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

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At least 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Micrometer-Scale Physical Structure and Microbial Composition of Soil Macroaggregates

Soil macroaggregates are discrete, separable units of soil that we hypothesize contain smaller assemblages of microorganisms than bulk soil, and represent a scale potentially consistent with naturally occurring microbial communities. We posed two questions to explore microbial community composition in the context of the macroaggregate: 1) Is there a relationship between macroaggregate physical structure and microbial community composition in individual macroaggregates? And, 2) How similar are the bacterial communities in individual sub-millimeter soil macroaggregates sampled from the same 5-cm core? To address these questions, individual macroaggregates of three arbitrary size classes (250–425, 425–841, and 841–1000 μm) were sampled from a grassland field. The physical structures of 14 individual macroaggregates were characterized using synchrotron-radiation based transmission X-ray tomography, revealing that a greater proportion of the pore space in the small- and medium-sized macroaggregates is as relatively smaller pores, resulting in greater overall porosity and pore–mineral interface area in these smaller macroaggregates. Microbial community composition was characterized using 16S rRNA pyrosequencing data. Rarefaction analyses indicated that the membership of each macroaggregate was sufficiently sampled with only a few thousand sequences; in addition, the community membership varied widely between macroaggregates and the structure varied from those communities strongly dominated by a few phylotypes to communities that were evenly distributed among several phylotypes. We found no strong relationship of physical structure with community membership; this may be due to the low number of aggregates (10) for which we have both physical and biological data. Our results do support our initial expectation that individual macroaggregate communities were significantly less diverse than bulk soil from the same grassland field site.

16S↗
DOE OSTI2017

Rapid Assessment of the Physical Stability of Amorphous Solid Dispersions

Amorphous solid dispersions (ASDs) can potentially increase the apparent solubility and thereby the oral bioavailability of poorly soluble compounds. Furthermore, their physical instability, i.e., potential to crystallize, is a major concern. Our objective was to explore methods for rapid assessment of the physical stability of ASDs. Ketoconazole ASDs were prepared with each of the three polymers, poly(acrylic acid) (PAA), poly(2-hydroxyethyl methacrylate) (PHEMA), and polyvinylpyrrolidone (PVP). The physical stability of these ASDs was evaluated after exposure to 90% RH (25 °C) in an automated water sorption analyzer. The onset time for ketoconazole crystallization, induced by water sorption, was rank ordered as PAA > PHEMA > PVP. Additionally, the ability of the polymers to inhibit crystallization on contact with aqueous medium was studied by powder X-ray diffractometry using synchrotron radiation. In this case, the rank ordering was PAA ~ PHEMA > PVP. To check the validity of our approach, the long-term stability of ketoconazole in ASDs was evaluated in the glassy state, both at 25 and at 40 °C, and was rank ordered as PAA > PHEMA > PVP. Crystallization, accelerated by water vapor sorption, can serve as an effective preliminary screening tool to rapidly evaluate and rank order the physical stability of ASDs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Physically Informed Machine Learning Prediction of Electronic Density of States

The electronic structure of a material, such as its density of states (DOS), provides key insights into its physical and functional properties and serves as a valuable source of high-quality features for many materials screening and discovery workflows. Still, the computational cost of calculating the DOS, most commonly with density functional theory (DFT), becomes prohibitive for meeting high-fidelity or high-throughput requirements, necessitating a cheaper but sufficiently accurate surrogate. To fulfill this demand, we develop a general machine learning method based on graph neural networks for predicting the DOS purely from atomic positions, six orders of magnitude faster than DFT. This approach can effectively use large materials databases and be applied generally across the entire periodic table to materials classes of arbitrary compositional and structural diversity. We furthermore devise a highly adaptable scheme for physically informed learning which encourages the DOS prediction to favor physically reasonable solutions defined by any set of desired constraints. This functionality provides a means for ensuring that the predicted DOS is reliable enough to be used as an input to downstream materials screening workflows to predict more complex functional properties, which rely on accurate physical features.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Constraining Data Mining with Physical Models: Voltage- and Oxygen Pressure-Dependent Transport in Multiferroic Nanostructures

