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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Whistler wave generation by anisotropic tail electrons during asymmetric magnetic reconnection in space and laboratory

Whistler wave generation near the magnetospheric separatrix during reconnection at the dayside magnetopause is studied with data from the Magnetospheric Multiscale (MMS) mission. The dispersion relation of the whistler mode is measured for the first time near the reconnection region in space, which shows that whistler waves propagate nearly parallel to the magnetic field line. A linear analysis indicates that the whistler waves are generated by temperature anisotropy in the electron tail population. This is caused by loss of electrons with a high velocity parallel to the magnetic field to the exhaust region. Here, there is a positive correlation between activities of whistler waves and the lower–hybrid drift instability (LHDI) both in laboratory and space, indicating the enhanced transport by LHDI may be responsible for the loss of electrons with a high parallel velocity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗
DOE OSTI2018

Whistler wave generation by anisotropic tail electrons during asymmetric magnetic reconnection in space and laboratory

Whistler wave generation near the magnetospheric separatrix during reconnection at the dayside magnetopause is studied with data from the Magnetospheric Multiscale (MMS) mission. The dispersion relation of the whistler mode is measured for the first time near the reconnection region in space, which shows that whistler waves propagate nearly parallel to the magnetic field line. A linear analysis indicates that the whistler waves are generated by temperature anisotropy in the electron tail population. This is caused by loss of electrons with a high velocity parallel to the magnetic field to the exhaust region. There is a positive correlation between activities of whistler waves and the lower-hybrid drift instability (LHDI) both in laboratory and space, indicating the enhanced transport by LHDI may be responsible for the loss of electrons with a high parallel velocity.

Dispersion relation↗
DOE OSTI2017

A Comparative study of two RVE modelling methods for chopped carbon fiber SMC

To achieve vehicle light-weighting, the chopped carbon fiber sheet molding compound (SMC) is identified as a promising material to replace metals. However, there are no effective tools and methods to predict the mechanical property of the chopped carbon fiber SMC due to the high complexity in microstructure features and the anisotropic properties. In this paper, the Representative Volume Element (RVE) approach is used to model the SMC microstructure. Two modeling methods, the Voronoi diagram-based method and the chip packing method, are developed for material RVE property prediction. The two methods are compared in terms of the predicted elastic modulus and the predicted results are validated using the Digital Image Correlation (DIC) tensile test results. Furthermore, the advantages and shortcomings of these two methods are discussed in terms of the required input information and the convenience of use in the integrated processing-microstructure-property analysis.

mesostructure reconstruction↗
DOE OSTI2025

Nanoscopic strain evolution in single-crystal battery positive electrodes

Single-crystal Ni-rich layered oxides (SC-NMC) with a grain-boundary-free configuration have effectively addressed the long-standing cracking issue of conventional polycrystalline Ni-rich materials (PC-NMC) in lithium-ion batteries, prompting a shift in optimization strategies. However, continued reliance on anisotropic lattice volume change—a well-established failure indicator in PC-NMC—as a metric for understanding strain and guiding compositional design for SC-NMC becomes controversial. Here, in this study, by leveraging multiscale diagnostic techniques, we unravelled the distinct nanoscopic strain evolution in SC-NMC during battery operation, challenging the conventional composition-driven strategies and mechanical degradation indicators used for PC-NMC. Through particle-level chemomechanical analysis, we reveal a decoupling between mechanical stability and lattice volume change in SC-NMC, identifying that structural instability in SC materials is primarily driven by multidimensional lattice distortions induced by kinetics-driven reaction heterogeneity and progressively deactivating chemical phases. Using this mechanical failure mode, we redefine the roles of cobalt and manganese in maintaining mechanical stability. Unlike cobalt’s detrimental role in PC-NMC, we find cobalt to be critical in enhancing the longevity of SC-NMC by mitigating localized strain along the extended diffusion pathway, whereas manganese exacerbates mechanical degradation.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Computerized detection of noncalcified plaques in coronary CT angiography: Evaluation of topological soft gradient prescreening method and luminal analysis

