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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A Distributed-Memory Package for Dense Hierarchically Semi-Separable Matrix Computations Using Randomization

In this paper, we present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable (HSS) representations. Such matrices appear in many applications, for example, finite-element methods, boundary element methods, and so on. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, relies on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization, and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. Finally, this work is part of a more global effort, the STRUctured Matrices PACKage (STRUMPACK) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Brief Announcement: Communication Optimal Sparse LU Factorization for Planar Matrices

We introduce a new parallel algorithm for solving sparse LU factorization of planar matrices, which commonly arise in the finite element method for 2D PDEs. Existing scalable methods, such as the multifrontal approach with subtree-to-subcube mapping by Gupta et al. [1] and right-looking with 3D mapping by Sao et al. [2] fail to achieve optimal communication costs for these matrices. Our new algorithm combines 3D mapping and subtree-to-subcube mapping to minimize communication costs while allowing trade-offs between extra memory and reduced communication. We demonstrate that our proposed algorithm attains the communication lower bound up to a factor of O(log log n) in the memory-optimal case and up to a factor of O(log P) in the memory-independent case for an n-dimensional planar sparse matrix on P processors.

Sao, Piyush↗
DOE OSTI2022

Pit Stability Predictions of Additively Manufactured SS316 Surfaces Using Finite Element Analysis

Stainless steels are susceptible to localized forms of corrosion attack, such as pitting. The size and lifetime of a nucleated pit can vary, depending on a critical potential or current density criterion, which determines if the pit repassivates or continues growing. This work uses finite element method (FEM) modeling to compare the critical pit radii predicted by thermodynamic and kinetic repassivation criteria. Experimental electrochemical boundary conditions are used to capture the active pit kinetics. Geometric and environmental parameters, such as the pit shape and size (analogous to additively manufactured lack-of-fusion pores), solution concentration, and water layer thickness were considered to assess their impact on the pit repassivation criterion. The critical pit radius (the transition point from stable growth to repassivation) predicted for a hemispherical pit was larger when using the repassivation potential (E rp ) criteria, as opposed to the current density criteria (pit stability product). Including both the pit stability product and E rp into its calculations, the analytical maximum pit model predicted a critical radius two times more conservative than the FEA approach, under the conditions studied herein. The complex pits representing lack-of-fusion pores were shown to have minimal impact on the critical radius in atmospheric conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Efficient Reformulation of Linear and Nonlinear Solid-Phase Diffusion in Lithium-ion Battery Models using Symmetric Polynomials: Mass Conservation and Computational Efficiency

Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms, along with suitable discretization or approximations for the solid-phase diffusion equation. The solid-phase diffusion equation represents the main computational burden for typical pseudo-2-dimensional (p2D) models since these equations in the pseudo r -dimension must be solved at each point in the computational grid. This substantially increases the complexity of the model as well as the computational time. Traditional approaches towards simplifying solid-phase diffusion possess certain significant limitations, especially in modeling emerging electrode materials which involve phase changes and variable diffusivities. A computationally efficient representation for solid-phase diffusion is discussed in this paper based on symmetric polynomials using Orthogonal Collocation and Galerkin formulation (weak form). A systematic approach is provided to increase the accuracy of the approximation (p form in finite element methods) to enable efficient simulation with a minimal number of semi-discretized equations, ensuring mass conservation even for non-linear diffusion problems involving variable diffusivities. These methods are then demonstrated by incorporation into the full p2D model, illustrating their advantages in simulating high C-rates and short-time dynamic operation of Lithium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

A Three-Dimensional, Thermodynamically and Variationally Consistent, Fully Coupled, Electro-Chemo-Thermo-Mechanical Model of Solid-State Batteries

