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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Feasibility of Formulating Ecosystem Biogeochemical Models From Established Physical Rules

Abstract To improve the predictive capability of ecosystem biogeochemical models (EBMs), we discuss the feasibility of formulating biogeochemical processes using physical rules that have underpinned the many successes in computational physics and chemistry. We argue that the currently popular empirically based approaches, such as multiplicative empirical response functions and the law of the minimum, will not lead to EBM formulations that can be continuously refined to incorporate improved mechanistic understanding and empirical observations of biogeochemical processes. Instead, we propose that EBM parameterizations, as a lossy data compression problem, can be better formulated using established physical rules widely used in computational physics and chemistry, and different biogeochemical processes can be more robustly integrated within a reactive‐transport framework. Through several examples, we demonstrate how mathematical representations derived from physical rules can improve understanding of relevant biogeochemical processes and enable more effective communication between modelers, observationalists, and experimentalists regarding essential questions, such as what measurements are needed to meaningfully inform models and how can models generate new process‐level hypotheses to test in empirical studies. Finally, while empirical models with more parameters are often less robust, physical rules‐based models can be more robust and show lower predictive equifinality, stemming from their enhanced consistency in representations of processes, interactions and spatial scaling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

First Principle based Thermal Conductivity Modeling of Metal Alloys with Applications on Uranium Alloys

Thermal conductivity is an important materials property related to heat transport, which is essential to many applications, ranging from thermoelectrics to nuclear reactor materials. High- quality thermal conductivity data is critical to these materials and their associated technologies, including the nuclear fuel materials like traditional oxide fuels and metallic uranium (U) fuels, e.g. U-Zr and U-Mo alloys. And thermal conductivity modeling is widely used to interpolate or extrapolate experimental data, to obtain high-quality thermal conductivity data over wide temperature and composition range, and to understand the impacts of different factors including defects and microstructures. However, a general thermal conductivity model for metal alloys which can work regrading different phase components is still missing. Also, for U alloys, a thermal conductivity model working with different phases is needed. Therefore, in this work, we developed thermal conductivity models for metal alloys, based on ab-initio calculations, semi-classical physics rules, and limited experimental data. The goals of these models are to help obtain high- quality thermal conductivity data within a little experimental input as possible, have models that are extendable to varying microstructures, including different types of irradiation effects, and provide mechanistic understanding of heat transfer in the modeled alloys. In this work, our model solves several challenges in the development. The DFT-BTE approach is applied to decrease the reliance on experimental data and make the model extendable to composition changes and defects. A practical and efficient way to combine the DFT inputs with physics rules is pointed out in this work too. A staged approach, which starts from simple cases of elemental metals, then extends to solid solutions, different compound phases, and multi-phase mixtures, is presented to work with the metal alloys in different phase components. Our models are demonstrated respectively on aU for elemental metal model, on U-Zr and U-Mo alloys in aU temperature range for multi-phase mixture model, on high-temperature U-Zr, U-Nb, and U-Mo alloys in the body-centered cubic phase for concentrated solid solution model, and on irradiated U-Mo alloys for irradiated metal alloy model. All models show great agreement with experimental data. In these demonstrations, our model shows its advantages compared to previous empirical fitting model. Our model requires fewer experimental data, due to the inputs from DFT. The quantitative insights into the different physical factors are provided in our model, as it incorporates the electron and phonon scattering mechanisms. Our model also can be extended to incorporate the contributions of point defects, grain boundaries, and noble gas bubbles, to integrate their effects on heat transfer. This model can serve as both a foundation for understanding the more complex thermal conductivity of realistic U alloy fuels and a useful tool to guide further modeling of thermal conductivity to aid materials and device design.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Monotonic Gaussian Process for Physics-Constrained Machine Learning With Materials Science Applications

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data are scarce and noisy, and monotonicity is supported by strong physical evidence.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Multi‐Scale Model‐Informed Deep Learning for Plasma‐Nanoparticle Interaction