Purpose: The buildup of noncalcified plaques (NCPs) that are vulnerable to rupture in coronary arteries is a risk for myocardial infarction. Interpretation of coronary CT angiography (cCTA) to search for NCP is a challenging task for radiologists due to the low CT number of NCP, the large number of coronary arteries, and multiple phase CT acquisition. The authors conducted a preliminary study to develop machine learning method for automated detection of NCPs in cCTA. Methods: With IRB approval, a data set of 83 ECG-gated contrast enhanced cCTA scans with 120 NCPs was collected retrospectively from patient files. A multiscale coronary artery response and rolling balloon region growing (MSCAR-RBG) method was applied to each cCTA volume to extract the coronary arterial trees. Each extracted vessel was reformatted to a straightened volume composed of cCTA slices perpendicular to the vessel centerline. A topological soft-gradient (TSG) detection method was developed to prescreen for NCP candidates by analyzing the 2D topological features of the radial gradient field surface along the vessel wall. The NCP candidates were then characterized by a luminal analysis that used 3D geometric features to quantify the shape information and gray-level features to evaluate the density of the NCP candidates. With machine learning techniques, useful features were identified and combined into an NCP score to differentiate true NCPs from false positives (FPs). To evaluate the effectiveness of the image analysis methods, the authors performed tenfold cross-validation with the available data set. Receiver operating characteristic (ROC) analysis was used to assess the classification performance of individual features and the NCP score. The overall detection performance was estimated by free response ROC (FROC) analysis. Results: With our TSG prescreening method, a prescreening sensitivity of 92.5% (111/120) was achieved with a total of 1181 FPs (14.2 FPs/scan). On average, six features were selected during the tenfold cross-validation training. The average area under the ROC curve (AUC) value for training was 0.87 ± 0.01 and the AUC value for validation was 0.85 ± 0.01. Using the NCP score, FROC analysis of the validation set showed that the FP rates were reduced to 3.16, 1.90, and 1.39 FPs/scan at sensitivities of 90%, 80%, and 70%, respectively. Conclusions: The topological soft-gradient prescreening method in combination with the luminal analysis for FP reduction was effective for detection of NCPs in cCTA, including NCPs causing positive or negative vessel remodeling. The accuracy of vessel segmentation, tracking, and centerline identification has a strong impact on NCP detection. Studies are underway to further improve these techniques and reduce the FPs of the CADe system.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Addressing Critical Problems in Materials Science Through Multiscale and Multimode Characterization (Project 1); Characterization and Optimization of Novel Triple-Conducting Oxide Materials for Energy Applications (Project 2) (CRADA Final Report)

PROJECT 1: Address critical problems in materials science and simultaneously advance the state-of-the-art in multiscale and multimode characterization using the combined advanced analytical capabilities and expertise of Colorado School of Mines (CSM) and the National Renewable Energy Laboratory (NREL). The primary effort of the Phase I of this CRADA is to establish the International Center for Multiscale Characterization using shared resources at both NREL and CSM. Phase II will focus on capability development and marketing, choosing candidate materials science issues in the areas of structure imaging, chemical composition mapping, and correlating properties and performance of materials for impact in energy-related, environmental and critical materials areas. The CRADA will be modified to include specific topics of concern in materials science to industry member partners. Advanced analytical capabilities and expertise at CSM and NREL will be used to advance materials understanding and performance through characterization of multiscale phenomena including structural imaging, chemical composition mapping, and other techniques correlating properties and performance of materials. PROJECT 2: As part of the International Center for Materials Characterization, work under Modification #1 will be led by Colorado School of Mines (CSM), working in collaboration with NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments. The purpose of the modification is to provide for NREL staff to mentor and advise CSM postdoctoral researchers on set up of diffusion annealing experiments, including mentoring and advising the CSM-NREL team on proper Secondary Ion Mass Spectrometry (SIMS) data analysis as needed. SIMS measurements of 10-20 samples will be performed at NREL during the project duration.

08 HYDROGEN↗
DOE OSTI2019

Multiscale modeling of fission gas behavior in U 3 Si 2 under LWR conditions

In this work, we present a model of fission gas behavior in U 3 Si 2 under light water reactor (LWR) conditions for application in engineering fuel performance codes. The model includes components for intra-granular and inter-granular behavior of fission gases. The intra-granular component is based on cluster dynamics and computes the evolution of intra-granular fission gas bubbles and swelling coupled to gas diffusion to grain boundaries. The inter-granular component describes the evolution of grain-boundary fission gas bubbles coupled to fission gas release. Given the lack of experimental data for U 3 Si 2 under LWR conditions, the model is informed with parameters calculated via atomistic simulations. In particular, we derive fission gas diffusivities through density functional theory calculations, and the re-solution rate of fission gas atoms from intra-granular bubbles through binary collision approximation calculations. Further, the developed model is applied to the simulation of an experiment for U 3 Si 2 irradiated under LWR conditions available from the literature. Results point out a credible representation of fission gas swelling and release in U 3 Si 2 . Finally, we perform a sensitivity analysis for the various model parameters. Based on the sensitivity analysis, indications are derived that can help in addressing future research on the characterization of the physical parameters relative to fission gas behavior in U 3 Si 2 . The developed model is intended to provide a suitable infrastructure for the engineering scale calculation of fission gas behavior in U 3 Si 2 that exploits a multiscale approach to fill the experimental data gap and can be progressively improved as new lower-length scale calculations and validation data become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

