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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Thermal Conductivity Degradation in High Burnup U-Pu-Zr Fuel

Recent advancements in the characterization of irradiated U-Pu-Zr fuels have revealed complexities that challenge existing understanding of constituent redistribution. Traditionally, models have proposed three concentric regions within the fuel, each characterized by distinctive phases and porosity. However, through detailed analysis of high burnup U-Pu-Zr, we discovered the presence of four distinct constituent redistribution regions. Particularly novel is the observation of significant Pu redistribution, a previously unreported phenomenon that necessitates a reevaluation of current models. This work aims to delve deeper into these findings, seeking to correlate mesoscale measurements of thermal diffusivity and respective thermal conductivity with the phases present in each redistribution region. To achieve this objective, we employed mesoscale thermoreflectance methods using the unique, Idaho National Laboratory (INL) developed, Thermal Conductivity Microscope (TCM) at INL’s Irradiated Materials Characterization Laboratory. The TCM employs two tightly focused lasers: one for heating to generate periodic thermal waves in the substrate, and another spatially separated probe laser to detect changes in the optical reflectivity of the gold-coated substrate resulting from thermal wave diffusion. We conducted several thermal diffusivity measurements within each region of constituent redistribution of a U-19Pu-10Zr fuel pin cross section irradiated to 11 at. % burnup. The TCM measurement positions strategically aligned with transmission electron microscopy (TEM) lift-out locations previously collected from the fuel sample. Complementary microstructural analysis techniques such as optical and scanning electron microscopy (OM/SEM), electron probe microanalysis for chemical compositions, and TEM-based selective area electron diffraction (SAED) analysis for crystallographic insights into each phase were also utilized. This comprehensive approach allowed us to correlate local thermal diffusivity data with microstructural characteristics, enabling the computation of local thermal conductivity at each position. The significance of this contribution lies in its pioneering use of the TCM for ternary fuel mesoscale examination, shedding light on the previously overlooked effects of Pu redistribution on local thermal conductivity. By informing current models capturing constituent redistribution and heat transfer, our findings pave the way for more accurate predictions of metallic fuel performance. Moreover, this work sets the stage for future comparisons with similar TCM examinations on U-19Pu-10Zr fuels at ultra-low burnup, facilitating a comprehensive understanding of thermal property changes across different burnup levels. Ultimately, our study not only enriches our understanding of the thermophysical properties of individual redistribution regions within U-Pu-Zr fuel but also offers valuable insights for the design and operational parameters of proposed next-generation fast reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

FY21 Status Report on the Molten Salt Thermal Properties Database (MSTDB) Development

This report describes the thermodynamic assessments for the ZrF 4 -BeF 2 , AlCl 3 -NaCl, AlCl3-KCl and the PuCl 3 -CsCl pseudo-binary subsystems. Calculation results are compared to the inputs used to optimize the adjustable model parameters. The data came from experimental studies reported in the open literature, from the MSR research community, and/or computational results generated within the NEAMS program and from collaborators. The models from the thermodynamic assessment of these salt systems are integrated into the MSTDB-TC (Molten Salt Thermal Properties Database-Thermochemical). A brief description of the companion MSTDB-TP (Molten Salt Thermal Properties Database-Thermophysical) is given along with an overview of the approach for predicting both thermodynamic and thermophysical property behavior of multicomponent systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

FFTF HT9 Cladding Microstructure Characterization

The sodium-cooled fast reactor (SFR) is a promising candidate for next generation nuclear reactors, operating at extreme conditions which include high temperatures (>500?C core outlet temperature) and significant neutron damage. High-Cr martensitic HT9 steel is an excellent candidate for SFR cladding and duct material due to its compatibility with liquid sodium, good thermal conductivity, resistance to void swelling, and strong creep rupture strength [1-4].However, the harsh in-core environment of SFRs can cause complex microstructural changes and mechanical property degradation in HT-9. Ensuring the safe use of HT9 cladding for metallic fuel requires both a thorough understanding of its mechanical response to microstructure evolution as well as reliable microstructure-sensitive modeling predictions. Microstructure-sensitive modeling of high temperature creep behavior in HT9 cladding for SFR applications currently lack experimental data to model the phenomena accurately. To fill this need, methods to perform microstructural characterization have been developed and performed on HT9.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of Hopper Flow Prediction based on Cubical Triaxial Tester Data and FEM

