New HPC4EI Project to Create 'Digital Twin' Models for Aerospace Manufacturing

A new High Performance Computing For Energy Innovation (HPC4EI)-funded collaboration between Lawrence Livermore National Laboratory and Allegheny Technologies Incorporated will leverage advanced LLNL-developed software to create a “digital twin” of the near-net shape mill-products (NNS-MP) system for producing aerospace parts. Shown is a lens housing design that applies multi-material additive manufacturing and was created using the Livermore design optimization (LiDO) code and Serac, a simulation code that will be used in the HPC4EI project. (Image: Kenny Swartz)

A partnership involving Lawrence Livermore National Laboratory aimed at developing “digital twins” for producing aerospace components is one of six new projects funded under the HPC for Energy Innovation (HPC4EI) initiative, the Department of Energy's (DOE) Office of Energy Efficiency and Renewable Energy (EERE) announced recently.

Sponsored by the HPC4Manufacturing (HPC4Mfg) Program, one of the pillars of HPC4EI, the collaboration between LLNL and specialty materials producer ATI will leverage advanced high performance computing (HPC) software to create a digital twin of the near-net shape mill-products (NNS-MP) system — a strategy in which components are initially fabricated to be as close to the finished product as possible.

The project will address reducing energy usage and CO2 production in aircraft manufacturing, where about 95 percent of metal used in the process is converted to scrap due to the complex shape of components.

Managed by LLNL, HPC4Mfg leverages the high performance computing (HPC) resources and expertise of the DOE national laboratories to improve manufacturing processes, address products’ lifecycle energy consumption and increase the efficiency of energy conversion and storage technologies. The Spring 2022 funding round was supported primarily by EERE’s Advanced Materials and Manufacturing Technologies Office (AMMTO).

NNS-MP manufacturing has significant potential for reducing energy use and CO2 in aerospace applications. However, NNS-MP production is limited due to the computational resources required, the products’ complex processing path and the lack of ad-hoc software, according to researchers.

For the collaboration with ATI, led at LLNL by co-principal investigators Aaron Fisher and Vic Castillo, researchers will simulate the multi-physics problem of multi-stand bar-shaped rolling and produce a machine learning model. The model will act as a digital object for a digital twin of the NNS-MP system, helping to optimize the process for manufacturing aerospace components.

Other projects receiving funding under the HPC4EI solicitation include efforts by:

  • The National Renewable Energy Laboratory and Daniel USA to develop computational simulation models of the melting processes of direct reduced iron and Hydrogen Direct Reduction for industrial use.
  • Sandia National Laboratories and Ford Motor Company to develop a high fidelity computational fluid dynamic model for solvent evaporation and transport in a porous structure during battery electrode drying.
  • Argonne National Laboratory and M2X Energy Inc. to optimize engine design for methane to syngas reformation.
  • Oak Ridge National Laboratory (ORNL) and Siemens Corporation, Technology to enable high-resolution modeling of the composite phase change material microstructure to design better materials for waste heat capture.
  • ORNL and Solar Turbines Incorporated to use a crystal plasticity finite element model to quantify the factors that drive additively manufactured surface fatigue behavior.

Each project will receive $300,000 in funding from DOE’s AMMTO with industry partners providing at least $75,000 in in-kind contributions.