In Situ Infrared Spectroscopy of a Plasma Jet and Data-Driven Solution of Multi-Scale Plasma Chemistry Problems

Multi-scale problems are commonly known in many scientific and engineering fields where microscopic behaviors are coupled with macroscopic processes. This is also an unsolved problem in low-temperature plasma chemistry where hundreds of chemical species are involved in thousands of chemical reactions. To address this problem, a physics-informed data-driven modeling is developed to solve such a multi-scale problem using the experimental Fourier-transform infrared spectroscopy (FTIR) measurements of several species’ concentrations. The modeling based on modern machine learning techniques provides concentrations of other relevant species along with the electron temperature and gas temperature at the location of FTIR measurements. For example, the concentrations O, OH, and H 2 O 2 play key roles in plasma-based cancer therapy. This approach overcomes the multi-scale difficulties of microscopic low-temperature plasma chemistry coupling with macroscopic gas flow and allows the acquisition of a full picture of output species concentrations. Presented here for the helium-air jet at atmospheric pressure, the ML-based modeling can be used to describe and possibly control multiscale systems using partial experimental data sets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

A forward analysis on the applicability of tracer breakthrough profiles in revealing the pore structure of tight gas sandstone and carbonate rocks

Here, we explore tracer breakthrough profiles (TBP) as a macroscopic property to infer the pore-space topology of tight gas sandstone and carbonate rocks at the core scale. The following features were modeled via three-dimensional multiscale networks: microporosity within dissolved grains and pore-filling clay, cementation in the absence and presence of microporosity (each classified into uniform, pore-preferred, and throat-preferred modes), layering, vug, and microcrack inclusion. A priori knowledge of the extent and location of each process was assumed to be known. With the exception of an equal importance of macropores and pore-filling micropores, TBPs show little sensitivity to the fraction of micropores present. In general, significant sensitivity of the TBPs was observed for uniform and throat-preferred cementation. Layering parallel to the fluid flow direction had a considerable impact on TBPs whereas layering perpendicular to flow did not. Microcrack orientations seemed of minor importance in affecting TBPs.

58 GEOSCIENCES↗
DOE OSTI2020

SIMPLE-G: A multiscale framework for integration of economic and biophysical determinants of sustainability

We introduce SIMPLE-G, a Simplified International Model of agricultural Prices, Land use, and the Environment- Gridded version, which is a novel tool for evaluating sustainability policies in a global context while factoring in local heterogeneity in land and water resources and natural ecosystem services. This multi-scale model can provide boundary conditions for local decision makers, as well as capturing feedback from local policies to national and global scales. Additionally, to illustrate its value in environmental analysis, we provide two applications of the model. First, we quantify the local stresses on land and water resources due to global changes in population, income, and productivity. Second, we quantify the global impacts of local policy responses and adaptations to water scarcity.

42 ENGINEERING↗
DOE OSTI2025

A tale of two towers: comparing NEON and AmeriFlux data streams at Bartlett Experimental Forest

Long-term ecological data are essential for detecting impacts of climate change and other global change factors, and for making informed predictions about future change. However, long-term measurements are rarely replicated at the site level, which raises questions about their representativeness. We used a multiscale approach to evaluate the agreement of parallel observations from AmeriFlux and NEON (National Ecological Observatory Network) towers at Bartlett Experimental Forest, New Hampshire, USA. The two towers are separated by a horizontal distance of 93 m. Here, we focused our analysis on standard meteorological variables; fluxes of CO 2 , sensible heat, and latent heat measured by eddy covariance; and phenology derived from PhenoCam imagery. Results suggest excellent agreement between AmeriFlux and NEON in meteorology and phenology, and good agreement in fluxes at the half-hourly scale. However, large disagreements in CO 2 and latent heat fluxes occurred at the annual scale, with implications especially for the forest carbon balance. The AmeriFlux tower measurements indicate a site that is close to carbon-neutral (-8 ± 65 g C m -2 y -1 , mean ± 1 SD), whereas the NEON tower measurements indicate a forest that is a carbon sink (-137 ± 10 g C m -2 y -1 ). Causes of this disagreement may include measurement height (26 m vs. 35 m), which resulted in different flux footprints being measured by the two towers, and differences in the flux measurement systems. Our results suggest the need for caution when attempting to merge long-term flux data from two different measurement platforms, and when using measurements from any one measurement platform to inform decision-making on issues related to carbon accounting or natural climate solutions.

