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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Geochemical constraints on chemolithoautotrophic reactions in hydrothermal systems

Thermodynamic calculations provide the means to quantify the chemical disequilibrium inherent in the mixing of reduced hydrothermal fluids with seawater. The chemical energy available for metabolic processes in these environments can be evaluated by taking into account the pressure and temperature dependence of the apparent standard Gibbs free energies of reactions in the S-H2-H2O system together with geochemical constraints on pH, activities of aqueous sulfur species and fugacities of H2 and/or O2. Using present-day mixing of hydrothermal fluids and seawater as a starting point, it is shown that each mole of H2S entering seawater from hydrothermal fluids represents about 200,000 calories of chemical energy for metabolic systems able to catalyze H2S oxidation. Extrapolating to the early Earth, which was likely to have had an atmosphere more reduced than at present, shows that this chemical energy may have been a factor of two or so less. Nevertheless, mixing of hydrothermal fluids with seawater would have been an abundant source of chemical energy, and an inevitable consequence of the presence of an ocean on an initially hot Earth. The amount of energy available was more than enough for organic synthesis from CO2 or CO, and/or polymer formation, indicating that the vicinity of hydrothermal systems at the sea floor was an ideal location for the emergence of the first chemolithoautotrophic metabolic systems.

Shock, Everett L.↗
NASA NTRS1989

Tank Pressure Control Experiment on the Space Shuttle

The tank pressure control experiment is a demonstration of NASA intent to develop new technology for low-gravity management of the cryogenic fluids that will be required for future space systems. The experiment will use freon as the test fluid to measure the effects of jet-induced fluid mixing on storage tank pressure and will produce data on low-gravity mixing processes critical to the design of on-orbit cryogenic storage and resupply systems. Basic data on fluid motion and thermodynamics in low gravity is limited, but such data is critical to the development of space transfer vehicles and spacecraft resupply facilities. An in-space experiment is needed to obtain reliable data on fluid mixing and pressure control because none of the available microgravity test facilities provide a low enough gravity level for a sufficient duration to duplicate in-space flow patterns and thermal processes. Normal gravity tests do not represent the fluid behavior properly; drop-tower tests are limited in length of time available; aircraft low-gravity tests cannot provide the steady near-zero gravity level and long duration needed to study the subtle processes expected in space.

Source record↗
DOE OSTI2025

A hybrid calorimetry-simulation model of mixing enthalpy for molten salt

Calorimetric determination of enthalpies of mixing (ΔH mix ) in multicomponent molten salts is often interpreted using empirical models that lack physically meaningful parameters. However, for improving pyrochemical separation of spent nuclear fuel, where lanthanides are major fission products and critical elements, a deeper thermodynamic understanding of the link between excess thermodynamic properties and solvation structure is critically needed. In this work, we implement a hybrid and physics-informed framework, MIVM+Calorimetry+AIMD, which integrates experimentally measured ΔH mix (via high temperature drop calorimetry) with solvation structures from ab initio molecular dynamics (AIMD). This approach is demonstrated using LaCl 3 mixed with eutectic LiCl-KCl (58 mol% – 42 mol%) at 873 K and 1133 K. MIVM-derived parameters enable extrapolation of excess Gibbs energy and La 3+ activity across compositions. In contrast, direct ΔH mix predictions from AIMD and polarizable ion model simulations deviate significantly. By incorporating experimentally benchmarked solvation structures into an interpretable thermodynamic model, the MIVM+Calorimetry+AIMD formalism achieves higher accuracy and generalizable method for studying molten salts, offering a robust path for understanding and optimizing molten salt chemistry relevant to nuclear fuel cycles and separation science.

Goncharov, Vitaliy G. [Washington State Univ., Pul↗
DOE OSTI2025

Thermodynamics of continuous spin photons

Special relativity allows massless particles to have states of different integer (or half-integer) helicities that mix under boosts, much like the spin states of a massive particle. Such massless particles are known as “continuous spin” particles (CSPs), a term coined by Wigner, and they are notable for their infinite tower of spin polarizations. The mixing under boosts is controlled by a spin scale 𝜌 with units of momentum. Normally, we assume 𝜌 = 0. The interactions of CSPs are known to satisfy certain simple properties, one of which is that the 𝜌 → 0 limit generically recovers familiar interactions of massless scalars, photons, or gravitons, with all other polarizations decoupling in this limit. Thus, one can ask if the photon of the Standard Model is a CSP at small but nonzero 𝜌. One concern about this possibility—originally raised by Wigner—is that the infinite tower of polarizations could pose problems for thermodynamics. To address this question, we study the thermal evolution of a CSP photon gas coupled to isothermal matter, across CSP helicity modes and phase space. We find that the structure of the interactions dictated by Lorentz symmetry implies well-behaved thermodynamics. When the CSP photon’s interactions with charged matter are turned on, the primary ℎ =±1 helicity modes thermalize quickly, while the other modes require increasingly long time scales to thermalize, set by powers of 𝑇/𝜌. In familiar thermal systems, the CSP photon behaves like the QED photon with small 𝜌- and time-dependent corrections to its effective relativistic degrees of freedom. Sizable departures from familiar thermal behavior arise at energy scales comparable to 𝜌 and could have testable experimental consequences.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Circuit complexity and functionality: A statistical thermodynamics perspective

