Projects

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.

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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.

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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.

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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.

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Figure. (a) Graphical representation of a conventional HPDC Al structural node to connect longitude rails and cross beams; (b) Graphical representation of an AM structural nodes designed at Ford using DfAM digital tools, which has a weight of 24.6kg and 46% weight saving compared to conventional HPDC Al structural nodes, and stress analysis results, showing that AM technology can significantly advance automotive products.

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.

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Snapshot from molecular dynamics simulations used to predict performance metrics for novel bio-based polymeric materials. Top: Simulation of O2 diffusion through a polymer matrix is used to estimate barrier properties. Bottom: Simulation of a water droplet on a polymer surface provides an estimate of the contact angle, a measure of hydrophobicity.

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.

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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.

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Scan of an incrementally formed aluminum alloy part, approximately 300 mm x 300 mm x 150 mm in size, depicting the redistribution of material across the part.

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.

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Feminine hygiene product with open cell foam as absorbent material.

Micro-CT of open cell foam structure (absorbent material).

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).

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Microwave-assisted chemical vapor infiltration of a stationary article. Ribbons visualize the flow of reactive gases around article from left to right. The temperature distribution of the article (yellow=low and white=high) affects the resulting densification quality. The concentration of gaseous reaction products that get transported out of the article is illustrated in the blue background (blue=low and red=high).

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.

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Model development of a ML-driven microwave-assisted plasma (MAP).

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.

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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.

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Schematic of a solid-state battery electrolyte, which typically requires energy-intensive processing to achieve sufficient ionic conductivity while maintaining mechanical integrity and suitable operating temperature.

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.

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VAST combustor test with sub ppm NOx and CO.

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.

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Image of 7 ton/day R&D Thermolyzer™

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.

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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.

Figure(s):

Figure F1: Example of 3D printed topology optimized multi-scale beam [Materials Sciences LLC]

Figure F2: Example of 3D printed jet impingement cooling solution [University of Pittsburgh, United Technologies Corp.]

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.

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Graph of CMOS Chip