Carbon cycle↗
DOE OSTI2026

Wavelet flow for extragalactic foreground simulations

Extragalactic foregrounds in cosmic microwave background (CMB) observations are both a source of cosmological and astrophysical information and a nuisance to the CMB. Effective field-level modeling that captures their non-Gaussian statistical distributions is increasingly important for optimal information extraction, particularly given the low-noise observations from current and upcoming experiments. Here, we explore the use of Wavelet Flow (WF) models to tackle the novel task of modeling the field-level probability distributions of multi-component CMB secondaries and foregrounds. Specifically, we jointly train correlated CMB lensing convergence (κ) and cosmic infrared background (CIB) maps with a WF model and obtain a network that statistically recovers the input to high accuracy — the trained network generates samples of κ and CIB fields whose average power spectra are within a few percent of the inputs across all scales, and whose Minkowski functionals are similarly accurate compared to the inputs. Leveraging the multiscale architecture of these models, we fine-tune both the model parameters and the priors at each scale independently, optimizing performance across different resolutions. These results demonstrate that WF models can accurately simulate correlated components of CMB secondaries, supporting improved analysis of cosmological data. Our code and trained models can be found on this GitHub repo.

cosmological simulations↗
DOE OSTI2023

Coupling of CTF and RELAP5-3D Within an Enhanced Fidelity Nuclear Power Plant Simulator

A robust and accurate multiphysics engineering simulator is being developed to model the core behavior and system response of pressurized water reactors. This simulator relies on the NESTLE and CTF computer codes to model the neutronics and thermal hydraulics (TH), respectively, inside the core on a nodal scale and on the Reactor Excursion and Leak Analysis Program—Three Dimensional (RELAP5-3D) to model the entire nuclear steam supply system. The RELAP5-3D model includes highly detailed nodalization and multidimensional flow modeling throughout the vessel. Previously, pin-resolved data generated via the Virtual Environment for Reactor Analysis core simulator were used to improve the accuracy of the NESTLE core predictions. The engineering simulator being developed as part of this work uses the 3KEYMASTER platform to couple the enhanced NESTLE model to a nodal-fidelity CTF model to balance run time with accuracy; NESTLE provides node-dependent powers to CTF, and CTF provides node-dependent coolant densities and fuel temperatures to NESTLE.An overlapping domain approach is used for the core TH in which RELAP5-3D provides core boundary conditions based on the system response and CTF provides a node-dependent coolant heating rate to the RELAP5-3D core solution. In the preliminary TH demonstration discussed in this paper, CTF and RELAP5-3D provided similar steady-state core predictions, indicating the hydraulic compatibility between the codes, as well as reasonable and expected behavior under hypothetical transient conditions. This provides an initial step in ongoing efforts toward a robust, multiscale TH/neutronics engineering simulator capability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Expandable Li Percolation Network: The Effects of Site Distortion in Cation-Disordered Rock-Salt Cathode Material

Cation-disordered rock-salt (DRX) materials receive intensive attention as a new class of cathode candidates for high-capacity lithium-ion batteries (LIBs). Unlike traditional layered cathode materials, DRX materials have a three-dimensional (3D) percolation network for Li + transportation. The disordered structure poses a grand challenge to a thorough understanding of the percolation network due to its multiscale complexity. In this work, we introduce the large supercell modeling for DRX material Li 1.16 Ti 0.37 Ni 0.37 Nb 0.10 O 2 (LTNNO) via the reverse Monte Carlo (RMC) method combined with neutron total scattering. Here, through a quantitative statistical analysis of the material’s local atomic environment, we experimentally verified the existence of short-range ordering (SRO) and uncovered an element-dependent behavior of transition metal (TM) site distortion. A displacement from the original octahedral site for Ti 4+ cations is pervasive throughout the DRX lattice. Density functional theory (DFT) calculations revealed that site distortions quantified by the centroid offsets could alter the migration barrier for Li + diffusion through the tetrahedral channels, which can expand the previously proposed theoretical percolating network of Li. The estimated accessible Li content is highly consistent with the observed charging capacity. The newly developed characterization method here uncovers the expandable nature of the Li percolation network in DRX materials, which may provide valuable guidelines for the design of superior DRX materials.