Development of new generation electronic devices requires understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite–cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Finally and furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Phases and interfaces from real space atomically resolved data: Physics-based deep data image analysis

Advances in electron and scanning probe microscopies have led to a wealth of atomically resolved structural and electronic data, often with ~1–10 pm precision. However, knowledge generation from such data requires the development of a physics-based robust framework to link the observed structures to macroscopic chemical and physical descriptors, including single phase regions, order parameter fields, interfaces, and structural and topological defects. Here, we develop an approach based on a synergy of sliding window Fourier transform to capture the local analog of traditional structure factors combined with blind linear unmixing of the resultant 4D data set. This deep data analysis is ideally matched to the underlying physics of the problem and allows reconstruction of the a priori unknown structure factors of individual components and their spatial localization. We demonstrate the principles of this approach using a synthetic data set and further apply it for extracting chemical and physically relevant information from electron and scanning tunneling microscopy data. Furthermore, this method promises to dramatically speed up crystallographic analysis in atomically resolved data, paving the road toward automatic local structure–property determinations in crystalline and quasi-ordered systems, as well as systems with competing structural and electronic order parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Ambient Aerosol Is Physically Larger on Cloudy Days in Bondville, Illinois

Particle chemical composition affects aerosol optical and physical properties in ways important for the fate, transport, and impact of atmospheric particulate matter. For example, hygroscopic constituents take up water to increase the physical size of a particle, which can alter the extinction properties and atmospheric lifetime. At the collocated AERosol RObotic NETwork (AERONET) and Interagency Monitoring of PROtected Visual Environments (IMPROVE) network monitoring stations in rural Bondville, Illinois, we employ a novel cloudiness determination method to compare measured aerosol physicochemical properties on predominantly cloudy and clear sky days from 2010 to 2019. On cloudy days, aerosol optical depth (AOD) is significantly higher than on clear sky days in all seasons. Measured angstrom ngstro''m exponents are significantly smaller on cloudy days, indicating physically larger average particle size for the sampled populations in all seasons except winter. Mass concentrations of fine particulate matter that include estimates of aerosol liquid water (ALW) are higher on cloudy days in all seasons but winter. More ALW on cloudy days is consistent with larger particle sizes inferred from angstrom ngstro''m exponent measurements. Aerosol chemical composition that affects hygroscopicity plays a determining impact on cloudy versus clear sky differences in AOD, angstrom ngstro''m exponents, and ALW. This work highlights the need for simultaneous collocated, high-time-resolution measurements of both aerosol chemical and physical properties, in particular at cloudy times when quantitative understanding of tropospheric composition is most uncertain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Physical Orphaning versus Chemical Instability: Is Dendritic Electrodeposition of Li Fatal?

The dendritic electrodeposition of lithium, leading to physical orphaning and chemical instability, is considered responsible for the poor reversibility and premature failure of electrochemical cells that utilize Li metal anodes. Herein we critically assess the roles of physical orphaning and chemical instability of electrodeposited Li on electrode reversibility using planar and non-planar electrode architectures. The non-planar electrodes allow the morphology of electrodeposited Li to be interrogated in detail and in the absence of complications associated with cell stacking pressure. We find that physical orphaning is a key determinant of the poor reversibility of Li. We report further that fiber-like, dendritic electrodeposition is an intrinsic characteristic of Li —irrespective of the electrolyte solvent chemistry. With guaranteed electronic access to prevent physical loss, we finally show that a Li metal electrode exhibits high levels of reversibility (99.4% CE), even when the metal electrodeposits are in obvious, dendritic morphologies. We take advantage of these findings to create high-loading (7 mAh/cm 2 ) Li||LFP full cells with nearly unity N:P ratio and demonstrate that these cells exhibit good reversibility.

