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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Decarbonization Scenarios in the United States: Comparing Biofuels Growth in Two Models - GCAM and BSM

Scenarios for deep decarbonization rely on biomass for biofuels, biopower, and bioproducts, often including negative emissions via carbon capture and storage or utilization. Despite the prominence of biomass in many deep decarbonization pathways, critical questions remain about biomass allocation, effects of transportation electrification, the pace of growth, and implications for agriculture and land use. We address these questions through a unique comparison of carbon pricing effects on the growth of biomass utilization and its effects on land use in the United States by comparing results from a multisectoral integrated assessment model, the Global Change Analysis Model [GCAM], with results from a biomass-to-biofuels system dynamics model, the Biomass Scenario Model [BSM]. We contribute to model comparison efforts by analyzing the biomass deployment needed for a scenario consistent with a "Middle of the Road" Shared Socioeconomic Pathway [SSP2] and a representative concentration pathway of 2.6 W/m2. The GCAM scenarios solve for global equilibrium conditions that are consistent with this pathway, including demands for biomass across all economic sectors and representing bioenergy with carbon capture and storage as a technology option. The BSM scenarios assess those biomass and biofuel results for the United States and identify challenges associated with that pace and amount of expansion. In the scenario analysis, we harmonize key factors such as carbon price trajectory, domestic ethanol fuel demand, ethanol blending, and arable land availability, and vary them in both models. In GCAM, we vary the carbon price, transportation electrification, ethanol blending constraints, and arable land availability inputs and the value of the carbon in land; in BSM, in addition to directly inputting certain GCAM results, we vary the maximum rate of biorefinery construction, flexibility of feedstock types across conversion processes, and policy incentives such as tax credits and renewable identification number payments. The selected carbon price trajectory results in a rapid increase in biofuel production in the United States, reaching about 9.4 EJ/year in 2060 in the highest scenario analyzed in GCAM. Results differ between the two models in timing and ultimate quantity of biomass and biofuel production. GCAM biofuel quantities generally exceed BSM amounts because CCS is applied to biofuel pathways in GCAM, and because of differences in capacity expansion and related dynamics of land allocation, biomass production, and price dynamics. These dynamics include rapid biorefinery capacity expansion in high demand cases. To satisfy this biomass demand, GCAM rapidly equilibrates land allocation, but the BSM limits the rate at which this re-allocation can occur. A further contrast with the equilibrium approach in GCAM is that the BSM represents a delay between planting and harvesting woody biomass resources. As a result of these model contrasts, feedstock costs in BSM increase more than in GCAM, and the absence of CCS in the BSM also reduces the relative economic attractiveness of biofuels production. The bottlenecks, lags, and price increases also lead to potential for volatility in feedstock price and land allocation to biomass in the BSM. GCAM has more biomass production than BSM in all scenarios, partly because of the broader, economy-wide coverage of GCAM, in contrast to BSM's exclusive focus on biofuels. In both models, trends like those of biofuels production were observed for biomass production: minimal growth without a carbon price and policy incentives, and increases with a carbon price, particularly with carbon capture and storage, because the inputs assume that biopower and biofuels decrease greenhouse gas emissions. In high policy scenarios, biomass demand is high, and the consequent high biomass prices due to the land re-allocation bottleneck in the BSM limit biofuel production even if the biorefinery capacity is expanded. However, because biomass prices do not increase as much in the low policy scenario, growth is slower and the land-reallocation bottleneck no longer dominates, such that the effect of increased capacity can be seen. Across both the models, a change in assumptions from less to more land availability increases biofuel production in both GCAM and BSM, as the upward pressure on feedstock price and volatility are both reduced.

biofuels↗
DOE OSTI2021

Kinetics of solid–liquid interface motion in molecular dynamics and phase-field models: crystallization of chromium and silicon

