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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Ambiguities in the hadro-chemical freeze-out of Au+Au collisions at SIS18 energies and how to resolve them

The thermal fit to preliminary HADES data of Au+Au collisions at $\sqrt{s_{NN}}$=2.4 GeV shows two degenerate solutions at T≈50 MeV and T≈70 MeV. The analysis of the same particle yields in a transport simulation of the UrQMD model yields the same features, i.e. two distinct temperatures for the chemical freeze-out. While both solutions yield the same number of hadrons after resonance decays, the feeddown contribution is very different for both cases. This highlights that two systems with different chemical composition can yield the same multiplicities after resonance decays. The nature of these two minima is further investigated by studying the time-dependent particle yields and extracted thermodynamic properties of the UrQMD model. It is confirmed, that the evolution of the high temperature solution resembles cooling and expansion of a hot and dense fireball. The low temperature solution displays an unphysical evolution: heating and compression of matter with a decrease of entropy. These results imply that the thermal model analysis of systems produced in low energy nuclear collisions is ambiguous but can be interpreted by taking also the time evolution and resonance contributions into account.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Effect of Impurity Atoms on Thermonuclear Yield

The presence of impurity atoms in an inertial confinement fusion (ICF) implosion can greatly affect the burning of the thermonuclear fuel. This is a serious concern for attaining ignition as high atomic number atoms, such as carbon, can mix into the hot spot and reach the deuterium–tritium (DT) fuel, absorb energy from the implosion, and radiate away that energy. The temperature of the DT fuel drops and reduces the reactivity of the fuel resulting in lower thermonuclear yield. Fuel impurities can also be a useful diagnostic as when higher-Z atoms, such as Ar, Kr, and Xe, are purposefully mixed into the fuel. From the spectra emitted by these atoms, the temperature and density of the fuel can be deduced. The long history of using such tracer atoms by the national ICF program shows the great advances in experimental technique, diagnostic instruments, and atomic-physics modeling over the past 50 years. A particular set of experiments, the “High-Z” campaign, is reviewed in depth as this was the most systematic study of tracer element composition and quantity ever reported. Difficulties in interpreting the results of those experiments due to the limited code capabilities at that time are discussed. A main concern for the High-Z campaign was the then current state of non-Local Thermodynamic Equilibrium (nLTE) atomic physics models. The present state of nLTE modeling and its implementation in Los Alamos’s multi-physics codes is discussed in this report. To further the understanding of the complex interaction of high-Z atoms in the gas, a survey of relevant existing experimental platforms at the National Ignition Facility (NIF) and the Omega Laser Facility are presented along with the current state of pertinent diagnostics at NIF and Omega. This review concludes with a discussion of the trade-off available between temperature and volume of the imploded ICF capsule with the goal of maximizing the total yield.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2022

Hot Circumsingle Disks Drive Binary Black Hole Mergers in Active Galactic Nucleus Disks

Abstract Binary black hole (BBH) mergers, particularly those with component masses in the pair-instability gap, may be produced by hierarchical mergers in the disks surrounding Active Galactic Nuclei (AGNs). While the interaction of an embedded BBH with an AGN disk is typically assumed to facilitate a merger, recent high-resolution hydrodynamical simulations challenge this assumption. However, these simulations often have simplified treatments for gas thermodynamics. In this work, we model the possible consequence of various feedback from an embedded BBH with a simple model that maintains an enhanced temperature profile around each binary component. We show that when the minidisks around each BH become hotter than the background by a factor of three, the BBH orbital evolution switches from expansion to contraction. By analyzing the gravitational torque profile, we find that this change in direction is driven by a weakening of the minidisk spirals and their positive torque on the binary. Our results highlight the important role of thermodynamics around BBHs and its effect on their orbital evolution, suggesting that AGN disks could be efficient factories for BBH mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Prediction of nonlinear specific heat during single crystal HMX phase transition

Here, we develop a thermodynamically consistent chemo-thermo-mechanical model for the β → δ phase transition of energetic HMX crystals. In contrast to previous models, which either considered specific heat to be a constant or utilized a calibrated function, this model provides novel expressions for the specific heats at constant volume and constant elastic strains derived directly from continuum mechanics. In addition, the model provides a novel prediction for the critical temperature at which the chemical heating rate achieves its extremum for Arrhenius kinetics. The numerical solution predicts highly nonlinear specific heat behavior including order of magnitude changes.

