli USA and NREL will partner to simulate the melting of H2DRI in steelmaking, a critical process that needs to be understood to use H2DRI. The project will deliver a computational simulation of the melting processes of DRI and H2DRI that will capture the key parameters relevant for industrial use, accelerating the adoption of low-carbon steelmaking.
Spring 2022
DEVELOPMENT OF A HIGH FIDELITY CFD MODEL FOR SOLVENT EVAPORATION AND TRANSPORT IN POROUS STRUCTURE DURING BATTERY ELECTRODE DRYING
Principal Investigator:
Wanjiao Liu, Maryam Akram | Ford Motor Company
National Lab Partner:
Jeffrey Horner, Scott Roberts | Sandia National Laboratories
Summary:
An efficient battery manufacturing process is the key to the mass production of Electric Vehicles (EV), in which drying is one of the most energy-intensive steps significantly influencing the battery performance. An accurate 3D CFD model for drying is essential for predicting the drying mechanism and optimizing its parameters. By optimizing the drying process, it is possible to reduce energy consumption during battery manufacturing, minimize binder loading and maximize active material loading to achieve superior electrochemical performances and facilitate faster public adoption of EV. Ford is seeking to leverage the expertise at Sandia National Laboratories to develop a high-fidelity model for solvent evaporation and transport during drying in a porous electrode structure. Potential savings from 10% improvement and speedup on the drying process is 300GWh /year of electrical energy and 10 million tonnes/year of CO2 emission at Ford and its supply base and five times the projected savings nationwide.
Spring 2022
PROCESS AND GEOMETRY OPTIMIZATION OF PARTIAL-OXIDATION ENGINE REFORMERS FOR DECARBONIZATION OF CHEMICAL MANUFACTURING
Principal Investigator:
Paul Yelvington, Josh Browne | M2X Energy Inc.
National Lab Partner:
Joohan Kim, Riccardo Scarcelli | Argonne National Laboratory
Summary:
Approximately 200 billion cubic meters of gas are flared or vented annually worldwide. M2X Energy Inc.’s mission is to mitigate methane and CO2 emissions by replacing flares with systems that produce economically viable, low-carbon chemical products. The selected approach is to apply the existing internal combustion (IC) engine technology and expertise to produce synthesis gas (a mix of CO and H2) via rich partial-oxidation in an engine reformer to later synthetize a liquid hydrocarbon product, such as methanol. M2X Energy Inc.’s objective is to be the leading supplier of small-scale, modular, autonomous gas-to-product systems, displace production of conventionally produced chemicals, and mitigate greenhouse gas (GHG) emissions. M2X Energy Inc. is targeting the deployment of 1,000 systems by 2030. At this scale, there will be a reduction of GHG emissions and energy consumption from the global upstream oil and gas sector by 43 MMton/year (million tons CO2e per year). Successful commercialization of this technology will use a waste stream (e.g., associated gas, biogas) to decarbonize chemical production.
Spring 2022
ADDITIVE MANUFACTURED COMPOSITE PHASE-CHANGE MATERIAL FOR THERMAL ENERGY STORAGE APPLICATIONS
Principal Investigator:
Santosh Narasimhachary | Siemens Corporation, Technology; Kai Kadau | Siemens Energy
National Lab Partner:
Balasubramanan Radhakishnan | Oak Ridge National Laboratory
Summary:
Composite Phase change materials (C-PCM) play a critical role in energy and storage industrial applications to drive efficiency improvements, thermal energy management, and carbon emissions reductions. This proposal establishes a computational framework to support fabrication of a container-free C-PCM (CC-PCM) using a metallic alloy through additive manufacturing (AM). It exploits metastable liquid-phase immiscibility that offers the potential to form a microstructure where the lower melting active phase is distributed in an inactive matrix in complex component geometries. Phase-field (PF) simulations utilizing HPC will provide a detailed distribution of different types of phases and their morphologies, and the compositions of the phases that make up the microstructure of the CC-PCMs during AM based manufacturing and service. These results will help Siemens accelerate the commercialization of CC-PCMs through AM for various energy and storage applications that support decarbonization goals.
Spring 2022
UNCERTAINTY QUANTIFICATION OF FATIGUE BEHAVIOR OF ROUGH AM SURFACES AND MICROSTRUCTURES TO ENABLE HYDROGEN GAS TURBINE COMBUSTION
Principal Investigator:
Daniel Ryan, Sudhakar Bollapragada, Brandon Kemerling | Solar Turbines Incorporated
National Lab Partner:
Jiahao Cheng, Patxi Fernandez-Zelaia | Oak Ridge National Laboratory
Summary:
Modification of fossil-fueled industrial gas turbines to accept no/low carbon fuels (Hydrogen, H2/natural gas blends) is a significant undertaking. Successful deployment of this technology sits at the intersection of three design criteria (1) new functional fuel injectors that can burn these fuels, (2) manufacturability to meet cost and time-to-market targets, and (3) durability in the harsh environment of an operating turbine. Additive manufacturing (AM) provides accelerated product development. However, uncertainty remains around the durability of parts with rough AM surfaces. A fully experimental approach towards quantifying fatigue performance of rough AM microstructures is costly and laborious. Instead, Solar Turbines Incorporated (Solar) proposes the use of a crystal plasticity finite element (CPFE) model to quantify the factors that drive AM surface fatigue behavior. Solar will use the CPFE results, along with targeted experimental data, to train a computationally efficient surrogate model that can be incorporated into existing turbine part lifing methods.
Fall 2021
OPTIMIZATION OF SCALABLE AEM ELECTROLYZER FOR HYDROGEN PRODUCTION EFFICIENCY AND LIFETIME USING 3D DEVICE-LEVEL CONTINUUM MODEL
Principal Investigator:
Jimmy Rojas, Ashutosh Devekar | EvolOH, Inc.
National Lab Partner:
Hans Johansen, Adam Weber | Lawrence Berkeley National Laboratory
Summary:
EvolOH’s novel pure-water anion-exchange-membrane (AEM) electrolyzer, featuring low system cost and made entirely of earth-abundant materials, is a transformational improvement over conventional electrolytic devices for producing clean hydrogen. However, inefficiencies and egradation are not yet minimized due to the complexity of the device. EvolOH and researchers from Lawrence Berkeley National Laboratory (LBNL) will use high-performance computing (HPC) resources to simulate a 3D representation of the electrolyzer cell using an existing microscale continuum model. This HPC effort will determine optimized device components (membrane, ionomer, catalyst, additives), their architecture (porosity, solid volume fractions, relative sizes), and operational parameters (temperature, current density). This optimization will help EvolOH reach commercial performance targets, ensuring high efficiency and long lifetime. This project will rapidly advance EvolOH’s commercialization pathway, making the DOE Hydrogen Energy Earthshot target of $1/kg-H2 production feasible and helping secure a domestic H2 supply chain.
