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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identification Uncertainty in Inverse Material Model Parameter Determination: A Sensitivity‐Based Decision Process for Load Path Selection

This research proposes a sensitivity-based framework for selecting the optimal prescribed loading path for a biaxial cruciform specimen. Optimality here is determined by the direction and magnitude of the prescribed displacement that minimizes the influence of random noise on the material model parameter identification. Using simulated experimental data based on finite element simulation, in this work, we identify the material model parameters of a Ludwik hardening model and plane stress implementation of the Hill-48 yield criterion using finite element model updating (FEMU). Our analysis reveals that the identification (or estimator) uncertainty of model parameters depends on the displacement boundary conditions (i.e., loading sequence) and the ground-truth value of the individual parameters. Optimal experimental design (OED) criteria based on the Fisher information matrix were investigated to mitigate indecision in the choice of optimal load path when the identification uncertainty of different material model parameters optimized at different load paths. The determinant of the Fisher information matrix was chosen here as the more useful metric due to its ability to capture uncertainty of the most influential material model parameters. The proposed framework demonstrates potential for real-time automated load step selection using scalar criteria derived prior to mechanical loading. The framework can be generalized to other geometries, boundary conditions and material models, allowing this procedure to be utilized for different experimental configurations and materials.

Fayad, Samuel S. [University of Illinois at Urbana↗
DOE OSTI2022

Validation of material models for puncture of 7075-T651 aluminum plate

Plate puncture simulations are challenging computational tasks that require advanced material models including high strain rate and thermal-mechanical effects on both deformation and failure, plus finite element techniques capable of representing large deformations and material failure. The focus of this work is on the material issues, which require large sets of experiments, flexible material models and challenging calibration procedures. In this study, we consider the puncture of 12.7 mm thick, 7075-T651 aluminum alloy plates by a cylindrical punch with a hemispherical nose and diameter of 12.7 mm. The plasticity and ductile failure models were isotropic with calibration data obtained from uniaxial tension tests at different temperatures and strain rates plus quasi-static notched tension tests and shear-dominated tests described here. Sixteen puncture experiments were conducted to identify the threshold penetration energy, mode of puncture and punch acceleration during impact, The punch was mounted on a 139 kg mass and dropped on the plates with different impact speeds. Since the mass was the same in all tests, the quantity of interest was the impact speed. The axis and velocity of the punch were perpendicular to the plate surface. The mean threshold punch speed was 3.05 m/s, and the mode of failure was plugging by thermal-mechanical shear banding accompanied by scabbing fragments. Application of the material models in simulations of the tests yielded accurate estimates of the threshold puncture speed and of the mode of failure. Time histories of the punch acceleration compared well between simulation and test. Remarkably, the success of the simulations occurred in spite of even the smallest element used being larger than the width of the shear bands.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Material Model Parameters Optimization in Liquid Mercury Target Dynamics Simulation With Machine Learning Surrogates

A pulsed spallation target is subjected to very short (∼0.7μs) but intense loads (23.3 kJ) from repeated proton pulses, which knock away neutrons from the mercury atoms’ nuclei for a wide range application in physics, engineering, medicine, petroleum exploration, biology, chemistry, etc. The effect of this pulsed loading on the stainless-steel target module which contains the flowing mercury target material is difficult to predict not only due to its short but intense explosive-like physical reaction, but also the nonlinear material behavior of the liquid mercury in the structure. Injecting small helium bubbles in the mercury has been an efficient method of mitigating the pressure wave at high power level stage. However, prediction of the resultant loading on the target is more difficult when helium gas is intentionally injected into the mercury. A 2-phase material model that incorporates the Rayleigh-Plesset (R-P) model is expected to address this complex multi-physics dynamics problem by including the bubble dynamics in the liquid mercury. A parameter sensitivity study was firstly employed to understand their impact on the simulation strains. The investigated parameters included E, μ, γ, σ, n, VFgas, and gas cumulative volume curve control parameters a and b. Verification and validation results from sparse polynomial expansions (SPE) method and directional Gaussian smoothing (DGS) optimization show that the surrogate model had training error of ∼7% and validation error of ∼15%, indicating that machine learning methods and surrogate models can help optimize the uncertain parameters in the complex 2-phase material model. This approach is expected to fill the knowledge gap between unknown liquid-gas mixture material model and measured vessel strain responses.

