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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Albedo-Induced Global Warming Impact at Multiple Temporal Scales within an Upper Midwest USA Watershed

Land surface albedo is a significant regulator of climate. Changes in land use worldwide have greatly reshaped landscapes in the recent decades. Deforestation, agricultural development, and urban expansion alter land surface albedo, each with unique influences on shortwave radiative forcing and global warming impact (GWI). Here, we characterize the changes in landscape albedo-induced GWI (GWI Δα ) at multiple temporal scales, with a special focus on the seasonal and monthly GWI Δα over a 19-year period for different land cover types in five ecoregions within a watershed in the upper Midwest USA. The results show that land cover changes from the original forest exhibited a net cooling effect, with contributions of annual GWI Δα varying by cover type and ecoregion. Seasonal and monthly variations of the GWI Δα showed unique trends over the 19-year period and contributed differently to the total GWI Δα . Cropland contributed most to cooling the local climate, with seasonal and monthly offsets of 18% and 83%, respectively, of the annual greenhouse gas emissions of maize fields in the same area. Urban areas exhibited both cooling and warming effects. Cropland and urban areas showed significantly different seasonal GWI Δα at some ecoregions. The landscape composition of the five ecoregions could cause different net landscape GWI Δα .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Heterogeneities at multiple length scales in 2D layered materials: From localized defects and dopants to mesoscopic heterostructures

Two-dimensional (2D) materials hold great promise for applications in optoelectronics, quantum information science, and energy conversion due to their remarkable properties imbued by their physical characteristics. Although heterogeneities in their intrinsic structure are the major challenges limiting their synthesis and predictable properties, they also provide a pathway to controllably tune the properties and broaden the potential of 2D materials. Heterogeneities that can be tailored, including defects, dopants, strain, edges, and layer stackings offer transformative opportunities in heterogeneous 2D materials through the introduction of novel properties for technological applications. This article provides a review of recent progress in studying heterogeneities in 2D materials. The review uses examples from our work to develop a strategy to understand the heterogeneities across multiple length scales to link the effect of heterogeneity at the nanoscale with the macroscale properties of 2D materials. We describe specific types of heterogeneities and explore novel synthesis and processing methods for their controlled production with example of the potential impact and applications enabled by their intriguing properties. Finally, we provide a perspective on how to extend the range of tunable properties through further engineering the heterogeneities in 2D materials.

2D materials↗
DOE OSTI2025

An effective field theory for muon conversion and muon decay-in-orbit

Muon conversion is one of the best probes of charged lepton flavor violation. The experimental limit is soon expected to improve by four orders of magnitude, thus calling for precise predictions of the shape of the signal spectrum. Equally important are precise predictions for muon decay-in-orbit, the main background for muon conversion. While the calculation of electromagnetic corrections to the two processes above the nuclear scale does not involve significant challenges, it becomes substantially more complex below that scale due to multiple scales, bound-state effects and experimental setup. Here, we present a systematic framework that addresses these challenges by resorting to a series of effective field theories. Combining Heavy Quark Effective Theory (HQET), Non-Relativistic QED (NRQED), potential NRQED, Soft-Collinear Effective Theory I and II, and boosted HQET, we derive a factorization theorem and present the renormalization group equations. Our framework allows for the proper calculation of precise predictions for the rates of the two processes, with crucial implications for the upcoming muon conversion searches. We also provide the most accurate prediction of the signal shape for those searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Renormalization scale setting for heavy quark pair production in $e^+e^–$ annihilation near the threshold region

Heavy fermion pair production in e + e – annihilation is a fundamental process in hadron physics and is of considerable interest for various phenomena. In this paper, we will apply the principle of maximum conformality (PMC) to provide a comprehensive analysis of these processes. The PMC provides a systematic, unambiguous method for determining the renormalization scales of the QCD coupling constant for single-scale and multiple-scale applications. The resulting predictions eliminate any renormalization scheme-and-scale ambiguities, eliminate the factorial renormalon divergences, and are consistent with the requirements of the renormalization group. It is remarkable that two distinctly different scales are determined by using the PMC for heavy fermion pair production near the threshold region. One scale is the order of the fermion mass m f , which enters the hard virtual corrections, and the other scale is of order v m f , where v is the quark velocity, which enters the Coulomb rescattering amplitude. The PMC scales yield the correct physical behavior and reflect the virtuality of the propagating gluons (photons) for the QCD (QED) processes. Moreover, we demonstrate the consistency of PMC scale setting from QCD to QED. Perfect agreement between the Abelian unambiguous Gell-Mann-Low and the PMC scale-setting methods in the limit of zero number of colors is demonstrated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Navigating Economies of Scale and Multiples for Nuclear-Powered Data Centers and Other Applications with High Service Availability Needs