Fall 2021
DEVELOP A NEW INTEGRATED MACRO→MICRO←NANO (MMN) MULTISCALE MODELING FRAMEWORK TO OPTIMIZE HIGH STRENGTH ALUMINUM ALLOYS AND PROCESSES FOR VEHICLE LIGHT-WEIGHTING
Principal Investigator:
Yang Huo, Garret Sankey | Ford Motor Company
National Lab Partner:
Xioahau Hu, Jiahao Cheng | Oak Ridge National Laboratory
Summary:
Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project proposes to utilize an innovative ORNL multiscale microstructure-based finite element (FE) code to further the understanding of microstructural relationship to fracture properties of high strength 6000 series alloys. Work will focus on plane strain bending to quantify the material’s self-pierce rivet-ability based on microstructural factors. If this project is successful, the multiscale framework could help to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.
Fall 2021
HIGH-PERFORMANCE COMPUTING (HPC) FOR SECONDARY LEAD FURNACE PROCESS OPTIMIZATION
Principal Investigator:
Alexandra Anderson | Gopher Resource LLC
National Lab Partner:
Vivek Rao, Prashant Jain | Oak Ridge National Laboratory
Summary:
Secondary lead is an important industry in the United States, with over 1 million tonnes produced in 2020 (Rainford, 2020). There is an excellent opportunity to improve the efficiency of pyrometallurgical furnaces used within the industry, which would provide environmental and energy-saving benefits. Gopher Resource (“Gopher”) is proposing a project on the use of high-performance computing (HPC) and numerical modeling to perform process optimization of secondary lead furnaces. This work would be a continuation of modeling efforts performed at Oak Ridge National Laboratory (ORNL) on multiphysics modeling of thermal transport with species chemistry, phase change, and turbulent flows. Improvements in furnace thermal efficiency can potentially result in energy savings of up to 750 billion BTUs per year and at least half a million tonnes of carbon dioxide emissions leading to a total cost savings of $30 million per year for the lead industry.
Fall 2021
MANUFACTURABLE HIGH TOUGHNESS, LOW THERMAL CONDUCTIVITY, THERMAL BARRIER MATERIALS FOR HYDROGEN COMBUSTION TURBINES
Principal Investigator:
Molly O’Conner | Praxair Surface Technologies
National Lab Partner:
Michael Gao | National Energy Technology Laboratory
Summary:
The use of hydrogen as clean fuel for industrial gas turbines is key to the decarburization of power generation in the United States. Although there is a demand to move towards 100% hydrogen fueled power generation turbines, current turbine materials are unable to accommodate the increased temperature and durability requirements. To address this limitation, new thermal barrier coatings (TBCs) with increased temperature and erosion capabilities are necessary. We will partner with NETL to expand the manufacturability and performance of a low conductivity/high toughness candidate, thermal barrier material, to enable its performance in 100% hydrogen fueled power generation. Specifically, the expansion of the non-transformable tetragonal phase field will be enabled by modeling targeted alloying of the oxide material allowing for the development of a single phase, low thermal conductivity, high temperature stable oxide with significant toughness imparted by ferroelastic toughening.
Fall 2021
CARBON NANOSPIKE BASED PHOTOELECTROCHEMICAL CO2 CONVERSION
Principal Investigator:
Yang Song, Brandon Iglesias, Thomas Harris | Reactwell, L.L.C.
National Lab Partner:
Mina Yoon | Oak Ridge National Laboratory
Summary:
ORNL reported the discovery of carbon nanospikes (CNS) in 2014, a unique nitrogen-doped graphene comprising 50-80 nm atomically sharp spikes grown using plasma-enhanced chemical vapor deposition (PE-CVD). Due to the atomically sharp texture, CNS are able to electrochemically reduce refractory molecules including CO2 and N2, and generate 2C+ hydrocarbons from electrocatalytic CO2 reduction reaction. This project will develop a first-principles-based atomistic model of metal-doped CNS for the optimal production of ethanol. First-principles data will be used to train machine learning (ML) schemes to identify key descriptors and efficiently compile complex parameters to guide experimental effort. Our goal is to understand the structural properties of metal-doped CNS at the atomic level and to relate them to their key function as electrochemical catalysts. In particular, we will study the role of metal particles doped on the CNS and their morphology to improve the efficiency and yield of the CO2 conversion reaction.
Fall 2021
HPC MODELING OF RAPID INFRARED SINTERING FOR LOW COST, EFFICIENT SOLID OXIDE ELECTROLYZER CELL MANUFACTURING
Principal Investigator:
Bryan Blackburn, Rebekah Patrick | Redox Power Systems, LLC.
National Lab Partner:
Adrian Sabau, Jean-Luc Fattebert | Oak Ridge National Laboratory
Summary:
Ceramic Solid Oxide Electrolyzer Cells (SOECs) can convert steam and renewable electricity into H2 with 98% efficiency. SOEC performance (e.g., current density) has improved significantly, but commercialization will benefit from manufacturing improvements. SOEC half-cells (fuel electrode/electrolyte) are sintered at ~1400-1500°C to achieve the necessary properties (e.g., strength, gas transport, electrical-connectivity, gas-tight membrane), but electric pusher kilns run continuously with firing taking 3+ days due to low energy flux and complex sintering profiles. Pulse thermal processing (PTP) offers 10,000X higher energy flux (e.g., using IR sources like plasma arc lamps), which can drive down cell cost, increase throughput, enhance properties, and improve manufacturing energy efficiency. PTP sintering is a complex, dynamic process requiring high performance computing (HPC) to greatly reduce the experimental cycle needed for optimization. We seek to model sintering using IR heat sources with different spectral properties to study dynamics of diffusion, microstructural evolution, and grain growth.
Fall 2021
MATERIALS RELIABILITY QUANTIFICATION FOR EFFICIENT HYDROGEN-FUELED GAS TURBINES FOR THE ENERGY TRANSITION
Principal Investigator:
Kai Kadau | Siemens Energy Inc
National Lab Partner:
Patxi Fernandez-Zelaia, Yousub Lee | Oak Ridge National Laboratory
Summary:
Hydrogen fueled land-based gas turbine engines are an essential component of the future hydrogen-based economy. Direct use of hydrogen fuels in existing engines, designed for fossil fuels, results in changes in the operational profile (temperatures and pressures) thus it is imperative to understand and quantify these effects on engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which impacts material performance and reliability. This proposal seeks to establish a computational framework for evaluating the probabilistic creep performance of high temperature materials where uncertainty comes from both microstructural and operational variability. A high-fidelity physics model will be used to evaluate the local microstructure-scale response. A computationally efficient surrogate will then be trained and used to sample a large number of grain-scale simulations to establish microstructure sensitive extreme value statistics. These results will be incorporated into Siemens’ probabilistic lifing models which will accelerate adoption of hydrogen fueled engines.
Fall 2021
COMPUTATIONAL MODELING OF COST-EFFECTIVE CARBON CAPTURE TECHNOLOGIES ON INDUSTRIAL GAS TURBINES TO REDUCE CO2 EMISSION
Principal Investigator:
Youduck Sung | Solar Tubines Incorporated
National Lab Partner:
Chao Xu | Argonne National Laboratory
Summary:
Small and mid-capacity gas turbines play an important role in power generation and field operations of many engineering projects, such as oil and gas, mining, and transportation. There are over 20,000 such machines worldwide and there is a tremendous opportunity to reduce greenhouse gas emissions from them with newly developed carbon capture systems. In this project, we propose high-fidelity large eddy simulation (LES) based modeling of novel carbon capture system on Solar Turbines’ industrial gas turbines. This new technology utilizes exhaust gas recirculation (EGR) with augmented oxygen supply in a semi-closed cycle to capture CO2 and has been successfully demonstrated on several pilot projects on Caterpillar’s (parent company of Solar Turbines) internal combustion engines. This technology is attractive because its application in Solar’s industrial gas turbines requires no significant engine hardware changes and can significantly reduce the capital and operating costs of CO2 removal by commercially available CO2 removal technologies.
