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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 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Integration of a physics-based direct normal irradiance ($\mathrm{DNI}$) model to enhance the National Solar Radiation Database ($\mathrm{NSRDB}$)

The National Solar Radiation Database (NSRDB) is an extensively used dataset that furnishes satellite-retrieved solar resource data across the United States and an expanding list of other countries. Although the NSRDB uses a physical model to compute global horizontal irradiance (GHI), it currently employs an empirical approach based on surface observations to estimate cloudy-sky direct normal irradiance (DNI). Recently, a new physics-based approach, known as the Fast All-sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to improve DNI forecasting. FARMS-DNI integrates direct and scattered solar radiances within the circumsolar region, resulting in improved day-ahead forecasting of DNI by incorporating it into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study incorporates FARMS-DNI into the NSRDB algorithm to produce high-spatiotemporal-resolution DNI data from satellite data. The accuracy of the NSRDB based on FARMS-DNI is analyzed using surface observations from 19 sites situated within the National Oceanic and Atmospheric Administration (NOAA) Surface Radiation Budget (SURFRAD) and Solar Radiation (SOLRAD) networks, the University of Oregon (UO) network, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) network, and at the National Renewable Energy Laboratory (NREL). The results demonstrate that FARMS-DNI reduces the significant overestimation of DNI in the conventional NSRDB at all surface sites, particularly in cloud overcast conditions classified using both satellite retrievals and surface observations. Consequently, this new model can effectively improve the overall accuracy of the NSRDB. The results also suggest that further improvement of DNI estimates at individual time steps, however, requires advanced satellite techniques and precise identification of clouds and retrieval of cloud properties.

14 SOLAR ENERGY↗
DOE OSTI2020

A new frontier in laboratory physics: magnetized electron–positron plasmas

We describe here efforts to create and study magnetized electron–positron pair plasmas, the existence of which in astrophysical environments is well-established. Laboratory incarnations of such systems are becoming ever more possible due to novel approaches and techniques in plasma, beam and laser physics. Traditional magnetized plasmas studied to date, both in nature and in the laboratory, exhibit a host of different wave types, many of which are generically unstable and evolve into turbulence or violent instabilities. This complexity and the instability of these waves stem to a large degree from the difference in mass between the positively and the negatively charged species: the ions and the electrons. The mass symmetry of pair plasmas, on the other hand, results in unique behaviour, a topic that has been intensively studied theoretically and numerically for decades, but experimental studies are still in the early stages of development. A levitated dipole device is now under construction to study magnetized low-energy, short-Debye-length electron–positron plasmas; this experiment, as well as a stellarator device that is in the planning stage, will be fuelled by a reactor-based positron source and make use of state-of-the-art positron cooling and storage techniques. Relativistic pair plasmas with very different parameters will be created using pair production resulting from intense laser–matter interactions and will be confined in a high-field mirror configuration. We highlight the differences between and similarities among these approaches, and discuss the unique physics insights that can be gained by these studies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

The high-field tokamak physics basis for the ARC fusion power plant

This editorial summarizes the physics basis underlying the ARC high-field tokamak fusion power plant, including recent advances in tokamak plasma understanding, the role of the SPARC device, and key remaining physics questions. It describes how these results guide the ARC Version 3A design and outlines opportunities for the broader fusion community to contribute to the development of first-generation fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Use of a Plasticizer for Physical Stability Prediction of Amorphous Solid Dispersions

Utilizing glycerol as a plasticizer, an accelerated physical stability testing method of amorphous solid dispersions (ASDs) was developed. The influence of glycerol concentration on the glass transition temperature and α-relaxation time (a measure of molecular mobility) of amorphous ketoconazole, celecoxib, and the solid dispersions of each prepared with polyvinylpyrrolidone was investigated. By temperature scaling (T g /T), the effects of glycerol concentration and temperature on the relaxation time were simultaneously evaluated. Glycerol, in a concentration dependent manner, accelerated crystallization in all of the systems without affecting the fragility. In celecoxib-PVP ASDs, the drug crystallization was well coupled to molecular mobility and was essentially unaltered at glycerol concentrations up to 2% w/w. The acceleration in crystallization brought about by glycerol expedited the determination of the coupling between molecular mobility and crystallization. As a result, we were able to predict the physical stability of the unplasticized ASD. Lastly, this approach is especially useful for ASDs with high polymer content where drug crystallization is extremely slow at the relevant storage temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Elucidation of Structure and Physical Properties of Pyrolytic Sugar Oligomers Derived from Cellulose Depolymerization/Dehydration Reactions: A Density Functional Theory Study

