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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

BISON BWR Fuel Modeling Capability: Material Models (FY2020)

Until this year the BISON fuel performance code has been developed primarily for pressurized water reactor (PWR), accident tolerant fuel (ATF), and advanced reactor (e.g., tri-structural isotropic (TRISO), metallic) analyses. The main focus of this work is to develop and document required material models that would aid predicting cladding failure with a specific hydride distribution to be investigated for boiling water reactor (BWR) applications in the next fiscal year. In terms of materials, the primary differences between a BWR and PWR is the use of gadolinia doping of the UO 2 fuel, the use of Zircaloy-2 for the cladding, and the addition of a liner on the inner surface of the cladding for improved pellet-clad mechanical interaction (PCMI)performance. The liner is typically made of pure zirconium or a lower tin content zirconium alloy (e.g. Zr-0.3%Sn). This work details the development and documentation of material and behavioral models for BWR analysis such as the addition of material and behavior models for gadolinia doped UO 2 , pure zirconium, Zr-0.3%Sn, and Zircaloy-2 as well as the ease-of-use additions to allow internal meshing capabilities to include liners. Another important aspect affecting cladding performance is hydrogen diffusion and hydride precipitation in the cladding. A model was added previously for non-lined, homogeneous claddings. In this work, instructions are provided on how to apply the model to BWR liner cladding. Representative experimental values are provided for the hydrogen diffusion parameters and precipitation-dissolution kinetics for typical liner materials of interest.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Modeling creep behavior in ceramic matrix composites

Here, in this work, a three-dimensional viscoplasticity formulation with progressive damage is developed and used to investigate the complex time-dependent constituent load transfer and progressive damage behavior in ceramic matrix composites (CMCs) subjected to creep. The viscoplasticity formulation is based on Hill's orthotropic plastic potential, an associative flow rule, and the Norton-Bailey creep power law with Arrhenius temperature dependence. A fracture mechanics-informed isotropic matrix damage model is used to account for CMC brittle matrix damage initiation and propagation, in which two scalar damage variables capture the effects of matrix porosity as well as matrix property degradation due to matrix crack initiation and propagation. The Curtin progressive fiber damage model is utilized to simulate progressive fiber failure. The creep-damage formulation is subsequently implemented as a constitutive model in the generalized method of cells (GMC) micromechanics formulation to simulate time-dependent deformation and material damage under creep loading conditions. The developed framework is used to simulate creep of single fiber SiC/SiC microcomposites. Simulation results are in excellent agreement with experimental and numerical data available in the literature.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Modeling swelling behavior of hydrogels in aqueous organic solvents

Hydrogel, a three-dimensional elastic structure of cross-linked polymer chains, swells or deswells when immersed in aqueous organic solutions. The swelling of hydrogels depends on temperature, solvent composition, and hydrogel structure as the thermodynamic driving forces for hydrogel swelling are due to solvent – hydrogel interactions and expansion of elastic network. Here in this work, polymer Nonrandom Two-Liquid activity coefficient model is used to account for the solvent – hydrogel interactions and a semi-empirical expression for rubber elasticity is used to describe the expansion of elastic network. The model successfully correlates data for both swelling and equilibrium phase compositions for n-isopropyl acrylamide hydrogels in water – ethanol, water – acetone, water – n-butanol, and water – methyl isobutyl ketone mixed solvents.

42 ENGINEERING↗
DOE OSTI2020

Phase Equilibria and Thermochemistry of Advanced Fuels: Modeling Burnup Behavior (Final Report)