Fall 2021
MIXING EQUIPMENT OPTIMIZATION USING COMPUTATIONAL FLUID DYNAMICS AND MACHINE LEARNING
Principal Investigator:
Chi-Wei Tsang | The Dow Chemical Company
National Lab Partner:
Lorenzo Nocivelli, Pinaki Pal | Argonne National Laboratory
Summary:
The U.S. chemical industry is striving to become more sustainable. Energy sustainability can be achieved by developing higher efficiency mixing and contacting equipment involved in the mixture of chemicals. Typically, such equipment can have a multitude of design parameters, and are difficult and time-consuming to optimize for highest efficiency using manual design-of-experiments type approaches or conventional optimizers. This is due to the expensive nature of computational fluid dynamics (CFD) simulations, especially those involving multiphase flows. Here, we propose using advanced machine learning (ML) techniques in conjunction with high-performance computing (HPC) to speed up and improve CFD-driven design optimization of a gas-liquid turbulent jet mixer. This work will lead to an efficient framework combining ML and HPC for optimizing process equipment, and will provide a demonstration case for ML approaches to enable their wide adoption across the chemical industry.
Fall 2021
HPC FOR OPTIMIZING PROCESS PARAMETERS TO CONTROL MATERIAL EVOLUTION IN SEAMLESS INDUCTION HARDENING OF WIND TURBINE MAIN SHAFT BEARINGS
Principal Investigator:
Rohit Voothaluru, Lee Rothleutner | The Timken Company
National Lab Partner:
Balasubramaniam Radhakrishnan, Sarma Gorti | Oak Ridge National Laboratory
Summary:
Global wind turbine market is expected to increase to about 1800GW by 2030, compared to 540GW in 2018. This increase requires wind turbine mainshaft bearings of increasingly large size produced within a short period of time. Existing carburizing furnaces are limited in terms of size and overall capacity which will limit the rapid growth. Seamless induction hardening (SIH) process has been used to great success in renewable energy sector for producing large mainshaft bearings for wind turbines. The small footprint, reduced energy consumption and low lead times for SIH make it an ideal solution for this rapid growth. However, the rapid scale up to SIH requires understanding the phase change and optimization of process parameters to reduce potential quench micro-cracking. This project will employ a phase field (PF) model that captures the evolution of the bearing steels and perform a parametric study of the effect of the process variables.
Spring 2021
AI-DRIVEN ACCELERATED INCLUSION ANALYSIS FOR ENERGY EFFICIENT STEELMAKING
Principal Investigator:
Tathagata Bhattacharya, Ming Tang | ArcelorMittal; Bryan Webler | Carnegie Mellon University; Gary Casuccio, Kai van Beek, Henry Lentz – | RJ Lee Group
National Lab Partner:
Yeping Hu, Victor Castillo | Lawrence Livermore National Laboratory
Summary:
The iron and steel industry consumes an estimated 6% (~1470 PJ) of the energy used by the U.S. manufacturing sector, with about 80% of this energy used to produce liquid steel. Since the final steel product quality and resulting yield is mostly determined at the liquid steel stage, control of non-metallic inclusions (oxide, sulfide, or nitride particles) in liquid steel can make or break the process. The state-of-the-art in inclusion control involves lab-based automated scanning electron microscopy (SEM) on samples taken from liquid steel. In this project, we will use computer vision and machine learning methods with HPC resources to accelerate the analysis process so that it can be used for near-real time process control on the shop floor. The project team aims to realize considerable benefits in terms of energy savings and reduction of CO2 emissions via minimizing waste and increasing quality by avoiding re-melting and re-processing. This technology could reduce this number by 1-2% and reduce CO2 emission by ~1.5 million tonnes/yr.
Spring 2021
MULTI-PHYSICS MODELING OF CARBON FIBER OXIDATION TO ENHANCE PROCESS ENERGY EFFICIENCY
Principal Investigator:
Cliff Eberle | Collaborative Composite Solutions Corporation
National Lab Partner:
Srikanth Allu | Oak Ridge National Laboratory
Summary:
Integration of Carbon Fiber Reinforced Polymer (CFRP) materials in automotive industry is driven by the requirements of light weighting of components for increased fuel savings. The conversion of polyacrylonitrile (PAN) precursor to carbon fiber is a thermochemical process where the material loses 50% of its mass, undergoes softening, shrinkage, recrystallization, and reorientation. Though carbon fibers are prized for their high stiffness- and strength-to-weight ratios, their production is extremely energy intensive with oxidation being the most energy-intensive and rate-limiting step. It is imperative that the temperature, gas flow, and chemical reactions, which critically affect properties, be simulated to ensure that the equipment and process design provides the necessary uniformity. Reducing carbon fiber’s manufacturing cost and energy intensity will enable its benefits for mass markets such as sustainable vehicles and buildings. High performance computer (HPC) modeling of oxidation is anticipated to increase carbon fiber production rate and energy efficiency by at least 10%.
Spring 2021
ADVANCED HPC THERMAL SIMULATIONS FOR WAFER-SCALE DIAMOND HEAT SPREADERS
Principal Investigator:
Jeroen van Duren | Diamond Foundry
National Lab Partner:
Daniel F. Martin | Lawrence Berekeley National Laboratory
Summary:
With increasing chip power, the choices boil down to wasting energy and chip longevity by running too hot or wasting performance by throttling. Designers need improved solutions to deal with increases in power leakage, thermal and electrical resistance. Given the ~5% annual growth rate of US data center energy consumption, technologies that can mitigate these effects are critical to improving chip efficiency. Diamond Foundry’s single crystal diamond heat spreaders have unmatched thermal conductivity and can be directly bonded to chips, greatly improving thermal management of computing and power conversion electronics. However, design tradeoffs for chip and package performance, power, and reliability are not well understood, as the relevant models span six orders of magnitude in scale (~10nm-10mm). We propose to leverage DOE’s investments in HPC algorithms and software to develop new thermomechanical analysis tools, which will accelerate the design and optimization of heat spreaders customized for each unique chip design. This technology could save about 0.03 Quads of electricity per year and reduce CO2 emissions by about 1 million tonnes/yr.
