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

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At least 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Comparison of Magnetospheric MultiScale (MMS) Formation Design Algorithms

The MMS mission consists of four identical spinning spacecraft in a tetrahedron-like formation during predefined orbital regions-of-interest [1]. The MMS formation will fly about a highly eccentric orbit to study magnetic reconnection in the Earth.s magnetosphere. The mission is divided in two main science phases of markedly different orbit size. Phase 1 is a 1.2 R(sub E) x 12 R(sub E) (R(sub E) equals one Earth radius) orbit phase where science will be performed on the day-side of the magnetosphere, and Phase 2 is a 1.2 R(sub E) x 25 R(sub E) orbit phase where science will be performed on the night-side of the magnetosphere. For each phase, the MMS formation initially will fly a series of tetrahedron sizes ranging between 10-km and 400-km to assist the science team in determining the best value for the remainder of the phase. This paper presents a preliminary comparison of the algorithms currently available to determine desired MMS formation states. After a brief description of the MMS formation flying metrics and associated requirements, the five formation design algorithm combinations considered here are presented. Monte Carlo simulations determine the performance of each algorithm defined in terms of formation lifetime and fuel consumption. Preliminary results focusing on the smaller formation size for Phase 1 are discussed and the conclusions are presented along with indications for future work.

Mann, L.↗
DOE OSTI2023

New formation and fate of Isoprene SOA markers revealed by field data-constrained modeling

Particulate 2-methyltetrols (2-MT) and 2-methylglyceric acid (2-MG) are typically used to indicate the abundance of isoprene-derived secondary organic aerosols (SOA). However, their formation and fate are not fully understood. In this study, we showed that particulate 2-MT and 2-MG collected at multiple monitoring sites under a wide range of atmospheric and emission conditions, with concentrations spanning six orders of magnitudes, are well reproduced with an expanded isoprene-SOA scheme implemented into the Community Multiscale Air Quality (CMAQ) model. The scheme considers their three-phase (gas-aqueous-organic phase) partitioning, formation from acid-driven multiphase reactions, and degradation by OH radicals in the gas and aqueous phases. The model results reveal that a non-aqueous formation pathway or direct biogenic emission is needed to supplement the commonly assumed acid-driven multiphase reaction process to explain the observed 2-MT concentrations. This missing pathway contributes to 20–40% of 2-MT in areas with aerosol pH < 2 and more than 70% under less acidic conditions (pH~2–5), such as those encountered in the western US and China. The typical summertime gas-phase photochemical lifetimes of 2-MT and 2-MG are estimated to be 4–6 and 20–30 h, respectively, and their aqueous lifetimes are approximately 20–40 h. Our simulations show that predicted 2-MT is mainly influenced by its aqueous phase loss to OH, but 2-MG is more sensitive to gas phase OH loss due to the preferential partitioning of the two tracers in the aqueous and gas phases, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

On the continuity of mean total normal stress in geometrical multiscale cardiovascular problems

In this work an iterative strategy to implicitly couple dimensionally-heterogeneous blood flow models accounting for the continuity of mean total normal stress at interface boundaries is developed. Conservation of mean total normal stress in the coupling of heterogeneous models is mandatory to satisfy energetic consistency between them. Nevertheless, existing methodologies are based on modifications of the Navier–Stokes variational formulation, which are undesired when dealing with fluid–structure interaction or black box codes. The proposed methodology makes possible to couple one-dimensional and three-dimensional fluid–structure interaction models, enforcing the continuity of mean total normal stress while just imposing flow rate data or even the classical Neumann boundary data to the models. This is accomplished by modifying an existing iterative algorithm, which is also able to account for the continuity of the vessel area, when required. Comparisons are performed to assess differences in the convergence properties of the algorithms when considering the continuity of mean normal stress and the continuity of mean total normal stress for a wide range of flow regimes. Finally, examples in the physiological regime are shown to evaluate the importance, or not, of considering the continuity of mean total normal stress in hemodynamics simulations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

GrainPaint: A multi-scale diffusion-based generative model for microstructure reconstruction of large-scale objects

Simulation-based approaches to microstructure generation can suffer from a variety of limitations, such as high memory usage, long computational times, and difficulties in generating complex geometries. Generative machine learning models present a way around these issues, but they have previously been limited by the fixed size of their generation area. Here, we present a new microstructure generation methodology leveraging advances in inpainting using denoising diffusion models to overcome this generation area limitation. We show that microstructures generated with the presented methodology are statistically similar to grain structures generated with a kinetic Monte Carlo simulator, SPPARKS.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Low-Resolution Models for the Interaction Dynamics of Coated Gold Nanoparticles with β2-microglobulin