A cubical triaxial tester (CTT) laboratory device was used to collect key bulk flow characteristics of Crumbles® 6mm rotary sheared woody biomass and Crumbles® 2mm rotary sheard corn stover. The flow properties and a CAD model of a hopper and conveyor system to predict the consistency of flow of material from the hopper. Prediction included the likeliness of jamming, bridging, or ratholing. Each material was then loaded into the hopper and the actual bulk flow was measured. Measured flow closely matched the predicted flow.

09 BIOMASS FUELS↗
DOE OSTI2022

Developments in Physics-Based Modeling and Machine Learning for Environmental Effects in Nuclear Materials [Slides]

This presentation is part of an annual review for the new Advanced Materials and Manufacturing Technologies program under the Office of Nuclear Energy within the Department of Energy. This presentation covers advances in physics-based modeling and machine learning for environmental effects in nuclear materials. It discusses efforts in combining density functional theory with machine learning to predict material properties, studying irradiation defect recombination, and improving MOOSE-based tools for crystal plasticity modeling and the Stochastic Tools Module.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Elucidating the Role of Hydrogen Bond Donor and Acceptor on Solvation in Deep Eutectic Solvents Formed by Ammonium/Phosphonium Salts and Carboxylic Acids

Deep eutectic solvents (DESs) constitute a rapidly emerging class of sustainable liquids that have been widely studied and employed in chemical separations, catalysis, and electrochemistry. The unique physicochemical and solvation properties of DESs can be highly tailored by choosing the appropriate hydrogen bond acceptor (HBA) and hydrogen bond donor (HBD). Understanding the role of the HBA and HBD on the multiple solvation interactions in DESs is important to enable their judicious selection for particular applications. As such, this work constitutes the first study to exploit chromatography to measure solute–solvent interactions of DESs using a wide array of known probe molecules. The constituent components of 20 DESs, formed by ammonium and phosphonium-based salts and carboxylic acids, are systematically modulated to delineate the contribution of the HBA and HBD toward individual solvation properties. Solute–solvent interactions measured in this study are used to interpret and explain the performance of DESs in desulfurization of fuels and extraction of natural products. The results from this study can be used to predict and understand the performance of DESs in various chemical processes where solvation interactions heavily influence outcomes.

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

Property Measurements of NaCl-UCl 3 and NaCl-KCl-UCl 3 Molten Salts (Rev.1)

Thermochemical and thermophysical property values of several salt compositions of interest are needed by molten salt reactor (MSR) developers to design, license, and operate their reactors. Thermochemical and thermophysical properties being measured at Argonne include thermal transitions, phase behavior, heat capacity, density, volumetric thermal expansion of the liquid phase, thermal diffusivity, thermal conductivity, and viscosity. Several properties of eutectic compositions in the ternary NaCl-KCl-UCl 3 and binary NaCl-UCl 3 systems that may be used by MSR developers as fuel bearing salts are being measured. A 65.8 mol % NaCl–34.2 mol % UCl 3 mixture and a near-eutectic mixture of 50.9 mol % NaCl–24.4 mol % KCl–24.7 mol % UCl 3 were synthesized and the thermochemical properties of the mixtures were measured by using differential scanning calorimetry (DSC). The measured transition temperatures were compared to transition temperatures predicted using two models. A thermodynamic model of the binary NaCl-UCl 3 system was constructed using data in the Molten Salt Thermal Properties Database–Thermochemical Version 2.0 (MSTDB-TC V2.0). A ternary NaClKCl-UCl 3 system model constructed at Argonne and was described in a previous report. These comparisons can be used to validate the models. Thermophysical property values of molten salts are needed to model how salt retains and transfers heat in an MSR system. These property values are essential to the entire MSR design because molten salt is used as both the fuel and the coolant material in a salt fueled reactor. Heat capacity of the synthesized NaCl-UCl 3 and NaCl-KCl-UCl 3 salt mixtures was measured by using DSC and thermal diffusivity was measured by using laser flash analysis (LFA) at temperatures spanning the typical operating range of an MSR.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Colloidal Stability of PFSA-Ionomer Dispersions Part II: Determination of Suspension pH Using Single-Ion Potential Energies

