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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

RASPA3

RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub.

Dubbeldam, David↗
DOE OSTI2024

Complete Optimization of LPBF Ni-Based Alloys Down-Selected from FY23 Candidate Materials Including, Thermodynamic Modeling, Sample Fabrication and Microstructure Characterization

The goal of the Advanced Materials and Manufacturing Technologies (AMMT) program is to accelerate the incorporation of new materials and manufacturing technologies into advanced nuclear-related systems. Although 316H stainless steel fabricated by laser powder bed fusion (LPBF) has already been identified as an alloy that could have a significant effect on various reactor technologies, many other materials and manufacturing techniques are being evaluated. Nickel-based alloys typically offer higher-temperature capabilities compared with advanced stainless steels, and previous reports looked at three Ni-based alloy categories: low-Co alloys with a potential use close to the reactor core; high-temperature, high-strength alloys; and molten salt–compatible alloys. In the first category, alloy 718 was studied in 2023, and creep testing at 600°C and 650°C revealed that the alloy exhibited great creep strength after the appropriate annealing but had low ductility. Advanced characterization was recently conducted to highlight the presence of strengthening γ' and γ" precipitates after creep testing and to show that brittle phases at grain boundaries might explain the low ductility of LPBF 718 compared with wrought 718. For the high-temperature, high-strength alloys, previously purchased powders of alloys 617, 230, and 625 were used to assess the printability of these three solution-strengthened alloys. Hot cracking could not be suppressed for alloy 617 and 230, and it was shown that these cracks, which were elongated along the build direction (BD), had a drastic effect on the ductility of alloy 230 at room temperature when specimens were machined perpendicular to the BD. On the contrary, LPBF printing of crack-free alloy 625 was achieved using similar printing parameters, and the alloy looked like a promising candidate for various reactor technologies. The fabrication of alloy 282 by LPBF, a γ'-strengthened alloy with great creep strength up to 800°C, was performed in 2023, and x-ray computed tomography (XCT) scans of the alloy before and after creep testing at 750°C were carried out to assess the effect of flaws on the alloy’s creep behavior. Correlation between the flaws’ volume fraction, creep ductility, and creep lifetime could be established, and future work on LPBF 625 will take full advantage of in situ printing data and ex situ XCT scans to accelerate the alloy qualification. Finally, single track experiments were performed on the two alloys previously identified as good molten salt–resistant, Ni-based candidates: Hastelloy N and 244. Various laser parameters were considered, and cracking was not observed for either of the two alloys. Wrought 244 offers better creep strength and molten salt compatibility than alloy 625, and future work will aim to establish the alloy LPBF processing window.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Field control of quasiparticle decay in a quantum antiferromagnet

Dynamics in a quantum material is described by quantized collective motion: a quasiparticle. The single-quasiparticle description is useful for a basic understanding of the system, whereas a phenomenon beyond the simple description such as quasiparticle decay which affects the current carried by the quasiparticle is an intriguing topic. The instability of the quasiparticle is phenomenologically determined by the magnitude of the repulsive interaction between a single quasiparticle and the two-quasiparticle continuum. Although the phenomenon has been studied in several materials, thermodynamic tuning of the quasiparticle decay in a single material has not yet been investigated. Here we show, by using neutron scattering, magnetic field control of the magnon decay in a quantum antiferromagnet RbFeCl 3 , where the interaction between the magnon and continuum is tuned by the field. At low fields where the interaction is small, the single magnon decay process is observed. In contrast, at high fields where the interaction exceeds a critical magnitude, the magnon is pushed downwards in energy and its lifetime increases. Our study demonstrates that field control of quasiparticle decay is possible in the system where the two-quasiparticle continuum covers wide momentum-energy space, and the phenomenon of the magnon avoiding decay is ubiquitous.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Particle-based high-temperature thermochemical energy storage reactors

