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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Thermodynamic Profiling Through ASSIST Observations and TROPoe Retrievals

This report reviews the most relevant theoretical aspects of thermodynamic profiling techniques based on spectral observations from ASSIST-II infrared radiometers and TROPoe retrievals. The ASSIST+TROPoe system is a cutting-edge remote sensing technology deployed during the AWAKEN and WFIP3 field campaigns to estimate high-frequency profiles of temperature and humidity in the atmosphere. These profiles are highly valuable for characterizing atmospheric stratification, improving wind models, and understanding the impacts of wind plants on the climate. In this document, we discuss the operating principles of ASSIST, the physics of atmospheric infrared radiation, and the mathematical framework and capabilities of TROPoe. Sources of uncertainties in both the instrument and the retrieval method are also thoroughly addressed. This guide is designed to help users of ASSIST, TROPoe, and thermodynamic data in collecting, estimating, and applying thermodynamic profiles rigorously and scientifically.

17 WIND ENERGY↗
DOE OSTI2023

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Thermodynamics of Uranium Tri-Iodide from Density-Functional Theory

Density-functional theory (DFT) is employed to investigate the thermodynamic and ground-state properties of bulk uranium tri-iodide, UI3. The theory is fully relativistic and electron correlations, beyond the DFT and generalized gradient approximation, are addressed with orbital polarization. The electronic structure indicates anti-ferromagnetism, in agreement with neutron diffraction, with band gaps and a non-metallic system. Furthermore, the formation energy, atomic volume, crystal structure, and heat capacity are calculated in reasonable agreement with experiments, whereas for the elastic constants experimental data are unavailable for comparison. The thermodynamical properties are modeled within a quasi-harmonic approximation and the heat capacity and Gibbs free energy as functions of temperature agree with available calculation of phase diagram (CALPHAD) thermodynamic assessment of the experimental data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Some Aspects of the Liquid Water Thermodynamic Behavior: From The Stable to the Deep Supercooled Regime

Liquid water is considered to be a peculiar example of glass forming materials because of the possibility of giving rise to amorphous phases with different densities and of the thermodynamic anomalies that characterize its supercooled liquid phase. In the present work, literature data on the density of bulk liquid water are analyzed in a wide temperature-pressure range, also including the glass phases. A careful data analysis, which was performed on different density isobars, made in terms of thermodynamic response functions, like the thermal expansion αP and the specific heat differences C P -C V , proves, exclusively from the experimental data, the thermodynamic consistence of the liquid-liquid transition hypothesis. The study confirms that supercooled bulk water is a mixture of two liquid “phases”, namely the high density (HDL) and the low density (LDL) liquids that characterize different regions of the water phase diagram. Furthermore, the C P -C V isobars behaviors clearly support the existence of both a liquid–liquid transition and of a liquid–liquid critical point.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Aerosol Thermodynamics (Aerosol Liquid Water Concentration and Aerosol pH) at S2 during CoURAGE

These data report aerosol liquid water content (ALWC) and aerosol pH for the S2 (Mt. Airy) site during CoURAGE for April - June 2025. The ISORROPIA-II aerosol thermodynamic equilibrium model was used to compute ALWC and pH. The model was run in forward mode, with solids formation disabled (metastable mode), according to Pye et al. (Atmospheric Chemistry and Physics, 2020). Information about the model can be found at https://www.epfl.ch/labs/lapi/models-and-software/isorropia/, and in Fountoukis and Nenes (Atmospheric Chemistry and Physics, 2007). Inputs to the model were: (1) measured Temperature and relative humidity, both from the DoE ARM “metwxt” data products at S2, (2) aerosol chemical composition from the ARM ACSM, and (3) gas-phase ammonia concentrations. For ISORROPIA-II, ambient RH values above 0.995 were input to the model as 0.995; therefore, caution should be exercised interpreting ALWC when RH was above 0.995. Aerosol composition or NH3(g) concentrations below the LOD were input into the model as 0.5*LOD. Model runs were only conducted for data points at 30-min resolution with all three inputs (meteorology, ACSM, and NH3). If any of the three model inputs were missing, aerosol thermodynamic outputs are flagged as -999. Similarly, aerosol thermodynamic outputs are flagged as -999 if measured NH3(g) and aerosol ammonium (NH4+) were simultaneously below the respective LODs. The ALWC includes liquid water from inorganic aerosol components, calculated directly by ISORROPIA-II, as well as liquid water from organics, estimated from the ACSM organics assuming a kappa value of 0.12 according to Guo et al. (Atmospheric Chemistry and Physics, 2015).