The Overarching Goal of this proposed research is to understand and quantitively determine the interactions between non-thermal plasma (hot electrons, reactive radicals, vibrationally excited species) and surface reactions on influencing the activity and selectivity of the desired reactions via developing multi-scale model informed deep learning algorithm. Investigating non-thermal plasma-surface interaction is feasible due to the low bulk temperature in the discharge region. To investigate the role of plasma-nanoparticle interaction on enhancing the reaction kinetics, we will focus on ammonia cracking to generate clean hydrogen over earth-abundant, non-critical metallic nanoparticles, which is of great significance for decarbonization. We hypothesize that (1) reactive radicals interacting with surface reaction species via Eley–Rideal mechanism will significantly lower the energetics of the potential rate-limiting step of nitrogen formation; (2) the surface will be charged heterogeneously under non-thermal plasma conditions and the charged site will lower the energetics of ammonia cracking through Langmuir– Hinshelwood mechanism; (3) vibrationally excited ammonia will further promote the initial N-H bond cleavage. To access the hypothesis, we will (1) reveal the surface charge effects on tunning the reaction energetics via interpretable, physics-informed deep learning accelerated density functional theory (DFT) calculations; (2) determine the reactive radicals interacting with surface reaction species on tuning the reaction energetics via DFT; (3) reveal the surface charge effects on tunning the reaction energetics via DFT and deep learning models, (4) quantify how vibrationally excited species, reactive radicals, and surface charging effects on enhancing the catalysis via developing DFT-based microkinetic modeling (MKM) and active learning. Deep and active learning of plasma-nanoparticle interactions effects on enhancing ammonia cracking to generate hydrogen represents a new paradigm for designing high performance plasma materials. The fundamental science of how plasma-nanoparticle interactions will change the plasma kinetics and will improve the energy efficiency for decarbonization and sustainability. The interpretable and physics-informed machine learning model will accelerate low temperature plasma chemistry and material discovery with physics rules and model interpretation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Interpreting the spatial distribution of soil properties with a physically-based distributed hydrological model

Digital soil maps are commonly data-driven as the development of physically-based models for soil mapping is difficult due to the complexity of soils. However, physically-based hydrologic models have been successful in simulating water dynamics. Since water movement is a major driver of pedogenesis, the physical rules that govern water movement might help explain and predict the spatial variation of soil properties. Here, we demonstrate the novel use of a physically-based, distributed hydrologic model to inform the spatial distribution of soil properties. The Distributed Hydrology Soil Vegetation Model (DHSVM) was utilized to simulate soil moisture content (SM) and water table depth (WTD) in two hillslope catchments under pasture and forest management wherein hydrologic model outputs were then compared with soil properties measured in situ. SM sensors and wells were installed in both catchments to validate simulations of soil water movement via Nash-Sutcliffe Efficiency (E). In-situ observations were made at 87 sites within both catchments to study the connection between simulated water movement (SM and WTD) and observed soil properties, namely the depth and thickness of the argillic (Bt), fragic (Btx), and C horizons, and the depth of redoximorphic features. The simulated time series of SM and WTD were also clustered per season using Dynamic Time Warping (DTW), which identified similarity among time series at varying timescales. Model validation suggested that simulations of surficial SM (0–20 cm) were reasonable (E = 0.45), however, simulated subsurface SM (45–60 cm) and WTD were not sufficiently accurate. The thickness of Btx horizons were spatially grouped into different populations by SM clusters from every season except spring. For the other properties, only SM dynamics of specific seasons grouped into significantly different populations, suggesting that the explanatory power of simulated water movement varies seasonally and was greater during winter. Here, we show clusters of simulated SM separated soil properties into statistically different populations, showing that hydrologic models could inform areas that followed different water dynamics related to pedogenic trajectories and related biogeochemical processes not necessarily simulated by the model. As such, physically-based modeling of water dynamics can, therefore, inform and advance digital soil mapping by linking water movement patterns stemming from hydrologic model outputs to spatial patterns of soil properties and pedogenesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2025

Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

Abstract We evaluate the ability of machine learning to predict whether a hypothetical crystal structure can be synthesized and explain those predictions to scientists. Fine‐tuned large language models (LLMs) trained on a human‐readable text description of the target crystal structure perform comparably to previous bespoke convolutional graph neural network methods, but better prediction quality can be achieved by training a positive‐unlabeled learning model on a text‐embedding representation of the structure. An LLM‐based workflow can then be used to generate human‐readable explanations for the types of factors governing synthesizability, extract the underlying physical rules, and assess the veracity of those rules. These explanations can guide chemists in modifying or optimizing non‐synthesizable hypothetical structures to make them more feasible for materials design.

