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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Influence of simple terrain on the spatial variability of a low-level jet and wind farm performance in the AWAKEN field campaign

In wind energy research, scientific challenges are often associated with complex terrain sites, where orography, vegetation, and buildings disrupt flow uniformity. However, even sites characterized as simple terrain can exhibit significant spatial variability in wind speed, particularly during stable boundary layers (SBLs) and low-level jets (LLJs). This study investigates these terrain interactions using both simulations and observations from the American WAKe ExperimeNt (AWAKEN). We employ a multiscale Weather Research and Forecasting (WRF) model simulation, integrating mesoscale forcing in the coarse domains and representing three rows of turbines from the King Plains wind farm as generalized actuator disks (GAD) in the large-eddy simulation (LES) domains. During a nocturnal LLJ event on 3 April 2023, the downstream, wake-affected turbine rows outperformed the upstream, unwaked row by 25 %–51 %. This counterintuitive result arises from terrain-induced streamwise variations in hub-height wind speed of approximately 4 m s −1 over 5 km – equivalent to ∼50 % of the upstream reference speed. This enhancement outweighs the wake-induced reduction in mean wind speed (∼12 %) and global blockage effects reported in the literature (∼1 %–3.4 %). The multiscale simulations capture the intra-farm spatial variability in power performance observed in SCADA data. Terrain-induced vertical displacement of the LLJ, coupled with large wind shear below the jet maximum, drives the substantial streamwise acceleration within the wind farm. These findings underscore the importance of accounting for spatial variability related to terrain, even in simple landscapes, particularly during LLJ conditions. Incorporating such effects into reduced-order modeling frameworks for wind farm design and control could significantly enhance their effectiveness.

17 WIND ENERGY↗
NASA NTRS2023

An Enabling Platform for Achieving Multiscale Multiphysics Analysis of Multiphase Materials

This paper will address current NASA GRC research efforts utilizing the NASA Multiscale Analysis Tool (NASMAT) which address technical gaps in three of the Vision 2040 key discipline areas. NASMAT is a versatile platform for performing computationally efficient multiscale analyses of heterogeneous materials and is available free through the NASA software catalog. It offers the user flexibility to define an arbitrary number of length scales (levels) where a variety of micromechanics theories can be implemented at each level. Micromechanics theories can be selected to balance accuracy and computational efficiency and range from analytical (Mori-Tanaka) to several semi-analytical (method of cells) formulations. The resulting anisotropic, evolving nonlinear, thermomechanical constitutive model can also be coupled with external software and used to perform multiscale analyses of more complex structures. A recent application to model a complex, three-dimensional woven composite, with a particular emphasis placed on multiscale measurements utilized to enhance the quality of the multiscale analysis will be discussed. Since typical NASMAT analyses can be completed on the order of seconds to minutes, a second example will demonstrate the ability to generate large quantities of data useful for sensitivity analysis, uncertainty quantification, or machine learning applications. Current progress on implementing NASMAT within a multiscale digital thread/digital twin framework will also be addressed. These examples will demonstrate that NASMAT is an enabling platform for achieving the goals in Vision 2040.

Multiscale↗
DOE OSTI2026

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

14 SOLAR ENERGY↗
DOE OSTI2025

Multiscale Thermal-hydraulic analysis of the MARVEL micro-reactor using coupled MOOSE Subchannel (SCM) and SAM