Fast pyrolysis of lignocellulosic materials is a promising research area to produce renewable fuels and chemicals. Dehydration is known to be among the most important reaction families during cellulose pyrolysis; water is the most important product. Together with water, dehydration reactions also form a range of poorly known oligomer species of varying molecular sizes, often collected as part of bio-oil water-soluble (WS) fraction. In this work, we used electronic structure calculations to evaluate the relative thermodynamic stabilities of several oligomer species resulting from up to three consecutive dehydration events from cellulose depolymerization intermediates. A library of the thermodynamically favored candidate molecular structures was compiled. Results revealed that most of the water molecules are eliminated from the non-reducing end, forming thermodynamically more stable conjugated compounds. This is consistent with results reported by other researchers in literature where dehydration reactions occur preferably at the non-reducing ends of oligomers. The physical-chemical properties of the proposed structures were estimated using quantitative structure-property relationships (QSPRs) and quantitative property-property relationships (QPPRs). The anhydro-sugars derived from cellulose are often blamed for coke formation during bio-oil hydrotreatment. Understanding their chemical structure could help to develop rational strategies to mitigate coke formation. Furthermore, the thermo-physical properties reported (boiling point, melting point, Gibb’s free energy of formation, enthalpy of formation, and solubility parameters among others) are also fundamental to conducting first principle engineering calculations to design and analyze new pyrolysis reactors and bio-oil up-grading units.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Molecular Modeling of the Physical Properties for Aqueous Amine Solution Containing a CO 2 Hydration Catalyst

The effects of an amphiphilic CO 2 hydration catalyst (C3P) on the physical properties of aqueous monoethanolamine (MEA) solutions were studied using molecular simulations and verified experimentally. Adding 2.7–27.7 g/L of C3P in 30 wt % MEA aqueous solution did not significantly affect the solution viscosity, surface tension, or CO 2 diffusivity. These results confirm that the previously reported increase in CO 2 mass transfer by C3P is due to CO 2 hydration catalysis and not due to changes in the physical properties of the MEA solution. Additional simulations indicate that the catalyst molecules tend to aggregate in MEA solution and are preferentially adsorbed at the gas–liquid interface region. Finally, for the catalyst molecules remaining in the bulk solution, the local concentrations of CO 2 and MEA in the area immediately around the catalyst are increased while the local water concentration is decreased, relative to their concentrations in the rest of the bulk MEA solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Diverse Physical States of Amorphous Precursors in Zeolite Sol Gel Syntheses

The assembly and structural evolution of amorphous precursors during zeolite crystallization is an important area of interest owing to their putative roles in the nucleation and growth of aluminosilicate microporous materials. Precursors range in complexity from oligomeric molecules and colloidal particles to gels comprised of heterogeneous silica and alumina domains. The physical state of precursors in most zeolite syntheses is generally not well understood; however, it is evident that the physicochemical properties of precursors depend on a wide range of conditions that include (but are not limited to) the selection of reagents, the composition of growth mixtures, the methods of preparation, and the use of inorganic and/or organic structure-directing agents. The fact that precursors evolve in size, shape, and/or microstructure during the course of nucleation and potentially throughout crystallization leads to questions pertaining to their mode of action in the formation of zeolites. Finally, this also highlights the diversity of species that are present in growth media, thus rendering the topic of zeolite synthesis essentially a black box to those attempting to better understand the fundamental role(s) of precursors. Here in this Article, we discuss the wide variety of precursors encountered in the synthesis of various framework types, emphasizing their complex physical states and the thermodynamic and kinetic factors that govern their heterogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. Further, this physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. In conclusion, not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations.