Lin, Lianshan↗
DOE OSTI2025

Conference Travel Fellowships: The 10th Annual International Conference on Multiscale Materials Modeling, Baltimore, Maryland, October 19-22, 2020 (Final Technical Report)

The original scope aimed to broaden participation in the 10th International Conference on Multiscale Materials Modeling (MMM 10) by supporting travel and accommodation for 10 junior scientists from U.S. Institutions. A funding request of $\$$10,000 was submitted to partially offset the cost of registration and local accommodation for these early-career participants. Since the award was made close to the date MMM 10 was to be held, we requested a no-cost extension to defer this travel support to the 11th International Conference on Multiscale Materials Modeling (MMM 11), which was held in Prague Congress Center in the Czech Republic. The scope of the travel award remained the same: supporting travel and accommodation for 10 junior scientists from U.S. Institutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unifying Quantum Materials Modeling and Experiments: The Role of Machine Learning Interatomic Potentials

Computational experiments have emerged as a powerful complement to traditional experiments in the design of new materials. The development of machine learning (ML) and deep learning techniques, combined with database construction and data mining, has significantly enhanced traditional quantum mechanical methods. This synergy enables the rapid development of structure-property relationships. In this talk, I will discuss our recent efforts in applying Machine Learning Interatomic Potentials (MLIAPs) to accelerate materials modeling across various material classes and challenging applications where traditional methods fall short. First, I will highlight the success of MLIAPs in accurately modeling the melting behavior of complex materials. Our results demonstrate high fidelity with experimental observations and also with calculated reference melting temperatures. In the second application, I will discuss how MLIAPs are trained and applied to elucidate the interplay between segregation tendencies and surface reconstructions in CuNi alloys under oxidizing conditions. A key factor in the success of these MLIAP applications is the design of minimalistic yet flexible datasets along with a computational framework for training MLIAPs.

Saidi, Wissam↗
DOE OSTI2021

Sensitivity Analysis of Tunable Equation of State Material Model In Pulsed Mercury Target Simulation

A pulsed neutron spallation target is subjected to very short but intense loads from repeated proton pulses. Approximately 60% of the energy from each proton pulse is deposited into the mercury target material and the stainless-steel target structure, leading to a high-pressure region in both the stationary target structure and the flowing mercury. The high-pressure region propagates and leads to fluid-structure interaction. The resultant loading on the target structure containing liquid mercury is difficult to predict, although various simulation approaches and material models for the mercury have been tried. To date, the best match of simulation to experimental data is obtained by using an equation of state (EOS) material model with a specified tensile cutoff pressure, which simulates the cavitation threshold. The inclusion of a threshold to represent cavitation is key to the successful predictions of stress waves triggered by the high-energy pulse striking the mercury and vessel. However, recent measurements of target structure strain show that significant discrepancies remain between the measured and simulated strain values in the EOS mercury model. These differences grow when noncondensable helium gas is intentionally injected into the flowing mercury to reduce the loading on the structure. An EOS-based proportional–integral–derivative (PID) mercury model has been proposed to reduce the gap between the measured and simulated vessel strain responses for targets with gas injection. The conceptual and numerical description and initial investigation of the PID model are presented in previous work. Further studies of this PID model — including the sensitivity of the structure’s strain response to model parameters (the tensile cutoff, PID parameters Kp, Ki, and Kd) — are reported in this article. Results show the strain response is more sensitive to changes in the tensile cutoff value than to changes in the model parameters Kp, Ki, and Kd. These results will aid in future work where the model parameters will be optimized to match simulation data to strain measurements.

Lin, Lianshan↗
DOE OSTI2026

Modeling Material Interfaces with the Six-Equation Model in FLEXO

FLEXO is a multiphysics code developed at Sandia National Laboratories for predictive simulation of pulsed power target physics with extended magnetohydrodynamics modeling. Given this application space, FLEXO must be able to perform high-fidelity simulations of pulsed-power systems that include multiple materials. This work presents the augmentation of FLEXO with a six-equation model for multimaterial flow. We design and analyze discretizations of the six-equation model, emphasizing desirable properties for robust multimaterial simulation. We also present solution limiting, bounds preservation, and pressure equilibration/relaxation techniques that enhance the robustness of the overall multimaterial scheme. We present a suite of test problems to document these new capabilities of the FLEXO code.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Training material models using gradient descent algorithms