Achieving the goal of developing advanced fuel concepts that meet the DOE objectives of being robust, demonstrating high performance, and are more tolerant of accident conditions than current fuel systems will require a thorough understanding of the thermophysical and thermochemical properties of the constituent materials. Non-oxide fuel systems are being explored under the Advanced Fuels Program that hold significant promise for improved performance and accident tolerance, including the uranium silicide-based system considered in the current work. Prospective cladding materials currently considered that contribute to improved accident tolerance include silicon carbide composites and ferritic alloys (Fe-Cr-Al base compositions). Thus, the effort developed thermochemical models and values, supported with targeted experiments, to evaluate the ferritic alloy and silicon carbide composite cladding systems in contrast to current zirconium alloy cladding. The developed detailed understanding will serve to aid in relatively early screening of candidate systems to avoid wasted effort, guide development of new fuel forms, and to provide a basis for predicting and modeling fuel performance. Major deliverables for the project included: Thermochemical assessment and models of phases in the U-Si and U-Si-N systems; thermochemical evaluation supported by experimental measurements of fuel-cladding interactions of silicide fuel with baseline zirconium, ceramic composite, and ferritic alloy cladding; thermochemical assessment and models of phases supported by experimental measurements for silicide fuel with key fission products provided in a dataset and reported in refereed publications. Within the project a significantly refined U-Si phase diagram was developed and reported that now includes homogeneity ranges for key phases, such at the U 3 Si 2 proposed fuel phase, and settles issues with regard to uncertainty in the stability of some phases. Computational efforts together with key experiments has determined phase formation in interactions between U 3 Si 2 and Zircaloy-4 cladding material, a ferritic FeCrAlY alloy of interest as an advanced cladding material, and silicon carbide, also of interest as a fiber-reinforced composite cladding. As expected, very significant reactions occur between U3Si2 and Zircaloy-4, with much less interaction at higher temperatures for the ferritic alloy, and finally interactions with SiC only in the region of contact. A potentially major issue is the stability of U 3 Si 2 fuel that has undergone significant burnup. The result is the loss of the uranium metal, liberating silicon, and the formation of concomitant fission product elements that either dissolve in the U 3 Si 2 phase or form independent, and possibly silicide phases. A combination of experimental determinations of phase formation of U 3 Si 2 reacted with representative fission products yttrium, gadolinium, cerium, zirconium, and molybdenum and first principles calculations has helped understand the fuel chemistry. The behavior of the U 3 Si 2 phase and the partitioning of silicon to possible fission product phases with burnup was thus determined, with significant dissolution in U 3 Si 2 of cerium, gadolinium, zirconium, and plutonium predicted along with independent phase formation of a U-Mo-Si ternary phase, yttrium silicide, and elemental selenium. It can be concluded that at significant burnup there will be a very minor amount of the U 3 Si 2 fuel phase that will decompose to a lower silicide or a uranium alloy as silicon preferentially forms a secondary phase.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Fatigue behavior and modeling for additive manufactured 304L stainless steel: The effect of surface roughness

The fatigue strength of additively manufactured metallic parts in their as-built surface condition is mainly dominated by the surface roughness. Post-processing is often inevitable to reduce surface roughness effects even though post-processing diminishes the main advantage of additive manufacturing, which is net-shaped direct-to-service production. This work investigates the underlying mechanisms responsible for fatigue failure of additively manufactured 304L stainless steel parts in as-built and machined/polished surface conditions. Both strain- and force-controlled, fully reversed fatigue tests were conducted to gain a comprehensive understanding of surface roughness effects on fatigue behavior. The sensitivity to surface roughness is shown to be dependent on the control mode, with stress-based fatigue tests showing greater sensitivity than strain-based fatigue tests. Moreover, the fatigue life estimation for as-built specimens was performed based on surface roughness parameters as well as the fatigue properties of the specimens in machined/polished surface condition of the material without using any fatigue data of specimens in as-built surface condition. Accordingly, the effect of surface roughness on the fatigue behavior could be estimated reasonably well in both strain-life and stress-life approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Modeling the behavior of concentrated aqueous HNO 3 using machine learning interatomic potentials

We develop two multi-defect machine learning interatomic potentials (MLIPs) trained at the BLYP-D2 and PBE-D3 density functional theories using the DeepMD-kit, allowing for the investigation of structural and thermodynamic properties of nitric acid over a wide range of concentrations via molecular dynamics (MD) simulations. We directly compute the degree of dissociation, α, and pK a from MD simulations, revealing that HNO 3 behaves as a weaker acid at higher concentrations, noting that our standard-state pK a value is in excellent agreement with the experimental one. In general, good agreement is observed with experimental results such as α and density outside the training dataset, with only modest deviations at low-to-medium concentrations. We benchmark our custom multi-defect DeepMD MLIPs against foundational models MACE-MP0 and MACE-OFF23. The foundation models capture some aspects of HNO 3 /NO 3 − solvation in concentrated nitric acid but show noticeable density errors and miss subtle structural features relevant to spectroscopy, whereas the bespoke DeepMD MLIPs yield more compact solvation shells, reproduce density-concentration trends, and run ∼12–15× faster than MACE-MP0. Although classical FFs are still more efficient and match experimental densities better, they lack chemical reactivity and thus cannot predict α or pK a , underscoring the need for system-specific reactive MLIPs beyond universal MLIPs.