Aerosol Liquid Water Content↗
NASA NTRS1973

Thermodynamic Properties of Nitrogen Including Liquid and Vapor Phases from 63K to 2000K with Pressures to 10,000 Bar

Tables of thermodynamic properties of nitrogen are presented for the liquid and vapor phases for temperatures from the freezing line to 2000K and pressures to 10,000 bar. The tables include values of density, internal energy, enthalpy, entropy, isochoric heat capacity, isobaric heat capacity velocity of sound, the isotherm derivative, and the isochor derivative. The thermodynamic property tables are based on an equation of state, P=P (p,T), which accurately represents liquid and gaseous nitrogen for the range of pressures and temperatures covered by the tables. Comparisons of property values calculated from the equation of state with measured values for P-p-T, heat capacity, enthalpy, latent heat, and velocity of sound are included to illustrate the agreement between the experimental data and the tables of properties presented here. The coefficients of the equation of state were determined by a weighted least squares fit to selected P-p-T data and, simultaneously, to isochoric heat capacity data determined by corresponding states analysis from oxygen data, and to data which define the phase equilibrium criteria for the saturated liquid and the saturated vapor. The vapor pressure equation, melting curve equation, and an equation to represent the ideal gas heat capacity are also presented. Estimates of the accuracy of the equation of state, the vapor pressure equation, and the ideal gas heat capacity equation are given. The equation of state, derivatives of the equation, and the integral functions for calculating derived thermodynamic properties are included.

Thermodynamic properties↗
NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement–Southern Great Plains Site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to get a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near boundary layer top. We also explore how differences in retrievals may impact results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval showed some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

boundary-layer observation↗
DOE OSTI2021

From classical thermodynamics to phase-field method

Phase-field method is a density-based computational method at the mesoscale for modeling and predicting the temporal microstructure and property evolution during materials processes. The focus of this article is on connecting the most common phase-field equations to the very basic first and second laws of classical thermodynamics through rudimentary irreversible thermodynamics. It briefly discusses the relations of the continuum phase-field equations to their counter parts at the microscopic and atomic levels. It attempts to clarify the contributions of long-range elastic, electrostatic, and magnetic interactions to domain structure evolution during structural, ferroelectric, and ferromagnetic phase transformations by separating order parameter changes due to the presence of quasi-static fields and those arising from phase transformations. A few examples are presented to demonstrate the possibility of employing the phase-field method to provide guidance to designing materials for optimum properties or discovering novel mesoscale phenomena or new materials functionalities. Here, the article ends with a brief perspective on a number of potential future directions on the development and applications of phase-field method beyond its traditional applications to structural alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2024

The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

A comprehensive thermodynamic model for high salinity produced waters

Abstract Reuse of produced waters in oil and gas production is a major concern due to high treatment cost and regulations on disposal in the environment. To support development of separation techniques and process innovations, we report a comprehensive thermodynamic model for the aqueous hexary system of Na + , K + , Mg 2+ , Ca 2+ , Cl − , and SO 4 2− , the major ionic species present in high salinity produced waters. Based on the electrolyte NRTL theory, the model accurately calculates thermodynamic and phase equilibrium properties with two binary interaction parameters per water‐electrolyte pair and electrolyte‐electrolyte pair sharing a common ion. This article presents the methodology to identify the binary interaction parameters from literature data and the model results for wide varieties of thermodynamic and phase equilibrium properties including salt solubility for selected binary, ternary, quaternary, and quinary subsystems. The model is validated with the electrolyte concentrations up to salt saturation and temperatures from 273 to 473 K.

Tanveer, Sheik↗
DOE OSTI2022

Linear relationships for modeling CO 2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way

A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Reduction Thermodynamics of Sr 1– x Ce x MnO 3 and Ce x Sr 2– x MnO 4 Perovskites for Solar Thermochemical Hydrogen Production

Herein, the compositional families Sr 1–x Ce x MnO 3 (SCMX, X = 100x, x = 0.10, 0.20, and 0.30) and Ce x Sr 2–x MnO 4 (CSMX, X = 100x, x = 0.10, 0.20, and 0.30) are studied to determine the effects of perovskite structure and cerium content on thermal reduction thermodynamics and the resulting impact on solar thermochemical hydrogen production (STCH). Relying on thermogravimetric results from oxygen nonstoichiometry experiments, fits for various thermodynamic quantities are produced, including defect-reaction specific enthalpy (Δ H) and entropy (Δ S ), as well as the δ-dependent standard partial molar enthalpy, Δh¯$^{°}_{Ο}$, and entropy Δs¯$^{°}_{Ο}$, of oxygen as a function of composition within these two perovskite families. Here, the results of this thermodynamic study are also discussed in the context of structure and cerium dopant level. Experimental hydrogen production results show that the SCM family produces slightly larger amounts of hydrogen per mole of oxide compared with the CSM family under similar reduction and oxidation temperature conditions, however, a direct correlation between structure, cerium content, and water-splitting capacity could not be discerned.

