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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Comparison of Single-Phase Convection in Additive Manufactured Versus Traditional Metal Foams

Abstract Metal foams have been often used for thermal management due to their favorable characteristics including high specific surface area (SSA), high thermal conductivity, and low relative density. However, they are accompanied by shortcomings including the significant contact resistances due to attachment method, as well as the need for characterization of foam parameters such as pore diameter and SSA. Additive manufactured (AM) metal foams would eliminate the substrate/foam thermal resistance, decrease the need for pre-usage characterization, and allow for tailoring structures, while also taking advantage of the characteristics of traditionally manufactured foams. A commercial, aluminum foam (nominally 5 pores per inch (PPI), 86.5% porosity) was analyzed using X-ray microcomputed tomography, and a custom-designed metal foam based on the cell diameter and porosity of the commercial sample was subsequently manufactured. Reduced domain computational fluid dynamics/heat transfer (CFD-HT) models were compared against experimental data. Postvalidation, the flow behavior, effect of varying attachment thermal conductivities, and thermal performance were numerically investigated, demonstrating the usefulness of validated pore-scale models, as well as the potential for improved performance using AM metal foams over traditionally manufactured foams.

Engineering↗
DOE OSTI2022

Modeling Cosmic Reionization

The transformation of cold neutral intergalactic hydrogen into a highly ionized warm plasma marks the end of the cosmic dark ages and the beginning of the age of galaxies. The details of this process reflect the nature of the early sources of radiation and heat, the statistical characteristics of the large-scale structure of the Universe, the thermodynamics and chemistry of cosmic baryons, and the histories of star formation and black hole accretion. A number of massive data sets from new ground- and space-based instruments and facilities over the next decade are poised to revolutionize our understanding of primeval galaxies, the reionization photon budget, the physics of the intergalactic medium (IGM), and the fine-grained properties of hydrogen gas in the "cosmic web". In this review we survey the physics and key aspects of reionization-era modeling and describe the diverse range of computational techniques and tools currently available in this field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Interface Coating Design for Dynamic Voltage Stability of Solid‐State Batteries

Abstract Intrinsic or interface thermodynamic voltage windows of solid electrolytes are often narrower than the operational voltage range needed by a full battery, thus various interface decomposition reactions can happen in a practical solid‐state battery. Experimentally, it is found that a proper battery design utilizing the reactions can lead to a dynamic evolution from interface instability to stability, giving the so‐called dynamic voltage stability for advanced battery performance. Here, first the state‐of‐the‐art understanding is articulated about how the dynamic voltage stability should be interpreted in physical picture and treated in computation, emphasizing the potential importance of nonequilibrium reaction pathways. The constrained ensemble computational approach is further applied across most types of solid‐state electrolytes to systematically evaluate and compare their dynamic stability voltage windows in response to the mechanical constriction effect. High‐throughput calculations are used to search for coating materials for different interfaces between sulfide, halide, and oxide electrolytes and typical cathode materials with enhanced dynamic voltage stability. A comparison with experiment is given to highlight the value of these computational predictions.

Wang, Yichao↗
DOE OSTI2023

Autonomous and dynamic precursor selection for solid-state materials synthesis

Abstract Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS 3 ) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS 3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS 3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Chemical bonding in actinyl( V / VI ) dipyriamethyrin complexes for the actinide series from americium to californium: a computational investigation