The results of molecular dynamics (MD) simulations of the crystallization process in one-component materials and solid solution alloys reveal a complex temperature dependence of the velocity of the crystal–liquid interface featuring an increase up to a maximum at 10–30% undercooling below the equilibrium melting temperature followed by a gradual decrease of the velocity at deeper levels of undercooling. At the qualitative level, such non-monotonous behaviour of the crystallization front velocity is consistent with the diffusion-controlled crystallization process described by the Wilson–Frenkel model, where the almost linear increase of the interface velocity in the vicinity of melting temperature is defined by the growth of the thermodynamic driving force for the phase transformation, while the decrease in atomic mobility with further increase of the undercooling drives the velocity through the maximum and into a gradual decrease at lower temperatures. At the quantitative level, however, the diffusional model fails to describe the results of MD simulations in the whole range of temperatures with a single set of parameters for some of the model materials. The limited ability of the existing theoretical models to adequately describe the MD results is illustrated in the present work for two materials, chromium and silicon. It is also demonstrated that the MD results can be well described by the solution following from the hodograph equation, previously found from the kinetic phase-field model (kinetic PFM) in the sharp interface limit. The ability of the hodograph equation to describe the predictions of MD simulation in the whole range of temperatures is related to the introduction of slow (phase field) and fast (gradient flow) variables into the original kinetic PFM from which the hodograph equation is obtained. The slow phase-field variable is responsible for the description of data at small undercoolings and the fast gradient flow variable accounts for local non-equilibrium effects at high undercoolings. The introduction of these two types of variables makes the solution of the hodograph equation sufficiently flexible for a reliable description of all nonlinearities of the kinetic curves predicted in MD simulations of Cr and Si. This article is part of the theme issue ‘Transport phenomena in complex systems (part 1)’.

Science & Technology - Other Topics↗
DOE OSTI2022

Analytical benchmark solution for 1-D neutron transport coupled with thermal conduction and material expansion

In this work we present fully analytical solutions for a class of finite, homogeneous, 1-D slab benchmark problems with nonlinear temperature feedback effects. The proposed class of benchmarks include multiplicative 1-D neutron transport (limited to quasistatic S{sub 2} with μ = ±1) coupled with thermal conduction, convection, Doppler broadening, and expansion effects along the length of the slab. This class of benchmark models, along with the corresponding analytical solutions, are valuable for validating multiphysics analysis frameworks that support coupled neutronics/thermal/structural calculations. Analytical solutions for the benchmarks are obtained by introducing an ansatz that the equilibrium flux and temperature distributions in the slab have the same shape. Specific values for the total microscopic cross section, σ{sub t,0}, and conductive heat transfer coefficient, h, that satisfy the assumed ansatz are then determined. A discussion of the procedure for generating benchmark models and analytical solutions is provided, along with numerical results for an example set of model parameters and thoughts on practical applications of the benchmarks for multiphysics code validation. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Data for Soil Oxygen Dynamics: A Key Mediator of Tile Drainage Impacts on Coupled Hydrological, Biogeochemical, and Crop Systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

Modeling↗
DOE OSTI2025

Soil oxygen dynamics: a key mediator of tile drainage impacts on coupled hydrological, biogeochemical, and crop systems

Tile drainage removes excess water and is an essential, widely adopted management practice to enhance crop productivity in the US Midwest and throughout the world. Tile drainage has been shown to significantly change hydrological and biogeochemical cycles by lowering the water table and reducing the residence time of soil water, although examining the complex interactions and feedbacks in an integrated hydrology–biogeochemistry–crop system remains elusive. Oxygen dynamics are critical to unraveling these interactions and have been ignored or oversimplified in existing models. Understanding these impacts is essential, particularly so because tile drainage has been highlighted as an adaptation under projected wetter springs and drier summers in the changing climate in the US Midwest. We used the ecosys model that uniquely incorporates first-principle soil oxygen dynamics and crop oxygen uptake mechanisms to quantify the impacts of tile drainage on hydrological and biogeochemical cycles and crop growth in corn–soybean rotation fields. The model was validated with data from a multi-treatment, multi-year experiment in Washington, IA. The relative root mean square error (rRMSE) for the corn and soybean yield in validation is 5.66 % and 12.57 %, respectively. The Pearson coefficient (r) of the monthly tile flow during the growing season is 0.78. Plant oxygen stress turns out as an emergent property of the equilibrium between the soil oxygen supply and biological demand. The impact of tile drainage on the system is achieved through a series of coupled feedback mechanisms. The model results show that tile drainage reduces the soil water content and enhances soil oxygenation. It additionally increases the subsurface discharge and elevates inorganic nitrogen leaching, with seasonal variations influenced by climate and crop phenology. The improved aerobic condition alleviates crop oxygen stress during wet springs, thereby promoting crop root growth during the early growth stage. The development of greater root density, in turn, mitigates water stress during dry summers, leading to an overall increase in the crop yield by ∼6 %. These functions indicate the potential of tile drainage in bolstering crop resilience to climate change and the use of this modeling tool for large-scale assessments of tile drainage. The model reveals the underlying causal mechanisms that drive the agroecosystem response to drainage on the coupled hydrology, biogeochemistry, and crop system dynamics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Activity coefficient acquisition with thermodynamics–informed active learning for phase diagram construction