Solar and other renewable energy driven gas-solid thermochemical energy storage (TCES) technology is a promising solution for the next generation energy storage systems due to its high operating temperature, efficient energy conversion, ultra-long storage duration, and potential high energy density. Experimental and theoretical studies suggest that the respective gravimetric and volumetric TCES energy storage densities vary from 200 to 3000 kJ kg –1 and 1–3 GJ m –3 . Solar radiation or heat generated from electric furnaces powered by renewable electricity can be stored in the form of chemical energy through endothermic reactions, while the stored chemical energy can be converted to thermal energy via an exothermic reaction when needed. The design of highly effective reactors requires a deep understanding of materials, thermodynamics, chemical kinetics, and transport phenomena. At time of writing, TCES reactors are yet to be deployed at commercially relevant scales, leaving a substantial gap between development efforts and commercial feasibility. Therefore, this review aims to examine the state-of-the-art design and performance of particle-based TCES reactors with different reactive materials. Fundamentals related to TCES reactive materials, reaction conditions, thermodynamics and kinetics, and transport phenomena are reviewed in detail to provide a comprehensive understanding of the reactor design and operation. Five major types of TCES reactors have been comprehensively reviewed and compared, including fixed, moving, rotary, fluidized, and entrained bed reactors. Most reported prototype reactors in the literature operate at lab scale with thermal inputs below 40 kW, and scaled TCES reactors (e.g., at megawatt level) are yet to be demonstrated. The nominal reactor operating temperatures range from 300 to 1500 °C, depending on the selected chemistry, reactive material, and heat sources. To evaluate their designs, the reactors are assessed in aspects of performance, cost, and durability. Discrepancies in performance indicators of energy storage density, extent of reaction, and various energy efficiencies are highlighted. The scale-up of reactors and power block integration, which hold the key to the successful commercialization of TCES systems, are critically analyzed. Furthermore, advanced materials (both reactive materials and ceramic reactor housing materials), effective particle flow control, advanced modeling tools, and novel system design may bring significant improvement to the energy efficiency, storage density and cost competitiveness of particle-based TCES reactors.

25 ENERGY STORAGE↗
DOE OSTI2026

Exploding Bridgewire (EBW) Detonators: An Example of Synergistic Multiphysics

Exploding bridgewire (EBW) detonators are highly temporally reproducible explosive devices that require the rapid discharge of a high‐voltage capacitance to operate and so are immune to most of the accidental hazards associated with traditional electric detonators. They have been demonstrated to be safe enough for use in high‐consequence explosive applications. Despite continued use for over 82 years, understanding the exact mechanism of operation has remained elusive. Various researchers have ascribed either deflagration‐to‐detonation (DDT) or shock‐to‐detonation (SDT) phenomena observed in other explosive events to explain the science behind the successful engineering; however, a rigorous justification has been absent. Previously, we have demonstrated a complex interaction in EBW detonators between large electrical currents, non‐equilibrium thermodynamic material states, plasma physics, powder compaction phenomena, shock physics, photochemistry, and rapid conventional explosive chemical reaction processes. Specifically, we have made progress in understanding the complex multiphysics that operates in these detonators and demonstrating that it is a serendipitous synergy between UV light emitted from the arc formed as the bridge is electrically exploded and the accompanying short‐duration shock transmitted into the explosive powder bed that allows these devices to function at practical capacitor sizes and charge voltages. This insight not only places the topic on a firmer scientific footing but potentially enables new approaches to safe detonator design.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning Thermodynamics And Kinetics of Defects For Accelerated Materials Discovery

Atomistic defects play a pivotal role in functional and structural materials’ performance across a myriad of technology applications. Quantitative prediction of the thermodynamics and kinetics of defect formation and migration, respectively, typically requires accurate but expensive first-principles approaches, such as density functional theory (DFT). Their computational expense limits the throughput needed to perform high-throughput materials discovery/screening exercises or to perform materials modeling tasks relying on extensive sampling techniques. Therefore, in this Sandia National Laboratories Laboratory Directed Research and Development (LDRD) project (Project #229366), we developed a variety of machine learning techniques, trained on density functional theory calculations, to accelerate the discovery and modeling of materials in which vacancy and interstitial defects primarily dictate material performance. These include applications such as metal oxides for water-splitting or mixed ionic-electronic conduction, metal hydrides for hydrogen storage, and transition metal dichalcogenides for electronics, and the approaches developed herein can further be applied to many other domains that similarly depend on materials’ thermodynamic and kinetic defect properties for their desired functionality.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interstitial solute segregation at triple junctions: Implications for nanomaterials and a case study of hydrogen in palladium

At very fine grain sizes, grain boundary segregation can deviate from conventional behavior due to triple junction effects. While this issue has been addressed in prior work for substitutional alloys, here we develop a framework that accounts for interstitial sites in the grains, grain boundaries, and triple junctions of model Pd(H) polycrystals. This approach allows computation of interstitial segregation spectra separately at both defect types, which permits an understanding of segregation at all grain sizes via a size-scaling spectral isotherm. Here, the size dependencies of dilute Pd(H) are found to be influenced not only by the triple junction content, but also by grain size–dependent lattice strains; the latter effect is evidenced by size dependencies of individual grain boundary and junction subspectra. The framework proposed here is applicable to interstitial alloys in general and may serve as a basis for interfacial engineering in interstitial nanocrystalline alloys. As an example, we show that using the dilute limit isotherm, hydrogen density can triple in nanocrystalline vis-à-vis microcrystalline Pd due to hydrogen adsorption at intergranular defect sites.

