The US Department of Energy (DOE) has announced $3 million for 10 new projects to allow private companies access to the HPC resources at the DOE's national laboratories to tackle major manufacturing challenges.
The announcement was made by David Danielson, assistant secretary for the Office of Energy Efficiency and Renewable Energy (EERE), during an online media event hosted at Berkeley Lab.
Danielson said: 'Access to supercomputers in the DOE’s labs will provide a resource to American firms inventing and building clean energy technologies right here at home that no international competitor can match. The HPC4Mfg initiative pairs leading clean energy technology companies with the world-class computing tools and expertise at our national labs to drive down the cost of materials and streamline manufacturing processes. The ultimate goal of their collaboration is to increase our global competitiveness in the race to develop clean energy technology and jobs.'
High Performance Computing for Manufacturing (HPC4Mfg) is a project led by Lawrence Livermore National Laboratory (LLNL) to advance clean energy technologies and increase energy efficiency while reducing risk of HPC adoption for US manufacturers. Originally rolled out in 2015, the programme is funded by the DOE Advanced Manufacturing Office (AMO) within the Energy Efficiency and Renewable Energy (EERE) Office.
Under the HPC4Mfg program, the 10 selected projects will leverage the national labs' high-performance computing capabilities to apply modeling, simulation, and data analysis to industrial products and processes to lower production costs and shorten the time it takes to bring new clean energy technologies to market.
‘While HPC technology is critical to competitiveness and many large companies have recognized this and invested in it, it is very difficult for small companies to access the technology and use it effectively,’ said Horst Simon, deputy director of Berkeley Lab.
‘Access to HPC will give these companies an edge in the international competition for the clean manufacturing capabilities of the future. DOE and its network of national labs have invested billions in hardware capabilities and developed the expertise to harness HPC. The HPC4Mfg initiative is an ideal way to make the unique capability of labs’ technology and expertise available to U.S. industry to increase their competitiveness’ Simon conlcuded.
Berkeley Lab will work with Globalfoundries to optimise the design of transistors under a project entitled ‘Computation Design and Optimisation of Ultra-Low Power Device Architectures’. Other new HPC4Mfg projects range from improving turbine blades in aircraft engines and cutting heat loss in electronics to reducing waste in paper manufacturing and improving fiberglass production. Each of the projects will receive approximately $300,000 to fund the national labs to partner closely with each chosen company to provide expertise and access to HPC systems aimed at high-impact challenges.
The other Phase I projects are listed as follows:
Globalfoundries will collaborate with LBNL to optimize the design of transistors under a project entitled: 'Computation Design and Optimization of Ultra-Low Power Device Architectures.'
The Lightweight Innovations for Tomorrow Consortium in Michigan will partner with LLNL to develop, implement and validate a defect physics-based model to predict mechanical properties of Al-Li forged alloy, under a project entitled: 'Integrated Computational Materials Engineering Tools for Optimizing Strength of Forged Al-Li Turbine Blades for Aircraft Engines.'
ZoomEssence, of Kentucky will partner with LLNL to optimize the design of a new food drying method using HPC simulations of dryer physics, under a project entitled: ‘High Performance Computing Analysis for Energy Reduction of Industry Spray Drying Technology.’
United Technologies Research Center, located in East Hartford, Connecticut, will partner with ORNL and LLNL to develop and deploy simulation tools that predict the material microstructure during the additive manufacturing process to ensure that critical aircraft parts meet design specifications for strength and fatigue resistance, under a project entitled: ‘Integrated Predictive Tools for Customising Microstructure and Material Properties of Additively Manufactured Aerospace Components.’
Procter & Gamble of Ohio will partner with LLNL to reduce paper pulp in products by 20 percent, which could result in significant cost and energy savings in one of the most energy intensive industries, under a project entitled: ‘Highly-Scalable Multi-Scale FEA Simulation for Efficient Paper Fiber Structure.’
General Electric (GE), New York, will partner with ORNL to assist in the local control of melt pool and microstructure in additive manufactured parts, under a project entitled: ‘Process Map for Tailoring Microstructure in Laser Powder Bed Fusion Manufacturing (LPBFAM) Process.’
In a separate project, GE will partner with ORNL and LLNL to improve the efficiency and component life of aircraft engines through design optimization, under a project entitled: ‘Massively Parallel Multi-Physics Multi-Scale Large Eddy Simulations of a Fully Integrated Aircraft Engine Combustor and High Pressure Vane.’
PPG Industries, of North Carolina will partner with LLNL to model thermo-mechanical stresses involved in forming and solidifying glass fibers to understand fracture-failures mechanisms to significantly reduce waste, under a project entitled: ‘Numerical Simulation of Fiber Glass Drawing Process via a Multiple-Tip Bushing.’
In a separate project, PPG Industries, Inc. of Pennsylvania will partner with LLNL to develop a reduced computational fluid dynamics (CFD) model of a glass furnace to make informed line adjustments in hours in near real-time, under the title: ‘Development of Reduced Glass Furnace Model to Optimize Process Operation.’
The AweSim programme at the Ohio Supercomputer Center (OSC) and the Edison Welding Institute (EWI) will partner with ORNL to deploy cloud-based advanced welding simulation tool for broad industry use, under a project entitled: ‘Weld Predictor App'.