A large number of low-resolution models have been proposed in the last decades to reduce the computational cost of molecular dynamics simulations for bio-nano systems, such as those involving the interactions of proteins with functionalized nanoparticles (NPs). For the proteins, “minimalist” models at the one-bead-per residue (Cα-based) level and with implicit solvent are well established. For the gold NPs, widely explored for biotechnological applications, mesoscale (MS) models treating the NP core with a single spheroidal object are commonly proposed. In this representation, the surface details (coating, roughness, etc.) are lost. These, however, and the specificity of the functionalization, have been shown to have fundamental roles for the interaction with proteins. We presented a mixed-resolution coarse-grained (CG) model for gold NPs in which the surface chemistry is reintroduced as superficial smaller beads. We compared molecular dynamics simulations of the amyloid β2-microglobulin represented at the minimalist level interacting with NPs represented with this model or at the MS level. Our finding highlights the importance of describing the surface of the NP at a finer level as the chemical-physical properties of the surface of the NP are crucial to correctly understand the protein-nanoparticle association.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Bridging Scales to Model Reactive Diffusive Transport in Porous Media

Published by ECS. Two novel scale-bridging algorithms to model reaction-diffusion transport in porous media are presented. The algorithms are based on direct numerical simulations and couple the information of a micro-scale model, which accounts for the large field of view provided by micro X-ray computed tomography (X-ray CT), and a nano-scale model, which locally resolves transport in the fine structure extracted from nano X-ray CT. The micro-scale model is discretized in the through-plane direction into a 1D grid, where effective properties and internal boundaries are determined based on the results from the nano-scale model. The validated algorithms are used to examine transport of oxygen in precious group metal-free electrodes considering both zero- and first-order kinetics. Unlike conventional methods, the results show that the effective diffusivity is not a passive property but increases in regions where the reaction-rate coefficient is large. The proposed algorithms account for the multiscale coupling of reaction-diffusion transport and material microstructure, thus improving the predictions compared to conventional methods.

42 ENGINEERING↗
NASA NTRS2009

A Continuum-Atomistic Analysis of Transgranular Crack Propagation in Aluminum

A concurrent multiscale modeling methodology that embeds a molecular dynamics (MD) region within a finite element (FEM) domain is used to study plastic processes at a crack tip in a single crystal of aluminum. The case of mode I loading is studied. A transition from deformation twinning to full dislocation emission from the crack tip is found when the crack plane is rotated around the [111] crystallographic axis. When the crack plane normal coincides with the [112] twinning direction, the crack propagates through a twinning mechanism. When the crack plane normal coincides with the [011] slip direction, the crack propagates through the emission of full dislocations. In intermediate orientations, a transition from full dislocation emission to twinning is found to occur with an increase in the stress intensity at the crack tip. This finding confirms the suggestion that the very high strain rates, inherently present in MD simulations, which produce higher stress intensities at the crack tip, over-predict the tendency for deformation twinning compared to experiments. The present study, therefore, aims to develop a more realistic and accurate predictive modeling of fracture processes.

Yamakov, V.↗
DOE OSTI2016

Perspective: Ultrafast magnetism and THz spintronics

This year the discovery of femtosecond demagnetization by laser pulses is 20 years old. For the first time, this milestone work by Bigot and coworkers gave insight directly into the time scales of microscopic interactions that connect the spin and electron system. While intense discussions in the field were fueled by the complexity of the processes in the past, it now became evident that it is a puzzle of many different parts. Rather than providing an overview that has been presented in previous reviews on ultrafast processes in ferromagnets, this perspective will show that with our current depth of knowledge the first applications are developed: THz spintronics and all-optical spin manipulation are becoming more and more feasible. The aim of this perspective is to point out where we can connect the different puzzle pieces of understanding gathered over 20 years to develop novel applications. Based on many observations in a large number of experiments. Differences in the theoretical models arise from the localized and delocalized nature of ferromagnetism. Transport effects are intrinsically non-local in spintronic devices and at interfaces. We review the need for multiscale modeling to address the processes starting from electronic excitation of the spin system on the picometer length scale and sub-femtosecond time scale, to spin wave generation, and towards the modeling of ultrafast phase transitions that altogether determine the response time of the ferromagnetic system. Today, our current understanding gives rise to the first usage of ultrafast spin physics for ultrafast magnetism control: THz spintronic devices. This makes the field of ultrafast spin-dynamics an emerging topic open for many researchers right now.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Revealing EDL-driven reduction mechanisms in binary, ternary, and quaternary fluorinated electrolytes via an integrated MD–DFT–ML framework