Circuit complexity, defined as the minimum circuit size required for implementing a particular Boolean computation, is a foundational concept in computer science. Determining circuit complexity is believed to be a hard computational problem. Recently, in the context of black holes, circuit complexity has been promoted to a physical property, wherein the growth of complexity is reflected in the time evolution of the Einstein-Rosen bridge (“wormhole”) connecting the two sides of an anti-de Sitter “eternal” black hole. Here, we are motivated by an independent set of considerations and explore links between complexity and thermodynamics for functionally equivalent circuits, making the physics-inspired approach relevant to real computational problems, for which functionality is the key element of interest. In particular, our thermodynamic framework provides an alternative perspective on the obfuscation of programs of arbitrary length—an important problem in cryptography—as thermalization through recursive mixing of neighboring sections of a circuit, which can be viewed as the mixing of two containers with “gases of gates.” This recursive process equilibrates the average complexity and leads to the saturation of the circuit entropy, while preserving functionality of the overall circuit. The thermodynamic arguments hinge on ergodicity in the space of circuits which we conjecture is limited to disconnected ergodic sectors due to fragmentation. The notion of fragmentation has important implications for the problem of circuit obfuscation as it implies that there are circuits of same size and functionality that cannot be connected via a polynomial number of local moves. Furthermore, we argue that fragmentation is unavoidable unless the complexity classes NP and coNP coincide, a statement that implies the collapse of the polynomial hierarchy of computational complexity theory to its first level.

Science & Technology - Other Topics↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
DOE OSTI2025

Thermodynamic Cloud Phase Classifications Using Machine Learning at NSA and ANX

Vertically resolved thermodynamic cloud phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) THERMOCLDPHASE Value-Added Product (VAP) uses a multi-sensor approach to classify thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave radiometer-derived liquid water path, and radiosonde temperature measurements. The measured voxels are classified as ice, snow, mixed-phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multilayer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with one year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1-score, and mean Intersection over Union (IOU). Analysis of ML confidence scores shows ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential datastreams for ML thermodynamic cloud phase predictions. The ML models’ generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. Finally, we evaluate the ML models’ response to simulated instrument outages and signal degradation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1987

Mixing-induced fluid destratification and ullage condensation

In many applications, on-orbit storage and transfer of cryogens will require forced mixing to control tank pressure without direct venting to space. During a no-vent transfer or during operation of a thermodynamic vent system in a cryogen storage tank, pressure control is achieved by circulating cool liquid to the liquid-vapor interface to condense some of the ullage vapor. To measure the pressure and temperature response rates in mixing-induced condensation, an experiment has been developed using Freon 11 to simulate the two-phase behavior of a cryogen. A thin layer at the liquid surface is heated to raise the tank pressure, and then a jet mixer is turned on to circulate the liquid, cool the surface, and reduce the pressure. Many nozzle configurations and flow rates are used. Tank pressure and the temperature profiles in the ullage and the liquid are measured. Initial data from this ground test are shown correlated with normal-gravity and drop-tower dye-mixing data. Pressure collapse times are comparable to the dye-mixing times, whereas the times needed for complete thermal mixing are much longer than the dye-mixing times.

Meserole, Jere S.↗
DOE OSTI2025

The Importance of an Ensemble Approach for Modeling Aerosol‐Convection Interactions

Aerosol-convection interactions modulate cloud microphysics, thermodynamics, and updraft intensity, contributing to climate-scale aerosol-radiative forcing. However, quantifying aerosol indirect effects in mixed-phase deep convection remains challenging due to uncertainties in parameterized physics and initial conditions driving nonlinear evolution of convective processes. This study investigates the convective updraft sensitivity to random initial temperature perturbations using an idealized ensemble modeling framework informed by in situ thermodynamic and aerosol observations from the DOE TRACER field campaign. We analyze the impact of small-scale initial perturbations on updraft velocity and supersaturation, and determine the ensemble size required to minimize stochastic internal variability. Results show that minor thermodynamic perturbations can produce updraft variability comparable to aerosol-induced changes reported in prior work. An ensemble of 10 members sufficiently reduces variability, enabling robust investigation of aerosol-related updraft invigoration and informing the design of future ensemble-based aerosol-convection interaction studies in environments with significant spatiotemporal mesoscale thermodynamic and aerosol heterogeneity.