The separation of minor actinides in their dioxocation (i.e., actinyl) form in high-valence oxidation states requires efficient ligands for their complexation. Here, in this work, we evaluate the complexation properties of actinyls including americyl, curyl, berkelyl, and californyl in their pentavalent and hexavalent oxidation states with the dipyriamethyrin ligand (L) using density functional theory calculations. The calculated bond parameters show shorter An&#x2550O yl bonds with covalent character and longer An–N bonds with ionic character. The bonding between the actinyl cation and the ligand anion shows a flow of charges from the ligand to actinyl in all [An V/VI O 2 –L] 1–/0 complexes. However, across the series, backdonation of charges from the metal to the ligand becomes prominent and stabilizes the complexes. The thermodynamic parameters in the gas phase and solution suggest that the complex formation reaction is spontaneous for [Cf V/VI O 2 –L] 1–/0 complexes and spontaneous at elevated temperatures (>298.15 K) for all other complexes. Spin–orbit corrections have a quantitative impact while the overall trend remains the same. Energy decomposition analysis (EDA) reveals that the interaction between actinyl and the ligand is mainly due to electrostatic contributions that decrease from Am to Cf along with an increase in orbital contributions due to the backdonation of charges from the actinyl metal center to the ligand that greatly stabilizes the Cf complex. The repulsive Pauli energy contribution is observed to increase in the case of [An V O 2 –L] 1– complexes from Am to Cf while a decrease is observed among [An VI O 2 –L] 0 complexes, showing minimum repulsion in [Cf VI O 2 –L] 0 complex formation. Overall, the hexavalent actinyl complexes show greater stability (increasing from Am to Cf) than their pentavalent counterparts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Unsupervised discovery of extreme weather events using universal representations of emergent organization

Spontaneous self-organization is ubiquitous in systems far from thermodynamic equilibrium. While organized structures that emerge dominate transport properties, universal representations that identify and describe these key objects remain elusive. Here, we introduce a theoretically grounded framework for describing emergent organization that, via data-driven algorithms, is constructive in practice. Its building blocks are spacetime lightcones that embody how information propagates across a system through local interactions. We show that predictive equivalence classes of lightcones—local causal states—capture organized behaviors in complex spatiotemporal systems. Employing an unsupervised physics-informed machine learning algorithm and a high-performance computing implementation, we demonstrate automatically discovering organized structures in two real-world domain science problems. We show that local causal states identify vortices and track their power-law decay behavior in two-dimensional fluid turbulence. We then show how to detect and track familiar extreme weather events—hurricanes and atmospheric rivers—and discover other novel structures associated with precipitation extremes in high-resolution climate data at the grid-cell level.

Rupe, Adam [Pacific Northwest National Laboratory ↗
DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗
DOE OSTI2023

Reliability of Lumped Thermodynamic Hydrogen Fueling Model under Slow-Fill Conditions

This study verifies the reliability of lumped thermodynamic fuel cell electric vehicle (FCEV) tank model under considerably slow-fill conditions. Many countries put efforts into research and development of FCEVs. As part of the efforts, thermodynamic hydrogen fueling models have been developed to understand the hydrogen temperature in the onboard tanks of FCEVs during the fueling process. Most of the models treat the hydrogen inside the tanks to be a lumped system and assume the hydrogen temperature to be uniform throughout the tanks. In other words, the hydrogen temperature is treated as average bulk temperature. This study certifies whether treating the hydrogen temperature in a tank as the bulk gas temperature is suitable by evaluating the temperature distributions inside the tank by a three-dimensional computational fluid dynamics (3D CFD) model.

DIRECT ENERGY CONVERSION,HYDROGEN↗
DOE OSTI2024

Fermionic mean-field theory as a tool for studying spin Hamiltonians

The Jordan–Wigner transformation permits one to convert spin 1/2 operators into spinless fermion ones, or vice versa. In some cases, it transforms an interacting spin Hamiltonian into a noninteracting fermionic one, which is exactly solved at the mean-field level. Even when the resulting fermionic Hamiltonian is interacting, its mean-field solution can provide surprisingly accurate energies and correlation functions. Furthermore, Jordan–Wigner is, however, only one possible means of interconverting spin and fermionic degrees of freedom. Here, we apply several such techniques to the XXZ and J 1 –J 2 Heisenberg models, as well as to the pairing or reduced Bardeen–Cooper–Schrieffer Hamiltonian, with the aim of discovering which of these mappings is most useful in applying fermionic mean-field theory to the study of spin Hamiltonians.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Experimental and Computational Heat Transfer Study of sCO 2 Single-Jet Impingement