Dinpajooh, Mohammadhasan [Pacific Northwest Nation↗
DOE OSTI2024

Forest structural complexity and ignition pattern influence simulated prescribed fire effects

Background: Forest structural characteristics, the burning environment, and the choice of ignition pattern each influence prescribed fire behaviors and resulting fire effects; however, few studies examine the influences and interactions of these factors. Understanding how interactions among these drivers can influence prescribed fire behavior and effects is crucial for executing prescribed fires that can safely and effectively meet management objectives. To analyze the interactions between the fuels complex and ignition patterns, we used FIRETEC, a three-dimensional computational fluid dynamics fire behavior model, to simulate fire behavior and effects across a range of horizontal and vertical forest structural complexities. For each forest structure, we then simulated three different prescribed fires each with a unique ignition pattern: strip-head, dot, and alternating dot. Results: Forest structural complexity and ignition pattern affected the proportions of simulated crown scorch, consumption, and damage for prescribed fires in a dry, fire-prone ecosystem. Prescribed fires in forests with complex canopy structures resulted in increased crown consumption, scorch, and damage compared to less spatially complex forests. The choice of using a strip-head ignition pattern over either a dot or alternating-dot pattern increased the degree of crown foliage scorched and damaged, though did not affect the proportion of crown consumed. We found no evidence of an interaction between forest structural complexity and ignition pattern on canopy fuel consumption, scorch, or damage. Conclusions: We found that forest structure and ignition pattern, two powerful drivers of fire behavior that forest managers can readily account for or even manipulate, can be leveraged to influence fire behavior and the resultant fire effects of prescribed fire. These simulation findings have critical implications for how managers can plan and perform forest thinning and prescribed burn treatments to meet risk management or ecological objectives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Modeling the Behavior of Complex Aqueous Electrolytes Using Machine Learning Interatomic Potentials: The Case of Sodium Sulfate

Understanding the structure and thermodynamics of solvated ions is essential for advancing applications in electrochemistry, water treatment, and energy storage. While ab initio molecular dynamics methods are highly accurate, they are limited by short accessible time and length scales whereas classical force fields struggle with accuracy. Herein, we explore the structure and thermodynamics of complex monovalent-divalent ion pairs using Na 2 SO 4 (aq) as a case study by applying a machine learning interatomic potential (MLIP) trained on density functional theory (DFT) data. Our MLIP-based approach reproduces key bulk properties such as density and radial distribution functions of water. We provide the hydration structure of the sodium and sulfate ions in the 0.1–2 M concentration range and the one-dimensional and two-dimensional potentials of mean force for the sodium–sulfate ion pairing at the low concentration limit (0.1 M), which are inaccessible to DFT. At low concentrations, the sulfate ion is strongly solvated, leading to the stabilization of solvent-separated ion pairs over contact ion pairs. Minimum energy pathway analysis revealed that coordinating two sodium ions with a sulfate ion is a multistep process whereby the sodium ions coordinate to the sulfate ion sequentially. Finally, we demonstrate that MLIPs allow the study of solvated ions beyond simple monovalent pairs with DFT-level accuracy in their low concentration limit (0.1 M) via statistically converged properties from ns-long simulations.

anions↗
DOE OSTI2020

Morphology and chemical behavior of model CsO x /Cu 2 O/Cu(111) nanocatalysts for methanol synthesis: Reaction with CO 2 and H 2

Cs is a promoter of Cu-based catalysts for the synthesis of alcohols from CO 2 hydrogenation. Scanning tunneling microscopy and ambient-pressure x-ray photoelectron spectroscopy were used to study the morphology and chemical properties of surfaces generated by the deposition of cesium on Cu 2 O/Cu(111) and Cu(111) substrates. CsO x nanostructures were formed after Cs metal was deposited on Cu 2 O/Cu(111) at 300 K. The formed CsO x protrude over the surface of copper oxide by 2–4 Å, with the dimension at the base of the nanostructures being in the range of 1–3 nm. Heating to elevated temperature induced significant changes in the size and dispersion of the CsO x nanostructures, and there was a clear reconstruction of the copper oxide substrate, which then exhibited long range order with a hexagonally packed structure. The as-deposited and annealed surfaces of CsO x /Cu 2 O/Cu(111) were more reactive toward CO 2 than plain Cu 2 O/Cu(111) or clean Cu(111). Yet, none of them were stable in the presence of H 2 , which fully reduced the copper oxide at 400–450 K. In CsO x /Cu(111), the CsO x nanoclusters were dispersed all over the metallic copper in no particular order. The CsO x species had an average width of 2 nm and ~1 Å height. The CsO x /Cu(111) systems exhibited the highest activity for the binding and dissociation of CO 2 , indicating that the CsO x -copper interface plays a key role in alcohol synthesis.

