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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Progressive Damage Response of 3D Woven Composites via the Multiscale Recursive Micromechanics Solution with Tailored Fidelity

Progressive failure simulations have been performed for orthogonal 3D woven composites consisting of RTM6 resin matrix and AS4 carbon fibers. The Multiscale Recursive Micromechanics approach has been used, which, while being computationally efficient, captures the primary effects of the microstructure at each considered length scale. This approach also enables use of any micromechanics theory at any length scale, and herein, the fidelity of the chosen theories across the scales has been tailored to strike a balance with computational efficiency. The Mori-Tanaka method is employed at the lowest length scale, the Generalized Method of Cells is used at intermediate scales, and the High-Fidelity Generalized Method of Cells is used at the highest woven composite repeating unit cell scale. Furthermore, two different damage models, also with different levels of fidelity and efficiency, have been used for the resin material at the lowest length scale. Results for the mechanical behavior in response to loading in various directions are compared for the two damage models and with available test data.

3D woven↗
DOE OSTI2022

Scalable algorithms for physics-informed neural and graph networks

Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available. In some instances, the objective is to discover part of the hidden physics from the available data, and PIML has been shown to be particularly effective for such problems for which conventional methods may fail. Unlike commercial machine learning where training of deep neural networks requires big data, in PIML big data are not available. Instead, we can train such networks from additional information obtained by employing the physical laws and evaluating them at random points in the space–time domain. Such PIML integrates multimodality and multifidelity data with mathematical models, and implements them using neural networks or graph networks. Here, we review some of the prevailing trends in embedding physics into machine learning, using physics-informed neural networks (PINNs) based primarily on feed-forward neural networks and automatic differentiation. For more complex systems or systems of systems and unstructured data, graph neural networks (GNNs) present some distinct advantages, and here we review how physics-informed learning can be accomplished with GNNs based on graph exterior calculus to construct differential operators; we refer to these architectures as physics-informed graph networks (PIGNs). We present representative examples for both forward and inverse problems and discuss what advances are needed to scale up PINNs, PIGNs and more broadly GNNs for large-scale engineering problems.

42 ENGINEERING↗
DOE OSTI2026

Toward durable stacks: glass-ceramic sealants for intermediate-temperature protonic ceramic electrochemical systems

Protonic ceramic electrochemical cells (PCECs) are emerging as promising technologies for efficient energy conversion and hydrogen production because they operate at intermediate temperatures with improved efficiency and durability compared with conventional solid oxide electrochemical cells. However, the long-term reliability and commercialization of PCEC stacks remain strongly limited by the performance of sealants, which are required to maintain gas tightness, electrical insulation, and mechanical integrity under harsh thermal and chemical environments. Among various sealing approaches, glass-ceramic sealants are considered the most practical and scalable due to their excellent wettability, chemical tunability, and strong interfacial adhesion. This review provides a comprehensive overview of recent advances in glass-ceramic sealants for intermediate-temperature protonic ceramic electrochemical systems. The fundamental design principles of sealant compositions are first discussed, followed by recent developments in deposition methods, sintering strategies, surface treatments, and degradation monitoring techniques. Particular attention is given to the unique challenges associated with PCEC operating conditions, including hydrothermal degradation, interfacial reactions with barium-containing electrolytes, and thermal mismatch. Finally, future opportunities involving sustainable materials, multiscale modeling, additive manufacturing, and artificial intelligence-assisted sealant optimization are highlighted.

glass–ceramic sealants↗
DOE OSTI2024

Complete Development of Critical Capabilities for TRISO Fission Product Source Term Calculations and Quantify Mechanisms for Pd Penetration of SiC

Overall fission product (FP) release will be an important consideration for the licensing and deployment of advanced reactors utilizing tristructural isotropic (TRISO) fuels. This work focuses on enhancing and applying the BISON models needed to predict FP transport within TRISO particles and particle failure probability, both of which factor directly into release predictions. Specifically, this report details (1) the development of the models needed to predict palladium (Pd) conservation at the engineering scale and the application of those models to characterize Pd fluxes for input into a mechanistic multiscale model for Pd penetration; (2) the refinement of sorption mass transfer models and the development of models for trapping in porous layers, which were applied and compared to particle scans from AGR-2 to provide proof of concept for a method of particle-scale validation that may reduce uncertainties compared to compact-scale validation using data from integral effects tests; (3) the development of a failure-statistics-informed, mesh-independent methodology for applying smeared cracking, enabling further study of the localized multiphysics behaviors associated with cascading particle failure mechanisms; and (4) the preliminary characterization of those coupled multiphysics particle failure behaviors using smeared, nonretentive diffusivities to provide a baseline for future study and to guide ongoing engineering applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2020

An Algorithm for Characterization of Fiber Aggregation in Composite Microstructures