Spring 2021
EFFECT OF PROCESSING ON PRECIPITATION KINETICS IN NANO POLYCRYSTALLINE 7075 ALUMINUM ALLOY
Principal Investigator:
Vis Madhaven | Fairmount Technologies, LLC
National Lab Partner:
Shinyang Hu | Pacific Northwest National Laboratory
Summary:
Fairmount Technologies (FT) seeks to optimize the thermomechanical processing of AA 7075. The target yield strength is 20% higher than commercially available 7075-T6 temper sheet which will enable structural light weighting by a similar magnitude and reduce the carbon footprint of the transportation sector. To reduce processing cost and make this commercially viable, it is important to understand physical mechanisms driving microstructural changes. A mesoscale phase-field model seeded with experimentally measured microstructures and assessed thermodynamic and kinetic properties will be developed and exercised using HPC at PNNL. It will account for the effect of deformation, temperature, and chemistry on precipitation kinetics in nanocrystalline microstructures. This will help (i) design the processing schedule to minimize cost, (ii) optimize precipitation heat treatment to maximize yield strength and ductility, and (iii) increase the stability of the final microstructure. Lighter weight cars made using this material could save 6 million gallons of gas per year and reduce CO2 emissions by 4 million tonnes/yr.
Spring 2021
COMBINED ELECTROMAGNETIC AND MICROMAGNETIC SIMULATION OF INTEGRATED MAGNETIC DEVICES FOR IMPROVED INTEGRATED CIRCUIT ENERGY EFFICIENCY
Principal Investigator:
Michael Lekas | Ferric Inc.
National Lab Partner:
Hans Johansen | Lawerence Berekeley National Laboratory
Summary:
In the U.S., data centers already represent 1.8% of total energy consumption, and this figure continues to grow at an annualized rate of approximately 4%. To improve server efficiency and size, electronics manufacturers are adopting novel DC-DC power converter technologies, such as Ferric’s integrated voltage regulators (IVRs) with thin-film (TF) ferromagnetic inductors. Accurate modeling of these magnetic devices in conjunction with integrated circuits is paramount for achieving high conversion efficiency (η) with these products. However, thin-film magnetics simulation remains computationally expensive, and is the primary obstacle to reducing design-cycle time and optimizing performance. This proposal outlines follow-on development of a coupled micromagnetic-electromagnetic (MM-EM) equations solver, which employs adaptive mesh refinement (AMR) techniques optimized for HPC systems, in order to rapidly and accurately model TF magnetic devices for integrated power conversion applications. This technology could save 50 billion KWh electrical energy per year and reduce CO2 emissions by 20 million tonnes/yr.
Spring 2021
CLEAN, DISPATCHABLE AND AFFORDABLE CHP USING A NOVEL ARGON POWER CYCLE
Principal Investigator:
Guillaume Beardsell | Noble Thermodynamic Systems, Inc.
National Lab Partner:
Ricardo Scarcelli | Argonne National Laboratory
Summary:
With a growing share of renewables producing electricity, an increasing demand for cost-competitive dispatchable power has emerged, with the added constraints of aligning with climate change and air pollution abatement goals. Noble Thermodynamics Systems’ mission is to satisfy this demand by developing the Argon Power Cycle, a noble gas closed-loop thermodynamic cycle which radically increases the reciprocating engine efficiency while capturing all air pollutants. This system can cleanly and cost-effectively provide the electric grid with improved resilience and reliability. This project is set forth to solve pressing technical challenges by designing an advanced ignition system optimized for Noble Thermodynamics Systems’ engine operating on the Argon Power Cycle. This ignition system is expected to radically increase engine reliability and efficiency, thus bringing the Argon Power Cycle one step closer to the marketplace. This could save 0.4 TerraBTUs over 20 years for one plant alone, and many times that if widely adopted. This technology captures 100% of the CO2 emissions.
Spring 2021
HIGH PERFORMANCE COMPUTING TO OPTIMIZE AN INDUCED FLOW POWER GENERATOR DEVICE FOR WASTE HEAT RECOVERY APPLICATIONS IN DATA CENTERS
Principal Investigator:
Gaurav Bazaz, Abhishek Saraf | Spar Energy LLC
National Lab Partner:
Jain Prashant, Nithin Panicker, Rao Vivek | Oak Ridge National Laboratory
Summary:
Industrial waste heat in the U.S. accounts for 5-13 quadrillion BTU/year, of which 60% is low-temperature (<450°F). This is a substantial fraction of the 90-100 quadrillion BTUs of total U.S. energy consumption. At low temperatures, current waste heat-to-power technologies are uneconomical and therefore, commercially unviable. Spar Energy has recently developed the Induced Flow Generator (’IFG’) technology, which allows low-temperature waste heat to be converted into power efficiently and cost-effectively. Patents 9,938,963 and 10,190,603 protect this technology, which uses ambient air as the working fluid and uses convergent-divergent nozzles to convert heat into kinetic energy. Additionally, it significantly reduces equipment requirements, thereby reducing capital and operating expenses, resulting in lower costs for customers such as industrial plants and data centers. In this project, IFG technology design will be optimized to meet competitive performance requirements and increase commercial readiness, by leveraging high performance computing and Oak Ridge National Lab expertise. If deployed nation-wide, this technology could save 0.5 Quads/yr of electricity and reduce CO2 emissions by 75 million tonnes/yr.
Spring 2021
ADSORPTIVE CO2 REMOVAL FROM DILUTE SOURCES (ACO2RDS)
Principal Investigator:
Donny Cooper | TotalEnergies E&P Research & Technology USA, LLC
National Lab Partner:
John Low | Argonne National Laboratory
Summary:
Among the many technological approaches needed to reach net zero emissions by 2050, CO2 capture and storage (CCS) from power generation and other industries are seen as key enablers. TotalEnergies, a broad energy company, has initiated an ambitious program to mitigate greenhouse gas emissions by capturing CO2 directly from the air and other dilute sources. Using HPC and combining atomistic modeling, detailed process modeling and leading-edge machine learning methods, we will accelerate the discovery of novel adsorbents and process improvements suitable to reduce the costs of CO2 capture at an industrial scale. CO2 emissions could be reduced by up to 10 million tonnes/yr.
Fall 2020
REDUCING CONSUMPTION OF MELT BLOWN FIBER MANUFACTURING PROCESSES
Principal Investigator:
Dr. Bill Klinzing | 3M Company
National Lab Partner:
Dr. Ian Foster, Dr. Sibendu Som, Dr. Debolina Dasgupta | Argonne National Laboratory
Summary:
This proposal aims at minimizing energy consumption of melt blown (MB) fiber manufacturing processes. Such processes are widely used for 3M products including filters, fabrics and insulation materials. The most impactful recent example is the base material for the making N95 mask during the COVID-19 pandemic. The process is extremely energy intensive since it relies heavily on compressed air and electrical heating. This proposal seeks methods to minimize energy consumption through a combination of High-Performance Computing, Computational Fluid Dynamics, and Machine Learning. It is estimated that the optimization will lead to a 20% reduction in energy consumption. Approximately 300 tons of MB nonwovens are produced worldwide each year by 3M and other manufacturers, consuming approximately 245 GW hour/year. A 20% (49 GW hour/year) reduction in energy consumption would have a global impact as 3M is a major player in the nonwoven manufacturing market and other manufacturers would likely follow suit.