25 ENERGY STORAGE↗
DOE OSTI2014

Physical insight into switchgrass dissolution in the ionic liquid 1-ethyl-3-methylimidazolium acetate

Small-angle neutron scattering was used to characterize solutions of switchgrass and the constituent biopolymers cellulose, hemicellulose, and lignin, as well as a physical mixture of them mimicking the composition of switchgrass, dissolved in the ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate. The results demonstrate that the IL dissolves the cellulose fibrils of switchgrass, although a supramolecular biopolymer network remains that is not present in solutions of the individual biopolymers and that does not self-assemble in a solution containing the physical mixture of the individual biopolymers. The persistence of a network-like structure indicates that dissolving switchgrass in the IL does not disrupt all of the physical entanglements and covalent linkages between the biopolymers created during plant growth. Reconstitution of the IL-dissolved switchgrass yields carbohydrate-rich material containing cellulose with a low degree of crystallinity, as determined by powder X-ray diffraction, which impacts potential down-stream uses of the biopolymers produced by the process. The data suggests that the use of chemical additives which would break bonds that exist between the lignin and hemicellulose might improve the purity of the resulting product, but may not be able to disrupt the highly physically-entangled biopolymer network sufficiently to facilitate their separation.

Wang, Hui↗
DOE OSTI2024

The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM

Abstract A significant portion of surface melt on the Greenland Ice Sheet (GrIS) is due to dark ice regions in the ablation zone, where solar absorption is influenced by the physical properties of the ice, light absorbing constituents (LACs), and the overlying crustal surface or melt ponds. Earth system models (ESMs) typically prescribe the albedo of ice surfaces as a constant value in the visible and near‐infrared spectral regions. This work advances ESM ice radiative transfer modeling by (a) incorporating a physically based radiative transfer model (SNow, ICe and Aerosol Radiation model Adding‐Doubling Version 4; SNICAR‐ADv4) into the Energy Exascale Earth System Model (E3SM), (b) determining spatially and temporally varying bare ice physical properties over the GrIS ablation zone from satellite observations to inform SNICAR‐ADv4, and (c) assessing the impacts on simulated GrIS albedo and surface mass balance associated with modeling of more realistic bare ice albedo. GrIS‐wide bare ice albedo in E3SMv2 is overestimated by ∼4% in the visible and ∼7% in the near‐infrared wavelengths compared to the Moderate Resolution Imaging Spectroradiometer. Our bare ice physical property retrieval method found that LACs, ice crustal surfaces, and melt ponds reduce visible albedo by 30% in the bare ice region of the GrIS ablation zone. The realistic bare ice albedo reduces surface mass balance by ∼145 Gt, or 0.4 mm of sea‐level equivalent between 2000 and 2021 compared to the default E3SM. This work highlights the importance of simulating bare ice albedo accurately and realistically to improve our ability to quantify changes in the GrIS surface mass and radiative energy budgets.

58 GEOSCIENCES↗
DOE OSTI2023

A physics informed bayesian optimization approach for material design: application to NiTi shape memory alloys

Abstract The design of materials and identification of optimal processing parameters constitute a complex and challenging task, necessitating efficient utilization of available data. Bayesian Optimization (BO) has gained popularity in materials design due to its ability to work with minimal data. However, many BO-based frameworks predominantly rely on statistical information, in the form of input-output data, and assume black-box objective functions. In practice, designers often possess knowledge of the underlying physical laws governing a material system, rendering the objective function not entirely black-box, as some information is partially observable. In this study, we propose a physics-informed BO approach that integrates physics-infused kernels to effectively leverage both statistical and physical information in the decision-making process. We demonstrate that this method significantly improves decision-making efficiency and enables more data-efficient BO. The applicability of this approach is showcased through the design of NiTi shape memory alloys, where the optimal processing parameters are identified to maximize the transformation temperature.

