Projects

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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.

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.

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.

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.

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.