Fall 2020
DEVELOPMENT OF HIERARCHICAL ODS HIGH ENTROPY ALLOYS
Principal Investigator:
Dr. Dongsheng, Dr. Joseph Wysocki | Advanced Manufacturing LLC
National Lab Partner:
Dr. Michael Gao | National Energy Technology Laboratory
Summary:
The objective of this project is to develop and manufacture cost-effective, oxide dispersion-strengthened (ODS), NiCrFeCo-rich high entropy alloys (HEAs) that are superior to Ni-based superalloys (e.g. IN740) for repair or replacement service in extreme environments. High throughput (HT), multiscale computer modeling will be performed to accelerate alloy discovery by interrogating the intrinsic properties of the alloys including phase stability, diffusion, stacking faults energy, short-range order, and yield strength. In particular, a hierarchical microstructure will be sought: A ductile high-entropy solid solution matrix in the face-centered cubic (FCC) structure that is strengthened by high-entropy coherent ordered L12 precipitates and nano oxides dispersion, and further toughened by transformation induced plasticity (TRIP) and/or twinning induced plasticity (TWIP) effects. Advanced Manufacturing LLC (AMLLC) and Connecticut Center of Advanced Technology (CCAT) will carry out validation and evaluation, including additive manufacturing (AM), microstructure characterization, mechanical properties test and oxidation experiments.
Fall 2020
DEVELOPMENT OF ADDITIVE MANUFACTURING OF REFRACTORY MATERIALS FOR CRITICAL APPLICATIONS
Principal Investigator:
Dr. Yuri Plotnikov, Dr. Kaushik Joshi, Dr. Rich Martukanitz, Dr. Nasser Ghariban, Dr. Gaurav Ameta
National Lab Partner:
Dr. Yousub Lee | Oak Ridge National Laboratory
Summary:
The proposed program is focused on establishing computational framework, foundational knowledge, and additive manufacturing (AM) capabilities for accelerating the use of refractory metals for gas turbine generators, which is considered a significant enabling technology for increasing operating temperatures and improving efficiency of these systems. The program will develop and apply high-fidelity process and material models for simulation of potential defects, deposition geometry, and resultant microstructure of refractory alloys produced using directed energy deposition (DED) AM. Upon validation of the developed model, virtual and physical experiments will be designed and conducted to create process and material maps. The maps will assist in establishing quantitative relationships to define the influence of primary processing parameters on attributes used to delineate process consistency and product quality for meeting the stringent requirements for this industry. The developed models will be used to conduct significant virtual experimentation at the supercomputing facilities within Oak Ridge National Laboratory.
Fall 2020
IMPROVING MODELING AND SIMULATION TOOLS TO INDUCTION PIPE BENDING
Principal Investigator:
Dr. John Shingledecker, Mr. Kavarana Firdosh
National Lab Partner:
Dr. Noah Paulson | Argonne National Laboratory
Summary:
The proposed project seeks to leverage high performance computing (HPC) and active machine learning to apply state-of-the-art modeling and simulation tools to induction pipe bending of nickel-based alloys for energy applications. Induction bending offers significant improvements to the production of energy application piping systems, which enable highly efficient power cycles. However, the process has largely been ignored by the modeling community, and therefore the introduction of new piping alloys designed for high-temperature service require a trial-and-error experience-based approach. HPC offers the possibility of developing an accurate model of the non-symmetric 3-D temperature and strain profiles during heating, bending, cooling, and heat-treatment. Active machine learning can then efficiently construct an optimal surrogate model for the high-fidelity simulations, and therefore enable a large multi-variable assessment of the wide range of potential pipe sizes and process controls to develop scientifically sound approaches to enhance product quality and reduce overall energy intensity.
Fall 2020
OPTIMIZATION OF SULFUR THERMAL ENERGY STORAGE
Principal Investigator:
Dr. Karthik Nithyanandam
National Lab Partner:
Dr. Zhiwen Ma, Dr. Michael Martin | National Energy Renewable Laboratory
Summary:
Industrial process heating (IPH) accounts for ~70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately, 1500 TWht (~60%) of IPH demand is in the temperature range of 100-300℃. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. This HPC4EI project will facilitate Element 16’s development and commercialization of low-cost and high-impact molten sulfur TES for dispatchable IPH and support its broad applications and deployment. The project will accelerate Element 16’s molten sulfur TES product design with a high-fidelity HPC model validated by experimental data.
Fall 2020
IMPROVING ADDITIVE MANUFACTURED COMPONENT PERFORMANCE
Principal Investigator:
Dr. Qigui Wang, Dr. Andy Wang
National Lab Partner:
Dr. Alex Plothowski | Oak Ridge National Laboratory
Summary:
Coupling scalable process and microstructure models to optimize fatigue performance of new high-performance aluminum alloys for additive manufacturing to increase performance and efficiency of automotive engines.
Fall 2020
OPTIMIZING COUNTER CURRENCY AND IMPROVE SELECTIVE GAS PERMEATION
Principal Investigator:
Mr. John Jensvold
National Lab Partner:
Dr. Ramanan Sankaran | Oak Ridge National Laboratory
Summary:
Several gas separation applications such as the removal of CO2 from natural gas require a highly efficient gas separation membrane device to purify the feed stream with minimal loss of methane or other light hydrocarbons and a minimal loss of natural gas pressure. In both ways, energy loss as well as green-house gas emissions are minimized. This is often carried out with shell-side fed hollow fiber membrane modules equipped with membranes that can selectively permeate CO2 from the feed gas by means of a partial pressure driving force across the membrane. To maximize this driving force, the preferred module design is counter-current in which the permeate gas runs counter to the feed gas that is being processed. We propose to develop a CFD model that enables optimizing the counter-current flow patterns in the module while minimizing pressure drop. The CFD model will use effective media models informed by fiber resolved direct numerical simulations and will be validated against experimental flow measurements.
Fall 2020
TRANSPORT ANALYSIS AND OPTIMIZATION IN A MW-SCALE CO2 ELECTROLYZER
Principal Investigator:
Dr. Sichao | Twelve (formerly Opus 12)
National Lab Partner:
Dr. Victor Beck | Lawrence Livermore National Laboratory
Summary:
Twelve (formerly Opus 12) is developing and scaling up a technology that converts CO2 into high-value chemicals and fuels using renewable electricity, which represents a significant opportunity to transform our global energy system, allowing society to utilize excess renewable energy and profitably utilize CO2 while closing the loop on greenhouse gas emissions. One important challenge relevant to scaling up is the need to manage heat generated at the electrodes to mitigate materials degradation and water management problems. Compared with exhaustive experimentations, High Performance Computing is a more viable and faster route to identify key fluid, thermal and reactive factors impacting scaled-up performance. Through this project, we seek to collaborate with LLNL to better understand the local thermal environment and its impact on water management in the MW-scale CO2 electrolyzers via developing a series of 3D electrolyzer cell and stack models, with the purpose of accelerating transformational technological advances in the industry.
Fall 2020
HIGH PERFORMANCE AND REDUCED-COST MANUFACTURABILITY OF ELECTROCHROMIC (EC) DEVICES
Principal Investigator:
Dr. Anoop Agrawal, Dr. John Cronin, Dr. Sahila Perananthan
National Lab Partner:
Dr. Stephan Irle, Dr. Debsindhu Bhowmich, Dr. Dmitry Ganyushin | Oak Ridge National Laboratory
Summary:
The use of electrochromic (EC) dyes in Glass Dyenamic’s devices has shown to significantly reduce assembly cost for smart glass building windows with improved energy efficiency. Low manufacturing cost and aesthetic consideration has the potential of significantly increasing the adaptation of this technology in commercial and residential glass markets. However, the experimental design of suitable EC dyes with desired photophysical properties is highly resource intensive. We therefore propose a combined high-performance computing (HPC)- and machine learning (ML)-driven inverse structural design of anodic EC dyes based on high-level electronic structure theory to predict their photophysical properties in neutral and oxidized states. Highly accurate ab initio multireference wavefunction methods will be employed on OLCF’s Summit supercomputer to compute UV/Vis absorption spectra for a large training set of dye molecules. This data will drive a novel ML approach to predict novel EC dyes with superior properties.
