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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

C3F: Collaborative Container-based Model Coupling Framework

Solving complex real-world grand challenge problems requires in-depth collaboration of researchers from multiple disciplines. Such collaboration often involves harnessing multiscale and multi-dimensional data and combining models from different fields to simulate systems. However, the progress on this front has been limited mainly due to significant gaps in domain knowledge and tools that are typically employed in silos of the domains. Researchers from different fields face considerable barriers to understanding and reusing each other’s data/models in order to collaborate effectively. For example, in solving the global sustainability problems, researchers from hydrology, climate science, agriculture, and economics need to run their respective models to study different components of the global and local food, energy and water systems while, at the same time, need to interact with other researchers and integrate the results of one model with another. Developing this kind of model coupling workflow calls for (1) a large amount of data being processed and exchanged across domains and organizations, (2) identifying and processing the output of one model to make it ready for integration into another model, (3) controlling the workflow dynamically so that it runs until a certain convergence condition or other criteria is met, and (4) close collaboration among the modelers to explore, tune, and test the configuration and data transformation needed to link the models. We have developed C3F, a flexible collaborative model coupling framework to help researchers accelerate their model integration and linking efforts by leveraging advanced cyberinfrastructure such as high-performance computing and virtual containers. In this paper, we describe our experience and lessons learned in developing this cyberinfrastructure solution to support the linking of Water Balance Model (WBM) and SIMPLE-G agricultural economic model in an NSF funded INFEWS project and a DOE-funded Program on Coupled Human and Earth Systems (PCHES) to study the implications of groundwater scarcity for food-energy-water systems. The C3F model coupling framework can be extended to facilitate other model linkages as well.

containerization↗
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↗
DOE OSTI2021

Algorithm-Driven Advances for Scientific CT Instruments: From model-based to deep learning-based approaches

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1 ). A collection of such high-resolution measurements is made by reorienting the sample, which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The specific choice of which instrument to use depends on the desired resolution and properties of the materials being imaged. Additionally, the end goal of SCT scans includes determining the morphology, chemical composition, or dynamic behavior of materials when subjected to external stimuli. In summary, SCT instruments are powerful tools that enable 3D characterization across multiple length scales and play a critical role in furthering the understanding of the structure–function relationships of different materials.

42 ENGINEERING↗
NASA NTRSDate not supplied

In-Situ Scanning Electron Microscope Experiments for Microscale Mechanical Testing and Validated Modeling of Fiber Reinforced Thermoplastics

A novel, in-situ, scanning electron microscope (SEM) mechanical testing capability for materials at the microscale which provides experimental validation to a machine learning (ML) toolset for full-field validation of physics-based micromechanics models is being developed by researchers at NASA Glenn Research Center. These are enabling technologies for the integration of multiscale digital twins for materials into system level models which will result in the improved performance, material discovery, reduced production cost and time, rapid characterization, and prognostic structural health monitoring (SHM) for materials and structures for extreme environments in support of NASA space exploration missions. In order to bridge the material structure-to-system gap for digital twins, physics-based models must be experimentally validated at multiple length scales. Seminal microscale experiments, conducted at the Air Force Research Laboratory (AFRL), were limited to transverse compression of single-layer, unidirectional thermoset polymer matrix composite (PMC) micropillar specimens [1]. The early phases of the current project followed those initial results and setup to reproduce the compression testing of PMC material on the custom-built piezoelectric actuated micromechanical testing rig built by MicroTesting Solutions LLC. In this work, samples of thermoplastic PMC material were first machined into 3 mm cubes, and then further machining and final milling was done using a Focused Ion Beam (FIB). The initial experiment was done on a pillar roughly 20 µm x 20 µm x 40 µm tall. Additional pillars were milled with final sizes ranging from 20 µm x 20 µm x 40 µm tall to 40 µm x 40 µm x 65 µm tall. A speckle pattern for in-situ full-field measurements using Digital Image Correlation (DIC) was applied with platinum, which was coated on the surface, and then the FIB was used to mill away some of the coating to produce an irregular pattern of Pt on the pillar surface. The samples were loaded into the custom testing rig and placed into the SEM and loaded under compression until failure. Images were collected in the SEM during testing. Post-processing of the images was conducted using DIC to obtain full-field displacement and strain measurements elucidating the role of the matrix as well as fiber-fiber interaction at the microscale within the composite subjected to compression loading well into the non-linear regime of the material. Moreover, the evolution of fiber-matrix debonding and matrix cracking is observed in-situ at the microscale. This data, along with images segmented with a newly developed ML toolset [2], was used to create and validate physics-based micromechanics models. An image of the failed micropillar is shown in Figure 1. The techniques developed in the initial compression experiment was tailored to the validation needs of the models and expanded to include different sized samples as well as possibly tension and fatigue.