MARVEL is a natural-convection-cooled sodium-potassium microreactor that is anticipated to generate 85 kilowatts of thermal energy. It will operate within Idaho National Laboratory’s Transient Reactor Test Facility and is being developed by the DOE Microreactor Program. MARVEL will be used to test microreactor applications, evaluate systems for remote monitoring, and develop autonomous control technologies. A thermal-hydraulic computational model of this facility is a valuable tool to study important transients and calculate the safety limits of the micro-reactor design. For this purpose, the authors have chosen to use a multiscale coupled simulation: SCM for modeling the reactor core and SAM for the reactor’s primary cooling system. SCM is MOOSE physics module for subchannel analysis, which was designed to model single-phase flows through liquid-metal cooled, wire-wrapped fuel pin sub-assemblies, ordered in a triangular lattice. The SCM code was modified to be able to model MARVEL’s unique geometry. SAM is a systems analysis module based on the MOOSE framework. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for various advanced reactor types. The coupling between the two SCM and SAM for MARVEL modeling is done implementing a domain over-lapping approach. The resulting coupled simulation can model transients such as reactor startup/shutdown and provide an intermediate fidelity picture of the temperature field and other variables, in the core. Results for the steady-state simulations are presented in the article as well as flow blockage transient.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multiscale Thermal-hydraulic analysis of the MARVEL microreactor using a coupled SAM and SCM simulation. (PPTX)

This is .pptx document presenting a summary of the paper of the same name that has been submitted and accepted to NURETH-21: MARVEL is a natural-convection-cooled sodium-potassium microreactor that is anticipated to generate 85 kilowatts of thermal energy. It will operate within Idaho National Laboratory Transient Reactor Test Facility and is being developed by the DOE Microreactor Program. MARVEL will be used to test microreactor applications, evaluate systems for remote monitoring, and develop autonomous control technologies. A thermal-hydraulic computational model of this facility is a valuable tool to study important transients and calculate the safety limits of the micro-reactor design. For this purpose, the authors have chosen to use a multiscale coupled simulation: SCM for modeling the reactor core and SAM for the reactor's primary cooling system. SCM is MOOSE physics module for subchannel analysis, which was designed to model single-phase flows through liquid-metal cooled, wire-wrapped fuel pin sub-assemblies, ordered in a triangular lattice. The SCM code was modified to be able to model MARVEL?s unique geometry. SAM is a systems analysis module based on the MOOSE framework. It aims to provide fast-running, whole-plant transient analyses capability with improved-fidelity for various advanced reactor types. The coupling between the two SCM and SAM for MARVEL modeling is done implementing a domain over-lapping approach. The resulting coupled simulation can model transients such as reactor startup/shutdown and provide an intermediate fidelity picture of the temperature field and other variables, in the core. Results for the steady-state simulations are presented in the article as well as flow blockage transient.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2026

Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems

Multiscale systems are ubiquitous in science and technology, but are notoriously challenging to simulate as short spatiotemporal scales must be appropriately linked to emergent bulk physics. When expensive high-dimensional dynamical systems are coarse-grained into low-dimensional models, the entropic loss of information leads to emergent physics which are dissipative, history-dependent, and stochastic. To machine learn coarse-grained dynamics from time-series observations of particle trajectories, we propose a framework using the metriplectic bracket formalism that preserves these properties by construction; most notably, the framework guarantees discrete notions of the first and second laws of thermodynamics, conservation of momentum, and a discrete fluctuation-dissipation balance crucial for capturing non-equilibrium statistics. We introduce the mathematical framework abstractly before specializing to a particle discretization. As labels are generally unavailable for entropic state variables, we introduce a novel self-supervised learning strategy to identify emergent structural variables. We validate the method on benchmark systems and demonstrate its utility on two challenging examples: (1) coarse-graining star polymers at challenging levels of coarse-graining while preserving non-equilibrium statistics, and (2) learning models from high-speed video of colloidal suspensions that capture coupling between local rearrangement events and emergent stochastic dynamics. We provide open-source implementations in both PyTorch and LAMMPS, enabling large-scale inference and extensibility to diverse particle-based systems.