Fall 2020
OPTIMIZATION OF PROCESSING PARAMETERS FOR METAL POWDER PRODUCTION
Principal Investigator:
Dr. Andrew Heidloff
National Lab Partner:
Dr. Iver Anderson | Ames Laboratory
Summary:
Additive Manufacturing (AM) technologies are redefining next generation, energy critical component/system designs and manufacturing (e.g., stationary gas turbines, heat exchangers for extreme environments, etc.). The ability to produce complex geometries coupled with rapid development of new materials capable of harsh environments allows for unprecedented energy efficiencies through AM. Gas atomization (GA) is one of the most promising methods of producing feedstock powders used in AM processes, but suffers from inefficient powder yields and poor powder quality characteristics. This follow-on project aims to further develop the current understanding of breakup mechanisms during GA by using 2D and 3D computational fluid dynamics (CFD) to study the key variables leading to enhanced efficiency/ precision and powder quality. The results will aid US powder manufacturers in optimizing GA technologies to improve powder yield/quality, reduce material and energy production costs, and expedite the availability of novel and fully developed alloy powders for the AM marketplace.
Fall 2020
DEFECT-FREE PRODUCTION OF SOLVENT-FREE DETERGENTS
Principal Investigator:
Dr. William Hartt IV
National Lab Partner:
Dr. Rehka Rao | Sandia National Laboratory
Summary:
A new-to-the-world product form may revolutionize consumer cleaning products such as laundry detergent, shampoo, dentifrice, and lotions, by lowering energy usage for manufacturing and transportation and significantly reducing the carbon footprint compared to traditional products. These novel products will eliminate the need for water transportation, yet still perform effectively and receive excellent consumers reviews. However, formulation and processing of these game-changing materials are challenging. P&G desires modeling and simulation technology to theoretically determine process windows and formulate new products optimized for reduced defects. The rheology of the precursor solution, containing surfactants, polymers, and other actives, must be optimized such that fibers may be formed and solidified without instabilities such as droplet formation, fiber folding, or breakage. Process development and optimization require significant insight into these physical process for predictivity and control; process breakdowns and low yield can be costly. In this proposal, P&G will utilize expertise in rheology and process development complemented by HPC transient 3D multiphase viscoelastic flow models developed at Sandia to provide a modeling and simulation approach, using machine learning, to capture the complex rheology and advance process design. This “digital manufacturing” approach will allow for defect-free production of solvent-free detergents with an accelerated timescale and reduced waste streams compared to traditional approaches such as build-test cycles.
Fall 2020
IMPROVING JET ENGINE LIFECYCLE ENERGY EFFICIENCY
Principal Investigator:
Dr. Michael Joly
National Lab Partner:
Dr. Pinaki Pal, Dr. Muhsin Ameen, Dr. Opeoluwa Owoyele | Argonne National Laboratory
Summary:
This proposal aims to quantify the impact of manufacturing uncertainties in gas turbine engines and to better assimilate lifecycle sensitivities in the development of next-generation energy-efficient technologies. Reliable film cooling drives durability and thermal efficiency of turbine stages in gas turbine engines, but is greatly sensitive to variations in the shape of cooling holes (such as, machining offset, blockage from thermal barrier coating, and surface roughness) induced by manufacturing processes. The primary objective of this proposal is to develop a machine learning technique to desensitize film cooling effectiveness to manufacturing variability. The novelty of this proposal is in the development and application of composite neural network as a surrogate of multi-fidelity computational fluid dynamics (CFD) simulations towards the development of a reduced-order model to inform gas turbine engine design practitioners of the impact of manufacturing uncertainties on the energy efficiency and durability of gas turbine engine components.
Fall 2020
AN ICME MODELING FRAMEWORK FOR METAL MATRIX COMPOSITES
Principal Investigator:
Tahany El-Wardany, Masoud Anahid | Raytheon Technologies Research Center
National Lab Partner:
Mark Messner | Argonne National Laboratory
Summary:
Widespread use of Metal Matrix Composites in automotive and aerospace industries could lead to substantial energy savings by light-weighting components and increasing operating temperatures and efficiency. MMCs have complex microstructures and current material design process largely relies on iterative experimentation, leading to long material design and qualification times. A physics-based modeling approach could greatly accelerate material development by providing a direct connection between key microstructural features and the resulting material properties. This project, a collaboration between Argonne National Laboratory (ANL) and Raytheon Technologies Research Center (RTRC), would develop a physics-based, full-field model for a key MMC system, run throughput simulations on high performance computing, and develop a surrogate model using the throughput simulations as training data to connect key microstructural features to the material properties of interest. RTRC would then incorporate this surrogate model into their material design process by manufacturing materials with microstructures tailored to optimize the components performance.
Spring 2020
LASER POWDER BED FUSION TO IMPROVE CAR PART QUALITY
Principal Investigator:
Dr. Mei Li and Dr. Yang Huo
National Lab Partner:
Dr. Xiaohua Hu | Oak Ridge National Laboratory
Summary:
Laser power bed fusing (L-PBF) additive manufacturing is a key enabling technology to manufacture highly complex and integrated automotive structures. L-PBF processes usually produce excessive and nonuniform residual stresses, which increase quality uncertainties and manufacture issues, leading to increases in cost and energy consumption in the form of rejected parts. We propose to extend an HPC-compatible in-house ORNL finite element (FE) code, which was demonstrated on pseudo-3D fully-coupled thermomechanical L-PBF simulations, to part scale and use it to predict temperature evolution and residual stress during L-PBF with experimental validation. The innovative multi-resolution and concurrent modeling approach adopted in this code ensures accuracy and computational efficiency, which will enable energy-efficient and high-yield, low-cost manufacturing of optimized, qualifiable automotive structures. The successful completion of this project will contribute towards reaching technical targets outlined in AMO’s Program Plan to develop additive manufacturing systems that deliver consistently reliable parts with predictable properties.
Spring 2020
NEXT GENERATION RECYCLABLE CELLULOSE-BASED PACKAGING MATERIALS
Principal Investigator:
Kelly Williams
National Lab Partner:
Dr. Peter Ciesielski | National Renewable Energy Laboratory
Summary:
Fossil plastics in single-use packaging is one of the top existential problems in the world, and post-consumer collection of discarded materials continues to be elusive. Compostable packaging offers substantial energy savings relative to plastic packaging that requires recycling or upcycling in circular economy scenarios, and brands across the globe are seeking compostable options for flexible packaging. Cellulose, particularly dissolvable pulp that can be converted into high barrier packaging films, is currently in very high demand. We will leverage high-performance computing to accelerate evolution of art and science related to cellulose-derived films to meet societal demands and displace environmentally detrimental incumbent products. Specifically, molecular variations of cellulose dissolving pulp will be designed in-silico and their performance metrics, including mechanical, thermal, and barrier properties, will be predicted by large-scale simulation of polymer assemblies. The results will be used to identify production targets for next generation cellulose-based packaging materials to meet industry needs.
