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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Asynchronous Reciprocal Coupling of Martini 2.2 Coarse-Grained and CHARMM36 All-Atom Simulations in an Automated Multiscale Framework

The appeal of multiscale modeling approaches is predicated on the promise of combinatorial synergy. However, this promise can only be realized when distinct scales are combined with reciprocal consistency. Here, we consider multiscale molecular dynamics (MD) simulations that combine the accuracy and macromolecular flexibility accessible to fixed-charge all-atom (AA) representations with the sampling speed accessible to reductive, coarse-grained (CG) representations. AA-to-CG conversions are relatively straightforward because deterministic routines with unique outcomes are achievable. Conversely, CG-to-AA conversions have many solutions due to a surge in the number of degrees of freedom. While automated tools for biomolecular CG-to-AA transformation exist, we find that one popular option, called Backward, is prone to stochastic failure and the AA models that it does generate frequently have compromised protein structure and incorrect stereochemistry. Although these shortcomings can likely be circumvented by human intervention in isolated instances, automated multiscale coupling requires reliable and robust scale conversion. In this work, we detail an extension to Multiscale Machine-learned Modeling Infrastructure (MuMMI), including an improved CG-to-AA conversion tool called sinceCG. This tool is reliable (~98% weakly correlated repeat success rate), automatable (no unrecoverable hangs), and yields AA models that generally preserve protein secondary structure and maintain correct stereochemistry. We describe how the MuMMI framework identifies CG system configurations of interest, converts them to AA representations, and simulates them at the AA scale while on-the-fly analyses provide feedback to update CG parameters. Application to systems containing the peripheral membrane protein RAS and proximal components of RAF kinase on complex eight-component lipid bilayers with ~1.5 million atoms is discussed in the context of MuMMI.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Hierarchical Composites Patterned via 3D Printed Cellular Fluidics

Additive manufacturing of freeform structures containing multiple materials with deterministic spatial arrangement and interactions remains a challenge for most 3D printing processes, due to complex fabrication tool requirements and limitations in printability of some material classes. Here, in this paper, a versatile method is reported to produce architected composites using the concept of cellular fluidics, in which lattices of unit cells are used as templating scaffolds to guide flowable infill materials in a programmed spatial pattern, upon which they are cured in place to produce a deterministically ordered multimaterial solid. The lattice design relies on the unit cell size, type, strut diameter, surface wetting, and distribution of cellular structures to control liquid flow and retention. Individual unit cells are tuned to achieve reliable infilling and combined into higher-order architectures to achieve multiscale composite materials with disparate mechanical properties, including those considered non-printable. Lattice design considerations for leveraging capillary phenomena and demonstrate several methods of patterning polymers in 3D-printed cellular fluidic structures are presented. The concept of tuning the compressive response of an architected composite using a flexible-elastomer as the lattice and a stiff-epoxy as the infill material is illustrated.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multiscale analysis in solids with unseparated scales: fine-scale recovery, error estimation, and coarse-scale adaptivity

There are several engineering applications in which the assumptions of homogenization and scale separation may be violated, in particular, for metallic structures constructed through additive manufacturing. Instead of resorting to direct numerical simulation of the macroscale system with an embedded fine scale, an alternative approach is to use an approximate macroscale constitutive model, but then estimate the model-form error using a posteriori error estimation techniques and subsequently adapt the macroscale model to reduce the error for a given boundary value problem and quantity of interest. Here, we investigate this approach to multiscale analysis in solids with unseparated scales using the example of an additively manufactured metallic structure consisting of a polycrystalline microstructure that is neither periodic nor statistically homogeneous. As a first step to the general nonlinear case, we focus here on linear elasticity in which each grain within the polycrystal is linear elastic but anisotropic.