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

Modeling the Behavior of Noble Metals During HLW Vitrification in the DM1200 Melter

The noble metals ruthenium (Ru), rhodium (Rh), and palladium (Pd) are present to various extents in defense and commercial high-level nuclear waste streams. Usually, their concentrations are higher in commercial waste. Ru, Rh, and Pd are sparingly soluble in silicate glass melts and they therefore form separate metal, oxide, or other phases. These phases are generally denser than the glass melt and tend to sediment to the bottom of the melter, where they form a "sludge" layer. Since the sludge layer has a higher electrical conductivity than the molten glass, accumulation of sludge can ultimately lead to electrical disruption of the operation of the melter. In addition, the sludge reduces the melt volume, which also changes the operational characteristics of the melter. The noble metals sludge is a highly viscous mixture of melt and noble metal phases. The concentration of noble metals in the sludge may be 10 to 100 times higher than that in the drained glass.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Integrated simulation of U-10Mo monolithic fuel swelling behavior

Here, a separate computational branch has been implemented within the DART (Dispersion Analysis Research Tool) computational code to simulate the swelling behavior of U-10Mo monolithic fuel under the operating conditions of high-power research and test reactors (RTRs). The monolithic branch of the DART code implements a mechanistic rate-theory-based fission-gas-behavior model for the calculation of fission gas swelling, as well as a suite of thermal, physical, and mechanical models to take into account various processes occurring in RTR fuels during irradiation. In order to accurately simulate and eventually predict U-10Mo monolithic fuel irradiation behavior, the code uses materials properties calculated with lower length-scale computational methods, such as gas atom diffusivity and U-Mo surface energy from atomic simulations and grain-morphology-specific recrystallization kinetics (recrystallized fuel volume fractions vs. fission density) predicted using the phase-field method. The remainder of fission gas behavior parameters used in the model were calibrated with measured intergranular bubble size distributions. With this integrated simulation approach, the swelling behavior of U-10Mo monolithic fuel was simulated for various initial grain sizes at different operating conditions and compared with measured data. Furthermore, because limited experimental data exist for parameter calibration detailed sensitivity studies for the important parameters used in the fission gas behavior model were performed in order to examine their impact on both intergranular gas bubble morphology at low fission density, and on total porosity at high fission density.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Unifying Energy-Management Problems for Inverter-Based Power Networks

This paper outlines a suite of energy-management problems for inverter-based power networks from the vantage point of optimal control and (non)linear optimization. The problems are categorized based on timescales dictated by the network dynamics, and organized methodologically based on the problem complexity. A growing body of literature has addressed problems in this domain, albeit, with poorly motivated assumptions and behavioral models that obscure precise device behavior. With a combination of circuit- and control-theoretic lenses, we establish appropriate dynamic models for the networked resources, illustrate how common engineering assumptions arise, uncover how problems are linked, and postulate open challenges.

(non)linear optimization↗
DOE OSTI2025

Machine learning-enabled discovery of ionic liquid–solvent electrolytes exhibiting high ionic conductivity

Ionic liquids (ILs), which are a class of materials with versatile nature and growing popularity, are facing impediments toward widespread usage as electrolytes due to various factors such as low ionic conductivity, high viscosity, high market price etc. One of the ways these limitations can be addressed is by mixing ILs with a molecular solvent. In a combinatorial sense, there exists an immense number of specific IL–solvent combinations. An exhaustive experimental or even simulation-based investigation of the chemical space spanned by such combinations can be extremely time-consuming, expensive, and nearly impossible. An alternative approach is to employ machine learning-based models developed from available databases. Although there exists prior literature that integrates machine learning to investigate mixtures of specific solvents with ILs, these models lack generalization necessitating development of a large number of ML models to handle various solvents. To remedy this shortcoming, as a part of designing green electrolytes with high ionic conductivity that can have potential applications in next-generation batteries and solar cells, this work aims to develop a unified machine learning model to predict ionic conductivity of any IL–solvent mixture system. In this regard, three models, namely, Random Forest, extreme gradient boosting (XGBoost), and artificial neural network (ANN) were formulated using the NIST ILThermo database. The dataset contained 549 unique ionic liquids from 16 cation families and 81 unique solvents, representing a total of 23 712 datapoints. SHAPLEY additive explanation (SHAP) method was used to assess the impact of various features on model prediction and their significance was compared with literature to gain physical insight about the model behavior. Finally, using the developed models, approximately 2.5 million IL–solvent mixtures at five different compositions were screened at room temperature. The high-throughput screening yielded nearly 19 000 IL–solvent mixtures for which ionic conductivity was found to exceed the ionic conductivity of conventional Li-ion battery electrolyte.

25 ENERGY STORAGE↗