Nuclear energy is increasingly being considered for such targeted energy applications as data centers in light of their high capacity factors and low carbon emissions. This paper focuses on assessing the tradeoffs between economies of scale versus mass production to identify promising reactor sizes to meet data center demands. A framework is then built using the best cost estimates from the literature to identify ideal reactor power sizes for the needs of the given data center. Results should not be taken to be deterministic but highlight the variability of ideal reactor power output against the required demand. While certain advocates claim that with the gigawatts of clean, firm energy needed, large plants are ideal, others advocate for SMRs that can be deployed in large quantities and reap the benefits from learning effects. The findings of this study showcase that identifying the optimal size for a reactor is likely more nuanced and dependent on the application and its requirements. Overall, the study does show potential economic promise for coupling nuclear reactors to data centers and industrial heat applications under certain key conditions and assumptions.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Computational Fluid Dynamics of Bioreactors with Micro-Aeration

Recent work has probed the potential for using Zymomonas mobilis as a flexible platform for the production of advanced fuels and intermediates. One set of recombinant organisms requires a micro-aerobic environment to promote the production of the desired product (2,3-BDO), but the economics and process-control implications of this approach are uncertain at industrial scale. Computational multi-physics models have the potential to describe the fermentation in detail at multiple scales, reducing the risk of scale-up. A two-phase Euler-Euler CFD model is used alongside a simple model of oxygen uptake to describe this bioreaction. A few reactor vessels, including airlift and CSTR bioreactors, are analyzed at multiple scales, regions of oxygen-depletion and over-oxygenation are characterized, and the bioreactors are compared. We show that it is difficult to achieve homogenous oxygen concentrations in industrial-scale CSTR reactors and that advanced bioreactor designs are needed for economical operation.

09 BIOMASS FUELS↗
DOE OSTI2024

Meshfree Methods

Meshfree methods have undergone substantial development and have received much attention in the last two decades. This new family of numerical methods is designed to inherit the main advantages of the finite element method such as compact supports of shape functions and good approximation properties while, at the same time, overcome the main disadvantages of the finite element method caused by the mesh dependence. The meshfree methods share a common feature that no mesh is needed and shape functions are constructed from sets of points, thus eliminating the need for time consuming mesh generation. The most significant advantage of meshfree methods is the flexibility in customizing approximation functions for desired regularity and for capturing essential physics and features of the particular problems of interest. Adaptivity formulation and multiple-scale solution strategies also can be implemented with relative ease. It has become clear that the meshfree methods provide considerable advantages over the conventional finite element methods in solving problems involving moving discontinuities, evolving material interfaces, multiple-scale phenomena, large material distortion and structural deformation, and fracture and damage processes. This Chapter gives an overview of many classes of meshfree methods, with more detailed discussions on Smoothed Particle Hydrodynamics (SPH), the Reproducing Kernel Particle Method (RKPM), Peridynamics (PD), the Material Point Method (MPM), as well as their applications in various challenging engineering problems.2

Chen, Jiun-Shyan↗
DOE OSTI2021

Multiresolution classification of turbulence features in image data through machine learning

During large-scale simulations, intermediate data products such as image databases have become popular due to their low relative storage cost and fast in-situ analysis. Serving as a form of data reduction, these image databases have become more acceptable to perform data analysis on. In this work, we present an image-space detection and classification system for extracting vortices at multiple scales through wavelet-based filtering. A custom image-space descriptor is used to encode a large variety of vortex-types and a machine learning system is trained for fast classification of vortex regions. By combining a radial-based histogram descriptor, a bag of visual words feature descriptor, and a support vector machine, our results show that we are able to detect and classify vortex features at various sizes at multiple scales. Once trained, our framework enables the fast extraction of vortices on new, unknown image datasets for flow analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Towards improved speed and accuracy of laser powder bed fusion simulations via representation of multiple time scales

Here, we report the result of investigations into alternative representations of the temporal evolution of the laser powder bed fusion (LPBF) process. In order to provide the reader with sufficient context for the following discussion, the modeling challenge and its underlying cause, as well as current attempts to provide a satisfactory solution, will be discussed. Next, the author will describe two numerical methods (multirate time integration and parallel-in-time) to better represent the temporal scales of the problem. Finally, the results of applying these methods to a two-dimensional version of the heat transfer portion of the LPBF problem will be presented, reporting on both convergence and performance behavior, which indicate the possibility of significant speedup of solution calculations (40–100 times), along with well-characterized effects on solution accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Insights into the Solvation Characteristics of Zwitterionic Deep Eutectic Solvents Using Multiple Polarity Scales