Here we present a theoretical framework for high fidelity modeling of three-dimensional electro-chemo-thermo-mechanical coupled systems such as solid-state batteries. The framework provides a useful means of utilizing molecular level information to predict the overall performance of the system at the continuum level. The central statement of the theoretical framework is a thermodynamically consistent inf-sup problem. The total potential of the coupled system consists of the Helmholtz free energy, the electrical kinetic potential, the chemical potential, the Fourier potential, the chemical reaction potential, and the external power expenditure. All governing equations derive from variational principles. We also show that the resulting initial-boundary-value problem (IBVP) is consistent with existing, widely adopted equations for modeling batteries. Moreover, the IBVP can be naturally discretized using the (variational) finite element method to simulate practical solid-state batteries with arbitrary geometries. Several examples illustrate multiphysics coupling between pairs of the physical field equations, culminating in a comprehensive three-dimensional model of charge-discharge in a solid state battery.

25 ENERGY STORAGE↗
DOE OSTI2024

Accelerating FEM-Based Corrosion Predictions Using Machine Learning

Atmospheric corrosion of metallic parts is a widespread materials degradation phenomena that is challenging to predict given its dependence on many factors (e.g. environmental, physiochemical, and part geometry). For materials with long expected service lives, accurately predicting the degree to which corrosion will degrade part performance is especially difficult due to the stochastic nature of corrosion damage spread across years or decades of service. The Finite Element Method (FEM) is a computational technique capable of providing accurate estimates of corrosion rate by numerically solving complex differential Eqs. characterizing this phenomena. Nevertheless, given the iterative nature of FEM and the computational expense required to solve these complex equations, FEM is ill-equipped for an efficient exploration of the design space to identify factors that accelerate or deter corrosion, despite its accuracy. In this work, a machine learning based surrogate model capable of providing accurate predictions of corrosion with significant computational savings is introduced. Specifically, this work leverages AdaBoosted Decision trees to provide an accurate estimate of corrosion current per width given different values of temperature, water layer thickness, molarity of the solution, and the length of the cathode for a galvanic couple of aluminum and stainless steel.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Shape Factor for Pits and Its Impact on Pit Stability

This study employs the finite element method (FEM) to predict the impact of pit shape on pit stability via shape factors of various pit geometries relevant to localized corrosion. Lower values of the shape factor indicate an increased ease in maintaining pit stability. Analyzed geometries include undercut pits, bispherical pit-within-pit structures, and covered pits with perforated (lacy) covers. The effect of the water layer thickness, transport properties of the electrolyte inside and outside the pit, and cathode location on the pit shape factor were also explored. Results show that occluded pits exhibit lower shape factors than open ones, with disk-shaped pits decreasing further as c/r ratio and occlusion angle increase. The findings also suggest minimal influence from a secondary pit if the primary remains active. An equation is presented that quantifies the impact of lacy covers, revealing significant shape factor value reduction. Additionally, high salt concentrations inside pits have a limited stabilizing effect compared to geometry, while thin water layers and adjacent cathodic/sinks reduce pit stability.

Shehi, A. (ORCID:0009000271548041)↗
DOE OSTI2016

Study of hypervelocity projectile impact on thick metal plates

Hypervelocity impacts generate extreme pressure and shock waves in impacted targets that undergo severe localized deformation within a few microseconds. These impact experiments pose unique challenges in terms of obtaining accurate measurements. Similarly, simulating these experiments is not straightforward. This paper proposed an approach to experimentally measure the velocity of the back surface of an A36 steel plate impacted by a projectile. All experiments used a combination of a two-stage light-gas gun and the photonic Doppler velocimetry (PDV) technique. The experimental data were used to benchmark and verify computational studies. Two different finite-element methods were used to simulate the experiments: Lagrangian-based smooth particle hydrodynamics (SPH) and Eulerian-based hydrocode. Both codes used the Johnson-Cook material model and the Mie-Grüneisen equation of state. Experiments and simulations were compared based on the physical damage area and the back surface velocity. Finally, the results of this study showed that the proposed simulation approaches could be used to reduce the need for expensive experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2011