Composite structures are susceptible to localized flaws, or variability, that drive global failure. To capture this variance, a multiscale model needs to be introduced that not only accurately represents the statistical nature of the composite microstructure but is also efficient. At the microscale, fiber aggregation creates local stress concentrations, where failure is likely to occur sooner than expected. A method of rapid microstructure generation, in conjunction with cluster characterization, is examined to develop accurate reproductions of 2D composite cross-sections. Parameterization of shape and size of fiber clusters is used to characterize representative volume elements. The discrete element method is used to generate pseudo-microstructures. The clustering parameters from the pseudo- and actual microstructure arrangements (obtained from micrographs) can be compared to determine the validity of the representative volume element generated with the discrete element method. Results show a promising approach to evaluating randomness of fiber distributions and how to accurately recreate microstructures for strength analysis.

Carbon fiber↗
DOE OSTI2021

Multiscale Concurrent Atomistic-Continuum (CAC) modeling of multicomponent alloys

We report strengthening in complex multicomponent systems such as solid solution alloys is controlled primarily by the dynamic interactions between dislocation lines and heterogeneously distributed solute species. Modeling of extended defect length scales in such multicomponent systems becomes prohibitively expensive, motivating the development of reduced order approaches. This work explores the application of the Concurrent Atomistic-Continuum (CAC) method to model dislocation mobility in random alloys at extended length scales. By employing recently developed average-atom interatomic potentials, the average “bulk” material response in coarse-grained regions interacts with true random solute species in the atomistic-scale domain. We demonstrate that spurious stresses in domain resolution transition regions are eliminated entirely due to the CAC formulation. Simultaneously, the key details of local stress fluctuation due to randomness in the dislocation core region are captured, and fluctuating stress smoothly decays to the long-range dislocation stress field response. Dislocation mobility calculations, for line lengths over 400 nm, are computed as a function of alloy composition in the model FeNiCr system and compared to full molecular dynamics (MD). The results capture the composition-dependent trends, while reducing degrees of freedom by nearly 40%. This approach can be readily extended to any system described by an EAM potential and facilitates the study of large-scale defect dynamics in complex solute environments to support computational alloy design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiscale and multiphysics FEA simulation and materials optimization for laser ultrasound transducers

In this study, the relationship between the nanocomposite design and the laser ultrasound transducer (LUT) characteristics was investigated through simulations in multiple scale levels for material behavior, device response, and acoustic wave propagation in media. First, the effects of the nanoparticle size and concentration on the effective properties of composites were quantitatively investigated with the finite element analysis (FEA) method. Second, the effective properties of the nanocomposite were assigned to the layer, which is modeled as a homogeneous material, in the FEA for the LUT simulating the energy conversion from the incident laser to the acoustic wave. Finally, the ultrasound propagation in the water was calculated by a theoretical wave propagation model. The FEA-based prediction was compared with the experimental data in the literature and a theoretical analysis for LUT based on Thermal-Acoustic coupling. As a result, the ultrasound waves on the transducer surface and at a distance in the water could be predicted. Based on the hierarchically integrated prediction procedure, the optimal conditions of the photoacoustic nanocomposites were investigated through the parametric study with the particle size and concentration as variables. The results guide the material designs optimized for different device characteristics, such as high pressure and broad bandwidth.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Validation of strongly coupled geomechanics and gas hydrate reservoir simulation with multiscale laboratory tests

In this work, we validate a coupled flow-geomechanics simulator for gas hydrate deposits, named T+M AM , performing two meter-scale laboratory experiments of gas hydrates for production by depressurization, replicating the gas hydrate deposit in the Ulleung Basin, East Sea, South Korea. The first experiment with a sand-only specimen is a 1D 1 m-scale depressurization test based on the excess gas method, which represents the grain coating hydrate growth. On the other hand, the second is a 3D 1.5 m-scale test with the excess water method for a sand-mud alternating layer system, representing the pore filling hydrate growth. We measure production and displacement at the top with different depressurization levels. In particular, the 3D test exhibits high coupling strength of substantial deformation induced by incompressibility of water and high deformability of the specimen. For validation, we match pressure, flow rate, and displacement between the experimental data and numerical results. Thus, we identify that T+M AM is a reliable simulator, which can be applied to fields in both permafrost and deep oceanic hydrate deposits of strongly coupled flow and geomechanics systems. This validation also implies that other coupled simulators based on the same coupling formulation as T+M AM can be validated when individual flow and geomechanics simulators are stable and reliable.

