13 Projects to Receive $3.7 Million for High Performance Computing Research at U.S. National Laboratories

PROJECT TITLE

SELECTEE NAME

CITY, STATE

DOE Funding Amount

PROJECT DESCRIPTION

Robust Film Cooling Under Manufacturing Uncertainty for Improved Jet Engine LifeCycle Energy Efficiency

Raytheon Technologies Research Center

East Hartford, CT

$300,000

Raytheon Technologies Research Center and Argonne National Laboratory will use HPC 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.

HPC- and Machine-Learning-Based Modelling of Electrochromic Dyes for High Performance and Reduced-Cost Manufacturability of Electrochromic (EC) Devices

Polyceed Inc. (dba Glass Dyenamics)

Tucson, Arizona

$300,000

Polyceed Inc. (dba Glass Dyenamics) and Oak Ridge National Laboratory will use 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.

Next Generation Nonwovens Manufacturing Based on Model-Driven Simulation Machine Learning Approach

3M Company

Woodbury, Minnesota

$297,000

3M Company in partnership with Argonne National Laboratory will use a combination of HPC based CFD simulations and a 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.

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage

Element 16 Technologies, Inc.

Arcadia, California

$300,000

Element 16 Technologies, Inc. and National Renewable Energy Laboratory will use HPC to improve Element 16’s molten sulfur TES product design with a high-fidelity HPC model validated by experimental data.

Reinventing the Green Consumer Products Landscape with Material and Process Design using High Performance Computing

The Procter & Gamble Co.

Cincinnati, Ohio

$300,000

The Procter & Gamble Co. and Sandia National Laboratories will use HPC 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.

Modeling Dynamic Stress-Strain-Temperature Profiles in Induction Pipe Bending to Improve Productivity and Avoid Cracking in Energy Intensive Applications            

Electric Power Research Institute, Inc.

Palo Alto, California

$300,000

Electric Power Research Institute, Inc. and Argonne National Laboratory will use HPC to apply state-of-the-art modeling and simulation tools to induction pipe bending nickel-based alloys for energy applications.

Modeling of Shell-Side Gas Membrane Modules to Optimize Counter-Currency and Improve Selective Gas Permeation

Generon IGS

Pittsburg, California

$300,000

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.

Improving Additive Manufactured Component Performance through Multi-Scale Microstructure Simulation and Process Optimization

General Motors LLC

Warren, Michigan

$300,000

General Motors LLC and Oak Ridge National Laboratory will use HPC to develop a high-performance lightweight additive manufacturing (AM) engine piston through material, shape and process optimization.

Integrated Process and Materials Modeling for Development of Additive Manufacturing of Refractory Materials for Critical Applications

Commonwealth Center for Advanced Manufacturing

Disputanta, Virginia

$300,000

Commonwealth Center for Advanced Manufacturing and Oak Ridge National Laboratory will use HPC to 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.

Optimization of Processing Parameters for Metal Powder Production by Gas Atomization Utilizing CFD Simulations

Praxair Surface Technologies

Indianapolis, Indiana

$150,000

Praxair Surface Technologies and Ames Laboratory will use HPC to improve quality and yield of metal powder for additive manufacturing produced via close-coupled gas atomization.

PROJECT TITLE

SELECTEE NAME

CITY, STATE

DOE Funding Amount

PROJECT DESCRIPTION

An ICME Modeling Framework for Metal Matrix Composites (MMC) Focusing on Ultrahigh Temperature Matrix Material and Tungsten Carbide Reinforcement Particulate

Raytheon Technologies Research Center

East Hartford, Connecticut

$300,000

Raytheon Technologies Research Center and Argonne National Laboratory will use HPC to design and fabricate ultra-high temperature metal matrix composite in order to obtain as-desired microstructure, strength, fracture toughness, creep resistance and oxidation resistance at operating temperature > 1850F to 2250F for enabling cost effective performance improvement in aerospace applications.

Development of Hierarchical ODS High Entropy Alloys under Guidance of ICME

Advanced Manufacturing LLC

East Hartford, Connecticut

$300,000

Advanced Manufacturing LLC and National Energy Technology Laboratory will use HPC 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.

Transport Analysis and Optimization in a MW-Scale CO2 Electrolyzer

Opus 12 Inc.

Berkley, CA

$300,000

Opus 12 and Lawrence Livermore National Laboratory will use HPC 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.  The project output will inform the design of Opus 12’s first-of-a-kind MW-scale electrolyzer development, capable of converting over one ton per day of CO2 into valuable products.