Laura Wilson↗
DOE OSTI2023

Particle resuspension: Challenges and perspectives for future models

Using what has become a celebrated catchphrase, Philip W. Anderson once wrote that “more is different” (Science, Vol. 177, Issue 4047, pp. 393–396, 1972). First formulated in the context of condensed matter, this statement carries far beyond the sole limits of solid-state physics. It emphasizes that collective behavior can be more than the mere sum of what happens for elementary constituents or the mere collation of the evolution of each degree of freedom. Said otherwise, complex phenomena can arise out of the interplay between multiple sub-phenomena each of which can be relatively simple. The process of particle resuspension, in which discrete particles adhering on a surface are pulled off and carried away by a fluid flow, is another example involving a web of phenomena pertaining to fluid mechanics, particle dynamics and interface chemistry whose cross-effects create an intricate topic. The purpose of this review is to analyze the physics at play in particle resuspension in order to bring insights into the rich complexity of this common but challenging concern. Following the more-is-different vision, this is performed by starting from a range of practical observations and experimental data. We then work our way through the investigation of the key mechanisms which play a role in the overall process. In turn, these mechanisms reveal an array of fundamental interactions, such as particle–fluid, particle–particle and particle–surface, whose combined effects create the tapestry of current applications. At the core of this analysis are descriptions of these physical phenomena and the different ways through which they are intertwined to build up various models used to provide quantitative assessment of particle resuspension. The physics of particle resuspension implies to hold together processes occurring at extremely different space and time scales and models are key in providing a single vehicle to lead us through such multiscale journeys. This raises questions on what makes up a model and one objective of the present work is to clarify the essence of a modeling approach. In spite of its ubiquitous nature, particle resuspension is still at the early stages of developments. Many extensions need to be worked out and revisiting the art of modeling is not a moot point. The need to consider more complex objects than small and spherical particles and, moreover, to come up with unified descriptions of mono- and multilayer resuspension put the emphasis on solid model foundations if we are to go beyond current limits. Further, this is very much modeling in the making and new ideas are proposed to stimulate interest into this everyday but challenging issue in physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2016

Thermal Vacuum Test Correlation of A Zero Propellant Load Case Thermal Capacitance Propellant Gauging Analytics Model

This thesis describes the development and test data validation of the thermal model that is the foundation of a thermal capacitance spacecraft propellant load estimator. Specific details of creating the thermal model for the diaphragm propellant tank used on NASA's Magnetospheric Multiscale spacecraft using ANSYS and the correlation process implemented to validate the model are presented. The thermal model was correlated to within plus or minus 3 degrees Centigrade of the thermal vacuum test data, and was found to be relatively insensitive to uncertainties in applied heat flux and mass knowledge of the tank. More work is needed, however, to refine the thermal model to further improve temperature predictions in the upper hemisphere of the propellant tank. Temperatures predictions in this portion were found to be 2-2.5 degrees Centigrade lower than the test data. A road map to apply the model to predict propellant loads on the actual MMS spacecraft toward its end of life in 2017-2018 is also presented.

Thermal↗
DOE OSTI2023

Variational quantum simulation of the critical Ising model with symmetry averaging

Here we investigate the use of deep multiscale entanglement renormalization ansatz (DMERA) circuits as a variational ansatz. We use the exactly solvable one-dimensional critical transverse-field Ising model as a test bed. Numerically exact simulation of the quantum circuit ansatz can in this case be carried out to hundreds of qubits by exploiting efficient classical algorithms for simulating matchgate circuits. We find that, for this system, the DMERA strongly outperforms a standard quantum approximate optimization algorithm (QAOA)–style ansatz, and that a major source of systematic error in correlation functions approximated using the DMERA is the breaking of the translational and Kramers-Wannier symmetries of the transverse-field Ising model. We are able to reduce this error by up to four orders of magnitude by symmetry averaging, without incurring additional cost in qubits or circuit depth. Here, we propose that this technique for mitigating systematic error could be applied to noisy intermediate-scale quantum (NISQ) simulations of physical systems with other symmetries.

