WFIP3 - NOAA ship - NREL Thermodynamic profiler (Assist II-12) / Raw Data
This dataset contains raw NREL Thermodynamic profiler (Assist II-12) data from the WFIP3 NOAA ship. The ship's movement is available in a separate dataset.
17 WIND ENERGY↗
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Results for “THERMODYNAMICS”
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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WFIP3 - CACO site - NREL Thermodynamic profiler (Assist II-12) / Processed Data
This dataset contains processed NREL thermodynamic profiler (Assist II-12) data from the WFIP3 CACO site.
17 WIND ENERGY↗
WFIP3 - CACO site - NREL Thermodynamic profiler (Assist II-12) / Raw Data
This dataset contains raw NREL Thermodynamic profiler (Assist II-12) data from the WFIP3 CACO site.
17 WIND ENERGY↗
Learning thermodynamic master equations for open quantum systems
The characterization of Hamiltonians and other components of open quantum dynamical systems plays a crucial role in quantum computing and other applications. Scientific machine learning techniques have been applied to this problem in a variety of ways, including by modeling with deep neural networks. However, the majority of mathematical models describing open quantum systems are linear, and the natural nonlinearities in learnable models have not been incorporated using physical principles. We present a data-driven model for open quantum systems that includes learnable, thermodynamically consistent terms. The trained model is interpretable, as it directly estimates the system Hamiltonian and linear components of coupling to the environment. We validate the model on synthetic two and three-level data, as well as experimental two-level data collected from a quantum device at Lawrence Livermore National Laboratory.
Mathematics and Computing↗
Thermodynamics of Liquid Uranium from Atomistic and Ab Initio Modeling
We present thermodynamic properties for liquid uranium obtained from classical molecular dynamics (MD) simulations and the first-principles theory. The coexisting phases method incorporated within MD modeling defines the melting temperature of uranium in good agreement with the experiment. The calculated melting enthalpy is in agreement with the experimental range. Classical MD simulations show that ionic contribution to the total specific heat of uranium does not depend on temperature. The density of states at the Fermi level, which is a crucial parameter in the determination of the electronic contribution to the total specific heat of liquid uranium, is calculated by ab initio all electron density functional theory (DFT) formalism applied to the atomic configurations generated by classical MD. The calculated specific heat of liquid uranium is compared with the previously calculated specific heat of solid γ-uranium at high temperatures. The liquid uranium cannot be supercooled below T sc ≈ 800 K or approximately about 645 K below the calculated melting point, although, the self-diffusion coefficient approaches zero at T D ≈ 700 K. Uranium metal can be supercooled about 1.5 times more than it can be overheated. The features of the temperature hysteresis are discussed.
36 MATERIALS SCIENCE↗
Thermodynamics of Bimetallic Joints between Titanium and Tantalum Produced by Laser Powder Bed Fusion
Laser powder bed fusion (LBPF) is currently the most mature metal additive manufacturing (AM) technology. While it does not have the same flexibility as directed energy deposition techniques to produce compositional gradients, LPBF can still be used to generate bimetallic parts by depositing one metal on a build plate made of another. Here, in this study, we print combinations of Ti-6Al-4V with Ta and characterize defects that occur at the interface. We use thermodynamic modeling to explain the formation of keyhole porosity and solidification cracks when Ta is built on a Ti baseplate, and the lack of defects when the materials are reversed. By understanding the mechanisms that lead to defect formation, the methodology demonstrated here can be applied to other material systems to efficiently design bimetallic LPBF processes.
36 MATERIALS SCIENCE↗
Thermodynamics-guided machine learning model for predicting convective boundary layer height and its multi-site applicability
Accurate estimation of convective boundary layer height (CBLH) is vital for weather, climate, and air quality modeling. Machine learning (ML) shows promise in CBLH prediction, but input parameter selection often lacks physical grounding, limiting generalizability. This study introduces a novel ML framework for CBLH prediction, integrating thermodynamic constraints and the diurnal CBLH cycle as an implicit physical guide. Boundary layer growth is modeled as driven by surface heat fluxes and atmospheric heat absorption represented with the low tropospheric stability, using the diurnal cycle as input and output. TPOT and AutoKeras are employed to select optimal models, validated against Doppler lidar-derived CBLH data, achieving an R 2 of 0.84 across untrained years. Comparisons of eddy covariance (ECOR) and energy balance Bowen ratio (EBBR) flux measurements show the same prediction capability. Models trained on the ARM SGP C1 site with ECOR data and tested at E37 and E39 yield R 2 values of 0.79 and 0.81, respectively, demonstrating their adaptability. The ML model trained with all sites' data slightly enhances the performance compared with ML models trained over single-site data. The interquartile range for predicted CBLH is consistently narrower than that for DL-derived CBLH, reflecting lower variability in predicted CBLH compared to DL-derived CBLH, which is influenced by additional factors, which are not well represented with the model inputs. The model's generalizability across multiple sites at the ARM SGP site demonstrates its potential for transfer to greater distances, offering a scalable approach for enhancing boundary layer parameterization in atmospheric models.