Computational Engineering, Finance, and Science (c↗
DOE OSTI2021

Upscaling Reactive Transport and Clogging in Shale Microcracks by Deep Learning

Fracture networks in shales exhibit multiscale features. A rock system may contain a few main fractures and thousands of microcracks, whose length and aperture are orders of magnitude smaller than the former. It is computationally prohibitive to resolve all the fractures explicitly for such multiscale fracture networks. One traditional approach is to model the small-scale features (e.g., microcracks in shales) as an effective medium. Although this fracture-matrix conceptualization significantly reduces the problem complexity, there are classes of physical processes that cannot be accurately upscaled by effective medium approximations, e.g., microcrack clogging during mineral reactions. Here, we employ deep learning in place of effective medium theory to upscale physical processes in small-scale features. Specifically, we consider reactive transport in a fracture-microcrack network where microcracks can be clogged by precipitation. A deep learning multiscale algorithm is developed, in which the microcracks are upscaled as a wall boundary condition of the main fractures. The wall boundary condition is constructed by recurrent neural networks, which take concentration histories as input and predict the solute transport from main fractures to microcracks. The deep learning multiscale algorithm is firstly employed in specific scenarios, then a general model is developed which can work under various conditions. The new approach is validated against fully resolved simulations and an analytical solution, providing a reliable and efficient solution for problems that cannot be upscaled by effective medium models.

58 GEOSCIENCES↗
DOE OSTI2023

Multiscale Electricity Modeling for Evaluating Carbon Capture and Sequestration Technologies (Final Report)

Carbon capture and sequestration (CCS) technologies that can operate with a high degree of operating flexibility could provide necessary electric grid flexibility in a system with high shares of variable renewables. This project examines the deployment and dispatch potential of twelve unique flexible CCS (FLECCS) technologies that encompass post-combustion carbon dioxide (CO 2 ) capture designs, concepts using a storage media to enable energy arbitrage, and hybrid processes that integrate CCS with direct air capture (DAC) for flexibility with net zero or negative CO 2 emissions. FLECCS technology potential is explored with a multi-model, multi-scale framework including the Regional Energy Deployment System (ReEDS) electric sector capacity expansion model (CEM) and the PLEXOS production cost model (PCM). Innovative methods were developed to represent FLECCS technology operating modes, performance, and cost in the two models. ReEDS was then used to simulate nine scenarios for each FLECCS technology, three CO 2 emissions price futures reaching $\$$150, $\$$225, and $\$$300/tCO 2 in 2050; and three scenarios for FLECCS technology deployment favorability relative to competing technologies. For each CO 2 price and reference FLECCS favorability, the 2050 infrastructures from ReEDS model are downscaled and implemented in PLEXOS to examine hourly dispatch under detailed operational constraints that are not included in ReEDS. FLECCS technologies exhibited a wide range of deployment potential ranging from none to several hundred gigawatts of capacity, with outcomes highly sensitive to input cost and performance parameters that are inherently highly uncertain. When deployed, FLECCS tended to displace a combination of wind, solar, and natural gas-based technologies rather than supporting increased renewable deployment. As a result, CO 2 emissions reductions facilitated by FLECCS deployment tended to come with higher overall system costs and electricity prices. When economically competitive, FLECCS technologies can contribute significant flexible generation and firm capacity to the grid, but continued technology development and an expanded analytical scope are necessary to fully understand FLECCS deployment potential its impact on the electric power sector. Follow-on analysis incorporating captured CO 2 tax credit value from the Inflation Reduction Act (IRA) and other potential policy scenarios could be particularly valuable, as this policy can substantially change the relative competitiveness of FLECCS technologies.

03 NATURAL GAS↗
DOE OSTI2025

Multiscale Physics of Atomic Nuclei from First Principles

Atomic nuclei exhibit multiple energy scales ranging from hundreds of MeV in binding energies to fractions of an MeV for low-lying collective excitations. As the limits of nuclear binding are approached near the neutron and proton drip lines, traditional shell structure starts to melt with an onset of deformation and an emergence of coexisting shapes. It is a long-standing challenge to describe this multiscale physics starting from nuclear forces with roots in quantum chromodynamics. Here, we achieve this within a unified and nonperturbative quantum many-body framework that captures both short- and long-range correlations starting from modern nucleon-nucleon and three-nucleon forces from chiral effective field theory. The short-range (dynamic) correlations which account for the bulk of the binding energy are included within a symmetry-breaking framework, while long-range (static) correlations (and fine details about the collective structure) are included by employing symmetry projection techniques. Our calculations accurately reproduce—within theoretical error bars—available experimental data for low-lying collective states and the electromagnetic quadrupole transitions in 20−30 Ne. In addition, we reveal coexisting spherical and deformed shapes in 30 Ne, which indicates the breakdown of the magic neutron number 𝑁 = 20 as the key nucleus 28 O is approached, and we predict that the drip line nuclei 32,34 Ne are strongly deformed and collective. By developing reduced-order models for symmetry-projected states, we perform a global sensitivity analysis and find that the subleading singlet 𝑆-wave contact and a pion-nucleon coupling strongly impact nuclear deformation in chiral effective field theory. The techniques developed in this work clarify how microscopic nuclear forces generate the multiscale physics of nuclei spanning collective phenomena as well as short-range correlations and allow one to capture emergent and dynamical phenomena in finite fermion systems such as atom clusters, molecules, and atomic nuclei.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