Alloys↗
DOE OSTI2024

Learning thermodynamically constrained equations of state with uncertainty

Numerical simulations of high energy-density experiments require equation of state (EOS) models that relate a material’s thermodynamic state variables—specifically pressure, volume/density, energy, and temperature. EOS models are typically constructed using a semi-empirical parametric methodology, which assumes a physics-informed functional form with many tunable parameters calibrated using experimental/simulation data. Since there are inherent uncertainties in the calibration data (parametric uncertainty) and the assumed functional EOS form (model uncertainty), it is essential to perform uncertainty quantification (UQ) to improve confidence in EOS predictions. Model uncertainty is challenging for UQ studies since it requires exploring the space of all possible physically consistent functional forms. Thus, it is often neglected in favor of parametric uncertainty, which is easier to quantify without violating thermodynamic laws. This work presents a data-driven machine learning approach to constructing EOS models that naturally captures model uncertainty while satisfying the necessary thermodynamic consistency and stability constraints. We propose a novel framework based on physics-informed Gaussian process regression (GPR) that automatically captures total uncertainty in the EOS and can be jointly trained on both simulation and experimental data sources. A GPR model for the shock Hugoniot is derived, and its uncertainties are quantified using the proposed framework. We apply the proposed model to learn the EOS for the diamond solid state of carbon using both density functional theory data and experimental shock Hugoniot data to train the model and show that the prediction uncertainty is reduced by considering thermodynamic constraints.

Sharma, Himanshu (ORCID:000900050235718X)↗
DOE OSTI2024

Predictions of Boron Phase Stability Using an Efficient Bayesian Machine Learning Interatomic Potential

Thermodynamic phase stability of three elemental boron allotropes, i.e., α-B, β-B, and γ-B, was investigated using a Bayesian interatomic potential trained via a sparse Gaussian process (SGP). SGP potentials trained with datasets from on-the-fly active learning achieve quantum mechanical level accuracy when employed in molecular dynamics simulations to predict wide-ranging thermodynamic, structural, and vibrational properties. The simulated phase diagram (500~1400 K and 0~16 GPa) agrees with experimental measurements. The SGP-based MD simulations also successfully predicted that the B13 defect is critical in stabilizing β-B below 700 K. At higher temperatures, the entropy becomes the dominant factor, making β-B the more stable phase over α-B. Furthermore, this Letter demonstrates that SGP potentials based on a training set consisting of defect-free-only systems could make correct predictions of defect-related phenomena in solid-state crystals, paving the path to investigate crystal phase stability and transitions.

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

Determining the Reaction Kinetics and Thermodynamics of a Diels–Alder Network Using Dynamic Gel Criteria

We undertook a detailed rheological investigation to evaluate the kinetic parameters of the forward and reverse Diels–Alder (DA) reactions of a model network cross-linked using a furan prepolymer and a common aromatic bismaleimide. At high temperature where the Winter–Chambon’s criterion of frequency-independence was more applicable, a multiwave technique permitted van’t Hoff analysis and calculation of the reaction thermodynamic parameters, specifically the enthalpy and entropy of the reaction: ΔH° = –38.3 ± 5.2 kJ mol –1 and ΔS° = –94.3 ± 13.4 J mol –1 . At mild temperatures where the G'–G" crossover point is experimentally convenient to measure gelation, isothermal tests were used to obtain reasonable fDA kinetic parameters from Eyring analysis such as the apparent activation enthalpy and entropy of ΔH$^{‡}_{fDA}$ = 76.8 ± 6.9 kJ mol –1 and ΔS$^{‡}_{fDA}$= –82.8 ± 22.2 J mol –1 K –1 . Comparable rheokinetic methods include cross-linking density measurements and stress relaxation tests to calculate effective kinetics, whereas the critical gel conversion was consistently applied here. As a result, rate data are fitted with the Arrhenius equation for comparison purposes and the Eyring equation to demonstrate its broader utility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Crystal structure, magnetism, and specific heat of lightly depleted layered honeycomb oxides, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$ ($D$ = Mg, Ga, Co; 0.10 ≤ $x$ ≤ 0.25)