42 ENGINEERING↗
DOE OSTI2025

Kinetically Dormant Ni‐Rich Layered Cathode During High‐Voltage Operation

Abstract The degradation of Ni‐rich cathodes during long‐term operation at high voltage has garnered significant attention from both academia and industry. Despite many post‐mortem qualitative structural analyses, precise quantification of their individual and coupling contributions to the overall capacity degradation remains challenging. Here, by leveraging multiscale synchrotron X‐ray probes, electron microscopy, and post‐galvanostatic intermittent titration technique, the thermodynamically irreversible and kinetically reversible capacity loss is successfully deconvoluted in a polycrystalline LiNi 0.83 Mn 0.1 Co 0.07 O 2 cathode during long‐term charge/discharge cycling in full cell configuration. Contradicting the dramatic capacity loss, the layered structure remains highly alive even after 1000 cycles at 4.6 V while undergoing a three‐order of magnitude reduction in the mass transfer kinetics, leading to almost fully recoverable capacity under kinetic‐free conditions. Such kinetic dormant behavior after cycling is not simply ascribed to poor chemical diffusion by reconstructed cathode surface but highly synchronizes with the lattice strain evolution stemming from the structural heterogeneity between deeply delithiated layered and degraded rock‐salt phases at high voltage. These findings deepen the degradation mechanism of high‐voltage cathodes to achieve long‐cycling and fast‐charging performance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Towards a multiscale approach for understanding irradiation induced swelling and creep in 316 stainless steels - A coupled cluster dynamics and crystal plasticity approach

Structural materials undergo mechanical degradation, in part due to irradiation-induced swelling and creep, under nuclear reactor conditions. While swelling results from the migration and clustering of irradiation-induced atomic-scale mobile defects, the interaction of mesoscale dislocations with these defects causes creep deformation. A coupled crystal plasticity (CP) and mean-field cluster dynamics (CD) approach is presented to investigate the effect of irradiation on the long-term mechanical behavior of 316 stainless steel, which are under consideration for use in nuclear reactors. The temporal evolution of Frenkel pairs and extended defect population, under a chosen irradiation flux and temperature, is predicted using the CD model. The impact of the irradiation defects on the stress state, and the resulting dislocation-mediated inelastic deformation, is modeled concurrently with the CP model. The inelastic deformation is irradiation flux dependent, and early-stage defect evolution determines the later-stage mechanical behavior in 316 stainless steel.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Designed Spin‐Texture‐Lattice to Control Anisotropic Magnon Transport in Antiferromagnets

Abstract Spin waves in magnetic materials are promising information carriers for future computing technologies due to their ultra‐low energy dissipation and long coherence length. Antiferromagnets are strong candidate materials due, in part, to their stability to external fields and larger group velocities. Multiferroic antiferromagnets, such as BiFeO 3 (BFO), have an additional degree of freedom stemming from magnetoelectric coupling, allowing for control of the magnetic structure, and thus spin waves, with the electric field. Unfortunately, spin‐wave propagation in BFO is not well understood due to the complexity of the magnetic structure. In this work, long‐range spin transport is explored within an epitaxially engineered, electrically tunable, 1D magnonic crystal. A striking anisotropy is discovered in the spin transport parallel and perpendicular to the 1D crystal axis. Multiscale theory and simulation suggest that this preferential magnon conduction emerges from a combination of a population imbalance in its dispersion, as well as anisotropic structural scattering. This work provides a pathway to electrically reconfigurable magnonic crystals in antiferromagnets.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Input specific neural networks

Neural networks have emerged as powerful tools for mapping between inputs and outputs. However, their black-box nature limits the ability to encode or impose specific structural relationships between inputs and outputs. Many scientific and engineering problems, such as constitutive modeling in solid mechanics, require networks that can enforce convexity, monotonicity, or other structural constraints to ensure physical consistency. Here, we introduce the Input Specific Neural Network (ISNN), a new architecture that enables multiple, distinct constraints to be imposed on different input subsets for scalar-valued outputs. This framework unifies convex, monotone–convex, monotone, and arbitrary mappings within a single network for the first time. Two ISNN architectures with analytical first- and second-order derivatives are developed. We demonstrate the performance on synthetic toy problems, inverse problems in isotropic hyperelasticity, and finite element simulations. ISNNs achieve improved extrapolation behavior, require fewer invariant inputs than standard input convex networks for polyconvex potentials, and enable significant computational savings via manual differentiation. We also show how ISNNs can be used to learn structural relationships between inputs and outputs via a binary gating mechanism. Particularly, ISNNs are employed to model a homogenized anisotropic free energy potential in a decoupled multiscale setting. The network learns whether or not the potential should be modeled as polyconvex and retains only the relevant layers while using the minimum number of inputs. ISNNs provide a flexible foundation for embedding structural priors into neural networks, enhancing both interpretability and stability. They are broadly applicable across computational mechanics and other scientific domains requiring constrained functional relationships.

