The ALCF partners with companies to provide access to world-class computing resources that drive technological breakthroughs, speed product development, and strengthen the nation’s innovation ecosystem.
Through ALCF’s Industry Partnerships Program, businesses of all sizes—from startups to Fortune 500 firms—can tackle R&D challenges that exceed the limits of traditional computing resources. ALCF’s advanced capabilities in simulation, AI, and data analytics allow researchers to build more accurate models, generate reliable predictions, and process massive datasets quickly, leading to faster innovation, reduced uncertainty, and fewer costly prototypes.
ALCF industry engagement extends across the laboratory, including collaboration with the Argonne’s Science and Technology Partnership Outreach (STPO) division. This integrated approach ensures companies gain a comprehensive view of Argonne’s capabilities and access to cross-disciplinary expertise.
Companies can request computing time through programs such as INCITE, ALCC, and ALCF Director’s Discretionary allocations. Industry projects addressing energy-related challenges can also leverage DOE’s HPC for Energy Innovation (HPC4EI) program, which includes HPC4Mfg for optimizing manufacturing and HPC4Mtls for developing advanced materials in challenging environments.
Accelerating R&D with HPC and AI
From AI-assisted manufacturing to energy conversion and advanced aerospace simulations, these examples illustrate how companies are leveraging ALCF resources to tackle complex research and development challenges, accelerate development cycles, and create practical, scalable solutions.
Spirit AeroSystems
In collaboration with Argonne National Laboratory, Northern Illinois University, and TRI Austin, Spirit Aerosystems is applying AI to transform aerospace inspections. With help from ALCF supercomputers, the team developed an anomaly detection tool trained on Spirit’s ultrasonic scan data to identify potential defects in composite materials. By highlighting areas most likely to require closer inspection, the AI system reduces evaluation time by up to 24 percent, cuts manufacturing flow time by nearly 9 percent, and saves roughly 3 percent in energy per aircraft. The model integrates domain-specific knowledge, ensuring reliability across different geometries and material systems, and provides inspectors with a practical, scalable tool for safer and faster quality assurance. This collaboration demonstrates how AI and HPC can improve efficiency, energy use, and throughput in complex industrial environments.
GE Aerospace Research
As part of an INCITE project, GE Aerospace Research, in partnership with Boeing, NASA, and Oak Ridge National Laboratory, is using DOE’s exascale supercomputers, including Aurora, to study the integration of open-fan propulsion systems on wing-mounted aircraft configurations. High-fidelity simulations capture complex flow physics around the aircraft-installed open fan, generating tens of billions of cell volumes to resolve turbulence and highly varying flow velocities. The simulation data are used to improve predictive models through machine learning and physics-based methods, enabling more efficient design optimization of the propulsion and aircraft system. This effort is providing insights to help the aviation industry explore energy-efficient propulsion architectures, reduce fuel burn and emissions, and advance sustainable aircraft design.
M2X Energy Inc.
Argonne National Laboratory is collaborating with M2X Energy to advance a gas-to-methanol system that converts low-value waste gas streams, such as landfill gas, into high-value methanol. Supported by DOE’s HPC4EI program, the team is using ALCF supercomputers to run high-fidelity computational fluid dynamics simulations of the company’s engine reformer technology. These simulations enable researchers to explore complex chemical and fluid dynamics inside the system and optimize operating strategies and compressor designs for improved performance. Building on earlier modeling work, the current effort focuses on refining the system for cost-effective operation and greater energy productivity. The project aims to accelerate the development of modular chemical manufacturing technology while creating an economically viable pathway to convert waste gas streams into valuable fuels and chemical feedstocks.