Chu, Yufei [Stony Brook Univ., NY (United States)]↗
Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models
Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.
54 ENVIRONMENTAL SCIENCES↗
Thermodynamic modeling of CsF with LiF-NaF-KF for molten fluoride-fueled reactors
Gibbs energy models were developed to describe the thermochemical behavior of CsF in molten FLiNaK (46.5LiF-11.5NaF-42KF mol%), a proposed molten salt reactor (MSR) fuel solvent and coolant, as cesium is of concern due to its high radiotoxicity and volatility. Initially, it was necessary to obtain a more accurate Gibbs energy function for CsF which required fitting parameters to reported vapor pressures over condensed phase CsF. The pseudo-binary systems CsF-LiF, CsF-NaF and CsF-KF were then evaluated utilizing phase equilibria and enthalpy of mixing (Δ mix H) values, together with original differential scanning calorimetry (DSC) measurements performed for the CsF-LiF and CsF-KF systems. The CsF-LiF-NaF, CsF-LiF-KF and CsF-NaF-KF pseudo-ternary system representations were obtained by interpolation of the constituent pseudo-binary systems, with DSC measurements performed for the CsF-LiF-NaF system to corroborate the calculated liquidus temperature. Ultimately, the pseudo-ternary systems were interpolated to obtain Gibbs energy models for the pseudo-quaternary CsF-LiF-NaF-KF system, supported by DSC measurements at low CsF compositions (1–10 mol%), yielding computed equilibria and cesium-containing vapor pressures that compare favorably with reported values. In conclusion, the Molten Salt Thermal Properties Database – Thermochemical (MSTDB-TC) was subsequently expanded to include these Gibbs energy models allowing description of the thermochemical behavior of the CsF-LiF-NaF-KF system.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Thermodynamic modeling of countercurrent chemical looping reverse water gas shift process for redox material screening
The reverse water gas shift (RWGS) reaction is a key pathway for CO 2 utilization, particularly within Power-to-X process chains aimed at sustainable fuel and chemical production. Countercurrent chemical looping (CL-RWGS) using non-stoichiometric oxides can overcome equilibrium limitations of conventional RWGS reactors, enabling significantly higher CO 2 conversions. However, modeling the limiting performance of such systems is challenging due to their multiphase nature and coupled spatial and temporal variation in chemical composition. In this work, we present a discretized batch equilibrium model that simulates CL-RWGS reactors as a series of localized equilibrium exchanges between gas and solid elements. The model is numerically stable, computationally efficient, and free of kinetic source terms, making it well-suited for parametric studies and system-level integration. It is validated against established convection–diffusion models and shown to predict reasonable upper bounds on experimental results. Application of the model to a range of oxygen carrier materials identifies cerium–zirconium solid solutions, particularly Ce 0.80 Zr 0.20 O 2 , as a promising class offering superior oxygen storage characteristics compared to state-of-the-art La 0.6 Sr 0.4 FeO 3 . This framework provides a robust platform for materials screening, reactor sizing, and performance optimization in chemical looping systems. The model implementation is available as open-source software to support further research and development.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Thermodynamics, local structure, and transport of protons in triple-conducing oxide, BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ (BCFZY4411)
Triple-conducting oxides (TCOs) are an emerging class of mixed ionic and electronically conducting materials that show great promise for oxygen reduction/oxygen evolution (ORR/OER) electrocatalysis—primarily in high-temperature ceramic electrochemical cells— but also in aqueous alkaline environments. Their high activity is attributed, at least in part, to their ability to incorporate and transport three mobile charge carriers: protons, oxygen vacancies, and electron-holes Despite their promise, fundamental studies of TCOs are challenging, as transport dynamics from three charge carriers cannot be fully disentangled via traditional electrical measurement techniques. Characterizing proton dynamics in TCOs is particularly difficult as protons are generally the minority carrier, and their conduction response is typically obscured by the oxygen vacancies and electron holes. Here, we demonstrate successful isolation of the proton behavior in an archetypal TCO, BaCo 0.4 Fe 0.4 Zr 0.1 Y 0.1 O 3-δ (BCFZY4411), using a combination of non-electrical techniques. We determine proton uptake and oxygen non-stoichiometry (δ) using thermogravimetric analysis (TGA). X-ray absorption near edge structure (XANES) and neutron diffraction (ND) are used to validate the oxidation state of Co and the δ values obtained through TGA. We apply 1H solid-state magic-angle-spinning (MAS) nuclear magnetic resonance (NMR) to provide insights into local structure, dynamics, and proton kinetics. Finally, the proton transport properties are further quantified using tracer isotope exchange with time-of-flight secondary ion mass spectrometry (ToF-SIMS). Despite the very low proton concentrations in BCFZY4411 (<0.2% under most conditions), our analysis suggests that the oxygen Manuscript File Click here to view linked References 2 reduction and evolution reactions are nevertheless limited by the oxygen ion kinetics (e.g., oxygen surface exchange) rather than the proton kinetics at the reduced operating temperatures (<500 °C) that are targeted for electrochemical cell applications. These findings provide a comprehensive understanding of proton behavior in BCFZY4411 and pave the way for advancing the fundamental study of TCOs.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Novel Thermodynamical Predictor of Tropical High‐Cloud Area Coverage: Estimated Anvil‐Outflow Stability
The stability at the lapse-rate tropopause (LRT) was previously defined as the upper-tropospheric stability (UTS and SUT) but might underestimate the stability control on high-level clouds, since there is little direct connection between convective processes and the thermal stratification at the LRT. Here, a novel estimated anvil-outflow stability (EAS) based on the minimum stability in the upper troposphere is proposed. At the Manus site, the results show that small and large values of the LRT-based UTS and SUT both correspond to strong divergence and frequent occurrence of high-level ice clouds. In contrast, smaller EAS corresponds to stronger convective outflows to produce likely more high-level ice clouds, with a strong negative correlation. In tropics, EAS better explains the geographic distribution of high-level cloud coverage (HCC) and its temporal variations than UTS and SUT. With a strong linear correlation, EAS is likely a simple useful predictor of HCC.
54 ENVIRONMENTAL SCIENCES↗
ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling
Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.
36 MATERIALS SCIENCE↗
Thermodynamic Modeling of Intrinsic Defects in MnBi₂Te₄
This repository contains the computational data supporting the manuscript titled “The critical role of intrinsic defects and many-body interactions on the stability of MnBi₂Te₄.” It includes: 1. DFT data generated using VASP, used for training and benchmarking electronic structure models. 2. Quantum Monte Carlo (QMC) data produced with QMCPACK, used to apply many-body corrections and validate the electronic and magnetic properties of MnBi₂Te₄. 3. Relevant scripts used to run, analyze, and process the calculations, enabling reproducibility and transparency of the workflows.
36 MATERIALS SCIENCE↗
A computational Tool for Thermodynamic Properties of Aqueous Species at HTHP
This computational tool provides the standard Gibbs energy of formation for aqueous species, the dielectric constant and ionization constant of water, over a wide range of temperatures, pressures, and densities both above and below the critical point of water.
Computational Tool↗
Effect of Thermodynamic and Environmental Factors on Crystallization of DNA‐Origami Superlattices
The directed self‐assembly of nanoscale materials into ordered superlattices presents a powerful strategy for creating next‐generation materials with programmable mechanical, optical, and photonic properties. Deoxyribonucleic acid (DNA) origami has emerged as a versatile scaffold for encoding nanoscale geometry and guiding the crystallization of complex 3D architectures. However, a systematic understanding of the parameters that govern the efficiency and quality of superlattice formation remains limited. In this study, we utilize octahedral DNA nanoscale frames as a model system to investigate the relative influence of key factors, including buffer composition, ionic strength, frame concentration, and thermal annealing protocols, on the size, order, and reproducibility of the resulting superlattices. Our findings provide a quantitative framework to rationally optimize DNA‐based assembly pathways. Structural characterization via small‐angle x‐ray scattering (SAXS), scanning electron microscopy (SEM), and optical microscopy validates the quality and fidelity of the assembled lattices. Moreover, by templating these DNA frameworks into inorganic replicas, we establish general design principles that extend beyond biomolecular systems, providing a foundation for the synthesis of programmable materials in broader nanofabrication contexts.
77 NANOSCIENCE AND NANOTECHNOLOGY↗