Kim, Seongmin [Department of Chemical and Biologic↗
DOE OSTI2020

Inverse design of two-dimensional graphene/h-BN hybrids by a regressional and conditional GAN

Design of materials with desired properties is currently laborious and heavily relies on intuition of researchers through a trial-and-error process. To tackle this challenge, in this work we propose a novel regressional and conditional generative adversarial network (RCGAN) for inverse design of representative two-dimensional materials, the graphene and boron-nitride (BN) hybrids. RCGAN incorporates a supervised regressor network, thus overcoming the common technical barrier in the traditional unsupervised GANs, which cannot generate data when fed with continuous and quantitative labels. RCGAN can autonomously generate graphene/BN hybrids given any target bandgap values. These structures are distinguished from the ones used for training and exhibit high diversity for a given bandgap. Moreover, they exhibit high fidelity, yielding bandgaps within ~10% MAE F of the desired bandgaps as validated by density functional theory (DFT) calculations. Analysis by the principle component analysis (PCA) and modified locally linear embedding (MLLE) reveals that the generator has successfully generated structures following the statistical distribution of the real structures. It implies the possibility of the RCGAN in recognizing physical rules hidden in the high-dimensional data. The novel strategy for designing regressional GAN architecture together with the successful application to inverse design of materials would inspire further exploration in research fields beyond materials.

36 MATERIALS SCIENCE↗
NASA NTRS1995

EUVE observations of Algol: Detection of a continuum and implications for the coronal (Fe/H) abundance

We report results from the first extreme ultraviolet spectrum of the prototypical eclipsing binary Algol (beta Per), obtained with the spectrometers on the Extreme Ultraviolet Explorer (EUVE). The Algol spectrum in the 80-350 A range is dominated by emission lines of Fe XVI-XXIV, and the He II 304 A line. The Fe emission is characteristic of high-temperature plasma at temperatures up to at least log T approximately 7.3 K. We have successfully modeled the observed quiescent spectrum using a continuous emission measure distribution with the bulk of the emitting material at log T greater than 6.5. We are able to adequately fit both the coronal lines and continuum data with a cosmic abundance plasma, but only if Algol's quiescent corona is dominated by material at log T greater than 7.5, which is physically ruled out by prior X-ray observations of the quiescent Algol spectrum. Since the coronal (Fe/H) abundance is the principal determinant of the line-to-continuum ratio in the EUV, allowing the abundance to be a free parameter results in models with a range of best-fit abundances approximately = 15%-40% of solar photospheric (Fe/H). Since Algol's photospheric (Fe/H) appears to be near-solar, the anomalous EUV line-to-continuum ratio could either be the result of element segregation in the coronal formation process, or other, less likely mechanisms that may enhance the continuum with respect to the lines.

Stern, Robert A.↗
NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center is developing a pre-Phase A tool called "Trade-space Analysis Tool for Constellations" (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper focuses on the tradespace search and how it can be streamlined by combining physical rules, as well as well-designed orbit and coverage computations, thus yielding significant speed-ups. Two use cases are shown as representative examples of the utility of TAT-C generated trades, and results are preliminarily validated against AGI's Systems Tool Kit.

