Industry

ALCF computing resources and expertise are powerful tools for driving innovation and competiveness in the private sector.

Engine simulation

Equipped with advanced capabilities for simulation, data, and machine learning, ALCF supercomputers help companies expedite R&D efforts for many applications, including combustion engines, novel materials for energy and medicine, and fusion energy devices, to name a few.

With access to ALCF systems and expertise, industry researchers can create higher-fidelity models, achieve more accurate predictions, and quickly analyze massive amounts of data. The results enable companies to accelerate critical breakthroughs, verify uncertainties, and drastically reduce or eliminate the need to build multiple prototypes.

Ultimately, industry partnerships with the ALCF help to strengthen the nation’s innovation infrastructure and expand the use of HPC resources to improve competitiveness.

To broaden this impact, the ALCF Industry Partnerships Program is focused on growing the facility’s community of industry users by engaging prospective companies of all sizes, from start-ups to Fortune 500 corporations, that could benefit from the facility’s leadership computing resources and leverage ALCF expertise.

Please reach out to industry@alcf.anl.gov to discuss your ideas for collaborating with the ALCF.

Industry Partnerships

Advancing Fusion Energy Research

Collaboration
Researchers from TAE Technologies are carrying out simulations on ALCF supercomputing resources to accelerate their experimental research program aimed at developing a clean, commercially viable, fusion-based electricity generator. The team will use simulation results to optimize a device for studying the confinement of energy with high plasma temperatures, and to inform the design of a future prototype reactor.
Sean Dettrick, TAE Technologies, Inc.

Reducing Energy Consumption in Fiber Manufacturing

Collaboration
Working with 3M, Argonne is using HPC, fluid dynamics simulations, and machine learning to minimize the energy used in the melt-blown fiber manufacturing process. This extremely energy-intensive process is widely used by 3M to produce filters, fabrics, and insulation materials, as well as the N95 mask used for protection during the COVID-19 pandemic. By improving this manufacturing process, engineers can reduce the energy consumed by 20%, which would save the industry nearly 50 gigawatt hours per year.
Argonne and 3M use supercomputing and machine learning to enhance material manufacturing process

Improving the Energy Efficiency of Jet Engines

Collaboration
Argonne is partnering with Raytheon Technologies Research Center to use machine learning to improve the performance, design, and energy efficiency of gas turbine engines. This project will use complex modeling to better quantify the impact of manufacturing uncertainties on the energy efficiency of Raytheon’s jet engines.
Argonne to collaborate with Raytheon Technologies