Accurately predicting solid electrolyte interphase (SEI) formation requires explicitly resolving the electric double layer (EDL) structure, which deviates significantly from that of the bulk electrolyte. Although an established molecular dynamics (MD) and Density Functional Theory (DFT) framework can model SEI formation by evaluating reduction reactions of local clusters in the EDL, it suffers from a combinatorial computational bottleneck. To overcome this limitation, we introduce a machine-learning-accelerated simulation workflow (MD–DFT–ML), integrating a gradient-boosted regression model trained on EDL composition data to efficiently predict reduction potentials. We apply this framework to seven fluorinated electrolytes comprising fluorinated anions, a fluorinated ester solvent, two types of diluent (ion-solvating ester vs. non-solvating ether), and an FEC additive. The analysis shows that the EDL selectively accumulates cation-binding species; consequently, the non–cation-binding ether diluent rarely enters the EDL and makes minimal contributions to SEI formation. DFT calculations on statistically representative EDL clusters provide reduction potentials and fluorine-release pathways, while the ML model, which substantially reduces the DFT workload, predicts cluster reduction energies with a mean absolute error of 0.1 eV. The combined MD–DFT–ML approach also quantifies contributions from different sources to LiF formation in the SEI. This methodology establishes a generalizable route for multiscale modeling electrolyte and interphase design for next-generation electrochemical energy-storage systems.

DFT-MD-ML workflow↗
DOE OSTI2024

Accelerating multiscale electronic stopping power predictions with time-dependent density functional theory and machine learning

Knowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Design, fabrication, and testing of a water-cooled tungsten-foam high-heat flux plasma-facing module

Here, we present a comprehensive study detailing the design, fabrication and testing a new modular “finger” component, featuring a hexagonal tungsten foam armor tile bonded to the top surface of the water-cooled finger unit. The mechanical, thermal, and particle transport properties of the tungsten foam armor were determined for a wide range of porosity and cell size, and then combined in an equivalent solid material with “effective” properties suitable for large-scale simulations. A series of 3-D multiphysics simulations for coupled thermal and mechanical behavior of the “finger” module are used to optimize the design before fabrication. The simulation results show that, for the expected experimental conditions, a safe range of heat flux and foam porosity is possible for both water-cooling and helium-cooling. Based on these multiphysics simulations, an optimum finger cooling module has been fabricated at Ultramet, Inc. A series of experiments were designed to study the thermomechanical performance of the “finger” unit under steady-state and transient thermal loading at surface heat flux up to 16 MW/m 2 in the High Energy Flux Test facility (HEFTY) at UCLA. Testing conditions included low-power, steady-state plasma exposure at 6 MW/m 2 , followed by severe high heat flux cycles at 16 MW/m 2 . Results of these tests are explained through comparisons with multiphysics simulations. To eliminate the effects of thermomechanical damage carried out on a module on its thermo-mechanical response on a subsequent test, three different yet identically designed and fabricated finger modules were tested. The results show that the current design is capable of cooling the walls of fusion devices at surface heat flux up to 6 MW/m 2 without failure, and that the failure heat flux limit is below 16 MW/m 2 .

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗
DOE OSTI2021

Damage Modeling of Solid Oxide Fuel Cells Accounting for Redox Effects

This work applies a multiscale mechanistic damage model developed for brittle ceramics and implemented in commercial finite element (FE) packages via user subroutines to study progressive damage in solid oxide fuel cells (SOFC) subjected to thermomechanical loading under normal operating and shutdown conditions including redox effects. The damage model captures the micromechanics of stiffness reduction due to material porosity change and microcracking and integrates the as-obtained stiffness reduction law into a continuum damage mechanics (CDM) formulation for the evolution of microcracks up to fracture. The volumetric “swelling” that occurs during redox is treated in constitutive modeling similarly to thermal expansion, but swelling strains are irreversible. Furthermore, this damage model was first validated through predictions of strength and stress-strain response for the SOFC electrode materials. Next, it has been applied to predict the potential for degradation in a generic planar SOFC stack with large active area cells. Multicell stack models were simulated in both co-flow and counter-flow configurations. In addition, a constant temperature redox cycle was also simulated to capture overall cell electrode damage due to volumetric swelling of the nickel (Ni)-based anode in the anode-supported cells.

