DOE selects 13 HPC4Mfg projects to advance applied science and technology in manufacturing; aluminum rolling and solid-state batteries
The US Department of Energy (DOE), in partnership with Lawrence Livermore National Laboratory, is awarding nearly $3 million to 13 projects to stimulate the use of high performance supercomputers to advance U.S. manufacturing.
The US DOE national laboratories have some of the most significant high performance computing (HPC) resources available, including some of the fastest supercomputers in the world. There is great potential for the US manufacturing industry to use the power of HPC to solve key challenges, but many manufacturers lack access to supercomputing resources.
Selected projects will be awarded up to $300,000 in federal funding to cover the costs associated with using the supercomputers and technical expertise provided by the laboratory partners. Industry partners will provide a participant contribution of at least 20% of the DOE funding for the project.
Among the projects selected is Arconic’s effort, in partnership with Oak Ridge National Laboratory (ORNL), to model rolling processes to observe the evolution of porosity.
Because light-weight materials are fuel efficient, aluminum, already well-established in the aerospace industry, is gaining significantly in the automotive industry and other markets. Currently, greater amounts of aluminum must be produced than are needed because of manufacturing waste and scrap inefficiencies that increase energy consumption and lead to higher costs to end consumers.
Arconic’s project—Computational Modeling of Industrial Rolling Processes Incorporating Microstructure Evolution to Minimize Rework Energy Losses—addresses rolled plate and sheet recovery issues in aerospace, industrial and automotive products.
Arconic will comprehensively model the commercial rolling process using high fidelity three-dimensional models to virtually observe the evolution of porosity in greater detail, with the ultimate objective of optimizing rolled plate properties. Arconic’s rolling and research facilities and more than 100 years of rolling experience provide specific knowledge of the process, and facilities for defining and validating processing conditions. DOE’s national labs will provide scientific support, computational resources and expertise, and data storage to close the current industrial computation gap.
Depiction of 3D rolling or high-fidelity models.
Also selected for an award is KeraCel’s project—All-Solid-State Lithium-Ion Battery—in partnership with Lawrence Livermore National Laboratory, which will model a new plan to push energy density in Li batteries with lithium oxide garnet with the goal of lowering the required temperature to reduce porosity in sintering.
Most current solid-state battery designs are limited by the difficulty in processing the ceramic electrolyte components, which requires high temperatures that lead to materials incompatibilities and high-energy costs. This proposal engages high-performance computing and simulation expertise to optimize materials processing for more energy-efficient, scalable manufacturing of robust solid-state batteries.
Other projects selected for awards are:
3M - This project will optimize the design of emissive films on building windows for cooling via modeling of glass-bead-filled polymers.
3M - This project will minimize energy consumption of the fiber spinning manufacturing process using computational fluid dynamics (CFD) and machine learning.
Alliance for Pulp & Paper Technology - This project will help create a fundamental understanding of alkali reactivity with wood components using molecular modeling.
GE Global Research Center - This project will extend GE Global Research Center’s TRUCHAS model to large-scale casting simulation of turbine blades.
Seurat Technologies - This project will use the ALE3D software to optimize Seurat’s innovative laser energy flux distribution to reduce spatter during laser powder bed fusion.
SFP Works, LLC - This project will use computational effort to understand phase transformations that occur during the flash heat treating process in order to better control parameters to obtain the desired phase distribution and chemistry.
Steel Manufacturing Simulation and Visualization Consortium - This project will create a shared database of heat exchange in 140 steel reheat furnaces whose inconsistencies lead to significant energy loss.
The Dow Chemical Company - This project will model methods to reduce the thermal conductivity of Dow’s insulating foam polyurethane products while using less polymer in products.
Transient Plasma Systems - This project will build a comprehensive numerical model for use in understanding and optimizing key parameters in electrical pulse generation of plasmas for dilute burn combustion.
United Technologies Research Center - This project will develop a novel heat treatment scheme that eliminates deleterious phases from the microstructure additively manufactured aerospace components while reducing the annealing time.
VAST Power Systems, Inc. - This project will optimize gas turbine combustors by developing and validating surrogate models using CFD.
The High Performance Computing for Manufacturing (HPC4Mfg) program, supported by DOE’s Advanced Manufacturing Office, unites DOE’s supercomputing capabilities and expertise with American manufacturers to optimize production processes and designs, enhance product quality, predict performance and failure, and speed up design and testing cycles while decreasing energy consumption.
Manufacturer-laboratory partnerships leverage expertise in advanced modeling, simulation, and data analysis to accelerate innovation and shorten the time of adoption of new technologies in US manufacturing.
The HPC4Mfg program is managed by Lawrence Livermore National Laboratory, with support from Oak Ridge National Laboratory and Lawrence Berkeley National Laboratory, and has supported more than 40 projects and provided more than $15 million for these public-private partnerships. Additionally, the National Renewable Energy Laboratory and Argonne National Laboratory provide computing cycles to support this program.