Projects

Awarded Projects by Year

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Fall 2020


 

Spring 2020


 

Winter 2020


Round 12 Selectees

 

HPC4Mfg
 | Argonne National Laboratory

3M Company in partnership with Argonne National Laboratory will use a combination of HPC based CFD simulations and machine learning to minimize energy consumption of melt blown (MB) fiber manufacturing processes. Such processes are widely used for 3M products including filters, fabrics and insulation materials. Project title is “Next Generation nonwovens Manufacturing Based on Model-driven Simulation Machine Learning Approach”.


HPC4Mtls
 | National Energy Technology Laboratory

Advanced Manufacturing LLC will utilize National Energy Technology Laboratory's HPC expertise to develop and manufacture cost-effective, oxide dispersion-strengthened (ODS), NiCrFeCo-rich high entropy alloys (HEAs) that are superior to Ni-based superalloys (e.g. IN740) for repair or replacement service in extreme environments. Project title is “Development of Hierarchical ODS High Entropy Alloys under Guidance of ICME”.


HPC4Mfg
  |  Oak Ridge National Laboratory

Commonwealth Center for Advanced Manufacturing and Oak Ridge National Laboratory will establish foundational knowledge for developing and implementing technologies that enable the use of directed energy deposition (DED) for additively producing large gas turbine components using refractory metals in a project titled “Integrated Process and Materials Modeling for Development of Additive Manufacturing of Refractory Materials for Critical Applications”.


HPC4Mfg
 | Argonne National Laboratory

Electric Power Research Institute, Inc. will leverage Agronne National Laboratory's HPC expertise to apply state-of-the- art modeling and simulation tools to induction pipe bending nickel-based alloys for energy applications in a project titled “Modeling Dynamic Stress-strain-Temperature Profiles in Induction Pipe Bending to Improve Productivity and Avoid Cracking in Energy Intensive Applications”.


HPC4Mfg
 | National Energy Technology Laboratory

With the computing expetise of National Renewable Energy Laboratory, Element 16 Technologies, Inc., will improve Element 16’s molten sulfur TES product design with a high-fidelity HPC model validated by experimental data in a project titled “High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage”.


HPC4Mfg
  |  Oak Ridge National Laboratory

General Motors LLC and Oak Ridge National Laboratory will utilize ICME tools to develop a high-performance lightweight additive manufacturing (AM) engine piston through material, shape and process optimization in a project titled “Improving Additive Manufactured Component Performance through Multi-Scale Microstructure Simulation and Process Optimization”.


HPC4Mfg
 | Oak Ridge National Laboratory

Generon IGS and Oak Ridge National Laboratory will use HPC to model the flow patterns in a shell-side fed gas separation module to maximize counter current flow patterns which could lead to a 50% reduction in the methane lost through the CO2 removal process in a project titled “Modeling of Shell-Side Gas Membrane Modules to Optimize Counter-Currency and Improve Selective Gas Permeation”.


HPC4Mtls
  | Lawrence Livermore National Laboratory

Twelve (formerly Opus 12) in partnership with Lawrence Livermore National Laboratory will use computational fluid dynamics and thermal analysis to better understand the heat distribution within the electrolyzer and optimize the flow field design for efficient heat removal in order to minimize cooling costs which decrease energy efficiency. Project title is “Transport Analysis and Optimization in a MW-scale CO2 Electrolyzer”.


HPC4Mfg
 | Oak Ridge National Laboratory

Polyceed Inc (dba Glass Dyenamics) and Oak Ridge National Laboratory will utilize HPC- and Machine-Learning-Based Modelling to develop new electrochromic dyes for smart glass building windows with improved roll to roll manufacturability and low-cost in a project titled “HPC- and Machine-Learning-Based Modelling of Electrochromic Dyes for High Performance and Reduced-Cost Manufacturability of Electrochromic (EC) Devices”.


HPC4Mfg
  | Ames Laboratory

In partnership with Ames Laboratory, Praxair Surface Technologies will use HPC improve quality and yield of metal powder for additive manufacturing produced via close-coupled gas atomization in a project titled “Optimization of Processing Parameters for Metal Powder Production by Gas Atomization Utilizing CFD Simulations”.


HPC4Mfg
 | Sandia National Laboratories

The Procter & Gamble Co will partner with Sandia National Laboratories to create an eco-system of HPC-enabled fiber manufacturing models to allow for defect-free production of solvent-free detergents with an accelerated timescale and reduced waste streams compared to traditional approaches such as build-test cycles in a project titled “Reinventing the Green Consumer Products Landscape with Material and Process Design using High Performance Computing”.


HPC4Mfg
  | Argonne National Laboratory

Raytheon Technologies Research Center will partner with Argonne National Laboratory to develop a physics-informed machine learning technique to desensitize film cooling effectiveness to manufacturing variability and to inform design practitioners of the impact of manufacturing uncertainties on the lifecycle energy efficiency of gas turbine engines in project a titled “Robust Film Cooling Under Manufacturing Uncertainty For Improved Jet Engine LifeCycle Energy Efficiency (P.E00.0623)”.


HPC4Mtls
  | Argonne National Laboratory

Raytheon Technologies Research Center and Argonne National Laboratory will use HPC to develop physics-based, full- field model for a key MMC system and a surrogate model using the throughput simulations as training data to connect key microstructural features to the material properties of interest. Project titled is “An ICME Modeling Framework for Metal Matrix Composites Focusing on Ultrahigh Temperature Matrix Material and Tungsten Carbide Reinforcement Particulate (P.E00.0631)”.