DOE awards $3M for 10 high-performance computing projects to improve energy efficiency and material performance
The US Department of Energy (DOE) announced $3 million in funding for 10 high-performance computing projects that will advance cutting-edge manufacturing and clean energy technologies. As part of the High-Performance Computing for Energy Innovation (HPC4EI) initiative, the selected projects will leverage the expertise and computing capabilities of the U.S. National Laboratories to improve manufacturing efficiency and explore new materials for clean energy application through state-of-the-art modeling, simulation, and data analysis.
Through the High-Performance Computing for Manufacturing (HPC4Mfg) Program, selected teams will help manufacturers shrink their carbon footprint, streamline their processes, and increase innovation—from optimizing the performance of equipment used in chemical manufacturing to improving the fuel efficiency of vehicles.
DOE’s Advanced Manufacturing Office has selected seven projects in the HPC4Mfg program focused on the following topics:
- Reductions in CO2 or CO2-equivalent emissions through electrification, improved carbon-capturing processes, and the integration of low-to-zero carbon fuels.
Improvements in manufacturing processes that result in significant national energy savings and carbon emissions.
Improvements in the lifecycle energy consumption and carbon emissions reduction of products of interest.
Efficiency improvements and carbon emissions reduction in energy conversion and storage technologies.
Teams selected through the High-Performance Computing for Materials (HPC4Mtls) Program will use high-performance computing to bolster the domestic materials supply chain needed for energy applications, including reduced material costs or improved carbon capture for power plants or clean hydrogen.
|PROJECT TITLE||COMPANY||DESCRIPTION||DOE FUNDING|
|HPC Modeling of Rapid Infrared Sintering for Low Cost, Efficient Solid Oxide Electrolyzer Cell Manufacturing||Redox Power Systems||Rodox Power Systems and Oak Ridge National Laboratory aim to use high performance computing to reduce the energy and cost in the manufacturing of Solid Oxide Electrolyzer Cells (SOEC) which convert steam into hydrogen by modeling sintering using pulse thermal processing (PTP). This technology can drive down cell cost, increase throughput, enhance properties, and improve manufacturing energy efficiency.||$300,000|
|Computational modeling of cost-effective carbon capture technologies on industrial gas turbines to reduce CO2 emission||Solar Turbines||Using Argonne National Laboratory's high performance computing for computational fluid dynamics (CFD) modeling, Solar Turbines will improve the performance of an Exhaust Gas Recirculation (EGR) carbon capture system designed for small industrial turbines. This technology can significantly reduce the capital and operating costs of CO2 removal by commercially available CO2 removal technologies.||$300,000|
|Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting||Ford||Oak Ridge National Laboratory and Ford will collaborate to use high performance computing to model high-strength automotive aluminum sheet alloys in bending tests to infer rivet-ability for light-weighting. Lightweighting of Ford vehicles would lead to greater fuel efficiency and reduced manufacturing time and energy use.||$300,000|
|HPC for optimizing process parameters to control material evolution in seamless induction hardening of wind turbine main shaft bearings||The Timken Company||The Timken Company and Oak Ridge National Laboratory will develop high performance computing simulations to study seamless induction hardening (SIH) for wind turbine main shaft bearings. The small footprint, reduced energy consumption and low lead times for SIH make it an ideal solution for the anticipated growth of the global wind turbine market.||$300,000|
|High-Performance Computing (HPC) for Secondary Lead Furnace Process Optimization||Gopher Resource LLC - Phase II||In a Phase II project, Gopher Resource LLC and Oak Ridge National Laboratory will continue their successful partnership to improve multiphysics modeling of secondary lead furnaces for environmental and energy efficiency. Improvements in furnace thermal efficiency can 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.||$300,000|
|Optimization of Scalable AEM Electrolyzer for Hydrogen Production Efficiency and Lifetime using 3D Device-Level Continuum Model||EvolOH||EvolOH will utlilize Lawerence Berkeley National Laboratory's high performance computing capabilities and expertise to design an anion-exchange-membrane (AEM) electrolyzer for optimal performance and best durability to produce clean hydrogen.||$300,000|
|Mixing Equipment Optimization using Computational Fluid Dynamics and Machine Learning||Dow Chemical||Dow Chemical Company will combine Argonne National Laboratory's modeling and machine learning (ML) capabilities to improve nozzle design for jet mixing in chemical manufacturing processes.||$300,000|
|Materials Reliability Quantification for Efficient Hydrogen-Fueled Gas Turbines for the Energy Transition||Siemens Energy||Using high performance computing to understand creep performance in turbine materials at Oak Ridge National Laboratory, Siemens Energy will work to establish a framework for determining safe operating windows for hydrogen-fueled gas turbine engines. Results will be incorporated into Siemens’ models to accelerate adoption of efficient hydrogen fueled engines.||$300,000|
|Manufacturable high toughness, low thermal conductivity, thermal barrier materials for hydrogen combustion turbines||Praxair Surface Technologies||Praxair Surface Technologies will partner with National Energy Technologies Laboratory 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.||$300,000|
|Carbon Nanospike Based Photoelectrochemical CO2 Conversion||Reactwell||Reactwell and Oak Ridge National Laboratory will develop first principles and machine learning based approach to nitrogen-doped carbon nano-spikes with metal nanoparticles in search of features that can improve efficiency of photoelectrochemical conversion of CO2 to ethanol.||$300,000|