25 ENERGY STORAGE↗
DOE OSTI2019

A Measurement‐Model Fusion Approach for Improved Wet Deposition Maps and Trends

Abstract Air quality models provide spatial fields of wet deposition (WD) and dry deposition that explicitly account for the transport and transformation of emissions from thousands of sources. However, many sources of uncertainty in the air quality model including errors in emissions and meteorological inputs (particularly precipitation) and incomplete descriptions of the chemical and physical processes governing deposition can lead to bias and error in the simulation of WD. We present an approach to bias correct Community Multiscale Air Quality model output over the contiguous United States using observation‐based gridded precipitation data generated by the Parameter‐elevation Regressions on Independent Slopes Model and WD observations at the National Atmospheric Deposition Program National Trends Network sites. A cross‐validation analysis shows that the adjusted annual accumulated WD for NO 3 − , NH 4 + , and SO 4 2− from 2002 to 2012 has less bias and higher correlation with observed values than the base model output without adjustment. Temporal trends in observed WD are captured well by the adjusted model simulations across the entire contiguous United States. Consistent with previous trend analyses, WD NO 3 − and SO 4 2− are shown to decrease during this period in the eastern half of the United States, particularly in the Northeast, while remaining nearly constant in the West. Trends in WD of NH 4 + are more spatially and temporally heterogeneous, with some positive trends in the Great Plains and Central Valley of CA and slightly negative trends in the south.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Multiscale Dynamics of Polymers in Particle-Rich Nanocomposites

In this paper we report on the dynamics of entangled polymers in polymer–nanoparticle composites (PNCs) with moderate and high particle loadings. Using composites comprised of poly(ethylene glycol) (PEG)-tethered silica (SiO 2 ) nanoparticles and poly(methyl methacrylate) (PMMA), we show by means of small-angle X-ray scattering (SAXS) analysis that the nanoparticles are uniformly dispersed over a range of particle concentrations. From oscillatory shear rheology measurements, we find that the time–temperature superposition (TTS) principle holds for these SiO 2 -PEG/PMMA PNCs up to a particle concentration Φ G ≈ 11% where the materials begin to exhibit soft glassy rheological properties. At particle concentrations below Φ G , we take advantage of TTS to create dynamic maps for PNCs that span time scales ranging from fast segmental motions to slow terminal relaxation of polymer chains. These maps reveal that at low Φ and depending on the host polymer (PMMA) molecular weight ( M w ), hairy nanoparticles may either speed up or slow down terminal relaxation of the host. In contrast, at high Φ, nanoparticles slow down polymer dynamics on intermediate and long time scales, irrespective of the host polymer M w . The slowdown coincides with particle concentrations at which the mean interparticle spacing lies below the equilibrium tube diameter of the entangled PMMA host and is thought to reflect the onset of confinement dynamics of polymer chains in PNCs. Studies of PNCs in the confinement regime reveal important analogies between glassy attributes of 3D-confined polymer chains in nanocomposites with those of polymer thin films supported on attractive substrates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Multiscale modeling of hydrogenolysis of ethane and propane on Ru(0001): Implications for plastics recycling

Plastic waste presents an environmental threat. Chemical recycling via hydrogenolysis can convert plastic waste into waxes, lubricants, and fuels. Among catalysts, Ru stands out for its superior activity and selectivity. The chemistry of light alkane hydrogenolysis can help understanding plastics deconstruction. Here, we perform first-principles calculations, develop descriptor-based relations, and conduct microkinetic modeling and analysis on ethane and propane. Predictions are in excellent agreement with experimental data. We identify a similar cracking pattern for both hydrocarbons entailing a deeply dehydrogenated species with the removal of four hydrogen atoms: CHCH*+* → 2CH* for ethane and CH 3 CCH*+2* → CH 3 C* + CH* for propane. We find that the rate-determining step is the C-C cracking for ethane and the first dehydrogenation from the terminal carbon (CH 3 CH 2 CH 3 *+* → CH 3 CH 2 CH 2 *+H*) for propane. The vinyl species CH 2 CH* produced from propane cracking is responsible for whether a single or multiple cracking events occur and effectively controls the selectivity. Specifically, ethane formation in propane hydrogenolysis is suppressed at elevated temperatures due to over-cracking via multiple (two here) cracking events being preferred over hydrogenation and desorption of ethane from the catalyst. Our workflow and models provide a baseline for future studies on heavier hydrocarbons. Insights into recent experimental studies of polyethylene over Ru-based catalysts are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