42 ENGINEERING↗
DOE OSTI2021

Integration Roadmap for Multi-Scale, Multi-Physics Mass Accountancy in Molten Salt Reactors

The objective of the integration roadmap is to describe the necessary physics required and a plan for modeling and simulation approach for predicting mass accountancy in molten salt reactors on an engineering scale. The thermophysical properties and the underlying thermodynamics are fundamental inputs. Therefore, the modeling will span length scales from first principles calculations to the engineering scale. The intention is to predict where material accumulates in a reactor core and loop and to understand perturbations on the systems level, for example the downstream effects from a turbine failure

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Multiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE

Advanced Materials and Manufacturing Technologies (AMMT) program under the Department of Energy Office of Nuclear Energy, aims to develop and qualify additively-manufactured materials for nuclear applications. The key challenges to these efforts are the microstructural variabilities observed on the AM products and their impact on the properties and performance of the material in extreme environments. AMMT is using a combination of high-through-put experimental and modeling techniques to accelerate the qualification efforts. Conventionally, in-situ and ex-situ characterizations and testing are performed to correlate different aspects of the AM process to the final product and its performance. However, adopting a trial-and-error approach to experimentally evaluate the vast range of process parameters required to capture the microstructural variabilities is cost-prohibitive. Modeling and simulation provide a comparatively inexpensive way to understand and correlate the microstructural evolution to the processing conditions. The modeling and simulation work-packages within the AMMT program aims to use physics-based and machine learning modeling capabilities to develop a digital twin for AM that can correlate the process conditions to the final product and establish a process-structure-property-performance (PSPP) correlation for AM materials. The melting and subsequent solidification that occurs during the AM process is a complex phenomenon that requires multiscale multiphysics analysis. Idaho National Laboratory’s (INL) Multiphysics Object-Oriented Simulation Environment (MOOSE), specifically the MOOSE Application Library for Advanced Manufacturing UTilitiEs (MALAMUTE) software, provides an ideal platform for developing the multiphysics multiscale model to explore the intricacies of the microstructural evolution during the AM processes within a single framework. Furthermore, given that such full-fidelity simulations can be computationally intensive, reduced order models are necessary to explore the PSPP space for AM materials in an efficient, reliable, and cost-effective way. This work package focuses on understanding the role of process variabilities on the various microstructural characteristics of the AM materials. Microstructures unique to AM materials, such as compositional micro-heterogeneity and dislocation cells, are of particular interest here since they can influence the creep properties and radiation performance. In fiscal year (FY) 24, we significantly advanced upon our work in the last fiscal year, both on physics-based and ML models. The alloy solidification model available in MOOSE has been extended to incorporate the thermodynamic properties and free energy relevant to 316SS. The model demonstrates the Cr segregation that occurs during solidifcation. It is demonstrated that rate of solidification and solute segregation is primarily influence by the cooling rate dictating the level of freezing. This work captures the microstructural variabilities at the subgrain level that are often missing in the part-scale models. With an aim to connect the microstructural evolution model to realistic process conditions, a reduced order model is developed for predicting the thermal conditions around meltpool from high-fidelity process simulations. Furthermore, machine learning approach is used to accelerate the temperature prediction during the AM process. In the following years, MALAMUTE will be used to connect different aspects of the models and quantitatively predict the microstructural evolution. The developed ML-based surrogate model will consider the process conditions as the input to predict the microstructural features in a cost-effective way. The generated microstructures can be used by other work packages under AMMT to evaluate the properties and environmental response of the material at the mesoscale. Thus, this work help identify the key microstructural features at the subgrain level that are significant in property/performance prediction of the AM products. This work will provide inputs to the large-scale process variability models to reevaluate and validate assumptions/simplifications made in the part-scale models. Furthermore, through active learning this work will help identify the data need from both modeling and experimental sides for development of a robust digital twin for AM.

36 MATERIALS SCIENCE↗
DOE OSTI2022

iNovo479: Metabolic Modeling Provides a Roadmap to Optimize Bioproduct Yield from Deconstructed Lignin Aromatics by Novosphingobium aromaticivorans

Lignin is an abundant renewable source of aromatics and precursors for the production of other organic chemicals. However, lignin is a heterogeneous polymer, so the mixture of aromatics released during its depolymerization can make its conversion to chemicals challenging. Microbes are a potential solution to this challenge, as some can catabolize multiple aromatic substrates into one product. Novosphingobium aromaticivorans has this ability, and its use as a bacterial chassis for lignin valorization could be improved by the ability to predict product yields based on thermodynamic and metabolic inputs. In this work, we built a genome-scale metabolic model of N. aromaticivorans, iNovo479, to guide the engineering of strains for aromatic conversion into products. iNovo479 predicted product yields from single or multiple aromatics, and the impact of combinations of aromatic and non-aromatic substrates on product yields. We show that enzyme reactions from other organisms can be added to iNovo479 to predict the feasibility and profitability of producing additional products by engineered strains. Thus, we conclude that iNovo479 can help guide the design of bacteria to convert lignin aromatics into valuable chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The Significant Differences in Solvation Thermodynamics of C1–C3 Oxygenates in Hydrophilic versus Hydrophobic Pores of a Hydrophilic Ti-FAU Zeolite Model