Science Data Processing↗
DOE OSTI2026

Perspective of Fermi’s golden rule and its generalizations in chemical physics

This perspective provides a succinct history of Fermi’s golden rule (FGR), an overview of its derivation, assumptions, and representative forms. Major applications of FGR, mostly in the field of chemical physics, are reviewed. These illustrate the broad applicability and success of FGR. Ambiguities and open issues encountered in practical applications of FGR are clarified. Recent advances in generalizations of FGR and computational methods for practical applications are addressed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1988

Limitations of the semirelativistic approach in sum rules and related calculations in atomic physics

The semirelativistic approach, as applied to sum-rule calculations, can yield very inaccurate results for high-Z systems; the apparently good agreement previously reported is fortuitous, due to the application of an approximate eigenstate of the semirelativistic Hamiltonian. Attention is given to one-electron atomic systems, and the results obtained are discussed with a view to their implications for many-electron systems. The many-electron problem is complicated by a lack of clarity as to the relative importance of contributions due to correlation effects among the electrons, their effect on the E2 oscillator strength, and the retardation correction to the E1 transition.

Rustgi, M. L.↗
DOE OSTI2022

Thermal Conductivity of BAs under Pressure

Abstract The thermal conductivity of boron arsenide (BAs) is believed to be influenced by phonon scattering selection rules due to its special phonon dispersion. Compression of BAs leads to significant changes in phonon dispersion, which allows for a test of first principles theories for how phonon dispersion affects three‐ and four‐phonon scattering rates. This study reports the thermal conductivity of BAs from 0 to 30 GPa. Thermal conductivity vs. pressure of BAs is measured by time‐domain thermoreflectance with a diamond anvil cell. In stark contrast to what is typical for nonmetallic crystals, BAs is observed to have a pressure independent thermal conductivity below 30 GPa. The thermal conductivity of nonmetallic crystals typically increases upon compression. The unusual pressure independence of BAs's thermal conductivity shows the important relationship between phonon dispersion properties and three‐ and four‐phonon scattering rates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Using statistical modeling to predict and understand fusion experiments

Over 300 cryogenic layered direct-drive inertial confinement fusion implosions have been successfully executed on the OMEGA Laser System in the last decade. However, extracting sufficient understanding from these experiments to develop new designs or to identify or mitigate degradation sources remains challenging. Recently, a statistical modeling approach was developed to successfully design and predict improved implosion experiments on OMEGA. Here, we show that one-dimensional simulations can be used to predict the outcomes of systematically perturbed three-dimensional simulations and that this statistical modeling approach can be used to identify or rule out physical mechanisms for some of the degradation sources observed on the OMEGA Laser System for direct-drive cryogenic inertial confinement fusion. In this instance, we investigate the fusion yield dependencies on the ion temperature asymmetries and laser beam size observed in experiments by comparing with trends in three-dimensional synthetic simulation databases. Using the statistical model on these systematically perturbed simulations, we find that the statistically inferred dependency on the measured ion temperature asymmetries is well explained by imposed ℓ =1 modes. However, we find that the dependency on the laser beam size is only dominated by the illumination non-uniformity for some extreme cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1989

Angular motion of a PAH molecule in interstellar environment

Polycyclic aromatic hydrocarbon (PAH) molecules have recently been proposed as an important and hitherto undetected component of the Interstellar Medium (ISM). The theory was based on an explanation of the Unidentified IR Emission Bands by Leger et al. It has already led to a verified prediction on extended galactic and extragalactic emissions measured by IRAS, or by a recent balloon borne experiment. The physics that rules the motion of such molecules in the ISM was studied, taking into account their coupling with the ambient gas, the radiation field (absorption and emission) and the static magnetic field. This is important for many implications of the PAH theory such as the radio emission by these molecules or the expected polarization of their IR emission. A reflection nebulae is considered where the situation is rather well known. Every day life of a mean PAH molecule in such a region is as follows: every 3 hrs a UV photon is absorbed heating the molecule to a thousand degs; the temperature decay due to cooling by IR emission follows then within a few seconds. A collision with a molecule of gas occurs typically once a week, while an H atom is ejected or captured at the same rate. A typical cooling cycle after a heat impulse is given. The PAH molecules studied as representative of the family has typically 50 atoms, a radius of 4.5 A, is circular and has a molecular mass of M = 300; its permanent dipole moment is 3 Debye.