Jadoon, Asghar A. [Univ. of Texas, Austin, TX (Uni↗
DOE OSTI2024

VOTCA: multiscale frameworks for quantum and classical simulations in soft matter

Many physical phenomena in liquids and soft matter are multiscale by nature and can involve processes with quantum and classical degrees of freedom occurring over a vast range of length- and timescales. Examples range from structure formation processes of complex polymers or even polymer blends (Svaneborg & Everaers, 2023) on the classical side to charge and energy transport and conversion processes (Lee et al., 2019) involving explicit electronic and, therefore, quantum information. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides multiscale frameworks built on a comprehensive set of methods for the development of classical coarse-grained potentials (VOTCA-CSG) as well as state-of-the art excited state electronic structure methods based on density-functional and many-body Green’s function theories, coupled in mixed quantum-classical models and used in kinetic network models (VOTCA-XTP).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cross-Scale Catalyst Modeling Applied to H 2 Storage and Release via Formic Acid

Here, we propose the Systems-to-Atoms (S2A) modeling framework that integrates the kinetics of reaction chemistry and structural configurations across various length scales with the aim of establishing a versatile template for multiscale modeling of reactive flow problems and to predict the operando activity of catalyst materials. The approach encompasses a microkinetic model to analyze surface reactions on individual facets of catalyst nanoparticles coupled with the computation of average surface reaction rates for catalyst nanoparticles of specific size distributions. Macro-homogeneous surface reaction kinetics are derived as a function of catalyst loading and used as input parameters for the continuum-scale reactor model. The cross-scale framework enables the optimization of catalyst utilization through reactor design and operating strategy. To demonstrate the framework, we studied the storage and release of hydrogen from formic acid, a promising liquid organic hydrogen carrier (LOHC), over Pd, Pt, and Cu catalysts. The framework predicts observed trends in formic acid dehydrogenation activity for catalysts with comparable weight loadings and metal particle diameters, demonstrating satisfactory quantitative alignment. Finally, the seamless transmission of parameter uncertainties between scales is also discussed.

08 HYDROGEN↗
DOE OSTI2024

Validation Framework for Post-Combustion Carbon Capture CFD Simulations

First-principles based computational fluid dynamics (CFD) simulations are proposed as a fundamental tool for investigating solvent-based CO2 absorption in packed columns, due to the ability to accurately represent the underlying non-linear multiscale dynamics. In this work, we employ such models to investigate hydrodynamics of columns with structured by assessing the key hydrodynamic metrics, such as pressure drop and liquid holdup. Our models are validated with experimental data from a specifically designed column for this line of work. The test cases of gas and liquid flowrates and operating conditions were selected through a comprehensive sequential design of experiments approach offered by the CCSI2 toolset.