This work explores the use of thermodynamics-informed Gaussian processes (GPs) and active learning (AL) to model activity coefficients and construct phase diagrams. Relying on synthetic data generated from an excess Gibbs energy model, GPs were found to accurately describe the activity coefficients of several binary mixtures across large composition and temperature ranges. Moreover, GPs could estimate their own uncertainty and identify composition/temperature regions where activity coefficient data provide the most information to the models. This was leveraged to build AL algorithms targeted at modeling phase equilibria. In many cases, a single active-learning-acquired data point was sufficient to describe the phase diagrams studied. Lastly, the ability of AL to greatly reduce the amount of data needed to obtain accurate models was further verified on experimental case studies, namely individual ion activity coefficients, the solid–liquid and vapor–liquid equilibrium of deep eutectic solvents, and phase equilibria in ternary mixtures.

25 ENERGY STORAGE↗
DOE OSTI2023

Crystallographic and temperature effects in low-energy collisions for plasma–material interactions

The interaction of plasma with materials is critical for the fundamental understanding of non-equilibrium processes and their wide application. Recent achievements in fusion energy research emphasize the importance of this problem. Because modelling and predicting plasma–material interactions (PMI) require considering a tremendous number of single plasma particle–surface interaction events, this type of modelling was, until recently, possible only within a binary collision approximation (BCA). The BCA approach considers materials as uniform temperature-insensitive media. The research presented here addresses the PMI problem within an atomistic-based approach for the first time at a statistically significant level. Approximately 10 5 molecular dynamics trajectories were generated for 100 eV deuterium ions interacting with a tungsten surface. The research demonstrated the critical importance of incorporating the discrete lattice structure of matter into the model. Deuterium penetration depth and fraction of backscattered deuterium ions strongly depend on the surface orientation, the impact incident ion directions, and the ions’ initial positions. On average, the BCA-based calculations underestimate the penetration depth by a factor of two or more, and the fraction of backscattered atoms is overestimated by a factor of two or more. As a result, increasing the material temperature from 500 to 3000 K reduces penetration depth by about 30% and increases the fraction of backscattered atoms by 250%.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Impact of Disorder and Defects on Bond Energies in Highly Fluorinated Graphene

Fluorinated graphite materials are of interest for an assortment of applications and can be synthesized under a variety of synthetic conditions from many different types of carbon. Due to such variations, structural disorders in the form of defects and polymorphism are often present. Here, we investigate the impact of local structural variations on the C–F bond dissociation energies (BDEs) in carbon-based fluoride materials using density functional theory (DFT) computational methods. Employing fluorographene (FG) cluster models, we determine the impact of different C–F bonding configurations in the core of each platelet on the equilibrium BDEs for each C–F bond. The introduction of structural disorder decreases the first C–F BDE by approximately 1 eV compared to the canonical arrangement of axial C–F bonds ordered as in a network of cyclohexane “chairs”. Variability of calculated BDEs among the different polymorphs decreases upon subsequent F removal. Common structural tendencies of the adiabatic defluorination pathways for each polymorph are identified. In conclusion, our analysis suggests that at F/C ratios near 1.0, disorder in the local structure can play a significant role in the energetics of the initial carbon fluoride defluorination and that the influence of this configurational disorder diminishes with decreasing F/C ratios.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Collisional excitation of C2H− by H2: New interaction potential and scattering calculations

