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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Structural Analysis of Multilayer Tapered Canted-Cosine-Theta Superconducting Magnet for the Interaction Region of Electron Ion Collider

The electron ion collider (EIC) will collide high energy and highly polarized hadron and electron beams with luminosities up to 10 34 cm -2 s -1 . The magnet designs at the interaction region (IR) are challenging due to the close proximity of the hadron and electron beams. A canted cosine theta (CCT) quadrupole magnet was designed due to the space restrictions at the IR. The CCT magnet has a tapered aperture, which allows higher gradients at the interaction point (IP) side of the magnet. The tapered geometry makes the magnet not symmetrical along the axial direction. In conclusion, to validate the design in terms of the mechanical strength and the allowed deformations, a detailed structural mechanical analysis for the tapered CCT quadrupole magnet has been carried out using the finite element method (FEM) considering both the shrinking due to cool down and the electromagnetic force on the conductors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Structural Analysis of a Serpentine Superconducting Magnet for the Interaction Region of the Electron Ion Collider

The electron ion collider (EIC) is under design to collide the high energy and highly polarized hadron with electron beams with luminosities up to 10 34 cm −2 s −1 . The superconducting magnet designs at the interaction region (IR) are quite challenging due to the close proximity of the hadron and electron beams. Several serpentine types of superconducting magnets have been designed due to the space restrictions at the IR. Here, to validate the designs in terms of the mechanical strength, structural integrity, as well as the allowed deformations, a detailed structural mechanical analysis for a 12-layer superconducting quadrupole serpentine magnet (Q1BpR) in the rear side hadron beams has been carried out using the finite element method (FEM) considering the pretension stress, shrinking due to cool down, and the electromagnetic force on the conductors. Anisotropy material properties and individual roving tension have been considered. The electro-magnetic (EM) simulations were performed using COMSOL, confirmed with RAT, the structural mechanical analysis were performed using COMSOL by considering the contact elements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Efficient and Flexible Sensitivity Matrix Computation for Adaptive Electrical Capacitance Volume Tomography

Electrical capacitance tomography is a widely used sensor modality for flow imaging in many industrial settings. Adaptive Electrical Capacitance Volume Tomography (AECVT) extends the capabilities of traditional ECT by enabling direct volumetric imaging and an improved resolution. Construction of the sensitivity matrix is a necessary step to obtain flow images. This step requires computation of the electric field inside the sensing domain, which is done via a typical field solver such as the finite element method. In this work, we present an efficient and flexible method to construct the sensitivity matrix for Adaptive Electrical Capacitance Volume Tomography (AECVT) based on individual electrode segment excitations and their judicious combination to form desired matrix elements. We illustrate how the proposed method yields the same sensitivity matrix as the traditional method but at a much lower computational cost. Once all segment contributions are obtained, we also indicate how the proposed method, unlike the traditional approach, can generate the sensitivity matrix on demand for an arbitrary combination of synthetic electrodes and obviating the need for any additional field computations. Finally, we present image reconstruction results for two different experimental scenarios where the mutual capacitance data and the corresponding sensitivity vectors are obtained through the proposed measurement combination scheme.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗
DOE OSTI2020

Heat Flux Analysis From IR Imaging on Proto-MPEX

The Prototype Material Plasma Exposure eXperiment (Proto-MPEX) is a linear device used to develop the source and heating concept for MPEX, an experiment to study plasma–material interactions for future fusion reactors. This paper highlights recent analysis and results on Proto-MPEX using an infrared (IR) camera, a key tool for imaging heat fluxes measured at the material target. The target heat fluxes are crucial to understanding the source and heating concepts. The recent analysis includes the application of homography and 3-D finite-element methods to provide between-shot measurements of the 2-D target heat flux profile. The IR camera inferred heat flux is shown to correlate with Langmuir probe inferred heat fluxes for a range of parameters in helicon only and helicon + electron cyclotron heated discharges. Results show both desirable central and undesirable edge heat flux for helicon source and electron heating on the target. These heat flux measurements are shown to be important for better understanding of the source and heating physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Computational Evaluation of Thermal Response of Open-Cell Foam With Circular Pore (Computional Evaluation of Thermal Response of Open-Cell Foam with Circular Pore)