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

Unveiling the Finite Temperature Physics of Hydrogen Chains via Auxiliary Field Quantum Monte Carlo

The ability to accurately predict the finite temperature properties and phase diagrams of realistic quantum solids is central to uncovering new phases and engineering materials with novel properties ripe for device applications. Nonetheless, there remain comparatively few many-body techniques capable of elucidating the finite temperature physics of solids from first principles. Here, we take a significant step toward developing such a technique by generalizing our previous, exact fully ab initio finite temperature Auxiliary Field Quantum Monte Carlo (FT-AFQMC) method to model periodic solids and employing it to uncover the finite temperature physics of periodic hydrogen chains. Our chains’ unit cells consist of 10 hydrogen atoms modeled in a minimal basis, and we sample 5 k-points from the first Brillouin zone to arrive at a supercell consisting of 50 orbitals and 50 electrons. Based upon our calculations of these chains’ many-body energies, free energies, entropies, heat capacities, double and natural occupancies, and charge and spin correlation functions, we outline their metal–insulator and magnetic ordering as a function of both H–H bond distance and temperature. At low temperatures approaching the ground state, we observe both metal–insulator and ferromagnetic–antiferromagnetic crossovers at bond lengths between 0.5 and 0.75 Å. We then demonstrate how this low-temperature ordering evolves into a metallic phase with decreasing magnetic order at higher temperatures. In order to contextualize our results, we compare the features we observe to those previously seen in one-dimensional, half-filled Hubbard models at finite temperature and in ground state hydrogen chains. Interestingly, we identify signatures of the Pomeranchuk effect in hydrogen chains for the first time and show that spin and charge excitations that typically arise at distinct temperatures in the Hubbard model are indistinguishably coupled in these systems. Beyond qualitatively revealing the many-body phase behavior of hydrogen chains in a numerically exact manner without invoking the phaseless approximation, our efforts shed light on the further theoretical developments that will be required to construct the phase diagrams of the more complex transition metal, lanthanide, and actinide solids of longstanding interest to physicists.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Physical Origins of the Transient Absorption Spectra and Dynamics in Thin-Film Semiconductors: The Case of BiVO 4

Transient absorption (TA) spectroscopy is uniquely suited for understanding kinetic processes initiated by light over vast ranges of time. In combination with white light probes, the recorded differential absorption spectra can contain spectroscopic signatures characteristic of specific charge carrier population densities. However, disentangling the often-complex and convoluted spectra is made challenging without robust analysis methods relating the underlying physical mechanisms to the spectral components. In this work, we address the origin of the transient spectra of a model system of emerging solar energy harvesting materials using a monoclinic BiVO 4 thin film. Using ground-state optical properties of the semiconductor, we find the main derivative-like spectral response to be related to shifting and broadening of oscillators, rather than specific carrier-related transitions. However, by using the Drude optical model of free carriers, we also identify the transient response related to free-hole density. Importantly, sample heating from the optical pumping, which begins at ~10 ps and plateaus by ~200 ps, dominates the overall spectral response at longer times. On the basis of a physical model of the spectral response, a kinetic model is developed that describes the pump power dependence of the free-hole density, as well as the temporal evolution of the spectral changes associated with shifting and broadening of oscillators. First-principles density functional theory calculations are used to rationalize experimental measurements. Here, this comprehensive approach to analyzing and modeling the TA spectra offers a generalizable basis for understanding the complex pump-probe data, reveals thermal heating artifacts that are frequently erroneously assigned to long-lived photocarriers, and offers a path to eliminating ambiguity in analysis of photocarrier dynamics in solid-state systems.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Controlling Interfacial Dynamics: Covalent Bonding versus Physical Adsorption in Polymer Nanocomposites