aerosol-convection interactions↗
DOE OSTI2022

Regimes of evaporation and mixing behaviors of nanodroplets at transcritical conditions

The objective of this paper is to examine the fundamental mechanisms responsible for the transition between subcritical evaporation and supercritical dense-fluid-mixing in the absence of convection effects, specifically focusing on the liquid–vapor interfacial dynamics. To isolate the dynamics of this transition process, we characterize the different physical behaviors exhibited by an -dodecane nanoscale droplet placed in different nitrogen ambient conditions across the fuel’s critical point. We employ a continuum-based interface-resolving diffuse-interface method to explore the underlying phase-exchange mechanisms that bring about such distinct dynamics. Following the comparison against molecular dynamics simulations and experiments of evaporating droplets and experimental data for vapor–liquid equilibria, a parametric study at various ambient conditions and droplet sizing is performed to identify four regimes of evaporation/mixing behaviors: sub- and supercritical droplet evaporation, and sub- and supercritical dense-fluid-mixing. It is shown that the distinction in the phase-exchange mechanisms in these four regimes are brought about by the different thermodynamic phases the droplet center can exhibit during the evaporation/mixing process: subcritical liquid, supercritical liquid-like, subcritical gaseous, and supercritical gas-like, respectively. It is shown that the subcritical dense-fluid-mixing behavior is a direct result of nanoconfinement of the liquid–vapor interfacial structure and thus is not present for large droplet sizes. Finally, the present study also shows that the supercritical phase-exchange dynamics can follow two different pathways: supercritical droplet-like evaporation and supercritical dense-fluid-mixing. Furthermore, promoting the early transition to supercritical dense-fluid-mixing can significantly expedite the phase-exchange process through the disintegration of the liquid-like droplet core.

42 ENGINEERING↗
NASA NTRS1986

Prediction of heat release effects on a mixing layer

A fully second-order closure model for turbulent reacting flows is suggested based on Favre statistics. For diffusion flames the local thermodynamic state is related to single conserved scalar. The properties of pressure fluctuations are analyzed for turbulent flows with fluctuating density. Closure models for pressure correlations are discussed and modeled transport equations for Reynolds stresses, turbulent kinetic energy dissipation, density-velocity correlations, scalar moments and dissipation are presented and solved, together with the mean equations for momentum and mixture fraction. Solutions of these equations are compared with the experimental data for high heat release free mixing layers of fluorine and hydrogen in a nitrogen diluent.

Farshchi, M.↗
NASA NTRS1986

Prediction of heat release effects on a mixing layer

A fully second-order closure model for turbulent reacting flows is suggested based on Favre statistics. For diffusion flames the local thermodynamic state is related to a single conserved scalar. The properties of pressure fluctuations are analyzed for turbulent flows with fluctuating density. Closure models for pressure correlations are discussed and modelled transport equations for Reynolds stresses, turbulent kinetic energy dissipation, density-velocity correlations, scalar moments and dissipation are presented and solved, together with the mean equations for momentum and mixture fraction. Solutions of these equations are compared with the experimental data for high heat release free mixing layers of fluorine and hydrogen in a nitrogen diluent.

Farshchi, M.↗
DOE OSTI2024

Chemistry Informed Machine Learning-Based Heat Capacity Prediction of Solid Mixed Oxides

Knowing heat capacity is crucial for modeling temperature changes with the absorption and release of heat and for calculating the thermal energy storage capacity of oxide mixtures with energy applications. The current prediction methods (ab initio simulations, computational thermodynamics, and the Neumann–Kopp rule) are computationally expensive, not fully generalizable, or inaccurate. Machine learning has the potential of being fast, accurate, and generalizable, but it has been scarcely used to predict mixture properties, particularly for mixed oxides. Here, we demonstrate a method for the generalizable prediction of heat capacity of solid oxide pseudobinary mixtures using heat capacity data obtained from computational thermodynamics and descriptors from ab initio databases. Further, models trained through this workflow achieved an error (mean absolute error of 0.43 J mol –1 K –1 ) lower than the uncertainty in differential scanning calorimetry measurements, and the workflow can be extended to predict other properties derived from the Gibbs free energy and for higher-order oxide mixtures.