The rates of catalytic reactions have been observed to be dramatically different in zeolites, depending on if they are hydrophobic or hydrophilic. Hypotheses aimed at explaining this behavior have pointed to various solvent molecule and zeolite properties as having influence on entropy. Herein, the influence of various solvent and adsorbate properties on the solvation energies, entropies, and free energies of eleven C1-C3 oxygenates in hydrophobic and hydrophilic pores within a hydrophilic model of Ti-FAU zeolite are tested. The results indicate significant variation in the calculated solvation thermodynamics depending on the adsorbate type, as well as if it is bound within a hydrophobic or hydrophilic pore. Further, while solvation energies are related to solvent-adsorbate interactions, solvation entropies have multiple contributions, and these differ depending on if the adsorbate is in a hydrophobic or hydrophilic pore. Specifically, solvation entropies in hydrophobic pores are related to solvent structural properties, whereas solvation entropies in hydrophilic pores are related to adsorbate polarity. Here, the large range of results obtained from two different pores within one zeolite model with minimal unique adsorption sites suggests that solvation behavior in zeolites is complicated and that the phenomena that control observed performance depend on the zeolite, reaction, and solvent.

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

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↗
DOE OSTI2022

Development of an Open-source Alloy Selection and Lifetime Assessment Tool for Structural Components in CSP

Lack of sufficient data on high temperature mechanical and corrosion behavior of structural materials is a huge barrier in the technological maturity of current and future Concentrating Solar Power (CSP) technologies. Rapid development and selection of materials cannot be achieved by expensive and time-consuming acquisition of experimental data. The goal of the proposed work is development of an open-source alloy selection and lifetime prediction tool that will integrate validated physics-based models to describe influence of temperature, alloy composition, environment and component geometry (thickness) on mechanical and corrosion behavior of Ni and Fe-based alloys employed in molten salts/sCO 2 heat exchangers. This one-year project leveraged the extensive dataset on the creep\corrosion behavior of candidate materials generated at ORNL through past projects and input from current collaborations with industrial partners. Based on previous experience and the feedback provided by industry (Brayton Energy and Echogen), three candidate materials of interest, Ni-based alloys 740H, 282 and 625 and application-specific operating conditions (max. temperature of 730 °C and stress of 150 MPa) were identified for the heat exchanger. An extensive corrosion and creep dataset was assimilated for the relevant operating conditions and was supported by detailed characterization of about 100 metallographic cross-sections. The corrosion dataset consisted of scanning electron microscopy images (secondary electron and backscatter electron), measured concentration profiles of alloying elements using energy dispersive X-ray spectroscopy (EDS), widths of denuded zones (dissolution of strengthening phases) and depths of attack in molten KCl-MgCl 2 mixtures using image analyses. The creep dataset comprised of creep rupture data and creep strain curves (for 740H and 282). Coupled thermodynamic-kinetic microstructure-based models were employed to predict the stress-corrosion induced compositional and phase evolutions in the alloy during operation under the identified operating conditions. Reduced order models were developed from advanced physics-based models and were integrated in a user-friendly alloy selection tool. The corrosion model was able to predict the time to a critical Cr concentration at the oxide/alloy interface (chemical lifetime) within ±10% (1 standard deviation) of typical statistical variation in corrosion tests and EDS measurement errors (±0.5 wt%). The initial scope of the project was limited to predict creep rupture times (Larson-Miller parameter). Based on the input provided by industry, the mechanical lifetime of the heat exchanger is governed by accumulated creep strains (2%) rather than creep rupture. To be able to predict the times to specific creep strains, a more extensive creep model development was undertaken largely beyond the initial scope of the project. The continuum damage mechanics creep model was able to predict times to 2% creep strain, t 2% with an accuracy of ±500h. Ultimately, a screening protocol for SiC was generated to demonstrate the pathway for integration of one of the currently immature materials from a commercial adoption standpoint in the current material evaluation tool. The modeling tool developed here is accessible to the science community and stakeholders and lays the foundation for methods that will enable a rapid evaluation of optimum materials for CSP applications and reliable prediction of material degradation thereby considerably reducing operational costs, improving reliability and increasing overhaul intervals. However, the complete potential of such a tool to include a wider range of materials and test conditions can only be realized with a more concentrated combined experimental-characterization-computation effort.