Chemistry↗
DOE OSTI2024

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Sub genome anchored physical frameworks of the allotetraploid Upland cotton (Gossypium hirsutum L.) genome, and an approach toward reference-grade assemblies of polyploids

Like those of many agricultural crops, the cultivated cotton is an allotetraploid and has a large genome (~2.5 gigabase pairs). The two sub genomes, A and D, are highly similar but unequally sized and repeat-rich, which pose significant challenges for accurate genome reconstruction using standard approaches. Here we report the development of BAC libraries, sub genome specific physical maps, and a new-generation sequencing approach that will lead to a reference-grade genome assembly for Upland cotton. Three BAC libraries were constructed, fingerprinted, and integrated with BAC-end sequences (BES) to produce a de novo whole-genome physical map. The BAC map was partitioned by sub genomes through alignment to the diploid progenitor D-genome reference sequence with densely spaced BES anchor points and computational filtering. The physical maps were validated with FISH and genetic mapping of SNP markers derived from BES. Two pairs of homeologous chromosomes, A11/D11 and A12/D12, were used to assess multiplex sequencing approaches for completeness and scalability. The results represent the first sub genome anchored physical maps of Upland cotton, and a new-generation approach to the whole-genome sequencing, which will lead to the reference-grade assembly of allopolyploid cotton and serve as a general strategy for sequencing other polyploid species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Abstract Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Differentiable multiphase flow model for physics-informed machine learning in reservoir pressure management

Accurate subsurface reservoir pressure control is extremely challenging due to geological heterogeneity and multiphase fluid-flow dynamics. Predicting behavior in this setting relies on high-fidelity physics-based simulations that are computationally expensive. Yet, the uncertain, heterogeneous properties that control these flows make it necessary to perform many of these expensive simulations, which is often prohibitive. To address these challenges, we introduce a physics-informed machine learning workflow that couples a fully differentiable multiphase flow simulator, which is implemented in the DPFEHM framework with a convolutional neural network (CNN). The CNN learns to predict fluid extraction rates from heterogeneous permeability fields to enforce pressure limits at critical reservoir locations. By incorporating transient multiphase flow physics into the training process, our method enables more practical and accurate predictions for realistic injection-extraction scenarios compared to previous works. To speed up training, we pretrain the model on single-phase, steady-state simulations and then finetune it on full multiphase scenarios, which dramatically reduces the computational cost. We demonstrate that high-accuracy training can be achieved with fewer than three thousand full-physics multiphase flow simulations – compared to previous estimates requiring up to ten million. This drastic reduction in the number of simulations is achieved by leveraging transfer learning from much less expensive single phase simulations.

25 ENERGY STORAGE↗
DOE OSTI2025

Electrodermal activity as a proxy for sweat rate monitoring during physical and mental activities

Electrodermal activity has long been used for mental activity monitoring by measuring skin conductance at specific locations, such as fingertips, with high sweat gland density. However, electrodermal activity has not been considered useful for physical activity monitoring, where large sweat volumes are generated, resulting in the accumulation of sweat at the skin–electrode interface and, thus, preventing further dynamic response to sweating events. Here we show that electrodermal activity can be used as a proxy for sweat loss measurement under both low and high physical activity levels. We use wearable sweat sensors that consist of water-permeable electrodes and microfluidic-based sweat analysers, and show that skin conductance is proportional to the instantaneous sweat loss. We demonstrate that sweat loss during exercise can be estimated by integrating skin conductance over time, which can be applied to assess the body hydration status of exercisers. From multisite measurements of skin conductance, we show that the wrist, forearm and upper arm are reflective of physical activity levels, whereas the finger is indicative of mental activity. Simultaneous measurement of two different sites selectively decouples mental and physical activities.