High temperature design requires accurate constitutive models to describe material inelastic deformation and failure behavior. Oftentimes, calibrating accurate models devolves into the problem of fitting the model parameters against experimental test data. Here, we present the pyopmat package, an open source framework for calibrating constitutive models against experiment data subjected to various loading conditions using machine learning techniques. The package calculates the exact gradient of the model response with respect to the parameters using a combination of automatic differentiation and the adjoint method. Given this exact gradient, we compare the performance of several gradient-based optimization techniques in fitting realistic constitutive models against data. Here, we demonstrate the efficiency and accuracy of our package through example problems using both synthetic data, generated using known parameter sets, under monotonic and cyclic loading conditions and also with an example applying the techniques developed here to actual high temperature creep-fatigue test data.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Summary of FY2024 Experimental Results to Support Development of New Inelastic Material Models and Validation of Section III, Division 5, Class A Design Rules

To address the data gap crucial for updating existing viscoplastic constitutive material models for the Class A materials in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code, Section III, Division 5, High Temperature Reactors (ASME, 2023) for inelastic design analysis, Oak Ridge National Laboratory (ORNL) conducted experimental studies on Alloy 800H and Alloy 617 across a range of temperatures up to their maximum temperature limits in Section III, Division 5. The studies aim to characterize the materials' deformation behavior under both strain-controlled mechanical cyclic loading and thermal cycling conditions. This report summarizes ORNL's FY 2024 experimental findings on Alloy 800H and Alloy 617, focusing on pure fatigue tests, cyclic stress-strain curves, thermomechanical fatigue experiments, and verification of the high temperature cyclic damage summation design rules.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Accelerating Multiscale Materials Modeling with Machine Learning

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10 2 atoms) to the mesoscale (10 4 atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Semi-Empirical Material Model for Hydrogen Uptake Kinetics by 1,4-bis(phenylethynyl)benzene (DEB)-based Getters

We report on development of a semi-empirical model describing the kinetics of H 2 /D 2 reaction with 1,4-bis(phenylethynyl)benzene (DEB)-based getters in pellet and powder form. The physical basis of the model is solid-state diffusion of DEB plus reaction of DEB with H atoms on the surface of nanometer-scale Pd catalyst particles. The purpose of this material model is to facilitate more accurate predictions of performance for DEB-based getter assemblies. These predictions will be accomplished by integrating the model presented here into finite element, reaction/diffusion simulations of getter assembly hydrogen uptake performance. As of Fall 2020, this integration is underway.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗
DOE OSTI2022

Atomistic Materials Modeling of High-Pressure Hydrogen Interactions in Ethylene Propylene Diene Monomer (EPDM) Rubber

Elastomeric rubbers serve a vital role as sealing materials in the hydrogen storage and transport infrastructure. With applications including O-rings and hose-liners, these components are exposed to pressurized hydrogen at a range of temperatures, cycling rates, and pressure extremes. Cyclic (de)pressurization is known to degrade these materials through the process of cavitation. This readily visible failure mode occurs as a fracture or rupture of the material and is due to the oversaturated gas localizing to form gas bubbles. Computational modeling in the Hydrogen Materials Compatibility Program (H-Mat), co-led by Sandia National Laboratories and Pacific Northwest National Laboratory, employs multi-scale simulation efforts to build a predictive understanding of hydrogen-induced damage in materials. Modeling efforts within the project aim to provide insight into how to formulate materials that are less sensitive to high-pressure hydrogen-induced failure. In this document, we summarize results from atomistic molecular dynamics simulations, which make predictive assessments of the effects of compositional variations in the commonly used elastomer, ethylene propylene diene monomer (EPDM).

08 HYDROGEN↗
DOE OSTI2026

An Anisotropic Yield and Damage Material Model to Improve the Contact Pressure Analysis in a Biomass Shredding System

Size reduction systems used in biomass processing break biomass into smaller pieces by utilizing the kinetic energy from the sharp rotating blades. Abrasive and/or erosive wear caused by biomass comminution results in blade wear of the sharp edged cutters, deteriorating the process efficiency. Here, this study aims to optimize the blade design and improve the system efficiency by attempting to understand the interactions between the blades and biomass particles. Since real-time monitoring of these interactions is impractical during operation, mechanical simulations offer a viable alternative for investigating the shredding process. Yet, the irregular geometry and complex mechanical properties of biomass—such as the anisotropic nature of woodchips and their nonlinear fracture behavior—pose significant challenges for accurately simulating contact pressure. In this work an anisotropic yield material model, along with a damage initiation and evolution function, is applied to the woodchip particle to study the contact pressure on shredder blade, offering a scientific basis for improved blade design and process efficiency. This approach can be extended to other biomass processing systems with similar anisotropic feedstocks, making it a valuable tool for advancing sustainable biomass utilization.

09 - BIOMASS FUELS↗
DOE OSTI2020

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware.

36 MATERIALS SCIENCE↗