Spring 2020
IMPROVEMENT OF CERAMIC COMPOSITES FOR AVIATION
Principal Investigator:
Dr. Joseph Shiang
National Lab Partner:
Dr. Dongwon Shin | Oak Ridge National Laboratory
Summary:
Current chemical vapor infiltration ceramic matrix composite (CVI-CMC) technology does not yet meet all requirements for commercialization in aircraft engines, in part due to the difficulty of optimizing CVI processes for batch scales and the significant capital expenses required. GE and ORNL will team to enhance ORNL’s recently demonstrated data-driven CVI simulation workflow (CVISim) by explicitly incorporating the complex chemical kinetics of the CVI process. This project will exploit high-throughput computational fluid dynamics (CFD) and modern data analytics on HPC to rapidly develop a high-fidelity CVI kinetics model. Project success will enable accurate physics, data-based forecasting of advanced processing costs, and description of the operational performance of the CVI process prior to capital equipment acquisition, simultaneously reducing scale-up risk and accelerating commercialization. Enabling the introduction of a CVI-CMC material system to both aircraft engines and land-based turbines is expected to result in significant fuel consumption reductions.
Spring 2020
ADVANCED MACHINE LEARNING FOR THE REAL-TIME PERFORMANCE-INFORMED THERMOMECHANICAL PROCESSING OF SHEET METAL PARTS
Principal Investigator:
Dr. Babak Raeisinia
National Lab Partner:
Dr. Victor Castillo | Lawrence Livermore National Laboratory
Summary:
The total onsite energy use for the Fabricated Metals (NAICS 332) sector in the U.S. is about 344 TBTU (with 11 MMT CO2-equiv of emissions) [1]. It is possible to reduce this energy and emissions footprint by ensuring that energy is only used when and where dictated by product performance needs. Uptake of such performance-informed processing strategies has been limited due to challenges in connecting product performance to processing parameters in real-time for control purposes. With current advancements in artificial intelligence and simulation capabilities, coupled with advanced sensors, it is now possible to overcome this challenge. This HPC effort is aimed at developing a lean, reduced-order model based on process simulation and sensor data to enable performance-informed thermo-mechanical processing of sheet metal parts. Broad adoption of such strategy across the industry would reduce the process energy of sheet metal parts, lead to development of novel products, while improving manufacturing yields.
Spring 2020
MULTI-PHYSICS SIMULATION FOR AN EFFICIENT ABSORBENT STRUCTURE
Principal Investigator:
Dr. Mel Allende and Dr. Ken Comer
National Lab Partner:
Dr. Scott Roberts | Sandia National Laboratories
Summary:
Open Cell Foams (Random Foams) manufacturing, as well as papermaking, is a highly energy intensive manufacturing process. Tremendous amounts of energy can be saved if the microstructures can be designed and optimized for dewatering/drying while maintaining a desirable consumer experience.
The objectives are: To utilize a model-based approach to predict the process parameters required to efficiently and effectively utilize raw materials while also reducing energy consumption in the dewatering/drying of random foam & structured papers while generating a final product which is consumer preferred. Meeting these objectives requires optimizing a truly multi-physics problem.
In this project, The Procter & Gamble Company (P&G) will use codes developed by Sandia National Laboratories to represent the needed multi-physics with high HPC scalability. These codes will then enable P&G to design and optimize foam/fiber structures that meet the consumer needs and require much less energy and cost to manufacture.
Spring 2020
MIRCOWAVE-ENHANCED MANUFACTURING OF CERAMIC MATIRIX COMPOSITES
Principal Investigator:
Dr. Ying She
National Lab Partner:
Dr. Vimal Ramanuj, Dr. Wenjun Ge, Dr. Ramanan Sankaran | Oak Ridge National Laboratory
Summary:
This project addresses the use of microwaves to intensify the manufacturing process of Ceramic Matrix Composites (CMCs) that enable light-weighting and energy efficiency improvements of gas turbines when deployed in the hot section. A conservative estimate is that CMCs can reduce thrust specific fuel consumption (TSFC) in commercial aerospace by ~2.5%, resulting in US annual nationwide energy savings of 113 TBTU. High Performance Computing (HPC) will be used to develop pore- and geometry-resolved modeling capabilities of an advanced Chemical Vapor Infiltration (CVI) process and corresponding reactor design. This will address the technical challenge of more uniform heating and temperature control, as required for manufacturing high-quality CMCs in a shorter manufacturing time. It will accelerate the development of CMCs for commercial aerospace and showcase HPC capabilities at Oak Ridge National Laboratory (ORNL).
Spring 2020
USE OF MACHINE LEARNING TO UPSCALE MAP TECHNOLOGY
Principal Investigator:
Dr. Yehia F. Khalil and Dr. Vadim Yakovlev
National Lab Partner:
Dr. Srdjan Simunovic, Dr. Merlin Theodore, Dr. Max L. Pasini | Oak Ridge National Laboratory
Summary:
U.S. carbon fiber (CF) annual demand reached ≈73.1 million lbs/yr in 2020 and the primary energy intensity of PAN carbonization-step is ≈13.4 TBtu/yr. Using microwave-assisted plasma (MAP), ORNL demonstrated ≈45% energy savings, ≈67% reduction in residence-time, ≈40% reduction in CF production cost at small-scale, which can lead to energy savings of ≈1.7 TBtu/yr, based on 2010 current typical technologies. Building on ORNL work, this project aims to: (i) develop robust multi-physics model and machine-learning (ML) optimization algorithms to upscale MAP-carbonization to industrial levels and (ii) further optimize and validate techno-economic viability of MAP-based PAN carbonization. Structural light-weighting will benefit from advancing MAP-based technology in U.S.-manufacturing for energy-efficiency and it will positively impact the commercial aircraft manufacturing-supply-chain (which includes Raytheon Technologies), and the reduction of CF manufacturing-energy consumption. ORNL HPC capabilities and expertise are crucial to overcome key challenges in the computationally intensive optimization, testing, and validation of ML-driven MAP systems.
Spring 2020
QUENCH HEAT-TREATMENT PROCESSES FOR GAS TURBINE PARTS
Principal Investigator:
Dr. Michael Glavicic and Dr. Chong Cha
National Lab Partner:
Dr. Ramanan Sankaran | Oak Ridge National Laboratory; Dr. Ik Jang | Lawrence Livermore National Laboratory
Summary:
To manufacture light-weight, advanced metal alloy components for gas turbine engines, quench heat-treatment processes are typically used. By quenching the component from elevated temperatures, the alloy sometimes undergoes a solid-state phase transformation which produces special microstructures with the required, enhanced mechanical properties. However, the quenching can also lead to cracks forming in the component. Addressing the quench cracking problems adds a significant burden to the cost, schedule, and energy demand of manufacture. Currently, optimizing the quench process to mitigate or avoid the cracking is performed largely by trial-and-error, relying heavily on costly experimental (thermocouple) trials to understand the local thermal gradients which cause the cracks to form. In this work, high-performance computing is employed to establish the ability of modern CFD (computational fluid dynamics) to alleviate or wholly replace the experimental quenching trials by virtual testing.