Panagakos, Grigorios↗
DOE OSTI2020

Multimodal, Multidimensional, and Multiscale X-ray Imaging at the National Synchrotron Light Source II

Over the last couple of decades, the synchrotron radiation research community has witnessed tremendous advancement in the field of x-ray imaging and microscopy. Continuing enhancement of the light sources’ brightness, advances in x-ray focusing optics, incorporation of precision instruments, and development of innovative imaging techniques are some of the leading contributors for the rapid progress. New imaging and microscopy beamlines, such as the ones at the National Synchrotron Light Source II (NSLS-II), a Department of Energy (DOE) Office of Science user facility located at DOE’s Brookhaven National Laboratory, are capable of performing more sophisticated and complicated measurements than ever before, either on their own or used together as a suite of tools. These sophisticated and complex measurements exhibit attributes for multimodal, multidimensional, and multiscale imaging. Recent popularity of these methods is strongly driven by the current trends in materials characterizations, where researchers desire to map out hierarchical materials structure over a large range of length scales, to understand structure-property correlation, to quantify materials structures in 3D, and/or to perform operando or in situ experiments. It is important to emphasize that “materials” under investigation are not only synthesized materials but also the natural materials. In this article, we describe multimodal, multidimensional and multiscale x-ray imaging capabilities of the NSLS-II beamlines and how these methods are used to tackle complex scientific problems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Pore-scale modeling has limited applicability at large scales due to its high computational cost. One common approach to upscale pore-scale models is the use of effective medium theories, which homogenize small-scale features in a porous structure and characterize the medium by macroscale properties (e.g., permeability) and equations (e.g., Darcy’s law). However, there are classes of physical processes for which effective medium approximations may become inaccurate, e.g., mineral precipitation and clogging during reactive transport. We have developed a deep learning upscaling framework, in which pore-scale modeling is directly employed in macroscale systems, without relying on effective medium approximations. The upscaling framework is first developed for general multiscale systems and then applied to modeling reactive transport with mineral precipitation in the altered layer in fracture-matrix structures. Solute transport from the fractures to the matrix is modeled as a wall boundary condition for the fractures, which, in turn, is predicted by recurrent neural networks using the concentration histories at the fracture-matrix boundary. Specifically, we consider a meter-scale fracture network embedded in sandstones, where the smallest feature is at the micron scale. Here the proposed framework allows us to span five orders of magnitude in length scales by capturing mineral precipitation in the altered layer of the rock matrix at the pore scale across the entire meter-scale fracture network.

42 ENGINEERING↗
DOE OSTI2025

Observation of a Knotted Electron Diffusion Region in Earth's Magnetotail Reconnection

Magnetic reconnection is a fundamental plasma process that alters the magnetic field topology and releases magnetic energy. Most numerical simulations and spacecraft observations assume a two‐dimensional diffusion region, with the electron diffusion region (EDR) embedded in the same plane as the ion diffusion region (IDR) and a uniform guide field throughout. Using observations from Magnetospheric Multiscale mission, we report a non‐coplanar, knotted EDR in Earth's magnetotail current sheet. The reconnection plane of the knotted EDR deviates by approximately 38° from that of the IDR, with the guide field exhibiting both a 38° directional shift and a twofold increase in amplitude. Moreover, the Hall magnetic field is bipolar in the EDR but quadrupolar in the IDR, indicating different Hall current structures at electron and ion scales. These observations highlight the importance of three‐dimensional effects and illustrate the complexity of multiscale coupling between the EDR and IDR during reconnection studies.

diffusion region↗
DOE OSTI2024

Assessment of effective elastic constants of U-10Mo fuel: A multiscale modeling and homogenization study

The significant microstructural changes that U-Mo fuel undergoes during operation degrades its mechanical properties and structural integrity. Microstructural evolution entails the formation, evolution, and redistribution of porosity in conjunction with grain refinement. In the present paper, we employ numerical approaches to assess the impact of the various microstructural features—grains, nanoscale intragranular fission gas bubbles, and mesoscale intergranular voids—on the degradation of elastic constants. Phase-field microstructure models are combined with the asymptotic expansion homogenization technique in order to derive the effective elastic constants as a function of porosity and fission density. Here the results are verified and compared against theoretical bounds. Using this approach, elastic degradation in operating nuclear fuels can be quantified when the distributions of microstructural features are known.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