Interstellar anions play an important role in astrochemistry as being tracers of the physical and chemical conditions in cold molecular clouds and circumstellar gas. The local thermodynamic equilibrium is generally not fulfilled in media where anions are detected and radiative and collisional data are required to model the observed lines. The C2H− anion has not yet been detected in the interstellar medium; however, collisional data could be used for non-LTE models that would help in identifying the most intense lines. For this purpose, we have computed the first 4D potential energy surface (PES) of the C2H−–H2 complex using an explicitly correlated coupled-cluster approach. The PES is characterized by a single deep minimum with a well-depth of 924.96 cm−1. From this interaction potential, we derived excitation cross sections and rate coefficients of C2H− induced by collisions with para- and ortho-H2. The results obtained for collisions with para-H2 are compared to previous calculations performed using a 2D-PES obtained from an average over H2 rotations.

Chemistry↗
DOE OSTI2021

Simulating dust grain-radiation coupling on a moving mesh

ABSTRACT We present a model for the interaction between dust and radiation fields in the radiation hydrodynamic code arepo-rt, which solves the moment-based radiative transfer equations on an unstructured moving mesh. Dust is directly treated using live simulation particles, each of which represent a population of grains that are coupled to hydrodynamic motion through a drag force. We introduce methods to calculate radiation pressure on and photon absorption by dust grains. By including a direct treatment of dust, we are able to calculate dust opacities and update radiation fields self-consistently based on the local dust distribution. This hybrid scheme coupling dust particles to an unstructured mesh for radiation is validated using several test problems with known analytic solutions, including dust driven via spherically symmetric flux from a constant luminosity source and photon absorption from radiation incident on a thin layer of dust. Our methods are compatible with the multifrequency scheme in arepo-rt, which treats UV, optical photons as single scattered and IR photons as multi scattered. At IR wavelengths, we model heating of and thermal emission from dust. Dust and gas are not assumed to be in local thermodynamic equilibrium but transfer energy through collisional exchange. We estimate dust temperatures by balancing these dust-radiation and dust-gas energy exchange rates. This framework for coupling dust and radiation can be applied in future radiation hydrodynamic simulations of galaxy formation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

SILCC – VII. Gas kinematics and multiphase outflows of the simulated ISM at high gas surface densities

ABSTRACT We present magnetohydrodynamic (MHD) simulations of the star-forming multiphase interstellar medium (ISM) in stratified galactic patches with gas surface densities Σgas = 10, 30, 50, and 100 $\mathrm{M_\odot \, pc^{-2}}$. The silcc project simulation framework accounts for non-equilibrium thermal and chemical processes in the warm and cold ISM. The sink-based star formation and feedback model includes stellar winds, hydrogen-ionizing UV radiation, core-collapse supernovae, and cosmic ray (CR) injection and diffusion. The simulations follow the observed relation between Σgas and the star formation rate surface density ΣSFR. CRs qualitatively change the outflow phase structure. Without CRs, the outflows transition from a two-phase (warm and hot at 1 kpc) to a single-phase (hot at 2 kpc) structure. With CRs, the outflow always has three phases (cold, warm, and hot), dominated in mass by the warm phase. The impact of CRs on mass loading decreases for higher Σgas and the mass loading factors of the CR-supported outflows are of order unity independent of ΣSFR. Similar to observations, vertical velocity dispersions of the warm ionized medium (WIM) and the cold neutral medium (CNM) correlate with the star formation rate as $\sigma _\mathrm{z} \propto \Sigma _\mathrm{SFR}^a$, with a ∼ 0.20. In the absence of stellar feedback, we find no correlation. The velocity dispersion of the WIM is a factor ∼2.2 higher than that of the CNM, in agreement with local observations. For $\Sigma _\mathrm{SFR} \gtrsim 1.5 \times 10^{-2}\, \mathrm{M}_\odot \, \mathrm{yr}^{-1}\, \mathrm{kpc}^{-2}$ the WIM motions become supersonic.