Rouan, D.↗
NASA NTRS1993

The case of missing CO-13 in mergers

We present a comparison of the CO-13 and CO-12 emissions of six systems of merging galaxies: NGC 828, NGC 3256, NGC 4194, NGC 6240, Arp 220, and Arp 299. The observations were made in both J=1-0 and J=2-1 transitions with the IRAM 30 m and SEST 15 m telescopes. In all galaxies but NGC 828, the CO-13 is much weaker than in spiral galaxies. The average emissivity ratios measured at the few kiloparsec scale are: CO-12(1-0)/CO-13(1-0) approx. equals 30, CO-12(2-1)/CO-13(2-1) approx. equals 40. These values are significantly larger than those usually measured in normal spiral galaxies, which are always between 5 and 15 for the J=1-0 line. We show that such a peculiar behavior cannot be interpreted as due to the dominant presence of diffuse gas and it cannot be attributed to optically thin CO emission either. The faint CO-13 emission of mergers must result from either an underabundance of CO-13 or an overabundance of CO-12. They may be accounted for by different mechanisms: CO-13 molecules are more easily photodissociated than CO-12 ones - however our observations seem to rule out physical conditions characteristic of photodissociation regions; in the course of the merging, large amounts of unprocessed gas, with a high (CO-12/CO-13) abundance ratio, are driven from the external regions of the progenitor galaxies to the new nucleus - this could increase the (CO-12/CO-13) ratio by a factor of 2; the interstellar medium can also be quickly enriched in C-12 (thus in CO-12) by a factor of 2, due to selective nucleosynthesis of this isotope (vs. C-13) in the massive stars born during the starburst. The last two processes can thus significantly contribute to the weakness of the CO-13 lines. The high CO-13-to-CO-12 line ratios that we have measured are due to the deep transformations that take place in the interstellar medium during the merging and the starburst: indeed, the only object with nearly normal line ratios, NGC 828, appears less perturbed and active in star formation than the other sources in the sample. In most mergers, the faintness of the CO-13 emission may indicate that the conversion factor from CO-12 emissivities to H2 column densities could differ substantially from the standard galactic value.

Casoli, Fabienne↗
NASA NTRS2010

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media.

Hamilton, Paul Lawrence↗
NASA NTRS2019

CMB-HD: an Ultra-Deep, High-Resolution Millimeter-Wave Survey over Half the Sky

A millimeter-wave survey over half the sky, that spans frequencies in the range of 30 to 350 gigahertz, and that is both an order of magnitude deeper and of higher-resolution than currently funded surveys would yield an enormous gain in understanding of both fundamental physics and astrophysics. By providing such a deep, high-resolution millimeter-wave survey (about 0.5 microK-arcminutes noise and 15 arcseconds resolution at 150 gigahertz), CMB-HD (Cosmic Microwave Background - Henry Draper catalog entry) will enable major advances. It will allow 1) the use of gravitational lensing of the primordial microwave background to map the distribution of matter on small scales (k approximately equal to 10 h per megaparsec), which probes dark matter particle properties. It will also allow 2) measurements of the thermal and kinetic Sunyaev-Zel’dovich effects on small scales to map the gas density and gas pressure profiles of halos over a wide field, which probes galaxy evolution and cluster astrophysics. In addition, CMB-HD would allow us to cross critical thresholds in fundamental physics: 3) ruling out or detecting any new, light (less than 0.1 electronvolts), thermal particles, which could potentially be the dark matter, and 4) testing a wide class of multi-field models that could explain an epoch of inflation in the early Universe. Such a survey would also 5) monitor the transient sky by mapping the full observing region every few days, which opens a new window on gamma-ray bursts, novae, fast radio bursts, and variable active galactic nuclei. Moreover, CMB-HD would 6) provide a census of planets, dwarf planets, and asteroids in the outer Solar System, and 7) enable the detection of exo-Oort clouds around other solar systems, shedding light on planet formation. The combination of CMB-HD with contemporary ground and space-based experiments will also provide powerful synergies. CMB-HD will deliver this survey in 5 years of observing 20,000 square degrees, using two new 30-meter-class off-axis cross-Dragone telescopes to be located at Cerro Toco in the Atacama Desert. The telescopes will field about 2.4 million detectors (600,000 pixels) in total. The CMB-HD survey will be made publicly available, with usability and accessibility a priority.

Sehgal, Neelima↗