1-dimensional spin chains↗
NASA NTRS2016

Multiscale Static Analysis of Notched and Unnotched Laminates Using the Generalized Method of Cells

The generalized method of cells (GMC) is demonstrated to be a viable micromechanics tool for predicting the deformation and failure response of laminated composites, with and without notches, subjected to tensile and compressive static loading. Given the axial [0], transverse [90], and shear [+45/-45] response of a carbon/epoxy (IM7/977-3) system, the unnotched and notched behavior of three multidirectional layups (Layup 1: [0,45,90,-45](sub 2S), Layup 2: [0,60,0](sub 3S), and Layup 3: [30,60,90,-30, -60](sub 2S)) are predicted under both tensile and compressive static loading. Matrix nonlinearity is modeled in two ways. The first assumes all nonlinearity is due to anisotropic progressive damage of the matrix only, which is modeled, using the multiaxial mixed-mode continuum damage model (MMCDM) within GMC. The second utilizes matrix plasticity coupled with brittle final failure based on the maximum principle strain criteria to account for matrix nonlinearity and failure within the Finite Element Analysis--Micromechanics Analysis Code (FEAMAC) software multiscale framework. Both MMCDM and plasticity models incorporate brittle strain- and stress-based failure criteria for the fiber. Upon satisfaction of these criteria, the fiber properties are immediately reduced to a nominal value. The constitutive response for each constituent (fiber and matrix) is characterized using a combination of vendor data and the axial, transverse, and shear responses of unnotched laminates. Then, the capability of the multiscale methodology is assessed by performing blind predictions of the mentioned notched and unnotched composite laminates response under tensile and compressive loading. Tabulated data along with the detailed results (i.e., stress-strain curves as well as damage evolution states at various ratios of strain to failure) for all laminates are presented.

GMC↗
DOE OSTI2021

Emergent Recursive Multiscale Interaction in Complex Systems

This project studied the potential for multiscale group dynamics in complex social systems, including emergent recursive interaction. Current social theory on group formation and interaction focuses on a single scale (individuals forming groups) and is largely qualitative in its explanation of mechanisms. We combined theory, modeling, and data analysis to find evidence that these multiscale phenomena exist, and to investigate their potential consequences and develop predictive capabilities. In this report, we discuss the results of data analysis showing that some group dynamics theory holds at multiple scales. We introduce a new theory on communicative vibration that uses social network dynamics to predict group life cycle events. We discuss a model of behavioral responses to the COVID-19 pandemic that incorporates influence and social pressures. Finally, we discuss a set of modeling techniques that can be used to simulate multiscale group phenomena.

42 ENGINEERING↗
DOE OSTI2021

Peridynamic Model for Single-Layer Graphene Obtained from Coarse Grained Bond Forces

An ordinary state-based peridynamic material model is proposed for single sheet graphene. The model is calibrated using coarse grained molecular dynamics simulations. The coarse graining method allows the dependence of bond force on bond length to be determined, including the horizon. The peridynamic model allows the horizon to be rescaled, providing a multiscale capability and allowing for substantial reductions in computational cost compared with molecular dynamics. The calibrated peridynamic model is compared to experimental data on the deflection and perforation of a graphene monolayer by an atomic force microscope probe.

36 MATERIALS SCIENCE↗
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↗
NASA NTRS2016

Thermal Vacuum Test Correlation of a Zero Propellant Load Case Thermal Capacitance Propellant Gauging Analytical Model

This thesis describes the development and correlation of a thermal model that forms the foundation of a thermal capacitance spacecraft propellant load estimator. Specific details of creating the thermal model for the diaphragm propellant tank used on NASA's Magnetospheric Multiscale spacecraft using ANSYS and the correlation process implemented are presented. The thermal model was correlated to within plus or minus 3 degrees Celsius of the thermal vacuum test data, and was determined sufficient to make future propellant predictions on MMS. The model was also found to be relatively sensitive to uncertainties in applied heat flux and mass knowledge of the tank. More work is needed to improve temperature predictions in the upper hemisphere of the propellant tank where predictions were found to be 2 to 2.5 C lower than the test data. A road map for applying the model to predict propellant loads on the actual MMS spacecraft toward its end of life in 2017-2018 is also presented.