14 SOLAR ENERGY↗
DOE OSTI2020

Electron Energy Partition across Interplanetary Shocks. III. Analysis

An analysis of model fit results of 15,210 electron velocity distribution functions (VDFs), observed within ±2 hr of 52 interplanetary (IP) shocks by the Wind spacecraft near 1 au, is presented as the third and final part on electron VDFs near IP shocks. The core electrons and protons dominate in the magnitude and change in the partialto-total thermal pressure ratio, with the core electrons often gaining as much or more than the protons. Only a moderate positive correlation is observed between the electron temperature and the kinetic energy change across the shock, while weaker, if any, correlations were found with any other macroscopic shock parameter. No VDF parameter correlated with the shock normal angle. The electron VDF evolves from a narrowly peaked core with flaring suprathermal tails in the upstream to either a slightly hotter core with steeper tails or much hotter flattop core with even steeper tails downstream of the weaker and strongest shocks, respectively. Both quasi-static and fluctuating fields are examined as possible mechanisms modifying the VDF, but neither is sufficient alone. For instance, flattop VDFs can be generated by nonlinear ion acoustic wave stochastic acceleration (i.e., inelastic collisions), while other work suggested they result from the combination of quasi-static and fluctuating fields. This three-part study shows that not only are these systems not thermodynamic in nature; even kinetic models may require modification to include things like inelastic collision operators to properly model electron VDF evolution across shocks or in the solar wind.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Ramp compression of tantalum to multiterapascal pressures: Constraints of the thermal equation of state to 2.3 TPa and 5000 K

We report measurements of the compressibility of ramp compressed tantalum to a final stress of 2.3 TPa corresponding to threefold volumetric compression. Using these data, we extended the experimental constraint on the Ta cold compression curve by an order of magnitude in pressure. Furthermore, by combining the resulting data with previous measurements of shock compression and ambient pressure heating, we construct an experimentally bounded and thermodynamically consistent equation of state model for Ta which has 2% uncertainty in pressure at 1 TPa. We therefore propose Ta as an in situ pressure scale for laser-heated static compression experiments which were recently able to reach terapascal pressures and thousands of degrees Kelvin. Our new equation of state of Ta is experimentally constrained at extreme pressures and temperatures relevant to a wide range of planetary interiors and will allow for more accurate comparison between experimental measurements and theory at extreme conditions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Machine learning for fundamental spectroscopic and thermodynamic data of actinides and lanthanides

Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.

Atomic spectroscopy↗
DOE OSTI2024

Rational Optimization of Microbial Processing for High Yield CO 2 -to-Isopropanol Conversion: Cooperative Research and Development Final Report, CRADA Number CRD-20-17114

This project focuses on the production of the fuel blendstock isopropanol using a CO 2 -fixing Clostridium by metabolic engineering and process optimizations. The project will initiate from a baseline isopropanol producer and pursue isopropanol production at high carbon-conversion efficiency. We will lead engineering work by in-depth pathway analyses including thermodynamics optimization, enzyme expense analysis, metabolic robustness analysis, and -omics analysis. The isopropanol production will be optimized via genome editing followed by fermentation optimizations. This project will deliver a novel microbial process that efficiently converts waste CO 2 to isopropanol at ~g/L titer level within 18-months. This project will layout a solid knowledge basis and technology platform for renewable CO 2 valorization to bio-blendstock that help achieve Co-Optima and Shell's goals.

09 BIOMASS FUELS↗
DOE OSTI2025

A phase-field fracture formulation for generalized standard materials: The interplay between thermomechanics and damage

Accurately modeling fracture of ductile materials poses open challenges in the field of computational mechanics due to the multiphysics nature of their failure processes. Integrating the interplay between thermodynamics and damage into ductile fracture models is vital for predicting critical failure modes. Here, in this paper, we develop a versatile phase-field (PF) framework for modeling ductile fracture, taking into account finite-strain elasto-plasticity. The framework stems from a variational formulation of constitutive relations for generalized standard materials (GSMs), whose response is described by a Helmholtz free energy and a dissipation pseudo-potential. Its variational structure is based on a minimum principle for a functional that expresses the sum of power densities for reversible and irreversible processes. By minimizing this functional with a constraint on a von Mises yield function, we derive the evolution equation for the equivalent plastic strain and an associative flow rule. This constrained optimization problem is analytically solved for a wide class of thermo-viscoplasticity models. The key innovations of the current work include (i) a cubic plastic degradation function that accounts for a non-vanishing damage-dependent yield stress, (ii) closed-form expressions of the Helmholtz free energy and dissipation pseudo-potential for three thermo-viscoplasticity models, (iii) an extended Johnson–Cook plasticity model with a nonlinear hardening law, and (iv) a plastic work heat source that depends on the plastic degradation function and a variable Taylor–Quinney (TQ) coefficient. The capabilities of the proposed framework are tested with the aid of four ductile fracture problems, including the Sandia Fracture Challenge. In each of these problems, we examine the evolution of relevant field variables such as the PF order parameter, the equivalent plastic strain, the temperature, and the internal power dissipation density, in addition to the overall structural response quantified by the force–displacement curve. These numerical studies demonstrate that the proposed framework effectively represents ductile fracture, yielding computational results that exhibit good agreement with experimental data.

36 MATERIALS SCIENCE↗