Biomedical engineering↗
DOE OSTI2023

Inferring adversarial behaviour in cyber‐physical power systems using a Bayesian attack graph approach

Abstract Highly connected smart power systems are subject to increasing vulnerabilities and adversarial threats. Defenders need to proactively identify and defend new high‐risk access paths of cyber intruders that target grid resilience. However, cyber‐physical risk analysis and defense in power systems often requires making assumptions on adversary behaviour, and these assumptions can be wrong. Thus, this work examines the problem of inferring adversary behaviour in power systems to improve risk‐based defense and detection. To achieve this, a Bayesian approach for inference of the Cyber‐Adversarial Power System (Bayes‐CAPS) is proposed that uses Bayesian networks (BNs) to define and solve the inference problem of adversarial movement in the grid infrastructure towards targets of physical impact. Specifically, BNs are used to compute conditional probabilities to queries, such as the probability of observing an event given a set of alerts. Bayes‐CAPS builds initial Bayesian attack graphs for realistic power system cyber‐physical models. These models are adaptable using collected data from the system under study. Then, Bayes‐CAPS computes the posterior probabilities of the occurrence of a security breach event in power systems. Experiments are conducted that evaluate algorithms based on time complexity, accuracy and impact of evidence for different scales and densities of network. The performance is evaluated and compared for five realistic cyber‐physical power system models of increasing size and complexities ranging from 8 to 300 substations based on computation and accuracy impacts.

Sahu, Abhijeet↗
DOE OSTI2021

Rapid 3D nanoscale coherent imaging via physics-aware deep learning

Phase retrieval, the problem of recovering lost phase information from measured intensity alone, is an inverse problem that is widely faced in various imaging modalities ranging from astronomy to nanoscale imaging. The current process of phase recovery is iterative in nature. As a result, the image formation is time consuming and computationally expensive, precluding real-time imaging. Here, we use 3D nanoscale X-ray imaging as a representative example to develop a deep learning model to address this phase retrieval problem. We introduce 3D-CDI-NN, a deep convolutional neural network and differential programing framework trained to predict 3D structure and strain, solely from input 3D X-ray coherent scattering data. Our networks are designed to be “physics-aware” in multiple aspects; in that the physics of the X-ray scattering process is explicitly enforced in the training of the network, and the training data are drawn from atomistic simulations that are representative of the physics of the material. We further refine the neural network prediction through a physics-based optimization procedure to enable maximum accuracy at lowest computational cost. 3D-CDI-NN can invert a 3D coherent diffraction pattern to real-space structure and strain hundreds of times faster than traditional iterative phase retrieval methods. Our integrated machine learning and differential programing solution to the phase retrieval problem is broadly applicable across inverse problems in other application areas.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2013

PHYSICS OF A PARTIALLY IONIZED GAS RELEVANT TO GALAXY FORMATION SIMULATIONS-THE IONIZATION POTENTIAL ENERGY RESERVOIR

Simulation codes for galaxy formation and evolution take on board as many physical processes as possible beyond the standard gravitational and hydrodynamical physics. Most of this extra physics takes place below the resolution level of the simulations and is added in a ''sub-grid'' fashion. However, these sub-grid processes affect the macroscopic hydrodynamical properties of the gas and thus couple to the ''on-grid'' physics that is explicitly integrated during the simulation. In this paper, we focus on the link between partial ionization and the hydrodynamical equations. We show that the energy stored in ions and free electrons constitutes a potential energy term which breaks the linear dependence of the internal energy on temperature. Correctly taking into account ionization hence requires modifying both the equation of state and the energy-temperature relation. We implemented these changes in the cosmological simulation code GADGET2. As an example of the effects of these changes, we study the propagation of Sedov-Taylor shock waves through an ionizing medium. This serves as a proxy for the absorption of supernova feedback energy by the interstellar medium. Depending on the density and temperature of the surrounding gas, we find that up to 50% of the feedback energy is spent ionizing the gas rather than heating it. Thus, it can be expected that properly taking into account ionization effects in galaxy evolution simulations will drastically reduce the effects of thermal feedback. To the best of our knowledge, this potential energy term is not used in current simulations of galaxy formation and evolution.

79 ASTRONOMY AND ASTROPHYSICS↗