Deep eutectic solvents (DESs) formed by mixing zwitterionic hydrogen bond acceptors (HBAs) with diol- and organic acid-based hydrogen bond donors (HBDs) are popular sustainable solvents. In addition to possessing an amphiphilic character, zwitterionic DESs offer unique solvation properties, unlike those composed of halide-containing HBAs. While all other popular classes of DESs have been investigated, no study has attempted to characterize the solvation characteristics of zwitterionic DESs. In this study, the solvation properties of betaine [Bet]-, sulfobetaine-, and l-carnitine [l-Car]-based DESs were thoroughly investigated for the first time using solvatochromic dye methods and inverse gas chromatography (IGC). Herein, the Nile red polarity scale revealed that DESs composed of organic acids were generally more polar than diol-based DESs. Kamlet–Taft solvent parameters revealed that glycerol-based DESs offered exceptionally high hydrogen bond donating ability compared to those composed of 1,4-butane diol and triethylene glycol. The Abraham solvation parameter model revealed hydrogen bond basicity to be the most dominant interaction, and it was generally higher for diol-based HBDs compared to organic acid-based HBDs. Additionally, sulfobetaine HBAs possessing longer alkyl chains generally offered higher dispersive-type interactions and hydrogen bond basicity compared to [Bet] and [l-Car] salts. This is the first study to combine and compare results from solvatochromic dye methods and IGC in an attempt to comprehensively explain and predict the solvation characteristics of zwitterionic DESs.

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

Assessing CLUBB PDF Closure Assumptions for a Continental Shallow-to-Deep Convective Transition Case Over Multiple Spatial Scales

Assumed-PDF (probability density function) higher-order turbulence closures (APHOCs) are now widely used for parameterizing boundary layer turbulence and shallow convection in Earth system models (ESMs). A better understanding of the resolution-dependent behavior of APHOCs is essential for improving the performance of next-generation ESMs with intended horizontal resolutions finer than 10 km. In this study, we evaluate the PDF family of Analytic double-Gaussian 1 implemented in Cloud Layers Unified By Binormals (CLUBB) over a range of spatial scales (Dx) from 2 to 100 km. A 120-km-wide large eddy simulation (LES) for a continental convection case during 2016 Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign serves as benchmark to evaluate the PDF closure using an off-line approach. We find during the shallow convection period, the CLUBB PDF closure tends to produce positive biases of cloud properties and liquid water flux near cloud base for all scales of analysis. It produces negative biases for these variables near cloud top that are more severe for Dx larger than 25 km. Results show that replacing the CLUBB-parameterized moisture and temperature skewnesses with LES-derived ones can fix most of the biases if clipping of input moments is allowed to prevent the occurrence of unrealizable solutions. Overall, the performance of the PDF closure is better for smaller Dx = 2–5 km than for larger Dx = 50–100 km; for a given grid spacing, it is better when the convective clouds become deeper in the late afternoon. Likely causes for the resolution dependence and implications for improving the PDF closure are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Drosophila melanogaster pigmentation demonstrates adaptive phenotypic parallelism over multiple spatiotemporal scales

Abstract Populations are capable of responding to environmental change over ecological timescales via adaptive tracking. However, the translation from patterns of allele frequency change to rapid adaptation of complex traits remains unresolved. We used abdominal pigmentation in Drosophila melanogaster as a model phenotype to address the nature, genetic architecture, and repeatability of rapid adaptation in the field. We show that D. melanogaster pigmentation evolves as a highly parallel and deterministic response to shared environmental variation across latitude and season in natural North American populations. We then experimentally evolved replicate, genetically diverse fly populations in field mesocosms to remove any confounding effects of demography and/or cryptic structure that may drive patterns in wild populations; we show that pigmentation rapidly responds, in parallel, in fewer than 15 generations. Thus, pigmentation evolves concordantly in response to spatial and temporal climatic axes. We next examined whether phenotypic differentiation was associated with allele frequency change at loci with established links to genetic variance in pigmentation in natural populations. We found that across all spatial and temporal scales, phenotypic patterns were associated with variation at pigmentation-related loci, and the sets of genes we identified at each scale were largely nonoverlapping. Therefore, our findings suggest that parallel phenotypic evolution is associated with distinct components of the polygenic architecture shifting across each environmental axis to produce redundant adaptive patterns.

Evolutionary Biology↗
DOE OSTI2024

Sublimation of Snow Field Campaign Report

Snow is a vital part of water resources, but sublimation may remove 10% to 90% of snowfall from the system. The processes controlling sublimation span multiple scales of measurement and multiple disciplinary fields. Due to a critical lack of reliable direct measurements of snow sublimation, we do not fully understand the physics that govern current rates of sublimation, let alone how those amounts might change with the climate.

54 ENVIRONMENTAL SCIENCES↗