02 PETROLEUM↗
DOE OSTI2020

ChIMES: A Machine-Learned Interatomic Model Targeting Improved Description of Condensed Phase Chemistry in Energetic Materials

In this report we detail completion of a Physics and Engineering Model Level Two Milestone targeting improved reactive interatomic potentials (IAPs) for energetic materials (EM) through machine learning. The specific goals of this milestone were to develop, validate, and document a new reactive molecular dynamics method for EM, based on machine learning by (1) generating databases of first-principles-derived forces, stresses, and energies for HN3 and 3,4-bis(3-nitrofurazan- 4-yl)furoxan (DNTF) (2) generate atomistic force fields from these databases via ML, and (3) benchmark model performance against first principles calculations. These goals were achieved by (1) further developing a machine learned reactive IAP and generation approach (i.e. the Chebyshev Interaction Model for Efficient Simulation or “ChIMES”), for which resulting IAPs can approach the predictive power of quantum-mechanical approaches at a fraction of the computational expense, and (2) applying the ChIMES framework to develop models for HN3 and DNTF. We find that for simple energetic materials like HN3, high accuracy ChIMES models can be obtained through application of a fitting approach that does not use active machine learning. We demonstrate the suitability of ChIMES models for simulations involving EM by using the HN3 model in multiscale shock technique simulations to predict the HN3 Chapman-Jouguet detonation state and investigate chemical evolution out to 1 ns following shock compression. This model is then used in larger direct shock (DS) simulations for a preliminary investigation of how bubbles (i.e. voids) influence material response under shock compression. We find that more complex EM (i.e. DNTF) necessitate a more sophisticated fitting approach, and develop a new active learning method and python tool to meet this challenge. We demonstrate that this fitting approach yields ChIMES models that out-perform commonly used standard reactive IAPs as well as semi-empirical quantum methods, and discuss the systematic improvability of these actively learned ChIMES models. We also describe challenges related to model development for EM such as DNTF, for which few experimental or previous simulation data are available (e.g. which could otherwise inform generation of training data). To overcome this issue, we establish a semi-empirical quantum ChIMES capability which can be used to efficiently map out relevant thermodynamic and configurational space, and generate ChIMES-IAP training data in a multiscale manner. We also show that these semi-empirical quantum ChIMES models can be used to generate predictions for the shock Hugoniot (the Hugoniot is the locus of thermodynamic states found in a shocked material) equation of state, investigate related thermochemistry, and explore carbon condensation following shock compression. This work represents a substantial advance in our atomistic modeling capability for EM that will provide much needed information on the chemistry of detonation for continued development of continuum models based on the Cheetah thermochemical code.

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

Paper or Plastic? Multiscale Material Handling Properties of Two Model Municipal Solid Waste Streams

Purpose: Municipal Solid Waste (MSW) is a potentially valuable sustainable feedstock for fuel and chemical production due to its carbon-rich content and low cost. This study aims to assess the material handling properties of paperand plastic-rich MSW feedstocks to mitigate equipment failure and processing downtime. Methods: The material handling properties of crumbled MSW feedstocks were measured using apowder rheometer with mass flow hopper calculations to assess handling performance. Inverse gas chromatography was use to measure the surface energy differences between feedstocks. Electron microscopy and Raman spectroscopy was used to evaluate microscale features that may contribute to material handling differences. Results: Plastic and paper rich feedstocks crumbled to a nominal 2 mm particle size were observed to have similar flow and handling characteristics with reasonable hopper outlets. 2 mm plastic rich crumbles, with their higher bulk density, exhibited superior flow performance. By contrast, 4 mm material required significantly larger hopper outlets, indicating poor flowability. Paper rich and 4 mm plastic rich samples displayed broad particle size distributions, which contributed to particle interlocking, jamming, and other flow issues. Electron microscopy revealed that plastic rich samples were significantly smoother, enhancing their flowability compared to the rougher, paper rich materials. Conclusions: This study establishes critical material handling baselines for processing MSW as a viable feedstock for fuel and chemical production. The findings highlight the importance of optimizing particle size and feedstock composition to improve flowability and handling performance.

09 BIOMASS FUELS↗
DOE OSTI2022

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multiscale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. Here, the resulting consistent timeseries alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

42 ENGINEERING↗
DOE OSTI2023

Multiscale Modeling of Radiation Damage in UO 2 under Accelerated Burnup Conditions

Accelerated fuel qualification (AFQ) is a methodology by which new nuclear fuels are developed in an accelerated time frame compared with historical fuel qualification approaches. AFQ generally relies on high-fidelity physics-based modeling and simulation tools to adequately describe fuel performance as well as on revolutionary methods to accelerate burnup accumulation and collect relevant data more quickly. This report summarizes the use of advanced fuel modeling and simulation tools to evaluate microstructures from commercially irradiated fuel and microstructures from proposed MiniFuel irradiations, in which burnup accumulation is accelerated while prototypic temperature conditions are maintained. In this milestone, we used the mesoscale fuel performance code MARMOT to model the evolution of irradiated UO 2 microstructures and their potential restructuring at high burnup. The simulation conditions were informed by BISON models of both commercially irradiated fuel and MiniFuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS2009