Gauging↗
DOE OSTI2025

The Observed and Projected Changes of Global Monsoons: Current Status and Future Perspectives

The global monsoon system, encompassing the Asian-Australian, African, and American monsoons, sustains two-thirds of the world’s population by regulating water resources and agriculture. Monsoon anomalies pose severe risks, including floods and droughts. Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms. Observational data reveal a 20th-century shift: increased rainfall pre-1950s, followed by aridification and partial recovery post-1980s, driven by both internal variability (e.g., Atlantic Multidecadal Oscillation) and external forcings (greenhouse gases, aerosols), while ENSO drives interannual variability through ocean-atmosphere interactions. Future projections under greenhouse forcing suggest long-term monsoon intensification, though regional disparities and model uncertainties persist. Models indicate robust trends but struggle to quantify extremes, where thermodynamic effects (warming-induced moisture rise) uniformly boost heavy rainfall, while dynamical shifts (circulation changes) create spatial heterogeneity. Volcanic eruptions and proposed solar radiation modification (SRM) further complicate predictions: tropical eruptions suppress monsoons, whereas high-latitude events alter cross-equatorial flows, highlighting unresolved feedbacks. The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons. Critical challenges remain. Model biases and sparse 20th-century observational data hinder accurate attribution. The interplay between natural variability and anthropogenic forcings, along with nonlinear extreme precipitation risks under warming, demands deeper mechanistic insights. Additionally, SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation. Addressing these gaps necessitates enhanced observational networks, refined climate models, and interdisciplinary efforts to disentangle multiscale drivers, ultimately improving resilience strategies for monsoon-dependent regions.

climate extreme events↗
DOE OSTI2021

Explicit physics-informed neural networks for nonlinear closure: The case of transport in tissues

In upscaling methods, closures for nonlinear problems present a well-known challenge. While a number of theoretical methods have been proposed for handling such closures, nonlinearities still remain a significant obstacle for many problems. In this work, we use a combination of formal upscaling and data-driven machine learning for explicitly closing a nonlinear transport and reaction process in multiscale tissues. The classical effectiveness factor model is used to formulate the macroscale reaction kinetics. We train a multilayer perceptron network using training data generated by direct numerical simulations over microscale examples. Once trained, the network is used in an algorithm for numerically solving the upscaled (coarse-grained) differential equation describing mass transport and reaction in two example tissues. The network is described as being explicit in the sense that the network is trained using macroscale concentrations and gradients of concentration as components of the feature space rather than incorporating them as part of a constraint in the optimization process. Network training and solutions to the macroscale transport equations were computed for two different tissues. The two tissue types (brain and liver) exhibit markedly different geometrical complexity and spatial scale (cell size and sample size). The upscaled solutions for the average concentration are compared with numerical solutions derived from the microscale concentration fields by a posteriori averaging. There are three outcomes of this work of particular note. 1) Our overall approach results in an upscaled nonlinear PDE. The PDE is closed using a neural network, and our approach results in the definition of the classical effectiveness factor for effecting closure. 2) We identify particular source terms for the closure problem that are important for representing the structure of the closure. These source terms involve macroscale concentrations and their gradients. We adopt these source terms to use as explicit features in the learning algorithm. We find the trained networks that include the macroscale source terms generate models that are able to predict the correction factor with increased fidelity over those that do not. 3) We find that the trained network exhibits good generalizability, and it is able to predict the effectiveness factor with high fidelity for realistically-structured tissues despite the significantly different scale and geometrical complexity of the two example tissue types. This latter result emphasizes our purposeful connection between conventional averaging methods with the use of machine learning for closure; this contrasts with some machine learning methods for upscaling where the exact form of the macroscale equation remains unknown.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Cobalt-free composite-structured cathodes with lithium-stoichiometry control for sustainable lithium-ion batteries

Abstract Lithium-ion batteries play a crucial role in decarbonizing transportation and power grids, but their reliance on high-cost, earth-scarce cobalt in the commonly employed high-energy layered Li(NiMnCo)O 2 cathodes raises supply-chain and sustainability concerns. Despite numerous attempts to address this challenge, eliminating Co from Li(NiMnCo)O 2 remains elusive, as doing so detrimentally affects its layering and cycling stability. Here, we report on the rational stoichiometry control in synthesizing Li-deficient composite-structured LiNi 0.95 Mn 0.05 O 2 , comprising intergrown layered and rocksalt phases, which outperforms traditional layered counterparts. Through multiscale-correlated experimental characterization and computational modeling on the calcination process, we unveil the role of Li-deficiency in suppressing the rocksalt-to-layered phase transformation and crystal growth, leading to small-sized composites with the desired low anisotropic lattice expansion/contraction during charging and discharging. As a consequence, Li-deficient LiNi 0.95 Mn 0.05 O 2 delivers 90% first-cycle Coulombic efficiency, 90% capacity retention, and close-to-zero voltage fade for 100 deep cycles, showing its potential as a Co-free cathode for sustainable Li-ion batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Weather and climate extremes in a changing Arctic

Weather and climate extremes are increasingly occurring in the Arctic. Here, in this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers.

atmospheric dynamics↗