It is generally believed that the strength of the polymer nanoparticle interaction controls the modification of near-interface segmental mobility in polymer nanocomposites (PNCs). However, little is known about the effect of covalent bonding on the segmental dynamics and glass transition of matrix-free polymer-grafted nanoparticles (PGNs), especially when compared to PNCs. In this article, we directly compare the static and dynamic properties of poly(2-vinylpyridine)/silica-based nanocomposites with polymer chains either physically adsorbed (PNCs) or covalently bonded (PGNs) to identical silica nanoparticles (RNP = 12.5 nm) for three different molecular weight (MW) systems. Interestingly, when the MW of the matrix is as low as 6 kg/mol (RNP/Rg = 5.4) or as high as 140 kg/mol (RNP/Rg= 1.13), both small-angle X-ray scattering and broadband dielectric spectroscopy show similar static and dynamic properties for PNCs and PGNs. However, for the intermediate MW of 18 kg/mol (RNP/Rg = 3.16), the difference between physical adsorption and covalent bonding can be clearly identified in the static and dynamic properties of the interfacial layer. We ascribe the differences in the interfacial properties of PNCs and PGNs to changes in chain stretching, as quantified by self-consistent field theory calculations. These results demonstrate that the dynamic suppression at the interface is affected by the chain stretching; that is, it depends on the anisotropy of the segmental conformations, more so than the strength of the interaction, which suggests that the interfacial dynamics can be effectively tuned by the degree of stretching a parameter accessible from the MW or grafting density.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Analytical Platform To Characterize Dopant Solution Concentrations, Charge Carrier Densities in Films and Interfaces, and Physical Diffusion in Polymers Utilizing Remote Field-Effect Transistors

Characterizing doping effects in a conductive polymer and physical diffusion in a passive polymer were performed using a remote-gate field-effect transistor (RG FET) detection system that was able to measure the electrical potential perturbation of a polymer film coupled to the gate of a silicon FET. Poly(3-hexylthiophene) (P3HT) film doped using various concentrations of 2,3,5,6-tetrafluoro-7,7,8,8-tetracyanoquinodimethane (F4TCNQ) solutions imposed additional positive potentials on the P3HT RG, resulting in a lower threshold voltage ($V_{th}$) on the n-channel silicon FET. Changes in $V_{th}$ were related to the induced hole concentrations and hole mobility in P3HT films by using our $V_{th}$ shifting model for the RG FET. We discovered that the electron-donating P3HT and even inorganic materials, indium tin oxide and gold, showed similar electrical potential perturbations dependent on the concentration of F4TCNQ in overlying solutions as the dopant radical anions maximally covered the surfaces. This suggests that there are limited electroactive sites for F4TCNQ binding on electron donor surfaces which results in a similar number of positive charges in film materials forming dipoles with the F4TCNQ radical counteranions. The effect of electron acceptors such as 7,7,8,8-tetracyanoquinodimethane and tetracyanoethylene was compared with that of F4TCNQ in terms of $V_{th}$ shift using our analytical tool, with differences attributed to acceptor size and reduction potential. Meanwhile, this FET analysis tool offered a means of monitoring the physical diffusion of small molecules, exemplified by F4TCNQ, in the passive polymer polystyrene, driven by concentration gradients. The technique allows for nondestructive, nonspectroscopic, ambient characterization of electron donor-acceptor interactions at surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Upstream and Downstream Boundary Conditions Control the Physical and Biological Development of River Deltas

Relative sea level rise is depleting coastal land. Success in coastal restoration depends on understanding interactions between physical and biological processes that influence landscape change. We present results from flume experiments examining the coevolution of vegetation and delta channel networks. We show that channel mobility—controlled by upstream and downstream boundary conditions and delta size—controls vegetation colonization. The spatial patterns of plants in turn control the stability and spatial distribution of channels. Experiments with low–channel mobility develop large, dense plant patches that become denser over time and stabilize channels. Experiments with more mobile channels have sparse vegetation patterns that limit vegetation growth within existing patches, maintaining sparse, patchy vegetation. Thus, the ecogeomorphic development of deltas depends on the rate of channel lateral migration and avulsion, suggesting that upstream and downstream boundary conditions control both the physical and biological evolution of coastal landscapes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Physics–Informed Neural Networks of the Saint–Venant Equations for Downscaling a Large–Scale River Model

Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using Machine Learning to Generate a GISS ModelE Calibrated Physics Ensemble (CPE)

A neural network (NN) surrogate of the NASA GISS ModelE atmosphere (version E3) is trained on a perturbed parameter ensemble (PPE) spanning 45 physics parameters and 36 outputs. The NN is leveraged in a Markov Chain Monte Carlo (MCMC) Bayesian parameter inference framework to generate a second posterior constrained ensemble coined a “calibrated physics ensemble,” or CPE. The CPE members are characterized by diverse parameter combinations and are, by definition, close to top-of-atmosphere radiative balance, and must broadly agree with numerous hydrologic, energy cycle and radiative forcing metrics simultaneously. Global observations of numerous cloud, environment, and radiation properties (provided by global satellite products) are crucial for CPE generation. The inference framework explicitly accounts for discrepancies (or biases) in satellite products during CPE generation. We demonstrate that product discrepancies strongly impact calibration of important model parameter settings (e.g., convective plume entrainment rates; fall speed for cloud ice). Structural improvements new to E3 are retained across CPE members (e.g., stratocumulus simulation). Notably, the framework improved the simulation of shallow cumulus and Amazon rainfall while not degrading radiation fields, an upgrade that neither default parameters nor Latin Hypercube parameter searching achieved. Analyses of the initial PPE suggested several parameters were unimportant for output variation. However, many “unimportant” parameters were needed for CPE generation, a result that brings to the forefront how parameter importance should be determined in PPEs. From the CPE, two diverse 45-dimensional parameter configurations are retained to generate radiatively-balanced, auto-tuned atmospheres that were used in two E3 submissions to CMIP6.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

WellPINN: Accurate Well Representation for Transient Fluid Pressure Diffusion in Subsurface Reservoirs With Physics‐Informed Neural Networks

Accurate representation of pumping wells is essential for reliable reservoir characterization and simulation of operational scenarios in subsurface flow models. Physics-informed neural networks (PINNs) are emerging as a promising alternative to numerical models for reservoir modeling, offering seamless integration of monitoring data and governing physical equations. However, existing PINN-based studies face major challenges in capturing fluid pressure near wells when using a source/sink term, particularly during the early stages after pumping begins. We address this problem by introducing WellPINN, a workflow in which an initially trained PINN infers fluid pressure across the entire reservoir domain using a large equivalent well radius. This initial PINN solution is then locally refined around the well by a set of subdomain PINNs that are trained for smaller equivalent well radii. Continuity across these subdomain interfaces as well as at the initial condition is ensured by hard-constraining each PINN on its subdomain boundary. Our results demonstrate WellPINN as the first workflow of its kind to focus on accurate inference of fluid pressure from pumping rates throughout the entire injection period, significantly advancing the potential of PINNs for inverse modeling and operational scenario simulations. All data and code for this paper are openly available at https://doi.org/10.20350/DIGITALCSIC/17260.

58 GEOSCIENCES↗
DOE OSTI2018

Machine learning at the energy and intensity frontiers of particle physics

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Magnetic field mapping of inaccessible regions using physics-informed neural networks

A difficult problem concerns the determination of magnetic field components within an experimentally inaccessible region when direct field measurements are not feasible. In this paper, we propose a new method of accessing magnetic field components using non-disruptive magnetic field measurements on a surface enclosing the experimental region. Magnetic field components in the experimental region are predicted by solving a set of partial differential equations (Ampere’s law and Gauss’ law for magnetism) numerically with the aid of physics-informed neural networks (PINNs). Prediction errors due to noisy magnetic field measurements and small number of magnetic field measurements are regularized by the physics information term in the loss function. We benchmark our model by comparing it with an older method. The new method we present will be of broad interest to experiments requiring precise determination of magnetic field components, such as searches for the neutron electric dipole moment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