The Development of Directional Decohesion Finite Elements for Multiscale Failure Analysis of Metallic Polycrystals

Atomistic simulations of intergranular fracture have indicated that grain-scale crack growth in polycrystalline metals can be direction dependent. At these material length scales, the atomic environment greatly influences the nature of intergranular crack propagation, through either brittle or ductile mechanisms, that are a function of adjacent grain orientation and direction of crack propagation. Methods have been developed to obtain cohesive zone models (CZM) directly from molecular dynamics simulations. These CZMs may be incorporated into decohesion finite element formulations to simulate fracture at larger length scales. A new directional decohesion element is presented that calculates the direction of Mode I opening and incorporates a material criterion for dislocation emission based on the local crystallographic environment to automatically select the CZM that best represents crack growth. The simulation of fracture in 2-D and 3-D aluminum polycrystals is used to illustrate the effect of parameterized CZMs and the effectiveness of directional decohesion finite elements.

Saether, Erik↗
DOE OSTI2020

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗
NASA NTRS2004

Stress Distribution During Deformation of Polycrystalline Aluminum by Molecular-Dynamics and Finite-Element Modeling

In this paper, a multiscale modelling strategy is used to study the effect of grain-boundary sliding on stress localization in a polycrystalline microstructure with an uneven distribution of grain size. The development of the molecular dynamics (MD) analysis used to interrogate idealized grain microstructures with various types of grain boundaries and the multiscale modelling strategies for modelling large systems of grains is discussed. Both molecular-dynamics and finite-element (FE) simulations for idealized polycrystalline models of identical geometry are presented with the purpose of demonstrating the effectiveness of the adapted finite-element method using cohesive zone models to reproduce grain-boundary sliding and its effect on the stress distribution in a polycrystalline metal. The yield properties of the grain-boundary interface, used in the FE simulations, are extracted from a MD simulation on a bicrystal. The models allow for the study of the load transfer between adjacent grains of very different size through grain-boundary sliding during deformation. A large-scale FE simulation of 100 grains of a typical microstructure is then presented to reveal that the stress distribution due to grain-boundary sliding during uniform tensile strain can lead to stress localization of two to three times the background stress, thus suggesting a significant effect on the failure properties of the metal.

Yamakov, V.↗
DOE OSTI2020

Multi-GPU immersed boundary method hemodynamics simulations

Large-scale simulations of blood flow that resolve the 3D deformation of each comprising cell are increasingly popular owing to algorithmic developments in conjunction with advances in compute capability. Among different approaches for modeling cell-resolved hemodynamics, fluid structure interaction (FSI) algorithms based on the immersed boundary method are frequently employed for coupling separate solvers for the background fluid and the cells within one framework. GPUs can accelerate these simulations; however, both current pre-exascale and future exascale CPU-GPU heterogeneous systems face communication challenges critical to performance and scalability. In this paper, we describe, to our knowledge, the largest distributed GPU-accelerated FSI simulations of high hematocrit cell-resolved flows with over 17 million red blood cells. We compare scaling on a fat node system with six GPUs per node and on a system with a single GPU per node. Through comparison between the CPU- and GPU-based implementations, we identify the costs of data movement in multiscale multi-grid FSI simulations on heterogeneous systems and show it to be the greatest performance bottleneck on the GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment FY2020: Part 3

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Sup- port for Advanced Non-Light Water Reactors.” The tasks completed for this report are: Task2c: Explicit modeling of pebble transient temperature response. In this simulation, the 400 MWth Pebble-Bed Modular Reactor (PBMR) design, PBMR- 400, experiences a 20-second power ramp from 100% to 150% power. This is followed by a similar reduction in the power back to 100%. Several multiscale pebble coupling approaches are tested with one pebble per mesh element in the active core region. The results show good conservation behavior and the stability of the coupling.; Extended scope part 1: An assessment of the computational efficiency of the Discontinuous Finite Element Method (DFEM) heat transfer solver shows good scalability. The DFEM solver is a factor of 4 more expensive in solution time than the Finite Element Method (FEM) solver for heat transfer problems due to the increased number of degrees of freedom. Nonetheless, the DFEM approach provides the user with the flexibility to model gap heat transfer problems.; Extended scope part 2: The GapHeatTransferInterfaceMaterial was improved to give the user increased flexibility with the modeling of heat transfer through gaps with the DFEM solver. A number of gap parameters can now be coupled both through functions and variables.; Extended scope part 3: Demonstration of how the gap width between hexagonal fuel cells can be calculated during a heat-up transient and used in the GapHeatTransferInterface model. A full-domain DFEM model with gap expansion is coupled to a SubApp that models the thermal expansion of the base plate. The results show the expected physical behavior, although have not been fully bench-marked at this point in time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