Panzer

Panzer is a finite element assembly engine used for research on solving partial differential equations (PDEs). Panzer takes a physics model implemented by a user and creates and populates the linear algrbra objects, such as residuals and Jacobians, for use in implicit finite element methods. Panzer can be considered "glue code" that pulls together the capabilities of a number of open-source Trilinos packages (Epetra, Intrepid, Sacado, FEI, and Phalanx). This code provides a generic framework for describing a set of partial differential equations. Panzer provides a simple example driver that solves the undergraduate textbook partial differential equation of heat transfer in a solid (see for example, "Transport Phenomena" by Bird Stewart, and Lightfoot 1960). Panzer is intended as a tool to simplify the use of the discretization tools in Trilinos (Intrepid and Phalanx) and is meant to aid in fundamental research on discretizations. Panzer will be released open-source as a package in Trilinos under a BSD license.

Pawlowski, Roger↗
DOE OSTI2017

XFEMParaviewPlugin

The XFEMClipPartialElements tool is a plugin for the open-source Paraview visualization software to correctly visualize results with discontinuities represented using the extended finite element method (XFEM) in MOOSE-based codes.

Spencer, Ben↗
DOE OSTI2017

Affect v. 0.1

Affect is a module written in the Python language that provides functions for post-processing data that are the result of computer simulations—usually the result of the finite element method—for engineering and physics systems and environments.

Copps, Kevin↗
DOE OSTI2019

SCPACK vR1.0

User-defined elements in ANSYS enable programming of the finite element method to simulate the unique behavior of superconducting magnets. A set of electromagnetic and thermal user elements incorporate both quench and interfilament coupling current (IFCC) effects in a consistent manner, allowing for simulation of strongly coupled, multiphysics behavior typical of superconducting devices.

Brouwer, Lucas↗
DOE OSTI2012

COYOTE

COYOTE is designed for the analysis of steady and transient, multi-dimensional, nonlinear heat conduction problems. The spatial discretization technique is based on the Galerkin finite element method. The code contains a library of linear and quadratic elements and incudes bars and shells. COYOTE Implicit and explicit time integration methods, with adaptive time step control, are available for transient problems and fixed-point methods are used for Conjugate gradient iterative methods are used for the matrix solution. Coupled enclosure radiation with efficient view factor computation is included. Simulation capabilities include material motion, dynamic radiation enclosures, contact boundary conditions, multipoint constraints, material (element) addition, and deletion, bulk nodes, chemically reactive materials, and coupling with other mechanics codes. The code is designed for single or multi-processor computing

Gartling, DavidK.↗
DOE OSTI2022

Tacho

SAND2022-7470 O Tacho is a performance portable sparse direct solver for use with various sparse matrix problems which typically arise from finite element methods. The code relies on the Kokkos parallel programming model porting to both CPUs and GPUs. It is also part of the Trilinos library. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Kim, Kyungjoo↗
DOE OSTI2023

WhatsCracking [SWR-23-03]

WhatsCracking can accurately predict cell fracture in crystalline silicon PV modules. The probability of PV module cell fracture is determined by a complicated interrelationship of module architecture, materials, and loading. WhatsCracking is a Finite Element Method tool which enables users to assess how module design impacts the propensity for cell fracture.

Deceglie, Michael↗
DOE OSTI2025

Coupled momentum balance and phase-field solver with fenicsx module

Code solves momentum balance and phase-field equations simultaneously. The differential equations are solved on a discretized domain with appropriate boundary and initial conditions using finite element method. Primary purpose of the code is to simulate brittle fracture under dynamic loading. Constitutive equations are that of linear elasticity with degradation of stress due to fracture. Small strain formulation is used.

Zecevic, Milovan↗
DOE OSTI2026

TensorFEM

The purpose of this research library is to demonstrate how to combine the BoBa tensor library and MFEM finite element library to enable tensorized finite element methods.

Guthrey, PiersonT [Lawrence Livermore National Lab↗