This study experimentally and computationally investigates the heat transfer capability of supercritical carbon dioxide (sCO 2 ) single jet impingement. The evaluated jet Reynolds number range is between 80,000 and 600,000, with a nondimensional jet-to-target surface spacing of 2.8. CO 2 -impinging jet stagnation conditions were maintained at approximately 200 bar and a temperature of ∼400 °C for most experiments. The goal is to understand how changes in the aforementioned parameters influence heat transfer between the working fluid and the heated surface. Additionally, due to the elevated Reynolds numbers and difference in thermodynamic properties between air and CO 2 , air-derived impingement correlations may not be appropriate for CO 2 impingement; these correlations will be evaluated against experimental sCO 2 impingement data. At the time of this study, no sCO 2 impingement data was available relevant to sCO 2 power cycles. The target surface is a 1.5-in. diameter copper block centered on the 3 mm jet orifice. A mica heating element bolted to the bottom of the copper block provides a uniform heat flux. Thermocouples embedded in the copper block are used to determine the surface temperature. Nusselt numbers obtained from experimental sCO 2 test data are compared to area-averaged Nusselt numbers from air-derived correlations. The comparisons showed that air correlations drastically underpredict the heat transfer when sCO 2 is used as the working fluid. A modified sCO 2 correlation using experimental data at discussed conditions is derived based on an existing air correlation. A CFD study is also performed to further investigate sCO 2 heat transfer characteristics, and assess the numerical model applicability to this problem type.

42 ENGINEERING↗
DOE OSTI2024

Deep energy-pressure regression for a thermodynamically consistent EOS model

Abstract In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Demonstration and Validation of Additively Manufactured Intensified Device for Enhanced Carbon Capture

In FY 2018 and 2019, ORNL personnel designed, prototyped, characterized, and tested the enhancement of amine-based carbon capture using additively manufactured intensified devices, i.e., devices combining multiple thermodynamic and kinetic processes into one unit operation. State of the art scrubbing technology often employs inter-stage cooling which requires multiple stages of single-purpose unit operations, leading to larger equipment size, higher capital cost, and sometimes less than optimal operating conditions for each piece of equipment. It was proposed that integrating multiple thermodynamic and kinetic operations into one unit could mitigate or negate these drawbacks. Heat and mass transfer studies demonstrated that an additively manufactured intensified device could significantly improve capture efficiency at bench scale. This project is a continuation of FEAA130 and aims at (1) demonstration and validation of enhanced CO 2 capture with 3D printed intensified devices (i.e., mass exchanger packing with internal cooling channels), for low-aqueous-solvent (LAS) based capture at the ORNL-constructed laboratory-scale (4.5-feet tall) column, namely Column A, with built-in cooling and (2) computational scoping on the conceptual design of a flexible and modular larger column, namely Column B, at ORNL for further demonstration of enhanced capture for aqueous solvent. To execute this multi-faceted project scope, an integrated project team from ORNL’s Manufacturing Science Division is tasked with applying capabilities in computational fluid dynamics (CFD), additive manufacturing, and absorber-scale demonstration/validation experiments.