Spring 2020
NEW CLASS OF LI-ION SOLID-STATE ELECTROLYTES
Principal Investigator:
Dr. Rana Mohtadi
National Lab Partner:
Brandon Wood | Lawrence Livermore National Laboratory
Summary:
Electrochemical energy storage technologies that are durable, efficient, energy dense, cheap, safe, and industrially scalable are highly demanded by a wide range of applications. Solid-state battery technologies are promising in this regard, but they remain challenged by difficulties in simultaneously achieving energy-efficient processability, mechanical durability, and efficient performance of manufactured electrolyte components. Toyota Research Institute of North America has developed a new class of Li-ion solid-state electrolytes that promise highly efficient performance and easier processability and therefore are expected to enable practical production of solid-state batteries. However, optimizing processing requires understanding the critical connection between mechanical robustness, ionic transport, and thermodynamic properties, which is very challenging utilizing available experimental tools due to the high levels of structural complexity. This project integrates experiments with a multiscale modeling approach that can offer the necessary insights to advance this area and accelerate the deployment of practical and easily processible solid-state batteries.
Spring 2020
ULTRA-CLEAN TRANSIENT TURBINE COMBUSTOR
Principal Investigator:
Dr. David L. Hagen, Dr. Gary Ginter, Dr. John N. O’Halloran
National Lab Partner:
Dr. A. Cody Nunno, Dr. Prithwish Kundu | Argonne National Laboratory; Dr. Victor Castillo | Lawrence Livermore National Laboratory
Summary:
This research furthers VAST® TriFluid™ combustor and VAST Power Cycle™ design optimization for ~70% higher net power through a single expander, and ~24% better single turbine efficiency, with NOx and CO emissions below mandates, without catalysts, or ammonia.
To prevent state-wide blackouts from large wind/solar dropouts, California requires rapid 10-minute and 5-minute dispatch Peaker turbines, and 1-minute emergency dispatch. Frequent ramping severely harms turbines, increasing replacement costs. Clean air emission mandates cause high catalyst expenses. Emission control is difficult during rapid turbine startups, for pilot flames, and hydrogen combustion.
VAST® FastRamp™ turbines offer higher profitability with faster dispatch over >5% to <50% capacity use with renewable energy constraints. VAST’s patented independent temperature control minimizes cyclic fatigue, improving relative operating life. Accurate temperature control extends blade life. FastRamp turbines enable >40% US renewable grid penetration and international deployment. They create a profitable new niche between peakers and constrained combined cycle turbines.
Winter 2020
SIMULATION OF COMPLEX REACTING MEDIA IN MULTIDIMENSIONAL REACTION CHAMBER
Principal Investigator:
Dr. Henry W. Brandhorst, Jr. | CHZ Technologies, LLC
National Lab Partner:
Dr. Hariswaran Sitaraman, Dr. Shashank Yellapantula, Dr. Vivek Bharadwaj, Dr. Marc Henry de Frahan | National Renewable Energy Laboratory
Summary:
Thermolyzer™ is the only technology that can convert all waste hydrocarbon materials cleanly and safely into a fuel gas and salable byproducts. This means that tons of plastics now in storehouses can be converted into energy, thereby conserving non-renewable fossil fuels. The impact on the U.S. economy can be huge. However, pyrolysis of plastics is a complex process. The feedstock material that is of high variability is continuously gasified creating multiple species as it gets converted to a complex synthesis gas and carbon. The geometry and temperature gradients within the reactor are also complex. Thus, computational modeling of the reactor using high performance computing is essential in order to understand the physico-chemical interactions and to derive the best operating conditions for maximum efficiency. This project will provide the capability to achieve efficient larger-scale Thermolyzer systems (~200 ton/day capacity) that can significantly reduce the backlog of scrap plastics in the US.
Winter 2020
DEVELOPMENT OF EFFICIENT PROCESS FOR MANUFACTURING OF THERMOPLASTIC COMPOSITES WITH TAILORED PROPERTIES
Principal Investigator:
Dr. Ravi Raveendra | ESI North America, Inc.
National Lab Partner:
Dr. Ram Devanathan | Pacific Northwest National Laboratory
Summary:
This HPC4EI proposal seeks to develop a data driven approach to link features of the material and manufacturing processes to the mechanical properties of thermoplastic composite parts. This work will leverage data from physics-based commercial codes for manufacturing simulation and micromechanical analysis. There is a need to develop and manufacture lightweight materials with enhanced performance to improve the energy efficiency of automobiles. With outstanding strength to weight ratio, good fatigue resistance and good corrosion/fire resistance, composite materials are well positioned to meet the lightweight challenge. However, computational tools are needed to develop composites with enhanced performance given the large number of parameters that can be tuned to improve the performance. The proposed work will use high performance computing (HPC) and data analytics to optimize the design, shorten the time to market and generate reduced order models that are ultimately usable by U.S. industry without the need for HPC resources.
Winter 2020
HPC-ENABLED OPTIMIZATION OF HIGH TEMPERATURE HEAT EXCHANGERS
Principal Investigator:
Mr. Devlin Hayduke | Materials Sciences LLC
National Lab Partner:
Dr. Boyan Lazarov | Lawrence Livermore National Laboratory
Summary:
The Project Team proposes to combine recent advances in topology optimization-based design, high performance computing (HPC), and additive manufacturing (AM) technology to develop high pressure and temperature heat exchangers (HEX) concepts with greater than 85% effectiveness and a 50% reduction in volume in order to overcome the current design and economic limitations of conventional manufacturing methods. If realized, this technology could provide significant energy savings for power generation, aviation, and space industries.
Winter 2020
DEVELOPMENT OF HPC BASED PHASE FIELD SIMULATIONS TOOL FOR MODIFICATION OF ALLOY MORPHOLOGY TO ENHANCE MATERIAL PROPERTIES DURING ADDITIVE MANUFACTURING (AM) PROCESS
Principal Investigator:
Dr. Tahany El-Wardany, Dr. Ranadip Acharya | Raytheon Technologies Research Center (RTRC)
National Lab Partner:
Dr. Radhakrishnan Balasubramaniam | Oak Ridge National Laboratory
Summary:
Raytheon Technologies Research Center (RTRC) in collaboration with Oak Ridge National Laboratory (ORNL) proposes use of model-based tools to design alloys for additive manufacturing (AM) in order to obtain as-desired microstructure for performance improvement in aerospace and automotive applications. The performance and cost of AM products still controls the business value of deploying AM to replace conventional manufacturing processes. The digital benefit of digitally designing a component and rapidly manufacturing it through AM is often lost due to extensive experimental iterations to remedy poor performance of fabricated components. The lack of performance is often attributed to intrinsic defects formation and undesirable microstructural features since the alloy composition and microstructure are not designed optimally for the given application. RTRC and ORNL will use HPC based phase-field simulations along with experimental validation to design novel Ti alloy compositions based on forming fine equiaxed grains during AM to potentially replace currently used wrought Ti alloys.