08 HYDROGEN↗
DOE OSTI2021

Complement to thermodynamics of dyonic Taub-NUT-AdS spacetime

We examine the thermodynamics of Euclidean dyonic Taub-Nut/Bolt-AdS 4 black holes for a variety of horizon geometries to understand how gauge field regularity conditions influence the thermodynamic relations. We find several distinct features that distinguish the NUT-charged case from its dyonic Reissner-Nordstrom counterpart. For the Nut solution, the gauge field vanishes at the horizon and so regularity is ensured. For the Bolt solution we find that the norm of the gauge field is required to vanish at the horizon in order to satisfy both regularity and the first law of thermodynamics. This regularity condition yields a constraint on the electric and magnetic charges and so reduces cohomogeneity of the system; for spherical horizons, the regularity condition removing the Misner string singularity further reduces cohomogeneity. We observe that bolt solutions with increasing electric charge have positive heat capacity, but upon turning on the magnetic charge to make the solution dyonic, we find that the properties of the uncharged one are retained, having both positive and negative heat capacity. We also study the extremal Bolt solution, finding that Misner string disappears at the horizon in the zero temperature limit. We find that the extremal solution has finite-temperature-like behaviour, with the electric potential playing a role similar to temperature.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Bulk entropy is crucial to validate the second law of the extended black hole thermodynamics

The extended black hole thermodynamics in which the cosmological constant plays the role of pressure significantly enriches the phase structure of the theory. In order to understand the extended black hole thermodynamics more precisely, we let the value of the cosmological constant vary dynamically via tunneling from one vacuum to another in a black hole induced vacuum decay. In this process, entropy of the matter/energy released by a black hole is crucial to validate the second law of thermodynamics. In other words, without taking this bulk entropy into account, entropy associated with the black hole and cosmological horizons may not always increase. Since the bulk entropy is not represented by the black hole and the cosmological horizons, this result calls for a more careful interpretation of the holographic principle in which environmental effects are taken into account.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Structure and thermodynamics of calcium rare earth silicate oxyapatites, Ca 2 RE 8 (SiO 4 ) 6 O 2 (RE = Pr, Tb, Ho, Tm)

Calcium rare earth silicate oxyapatites, (Ca 2 RE 8 (SiO 4 ) 6 O 2 ), are of interest as components of glass-ceramic nuclear waste forms. To assess their long-term behavior in a geologic repository, it is essential to determine their structure and thermodynamic stability at relevant conditions. Here, in this work, we performed detailed structural and thermodynamic investigations on Ca 2 Pr 8 (SiO 4 ) 6 O 2 , Ca 2 Tb 8 (SiO 4 ) 6 O 2 , Ca 2 Ho 8 (SiO 4 ) 6 O 2 , and Ca 2 Tm 8 (SiO 4 ) 6 O 2 by high energy synchrotron powder X-ray diffraction combined with Rietveld analysis and high temperature oxide melt drop solution calorimetry. Enthalpies of formation from constituent oxides (ΔH f,ox ) were determined to be -765.1 ± 22.8 kJ/mol for Ca 2 Pr 8 (SiO 4 ) 6 O 2 ; -638.9 ± 20.5 kJ/mol for Ca 2 Tb 8 (SiO 4 ) 6 O 2 ; -643.3 ± 10.3 kJ/mol for Ca 2 Ho 8 (SiO 4 ) 6 O 2 ; and -403.2 ± 5.1 kJ/mol for Ca 2 Tm 8 (SiO 4 ) 6 O 2 . These thermodynamic parameters were used in assessing the thermochemical stability of these phases in the presence of water vapor from room temperature to 600 K, as encountered in the subsurface environments of a geological repository.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Review of computational approaches to predict the thermodynamic stability of inorganic solids

Improvements in the efficiency and availability of quantum chemistry codes, supercomputing centers, and open materials databases have transformed the accessibility of computational materials design approaches. Thermodynamic stability predictions play a central role in the efficacy of these approaches and should be considered carefully. This review covers the fundamentals of calculating thermodynamic stability using first-principles methods. Stability is delineated into two main topics — stability with respect to decomposition into competing phases and stability with respect to phase transition into alternative structures at fixed composition. For each topic, a summary of the state-of-the-art is provided along with a tutorial overview of practical considerations. The application of machine learning to both kinds of stability predictions is also covered. Finally, the limitations of thermodynamic stability predictions are discussed within the context of predicting the synthesizability of materials.

36 MATERIALS SCIENCE↗