Layered oxides, in which transition metal atoms form a honeycomb lattice, are known for complex magnetic phase diagrams. In this work we study the doped compounds, Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, $D$ = Mg, Ga, and Co (0.10 ≤ $x$ ≤ 0.25), to understand how chemical dilution at the nickel site influences the stability of magnetic exchanges within the honeycomb layer of the parent, Na$_2$ Ni$_2$ TeO$_6$. Here, the studied compounds were found to crystallize in $P6_3/mcm$ space group common to P2 type layered oxides. In general, the lattice parameters and the unit cell volume expanded with increased doping. The oxidation states determined from X-ray photoelectron spectroscopy were consistent with bond valence sum estimates, that confirmed the presence of Ni$^{2+}$, Mg$^{2+}$, Ga$^{3+}$, Co$^{2+}$ in our samples. Irrespective of magnetic or non-magnetic doping, the magnetic phase transition temperature ($T_N$) of the doped compounds were reduced from that of Na$_2$ Ni$_2$ TeO$_6$ or Na$_2$ Co$_2$ TeO$_6$, and lay between 21.7 K and 26.8 K. The effective paramagnetic moments extracted from Curie–Weiss analysis were in the range 3.17 μ B to 3.84 μ B and the Weiss temperatures were between $-15$ K and $-25$ K, suggesting antiferromagnetism. Mg- and Ga-doped compounds showed free spin formation, indicated by Curie–Weiss tails in their magnetic susceptibility. Our results suggest emergence of short-range magnetic correlations in Na$_2$(Ni$_{2-x}$D$_x$)TeO$_6$, enhanced due to the depletion of the Ni honeycomb lattice.

75 - CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY A↗
DOE OSTI2026

Dopant adsorption as a function of bulk concentration at a near 42º (100) twist grain boundary in SrTiO3

The enrichment of grain boundaries with dopant atoms is of critical importance for the macroscopic physical properties of materials. In thermodynamic equilibrium the Gibbs adsorption isotherm relates grain boundary excess of dopant atoms, their chemical potential in the adjacent bulk, and the respective interface energy. This study has used bicrystals with a near 42º (100) twist grain boundary in SrTiO3 to demonstrate that different kinetic pathways of Fe dopant atom additions converge towards comparable grain boundary configurations. Despite differences in bulk chemical potentials remarkably similar grain boundary excess quantities were observed. The experimental results indicate the general experimental feasibility to establish quantitative relationships between variations of grain boundary energy, interfacial excess, and overall dopant concentration. Improved experimental counting statistics are needed to distinguish solute interface excess as a function of bulk chemical potential.

Hahn, William [University of California, Davis]↗
DOE OSTI2024

Polyconvex neural network models of thermoelasticity

Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. Here, in this work, we extend a polyconvex hyperelastic neural network framework to (isotropic) thermo-hyperelasticity by specifying the thermodynamic and material theoretic requirements for an expansion of the Helmholtz free energy expressed in terms of deformation invariants and temperature. Different formulations which a priori ensure polyconvexity with respect to deformation and concavity with respect to temperature are proposed and discussed. The physics-augmented neural networks are furthermore calibrated with a recently proposed sparsification algorithm that not only aims to fit the training data but also penalizes the number of active parameters, which prevents overfitting in the low data regime and promotes generalization. The performance of the proposed framework is demonstrated on synthetic data, which illustrate the expected thermomechanical phenomena, and existing temperature-dependent uniaxial tension and tension-torsion experimental datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

In-situ kinetic study of irradiation induced crystallization in amorphous Al 2 O 3

In the last ten years amorphous alumina coatings, deposited by Pulsed Laser Deposition, emerged as potential key enabling technology in the fields of heavy liquid metal fast reactors (lead and lead-bismuth) and fusion. In the former, as coating of the steel fuel cladding and in the latter as multifunctional coating providing a barrier against tritium permeation, steel corrosion and electrical insulation. Nevertheless, a detailed knowledge of the behavior of this thermodynamically metastable material at high temperatures and under neutron irradiation is still unknown. A knowledge gap that is mandatory to fill up for the deployment of this barrier technology. In the present work, we present a first step towards this goal, by the in-situ dynamic observation of the radiation induced crystallization processes of thin films of amorphous Al 2 O 3 , induced by ion-irradiation over an extensive range of temperatures (400-800 °C). The study was performed at the Intermediate Voltage Electron Microscope (IVEM)-Tandem Facility at Argonne National Laboratory. The experimental findings allow to elucidate the dependence of the grain growth on ion dose and temperature. A kinetic approach has been used to derive the process activation energies and other important parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Monte Carlo Simulations of Crystal Defects in Open Ensembles

Zero- and two-dimensional crystal defects form in open statistical ensembles, such as the grand canonical, that are usually inaccessible with conventional simulation techniques. This longstanding challenge is overcome with a new Hamiltonian Monte Carlo method that samples energy-biased gradual transformations. In conclusion, the method enables free energy calculations for nonideal point defects and the direct prediction of finite-temperature interface structures.

Grain boundaries↗