A mechanistic, multiscale model for predicting Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles use silicon carbide (SiC) as the primary structural member and barrier against metallic fission product (FP) release. palladiums (PDs), produced by fission in the fuel kernel, can diffuse to and chemically interact with the SiC layer, degrading its structural integrity and ability to contain radioactive FPs. Existing temperature-dependent correlations for predicting Pd penetration rely on experimental data with significant scatter due to varying conditions, potentially complicating ongoing fuel qualification and licensing efforts for advanced reactors that would subject TRISO fuels to operating conditions outside of those examined in the experiments. A mechanistic model of Pd production, transport, and reaction is developed in this work to better understand and predict PD attack of SiC in TRISO particles. molecular dynamicss (MDs) simulations are utilized to calculate the Pd diffusivity in SiC grain bulk and grain boundaries. A mesoscale phase-field diffusion model, informed by the MD diffusivities, is used to develop a reduced order model (ROM) for the effect of SiC microstructure on the Pd penetration rate. The engineering scale BISON model calculates Pd production and transport, utilizing the ROM to predict penetration rates consistent with experimental data. This novel mechanistic ROM captures the effect of temperature, microstructure, and irradiation history on Pd penetration. In conclusion, these new capabilities are expected to support ongoing qualification and licensing efforts associated with near-term TRISO-fueled reactor applications.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

First Observations of an Ion Vortex in a Magnetic Hole in the Solar Wind by MMS

The coronal heating region is able to generate mirror mode structures by ion mirror instabilities. Linear magnetic holes are believed to be the remnants of mirror mode structures, thus they are believed to be messengers from the coronal heating region. They can be convected to ∼9 au with the solar wind flow, indicating that a stabilizing mechanism is necessary to make the magnetic holes survive for such a long time. Here, we investigate a magnetic hole with a size of ∼6.7 ρ {sub i} in the solar wind based on observations by the Magnetospheric Multiscale mission. The unprecedented high-resolution data enable us to reveal the existence of the ion vortex inside the structure for the first time. Such an ion vortex forms a ring-like current, which is consistent with the magnetic field depression. The self-consistent structure of the magnetic hole contributed by the ion vortex can help to further shed light on the mechanism of the long-term survival of magnetic holes in the astrophysical plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Multiscale Modeling of Silicon Carbide Cladding for Nuclear Applications: Thermal Performance Modeling

The complex multiscale and anisotropic nature of silicon carbide (SiC) ceramic matrix composite (CMC) makes it difficult to accurately model its performance in nuclear applications. The existing models for nuclear grade composite SiC do not account for the microstructural features and how these features can affect the thermal and structural behavior of the cladding and its anisotropic properties. In addition to the microstructural features, the properties of individual constituents of the composites and fiber tow architecture determine the bulk properties. Models for determining the relationship between the individual constituents’ properties and the bulk properties of SiC composites for nuclear applications are absent, although empirical relationships exist in the literature. Here, a hierarchical multiscale modeling approach was presented to address this challenge. This modular approach addressed this difficulty by dividing the various aspects of the composite material into separate models at different length scales, with the evaluated property from the lower-length-scale model serving as an input to the higher-length-scale model. The multiscale model considered the properties of various individual constituents of the composite material (fiber, matrix, and interphase), the porosity in the matrix, the fiber volume fraction, the composite architecture, the tow thickness, etc. By considering inhomogeneous and anisotropic contributions intrinsically, our bottom-up multiscale modeling strategy is naturally physics-informed, bridging constitutive law from micromechanics to meso-mechanics and structural mechanics. The effects that these various physical attributes and thermo-physical properties have on the composite’s bulk thermal properties were easily evaluated and demonstrated through the various analyses presented herein. Since silicon carbide fiber-reinforced SiC CMCs are also promising thermal–structural materials with a broad range of high-end technology applications beyond nuclear applications, we envision that the multiscale modeling method we present here may prove helpful in future efforts to develop and construct reinforced CMCs and other advanced composite nuclear materials, such as MAX phase materials, that can service under harsh environments of ultrahigh temperatures, oxidation, corrosion, and/or irradiation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markers

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

59 BASIC BIOLOGICAL SCIENCES↗