42 ENGINEERING↗
DOE OSTI2021

Progress report on analytic and numerical studies of x-ray-induced impulse

In a variety of high-energy-density (HED) systems, x-rays of a given energy are used to generate shockwaves, bulk motion, and impulse in materials. As radiation energy is deposited within the material, the material is heated. The number of photons of a certain wavelength absorbed is determined by the spectral intensity of the radiation and the material’s opacity evaluated at that wavelength. This heating results in a pressure increase dictated by the material’s equation of state. Depending on the intensity of the absorbed radiation, it may also cause the material to change phase into a liquid, gas, or plasma. The increased pressure drives the heated surface layer to blow off, imparting impulse and sending a compressive wave into the bulk of the material. Additionally, the compression wave interacts with the solid boundary of the material, resulting in a tensile wave that may cause the material to spall. The impulse generated by the deposition of x-ray energy within the sample can be modeled using purely analytical methods, e.g. the Bethe, Bade, Averell, and Yost (BBAY) model. However, the blow-off process is rather complicated, and proper modeling efforts must account for material ejected by spallation, vaporization, jetting, and plasma ablation. For this reason, analytical models have an unclosed term describing the final energy of the blown-off material Ef(z). Prior modeling efforts have arbitrarily fixed this at some value or modeled it using a limiting set of thermodynamic assumptions. The work we are currently performing uses validated simulations using sophisticated photon transport, equation of state, and strength models/data to provide a fit for Ef(z) that is useful for predictive calculation of impulse. We will apply our methodology and show results for different materials and x-ray sources. This work is particularly useful for the design of experiments studying x-ray impulse generation. The present report is outlined as follows. Section 1.2 describes a series of HED experiments investigating x-ray-generated impulse in materials. Section 1.3 describes the computational approach we employ in this study, and presents validation results against the aforementioned experiments. Section 1.4 introduces analytical models for impulse generation, as well as our method for utilizing impulse from simulations to close the models. We discuss the concept of impulse-spectrum sensitivity, it’s application to uncertainty quantification, and derive a very useful analytical expression for it in 1.5. We then summarize recent progress in this project and discuss future work in section 1.6.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modeling Equilibrium Solid–Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials

The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na 2 SO 4,aq –graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl aq –air and NaCl aq –graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid–liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. Here, we show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region.

Chemical structure↗
DOE OSTI2024

Water–Hydrocarbon Interactions in Anionic Pyrene Monohydrate

Interactions between water and polycyclic aromatic hydrocarbons are essential in many aspects of chemistry, from interstellar and atmospheric processes to interfacial hydrophobicity and wetting phenomena. Despite their growing importance, the intermolecular potentials of the water-hydrocarbon interactions are underdeveloped compared to water-water potentials, and there are similarly few experimental probes that are sensitive to the details of the water-hydrocarbon potential. We present a combined experimental and computational study of anionic pyrene monohydrate, one of the simplest water/hydrocarbon clusters. The action spectrum in the OH region of the mass-selected cluster ion provides a rigorous benchmark for intermolecular potentials and computational methodologies. We identify missing intermolecular interactions and shortcomings in conventional dynamics calculations by comparing experimental data to density functional theory and classical molecular dynamics calculations. Kinetic trapping is prevalent, even for one water molecule and one pyrene molecule, leading to slow equilibration in conventional molecular dynamics calculations, even on nanosecond timescales and at low temperatures (50 K). At constant energy, temperature fluctuations for the pair of molecules are substantial. Immersing the system in a bath of soft spheres and employing parallel tempering alleviates kinetic trapping and dampens temperature fluctuations, bringing the system closer to the thermodynamic limit. With such augmented sampling, a simple, flexible water model reproduces the linewidth and the asymmetric broadening of the symmetric OH stretching mode, which we assign to spectral diffusion. In the OH stretching region, dynamics calculations predict a more intense antisymmetric peak than experiments observe but do not predict the bimodal split symmetric peak that the experiments show. Furthermore, our work suggests that electronic polarization, missing in the empirical force field, is responsible for the first discrepancy and that quantum nuclear effects, captured neither in density functional theory nor in classical dynamics, may be responsible for the second.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Benchmarking nitrous oxide adsorption and activation in metal–organic frameworks bearing coordinatively unsaturated metal centers