Astronomy & Astrophysics↗
DOE OSTI2021

Electrochemical Modeling of Iodide Oxidation in Metal-Halide Molten Salts

The oxidation of iodide in NaI-AlBr 3 , NaI-AlCl 3 , and NaI-GaCl 3 molten salts was analyzed using simulation software to extract relevant kinetic parameters. The experimental oxidation potentials were ordered AlCl 3 < AlBr 3 < GaCl 3 , with higher oxidation potentials correlating with softer Lewis acidity of the metal halide. An iodide oxidation and metal halide speciation model was developed and simulated to fit the electrochemical response, enabling determination of electrochemical charge transfer parameters and chemical equilibrium constants. NaI-AlBr 3 displayed the fastest electron transfer rates yet showed the lowest current densities. All salts revealed smaller than expected current densities, explained by equilibrium between various species, where some are not electrochemically active at the studied potentials. These equilibrium reactions are due to the various metal halide species, controlling the reactant concentration of iodide and the resultant current. We hypothesize the electrochemically active iodide species, present as a metal halide monomer (MX 3 I − ), is decreased dramatically from the expected concentration, sequestered as a more stable metal halide dimer species (M 2 X 6 I − ) with a higher oxidation potential. Traditional Tafel analysis of the experimental data supports the validity of the simulations. These results increase understanding of iodide oxidation in low-temperature Lewis acidic molten salts and inform task-specific molten salt design.

Lee, Rose Y. (ORCID:0000000226587220)↗
DOE OSTI2021

Automated discovery of a robust interatomic potential for aluminum

Machine learning, trained on quantum mechanics (QM) calculations, is a powerful tool for modeling potential energy surfaces. A critical factor is the quality and diversity of the training dataset. Here we present a highly automated approach to dataset construction and demonstrate the method by building a potential for elemental aluminum (ANI-Al). In our active learning scheme, the ML potential under development is used to drive non-equilibrium molecular dynamics simulations with time-varying applied temperatures. Whenever a configuration is reached for which the ML uncertainty is large, new QM data is collected. The ML model is periodically retrained on all available QM data. The final ANI-Al potential makes very accurate predictions of radial distribution function in melt, liquid-solid coexistence curve, and crystal properties such as defect energies and barriers. We perform a 1.3M atom shock simulation and show that ANI-Al force predictions shine in their agreement with new reference DFT calculations.

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

Modeling active nematics via the nematic locking principle

Active nematic systems consist of rod-like internally driven subunits that interact with one another to form large-scale coherent flows. They are important examples of far-from-equilibrium fluids, which exhibit a wealth of nonlinear behavior. This includes active turbulence, in which topological defects in the nematic order braid around one another in a chaotic fashion. One of the most studied examples of active nematics consists of a dense two-dimensional layer of microtubules, crosslinked by kinesin molecular motors that inject extensile deformations into the fluid. Though numerous theoretical studies have modeled microtubule-based active nematics, no consensus has emerged on how to fully and quantitatively capture the features of the experimental system. Here, to better understand the theoretical foundations for modeling this system, we propose a fundamental principle we call the nematic locking principle—individual microtubules cannot rotate without all neighboring microtubules also rotating. Physically, this is justified by the high density of the microtubules, their elongated nature, and their corresponding steric interactions. We assert that the nematic locking principle holds throughout the majority of the material, but breaks down in the neighborhood of topological defects and other regions of low density. We derive the most general nematic transport equation consistent with this principle and also derive the most general term that violates it, introducing fracturing into the material. We then examine the standard Beris–Edwards approach, commonly used to model this system, and show that it violates the nematic locking principle throughout the majority of the material due to fracturing. We then propose a modification to the Beris–Edwards model that enforces nematic locking nearly everywhere. This modification shuts off fracturing except in regions where the order parameter (a proxy for density) is reduced. In these regions fracturing is turned on. The resulting simulations in turn show strong nematic locking throughout the bulk of the material, with narrow bands of fracturing, consistent with experimental observation. One additional advantage of enforcing nematic locking is that nontrivial stationary state solutions, common in Beris–Edwards simulations but not seen in experiments, are eliminated.