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

Additive Manufacturing of Pure Mo and Mo + TiC MMC Alloy by Electron Beam Powder Bed Fusion

A metal matrix composite powder of molybdenum (Mo) + TiC was produced by mechanical alloying (MA) and used in additive manufacturing by electron beam powder bed fusion along with pure Mo powder to form sandwich structures. The Mo + TiC solid layers formed mixed structures of Mo with discrete TiC particles, eutectic Mo + TiC, and Mo dendrites. Thermodynamic modeling showed that the system contained an invariant eutectic reaction in the composition range used and indicated that the system was highly sensitive to changes in composition and temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Navigating thermal stability intricacies of high-nickel cathodes for high-energy lithium batteries

High-nickel oxide cathodes, LiNi x M 1−x O 2 (x ≥ 0.8), are preferred in automotive lithium batteries, but they face thermal instability challenges. Inconsistent literature reports and unstandardized testing protocols further complicate quantitative assessments of the thermal stability of these cathodes. Here, we present here a statistical thermal analysis based on the differential scanning calorimetry measurements of 15 representative cathode materials with different compositions, morphologies and states of charge. The findings reveal that each cathode has a critical state of charge that defines its safe operating limit, which is affected by the metal–oxygen bond strength and surface reactivity. The thermal runaway temperature is dictated by the layered Li 1−x NiO 2 to LiNi 2 O 4 spinel-like phase transition, which is thermodynamically determined by the metal–oxygen bond covalency and kinetically influenced by the cation mixing and particle size. Raman spectroscopy is used to predict the thermal runaway temperature on the basis of the linear relationship between them. Finally, we propose a thermal stability index to quantify cathode thermal stability as a guide for developing safer high-nickel cathodes.

batteries↗
NASA NTRS1983

Water and acid soluble trace metals in atmospheric particles

Continental aerosols are collected above a deciduous forest in eastern Tennessee and subjected to selective extractions to determine the water-soluble and acid-leachable concentrations of Cd, Mn, Pb, and Zn. The combined contributions of these metals to the total aerosol mass is 0.5 percent, with approximately 70 percent of this attributable to Pb alone. A substantial fraction (approximately 50 percent or more) of the acid-leachable metals is soluble in distilled water. In general, this water-soluble fraction increases with decreasing particle size and with increasing frequency of atmospheric water vapor saturation during the sampling period. The pattern of relative solubilities (Zn being greater than Mn, which is approximately equal to Cd, which is greater than Pb) is found to be similar to the general order of the thermodynamic solubilities of the most probable salts of these elements in continental aerosols with mixed fossil fuel and soil sources.

Lindberg, S. E.↗
DOE OSTI2024

High-Pressure Melting Experiments of Fe 3 C and a Thermodynamic Model of Fe-C Liquids for the Earth's Core

Melting experiments of Fe 3 C were conducted to 85 GPa in laser-heated diamond anvil cells with in situ X-ray diffraction and post-experiment textural observation. From the determined pressure-temperature conditions of the melting curve for Fe 3 C, together with literature data on the melting point of diamond and eutectic point of the system Fe-Fe 3 C/Fe 7 C 3 under high pressures, we established a self-consistent thermodynamic model for high-pressure melting of the system Fe-C including the mixing parameters for liquids. The results show that mixing of Fe and C liquids is negatively nonideal from 1 bar to the pressure at the center of the Earth. The departure from ideal mixing becomes progressively larger with increasing pressure, which leads to greatly stabilized liquids under core pressures. The modeled carbon content in eutectic melts under core pressures is 3.3–4.4 wt%. From the Gibbs free energy, we derived an internally consistent parameters for Fe-C outer cores which included the crystallizing points at their bottoms, isentropic thermal profiles, and densities and longitudinal seismic wave speeds (Vp). While the addition of carbon in excess of the eutectic melt composition effectively reduces the density of iron liquid, the Vp of iron liquid is not greatly changed. Therefore, the low density and high Vp of PREM relative to pure iron cannot be reconciled by an Fe-C liquid. Therefore, the Earth's core cannot be approximated by the system Fe-C and should include another light element.

58 GEOSCIENCES↗
DOE OSTI2021

Thermodynamic non-ideality and disorder heterogeneity in actinide silicate solid solutions

Non-ideal thermodynamics of solid solutions can greatly impact materials degradation behavior. We have investigated an actinide silicate solid solution system (USiO 4 –ThSiO 4 ), demonstrating that thermodynamic non-ideality follows a distinctive, atomic-scale disordering process, which is usually considered as a random distribution. Neutron total scattering implemented by pair distribution function analysis confirmed a random distribution model for U and Th in first three coordination shells; however, a machine-learning algorithm suggested heterogeneous U and Th clusters at nanoscale (~2 nm). The local disorder and nanosized heterogeneous is an example of the non-ideality of mixing that has an electronic origin. Partial covalency from the U/Th 5f–O 2p hybridization promotes electron transfer during mixing and leads to local polyhedral distortions. The electronic origin accounts for the strong non-ideality in thermodynamic parameters that extends the stability field of the actinide silicates in nature and under typical nuclear waste repository conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