The evaluation of effective material properties in heterogeneous materials (e.g., composites or multicomponent structures) critically relevant to a wide spectrum of applications, including nuclear power, electronic packaging, flame retardants, hypersonics, and gas turbine power. The work described in this paper is centered around the numerical assessment of the thermal behavior of porous materials obtained from finite element thermal modeling and simulation. Here, two-dimensional, steady state analyses were performed on unit cells with centered, circular pores using a second order accurate Galerkin finite element method (FEM). The effective thermal conductivities of the porous systems were examined, encompassing a range of porosities from 4.9% to 60.1%. The geometries of the models were generated based on ordered circular pores for each modeled porosity level. The system response quantity (SRQ) under investigation was the dimensionless effective thermal conductivity across the unit cell. The dimensionless effective thermal conductivity was compared across all simulated cases, producing a trend between porosity and effective thermal conductivity. In the presented investigation, the method of manufactured solutions (MMS) was used to perform code verification, and the grid convergence index (GCI) was employed to estimate discretization uncertainty as solution verification. Code verification concluded an approximately second order accurate Galerkin FEM solver. It was found that the introduction of porosity to the unit cell material structure reduces effective thermal conductivity, as anticipated. Numerical results obtained in this study are compared to an analytical solution and to a sample of empirical data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2022

Computational Modeling of Multi-Pass Rolling Parameters Effect on Resulting Fuel Foil Shape

A focus of the U.S. Department of Energy is to improve production yield and reduce the cost of Low Enriched Uranium (LEU)-molybdenum alloy (U-10Mo) monolithic fuel plates that will be replacing High Enriched Uranium (HEU) oxide dispersion fuels used currently in the United States High Performance Research Reactors (USHPRR). One area of improvement is lowering the high transverse waviness and longitudinal waviness currently present within rolled foils prior to cladding to produce fuel plates. Traditional rolling manufacturing techniques for other metal foils use winders to pull and straighten the foil as it is rolled back and forth to the final thickness. This approach cannot be used to roll thin U-10Mo foils (0.008-0.025” thick) because only small castings can be rolled due to nuclear criticality safety concerns. As a result, the fuel foils are too short (1 m in length) to use traditional winders. Therefore, it is crucial to identify other rolling parameters (i.e., roller friction, axial tension load, roller diameter, and roll pass reduction percent) that might reduce transverse waviness and longitudinal waviness in the rolled fuel foil and develop a high-yield, low-cost multi-pass rolling manufacturing process. This report documents a systematic finite element modeling study to investigate the effects of numerous rolling parameters to reduce resulting transverse waviness and longitudinal waviness in the fuel foil during multi-pass rolling of U-10Mo foils. The rolling of a U-10Mo plate with initial dimensions of 1”x1”x 0.048” is modeled using Abaqus CAE. This rolling is modeled to undergo eight 20% reduction roll passes to a final fuel foil thickness of 0.01”. The elastic-plastic constitutive model of the U-10Mo alloy was input to the fuel foil rolling model. The rollers were modeled as rigid bodies. A comparison of rolling friction coefficients of 0.3 and 0.7 over a wide range of applied axial tension loads were investigated in order to evaluate the effect of using a lubricant during rolling. The effect of roller diameter on the resulting transverse waviness and longitudinal waviness of the fuel foil over a wide range of axial tension loads were also investigated by modeling rollers 7/8” and 3.75” in diameter. The results of this systematic finite element method study will aid manufacturers in producing low transverse waviness and reduced longitudinal waviness in U-10Mo fuel foils.

U-10Mo, FEA, Rolling, Residual Stress, Fuel Foil↗
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 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↗