2020 Multiscale Microbial Dynamics Modeling Course

The 2020 Multiscale Microbial Dynamics course is adapted from the virtual 2020 Mutliscale Microbial Dynamics Summer School that was hosted by Environmental Molecular Sciences Laboratory (EMSL), a U.S. Department of Energy (DOE) science user facility located on the Pacific Northwest National Laboratory (PNNL) campus, in collaboration with the Joint Genome Institute (JGI) and the DOE Systems Biology Knowledgebase (KBase). The course course covers how to incorporate microbial metagenomic and environmental metabolite data from watershed ecosystems into metabolic and community modeling using computational frameworks, such as KBase and PFLOTRAN. The curriculum includes lectures and software and data analysis tutorials. All materials are freely accessible to the community as part of the 2020 Microbial Dynamics Summer School Organization in KBase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Revealing multiscale competing processes in the solid-state synthesis of single-crystalline layered oxide positive electrodes

Solid-state synthesis involves a web of coupled chemical reactions and physical changes that unfold across multiple scales. Efforts to fine-tune its parameters have historically followed heuristic, trial-driven workflows that demand significant time and resources. In this study, we aimed to open this black box by employing multiscale in situ synchrotron imaging and diffraction. Using LiNi 0.5 Mn 0.3 Co 0.2 O 2 battery positive electrode material as a model system and Ba-based sintering aids, we reveal dopant segregation, intergranular mass transport, and porosity evolution as key drivers of single-crystalline particle formation. Notably, we uncovered a dynamic competition between particle-level grain coalescence and atomic-scale cation disordering, both of which are thermally activated yet have opposing impacts on battery performance. These findings highlight the coupled, multiscale nature of structure development and offer a mechanistic basis for optimizing the solid-state synthesis process. This framework provides a path toward more controlled, efficient, and scalable production of high-performance battery positive electrode materials.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Multiscale Simulations of Magnetic Island Coalescence

We describe a new interactive parallel Adaptive Mesh Refinement (AMR) framework written in the Python programming language. This new framework, PyAMR, hides the details of parallel AMR data structures and algorithms (e.g., domain decomposition, grid partition, and inter-process communication), allowing the user to focus on the development of algorithms for advancing the solution of a systems of partial differential equations on a single uniform mesh. We demonstrate the use of PyAMR by simulating the pairwise coalescence of magnetic islands using the resistive Hall MHD equations. Techniques for coupling different physics models on different levels of the AMR grid hierarchy are discussed.

Dorelli, John C.↗
DOE OSTI2020

Primal interface debonding formulation for finite strain isotropic plasticity

In this work, a framework is developed for modeling ductile damage of nonlinear materials whose plastic deformation is characterized using rate independent classical plasticity. This method relies on the assumption that the free energy can be decomposed into elastic, plastic and damage parts. A thermodynamically consistent method is derived which satisfies the second law of thermodynamics in the Clausius–Duhem inequality form. The dissipation associated with plasticity takes place in the domain only, while damage dissipation is localized to the interface. The method is developed using Variational Multiscale ideas to obtain definitions of the interface fluxes within a primal formulation analogous to the Discontinuous Galerkin method, which ensures weakly vanishing interface gap prior to reaching a damage initiation criterion. The local nonlinear problem to calculate both plastic deformation gradient and damage variable follows an incremental approach similar to classical plasticity return mapping algorithm. This elastoplastic damage formulation is developed for material undergoing finite strain, and it naturally accommodates a trapezoidal traction separation law (TSL) whose shape can be varied to model either ductile interface behavior or brittle interface behavior. The formulation's performance is assessed through modeling a patch test and a compact tension specimen.

