DOE to invest up to $100M in two new consortia to advance hydrogen and fuel cell R&D
Light-truck sales declined less during the current pandemic than car sales

DOE selects 11 manufacturing and materials projects to receive $3.3M for high performance computing research

The US Department of Energy (DOE) selected 11 projects to receive $3.3 million in federal funding for cost-shared research and development. The projects will use high performance computing to address key technical challenges in US manufacturing and materials development.

Through DOE’s High Performance Computing for Energy Innovation (HPC4EI) initiative, selected teams will work with the Department’s National Laboratories to apply advanced modeling, simulation, and data analysis for projects that improve manufacturing productivity and explore materials that can withstand extreme conditions.

HPC4EI is the umbrella initiative for DOE's HPC4Manufacturing, HPC4Materials, and HPC4Mobility programs. Individual projects will be awarded up to $300,000 of DOE funding to support high performance computing processing time and work performed by the National Laboratories.


  • ArcelorMittal – ArcelorMittal, Lawrence Livermore National Laboratory (LLNL), and Argonne National Laboratory (ANL) will work together to develop the next generation of lightweight, advanced high-strength steels with the help of high performance computing and artificial intelligence to positively impact the US energy landscape during both production and use in a project titled, “Ab-initio Guided Design and Materials Informatics for Accelerated Product Development of Next Generation Advanced High Strength Steels.”

  • ArcelorMittal – ArcelorMittal and Oak Ridge National Laboratory (ORNL) will collaborate to reduce the yield loss caused by inclusions forming in the refining ladle process in a project titled, “Reduced Order Modeling and Performance Prediction for Steel Refining Ladle Processing via High Performance Computing.”

  • Flawless Photonics – Flawless Photonics and LLNL will simulate both the heated glass flow and nucleation and growth of crystal nuclei to find the drawing conditions that suppress the growth of light scattering crystalline defects in the fluoride glass optical fiber ZBLAN in a project titled, “Modeling and Simulation of the Manufacture of a Superior Fiber Optic Glass.”

  • Guardian Glass, LLC – Guardian Glass, LLC and LLNL will collaborate to reduce energy consumption in glassmaking by using computational fluid dynamics (CFD) simulations and machine learning in a project titled, “Rapid CFD Using Machine Learning Algorithms.”

  • NLMK USA – NLMK USA will partner with ORNL to use CFD methodology to optimize scrap melting using the electric arc in electric arc furnaces in a project titled, “Optimization of Scrap Melting Using an Electric Arc in Steel Manufacturing.”

  • OxEon Energy, LLC – OxEon Energy, LLC will partner with LLNL to reduce the number of reactor tubes in Fischer-Tropsch reactors in order to lower cost and increase performance in a project titled, “Topology Optimization of Fischer-Tropsch Reactor Design for Synthetic Fuel Production.”

  • PPG Industries – PPG Industries will partner with Lawrence Berkeley National Laboratory to use high performance computing in the modeling of the paint drying process to enable energy savings through co-curing in a project titled, “Modeling Coating Flow and Dynamics During Drying.”

  • 3M Company – 3M Company will continue to partner with Sandia National Laboratories for a follow-on project to enhance non-equilibrium thermal radiation computation capability in a multi-physics framework of a passive cooling installation on a project titled, “Passive Cooling Film Optimization.”


  • 8 Rivers Capital, LLC – 8 Rivers Capital, LLC and the National Renewable Energy Laboratory will develop a surrogate model which will be used to inform liner and turbine design along with material selection for supercritical carbon dioxide (sCO2) combustion in a project titled, “Development of Novel Combustion Codes for Supercritical CO2 Combustion.”

  • Raytheon Technologies Research Center – Raytheon Technologies Research Center will partner with the National Energy Technology Laboratory to combine machine learning with national lab-based high-performance computing to identify candidate high entropy alloys for advanced ultra-supercritical power plant operating conditions in a project titled, “Accelerating High Temperature Operation Development of High Entropy Alloys via High Performance Computation.”

The HPC4Manufacturing and HPC4Materials programs will also co-fund one selection:

  • Raytheon Technologies Research Center – Raytheon Technologies Research Center will partner with ANL to predict flow and heat transfer characteristics of cooling air in gas turbine hot section combustion liners. This will increase operating efficiency and reduce fuel consumption in a project titled, “Pseudo-Spectral Method for Conjugate Heat Transfer Prediction of Impinging Flows Over Rough Surfaces.”

HPC4Manufacturing is funded by DOE’s Office of Energy Efficiency and Renewable Energy’s Advanced Manufacturing Office. DOE’s Office of Fossil Energy will fund the project selections in HPC4Materials.


The comments to this entry are closed.