Mitchell, Kevin A. [Univ. of California, Merced, C↗
DOE OSTI2021

AMOC stability and diverging response to Arctic sea ice decline in two climate models

This study compares the impacts of Arctic sea ice decline on the Atlantic meridional overturning circulation (AMOC) in two configurations of the Community Earth System Model with different horizontal resolution. In a suite of model experiments, we impose radiative imbalance at the ice surface, replicating a loss of sea ice cover comparable to that observed during 1979–2014, and we find dramatic differences in the AMOC response between the two models. In the lower-resolution configuration, the AMOC weakens by about one-third over the first 100 years, approaching a new quasi-equilibrium. By contrast, in the higher-resolution configuration, the AMOC weakens by ~10% during the first 20–30 years followed by a full recovery driven by invigorated deep water formation in the Labrador Sea and adjacent regions. We investigate these differences using a diagnostic AMOC stability indicator, which reflects the AMOC freshwater transport in and out of the basin and hence the strength of the basin-scale salt-advection feedback. This indicator suggests that the AMOC in the lower-resolution model is less stable and more sensitive to surface perturbations, as confirmed by hosing experiments mimicking Arctic freshening due to sea ice decline. Differences between the models’ mean states, including the Atlantic Ocean mean surface freshwater fluxes, control the differences in AMOC stability. Our results demonstrate that the AMOC stability indicator is indeed useful for evaluating AMOC sensitivity to perturbations. We emphasize that, despite the differences in the long-term adjustment, both models simulate a multidecadal AMOC weakening caused by Arctic sea ice decline, relevant to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Shadow masks predictions in SPARC tokamak plasma-facing components using HEAT code and machine learning methods

Here, this work uses machine learning (ML) to complement HEAT (Heat flux Engineering Analysis Toolkit) by developing 3-D footprint surrogate models for fast and accurate heat load calculations in the divertor of the SPARC tokamak. The focus is on shadowed regions, or magnetic shadows, caused by the 3-D geometry of plasma-facing components (PFCs). ML classifiers are employed to create a surrogate model for HEAT generated shadow masks, predicting these shadow masks and divertor heat flux profiles based on a diverse range of equilibria and only the plasma current, safety factor(q95) at the edge, and magnetic flux angles as input parameters. The ultimate goal is to integrate the model for real-time control and future operational decisions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Thermodynamic modeling with uncertainty quantification using the modified quasichemical model in quadruplet approximation: Implementation into PyCalphad and ESPEI

The modified quasichemical model in the quadruplet approximation (MQMQA) considers the first- and the second-nearest-neighbor coordination and interactions, particularly useful in describing short-range ordering (SRO) in complex liquids such as molten salts, slag in metal processing, and electrolytic solutions. Here, the present work implements the MQMQA into the Python based open-source software PyCalphad for thermodynamic calculations. This endeavor facilitates the development of MQMQA-based thermodynamic database with uncertainty quantification (UQ) and propagation (UP) using the open-source software ESPEI. A new database structure based on Extensible Markup Language (XML) is proposed for ESPEI evaluation of MQMQA model parameters. Using the KF-NiF 2 , KCl-NaCl-MgCl 2 , and CaCl 2 -CaF 2 -LiCl-LiF salt systems as examples, we demonstrate the successful implementation of MQMQA in PyCalphad through thermodynamic calculations of Gibbs energy, equilibrium quadruplet fractions, and phase diagram, as well as database development with UQ and UP using ESPEI. Furthermore, as an application of the present implementation, both the LiF–TbF 3 and LiF-HoF 3 systems have been modeled by MQMQA for the first time, which are in good agreement with experiments. The present implementation hence offers an open-source capability for performing CALPHAD modeling for complex liquids with SRO using MQMQA plus a new XML database structure.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamics near equilibrium for intense beams in a nonlinear integrable focusing channel

Accelerator storage ring designs based on nonlinear integrable Hamiltonian systems provide a novel test-bed for studying the interplay between nonlinear dynamics and space charge at high intensity. In this work, the structure of beam Vlasov equilibria is explored for a constant focusing channel based on the nonlinear focusing potential of the Integrable Optics Test Accelerator. Finally, the dynamics of the single-particle orbits is explored in the combined space charge and external focusing fields as a function of beam current, and the self-consistent relaxation of a mismatched beam to equilibrium is characterized.

43 PARTICLE ACCELERATORS↗