Anthropogenic emissions of N 2 O, the third most abundant greenhouse gas after CO 2 and CH 4 , are contributing to global climate change. Although metal–organic frameworks (MOFs) have been widely studied as adsorbents for CO 2 and CH 4 , less effort has focused on the use of MOFs to remove N 2 O from emission streams or from air. Further, N 2 O activation would enable its use as an inexpensive oxidant for fine chemical synthesis. Here, in this paper, we identify features that contribute to strong binding and high uptake of N 2 O at coordinatively unsaturated metal sites in the M 2 Cl 2 (btdd) (M = Mn, Co, Ni, Cu; btdd 2– = bis(1,2,3-triazolo[4,5-b],[4',5'-i])dibenzo[1,4]dioxin) and M 2 (dobdc) (M = Mg, Mn, Fe, Co, Ni, Cu, Zn; dobdc 4– = 2,5-dioxido-1,4-benzenedicarboxylate) series of MOFs. Combined experimental and computational studies suggest that N 2 O adsorption at open-metal-sites is primarily based on electrostatic interactions, rather than π-backbonding, causing MOFs with more Lewis acidic metal centers to be superior N 2 O adsorbents. As a result, Mg 2 (dobdc) demonstrates strong binding and record-setting N 2 O uptake (8.75 mmol g –1 at 1 bar and 298 K). Using density functional theory (DFT) to characterize reactive intermediates and transition states, we demonstrate that N 2 O activation to form a M(IV)–oxo species and N 2 is thermodynamically favorable in Mn 2 (dobdc) and Fe 2 (dobdc) but appears to be kinetically limited in Mn 2 (dobdc). Our work lays a foundation for understanding N 2 O adsorption and activation in MOFs, paving the way for the design of promising next-generation materials for N 2 O capture and utilization.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Synergistic Effect of Diatomic Mo-B Site Confined in Graphene-Like C 2 N Enables Electrocatalytic Nitrogen Reduction via Novel Mechanism

Structural modulation of active site with atomic-level precision is of great importance to meet the activity and selectivity challenges that electrocatalysts are commonly facing. In this work, we have designed a metal (M) - nonmetal (NM) diatomic site embedded in graphene-like C 2 N (denoted as Mo-B@C 2 N), where electrocatalytic N 2 reduction reaction (eNRR) was thoroughly explored using density functional theory combined with computational hydrogen electrode method. Compared to M-M diatomic sites, Mo-B site can generate pronounced synergistic effect that led to eNRR proceeding via a novel quasi-dissociative reaction mechanism that has not been reported relative to the conventional enzymatic, consecutive, distal, and alternating associative mechanism. This newly uncovered mechanism in which N-N bond scission takes place immediately after the first proton-coupled electron transfer (PCET) step (i.e., *NH-*N + H + + e – ® *NH 2 *N) has demonstrated much advantage in PCET process over the four conventional mechanism in terms of thermodynamic barrier, except that the adsorption of side-on *N 2 seemed thermodynamically unfavorable (DG ads = 0.61eV). Our results have revealed that the activation of inert NºN triple bond is dominated by π*-backdonation mechanism as a consequence of charge transfers from both B and Mo site, and unexpectedly, from the substrate C 2 N itself as well. Moreover, the hybrid Mo-B diatomic site demonstrated superior performance over either Mo-Mo or B-B site for driving eNRR. Furthermore, our study could provide insight into the delicate relationships among atomic site, substrate and electrocatalytic performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Consistent thermodynamic properties for alicyclic components of jet fuels: Experimental data, estimation methods, and homologous series trends

Alkylcycloalkanes represent a significant fraction of jet fuel components. An evaluation of their thermodynamic properties, enthalpies of formation in liquid and gas phases and enthalpies of vaporization, was conducted. A combination of available experimental data, up-to-date group-contribution methods, high-level quantum-chemical calculations, and homologous series trends was used to identify outliers and to recommend the most reliable values. The group-contribution approach was found to work well for the enthalpies of vaporization. Its performance for the enthalpies of formation in the liquid and gas phases was found to be substantially less effective, especially considering notable differences in this property among stereoisomers. Computationally affordable high-level ab initio results and homologous series trend analysis appeared more reliable. In conclusion, the recommended property values for 212 individual compounds and their isomeric mixtures were provided.

09 BIOMASS FUELS↗