42 ENGINEERING↗
DOE OSTI2021

Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to understand and describe their behavior. However, there is a scarcity of plasma models of lower fidelity than magnetohydrodynamics (MHD), although these reduced models hold promise for understanding key physical mechanisms, efficient computation, and real-time optimization and control. Galerkin models, obtained by projection of the MHD equations onto a truncated modal basis, and data-driven models, obtained by modern machine learning and system identification, can furnish this gap in the lower levels of the model hierarchy. This work develops a reduced-order modeling framework for compressible plasmas, leveraging decades of progress in projection-based and data-driven modeling of fluids. We begin by formalizing projection-based model reduction for nonlinear MHD systems. To avoid separate modal decompositions for the magnetic, velocity, and pressure fields, we introduce an energy inner product to synthesize all of the fields into a dimensionally consistent, reduced-order basis. Next, we obtain an analytic model by Galerkin projection of the Hall-MHD equations onto these modes. We illustrate how global conservation laws constrain the model parameters, revealing symmetries that can be enforced in data-driven models, directly connecting these models to the underlying physics. We demonstrate the effectiveness of this approach on data from high-fidelity numerical simulations of a three-dimensional spheromak experiment. Finally, this manuscript builds a bridge to the extensive Galerkin literature in fluid mechanics and facilitates future principled development of projection-based and data-driven models for plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Coupled Monte Carlo Transport and Conjugate Heat Transfer for Wire-Wrapped Bundles Within the MOOSE Framework

Cardinal is an open-source application that couples OpenMC Monte Carlo transport and NekRS computational fluid dynamics (CFD) to the Multiphysics Object-Oriented Simulation Environment (MOOSE), closing neutronics and thermal-fluid gaps in conducting high-resolution multiscale and multiphysics analyses of nuclear systems. We first provide a brief introduction to Cardinal's software design, data mapping, and coupling strategy to highlight our approach to overcoming common challenges in high-fidelity multiphysics simulations. Here we then present two Cardinal simulations for hexagonal pin bundles. The first is a validation of Cardinal's conjugate heat transfer coupling of NekRS's Reynolds-Averaged Navier Stokes model with MOOSE's heat conduction physics for a bare seven-pin Freon-12 bundle flow experiment. Predictions for pin surface temperatures under three different heating modes agree reasonably well with experimental data and similar CFD modeling from the literature. The second simulation is a multiphysics coupling of OpenMC, NekRS, and BISON for a reduced-scale, seven-pin wire-wrapped version of an Advanced Burner Reactor bundle. Wire wraps are approximated using a momentum source model, and coupled predictions are provided for velocity, temperature, and power distribution.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Emerging multiscale insights on microbial carbon use efficiency in the land carbon cycle

Microbial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

components for the MuMMI software release

The Department of Energy and the National Cancer Institute have developed new software for conducting multi-scale simulations of complex systems. This software, called the Multiscale Machine-Learned Modeling Infrastructure (MuMMI), couples simulations on three spatial scales to study slow, large-scale reorganizations of biomolecular systems with the speed of continuum and coarse-grained models while revealing selected interactions at full atomic precision. In these simulations, coarse-to-fine model conversions are used to spawn relevant fine-scale simulations along chosen order parameters, and fine-to-coarse feedback is used to iteratively improve the accuracy and multi-scale consistency of coarse-scale and continuum simulations. The complete MuMMI framework will be reviewed and released as open-source software by Lawrence Livermore National Laboratory. This review covers a subset of the MuMMI components that were developed exclusively at the Los Alamos National Laboratory.

Neale, Christopher↗