25 ENERGY STORAGE↗
DOE OSTI2022

Coupled Monte Carlo and thermal-hydraulics modeling of a prismatic gas reactor fuel assembly using Cardinal

Cardinal is a MOOSE application that couples OpenMC Monte Carlo transport and NekRS computational fluid dynamics to the MOOSE framework, closing the neutronics and thermal-fluid gaps in conducting tightly-coupled, high-resolution multiscale and multiphysics analyses. By leveraging MOOSE's interfaces for wrapping external codes, Cardinal overcomes many challenges encountered in earlier multiphysics coupling works, such as file-based I/O or overly-restrictive geometry mapping requirements. In this work, we leverage a subset of the multiphysics interfaces in Cardinal to perform coupling of OpenMC neutron transport, MOOSE heat conduction, and THM thermal-fluids for steady-state modeling of a prismatic gas reactor fuel assembly.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Digital Twins for Materials

Digital twins are emerging as powerful tools for supporting innovation as well as optimizing the in-service performance of a broad range of complex physical machines, devices, and components. A digital twin is generally designed to provide accurate in-silico representation of the form (i.e., appearance) and the functional response of a specified (unique) physical twin. This paper offers a new perspective on how the emerging concept of digital twins could be applied to accelerate materials innovation efforts. Specifically, it is argued that the material itself can be considered as a highly complex multiscale physical system whose form (i.e., details of the material structure over a hierarchy of material length) and function (i.e., response to external stimuli typically characterized through suitably defined material properties) can be captured suitably in a digital twin. Accordingly, the digital twin can represent the evolution of structure, process, and performance of the material over time, with regard to both process history and in-service environment. This paper establishes the foundational concepts and frameworks needed to formulate and continuously update both the form and function of the digital twin of a selected material physical twin. The form of the proposed material digital twin can be captured effectively using the broadly applicable framework of n-point spatial correlations, while its function at the different length scales can be captured using homogenization and localization process-structure-property surrogate models calibrated to collections of available experimental and physics-based simulation data.

36 MATERIALS SCIENCE↗
NASA NTRS2011

A Globally Stable Lyapunov Pointing and Rate Controller for the Magnetospheric MultiScale Mission (MMS)

The Magnetospheric MultiScale Mission (MMS) is scheduled to launch in late 2014. Its primary goal is to discover the fundamental plasma physics processes of reconnection in the Earth's magnetosphere. Each of the four MMS spacecraft is spin-stabilized at a nominal rate of 3 RPM. Traditional spin-stabilized spacecraft have used a number of separate modes to control nutation, spin rate, and precession. To reduce the number of modes and simplify operations, the Delta-H control mode is designed to accomplish nutation control, spin rate control, and precession control simultaneously. A nonlinear design technique, Lyapunov's method, is used to design the Delta-H control mode. A global spin rate controller selected as the baseline controller for MMS, proved to be insufficient due to an ambiguity in the attitude. Lyapunov's design method was used to solve this ambiguity, resulting in a controller that meets the design goals. Simulation results show the advantage of the pointing and rate controller for maneuvers larger than 90 deg and provide insight into the performance of this controller.

Shah, Neerav↗
DOE OSTI2024

3.0 - MOOSE: Enabling massively parallel multiphysics simulations

The development of MOOSE has kept accelerating since the last release, with over 2,100 pull requests merged over the last 30 months that involved nearly fifty contributors across close to a dozen institutions internationally. The growth in MOOSE's capabilities and downstream applications is reflected in the growth of the community. User support provided on the GitHub discussions forum has steadily increased to nearly 50 daily interactions. New simulation projects, notably to model advanced nuclear reactor and fusion devices, are driving a significant expansion of the capabilities. This paper reports on these developments, with several major released features, new physics modules, and key improvements to the user experience and simulation workflow.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Bridging Scales in Bioenergy and Catalysis: A Review of Mesoscale Modeling Applications, Methods, and Future Directions

Between the molecular and reactor scales, which are familiar to the chemical engineering community, lies an intermediate regime, here termed the “mesoscale,” where transport phenomena and reaction kinetics compete on similar time scales. Bioenergy and catalytic processes offer particularly important examples of mesoscale phenomena owing to their multiphase nature and the complex, highly variable porosity characteristic of biomass and many structured catalysts. In this review, we overview applications and methods central to mesoscale modeling as they apply to reaction engineering of biomass conversion and catalytic processing. A brief historical perspective is offered to put recent advances in context. Applications of mesoscale modeling are described, and several specific examples from biomass pyrolysis and catalytic upgrading of bioderived intermediates are highlighted. Methods including reduced order modeling, finite element and finite volume approaches, geometry construction and import, and visualization of simulation results are described; in each category, recent advances, current limitations, and areas for future development are presented. Owing to improved access to high-performance computational resources, advances in algorithm development, and sustained interest in reaction engineering to sustainably meet societal needs, we conclude that a significant upsurge in mesoscale modeling capabilities is on the horizon that will accelerate design, deployment, and optimization of new bioenergy and catalytic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