Perfluorosulfonic acid (PFSA) ionomers serve a vital role in the performance and stability of fuel-cell catalyst layers. These properties, in turn, depend on the colloidal processing of precursor inks. Here, to understand the colloidal structure of fuel-cell catalyst layers, we explore the aggregation of PFSA ionomers dissolved in water/alcohol solutions and relate the predicted aggregation to experimental measurements of solution pH. Not all side chains contribute to measured pH because of burying inside particle aggregates. To account for the measured degree of dissociation, a new description is developed for how PFSA aggregates interact with each other. The developed single-counterion electrostatic repulsive pair potential from Part I is incorporated into the Smoluchowski collision-based kinetics of interacting aggregates with buried side chains. We demonstrate that the surrounding solvent mixture affects the degree of aggregation as well as the pH of the system primarily through the solution dielectric permittivity, which drives the strength of the interparticle repulsive energies. Successful pH prediction of Nafion ionomer dispersions in water/n-propanol solutions validates the numerical calculations. Nafion-dispersion pH measurements serve as a surrogate for Nafion particle-size distributions. The model and framework can be leveraged to explore different ink formulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerating Materials Discovery: Artificial Intelligence for Sustainable, High-Performance Polymers

PolyID enables the discovery of polymers with advanced performance and greater sustainability while reducing material development timelines. The material design space is immense and cannot be reasonably probed using an Edisionian approach. High-throughput property prediction, enabled by artificial intelligence provides a hypothesis driven approach for down selection of candidate polymers to pursue experimentally. To aid experimentalists in the down selection of material targets this high-throughput, machine learning-based tool is capable of predicting polymer properties simply from molecular structures. Currently, transport, thermal, and mechanical properties across 7 polymer class (polyamides, polyesters, polycarbonates, polyimides, polyolefins, polyacrylates, and polyurethanes) can be predicted, and the PolyID platform has been flexibly designed so new materials and properties can be added.

artificial intelligence↗
DOE OSTI2025

The search for high-entropy fuel-cell catalysts using disorder descriptors

The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces.

fuel-cell catalysts↗
DOE OSTI2020

On the nonlinear temperature dependence of residual stresses in solid oxide fuel cells

The residual stresses in a dense yttria-stabilized zirconia (YSZ) layer bonded to a porous NiO-YSZ substrate were measured by X-ray diffraction in air between 25°C and 900°C. In this work, it was found that the residual stresses on the outer surface of the YSZ layer were compressive, that their magnitude increased with decreasing temperature, and that the temperature dependence was nonlinear particularly at temperatures below 600°C. An elastic model that incorporates micromechanics and laminate theory was formulated to successfully predicts the nonlinearity of the temperature dependence of the residual stresses by accounting for the effect of the order-disorder transition experienced by YSZ around 600°C and the magnetic transition of NiO at the Néel temperature around 265°C, on the properties of these materials. The implications of the results for the reliability of solid oxide fuel cells are discussed.

25 ENERGY STORAGE↗
DOE OSTI2024

Computational Approaches for Clean Energy Materials

Currently, 80% of the global final energy consumption occurs in form of fuels and only 20% as electricity. On the other hand, renewable energy additions come almost exclusively in the form of electricity (dominantly photovoltaics and wind). Thus, a successful energy transition will require enormous growth in renewables, sufficient to convert excess electricity into fuels, as well as the development of non-electricity based solar fuel technologies. As much as photovoltaic capacities have grown over the past 20 years, it is far from clear that current technologies and materials are up to the task to grow from here by yet another factor 100 until 2050. Therefore, sustained research efforts on emerging inorganic semiconductors for solar electricity and fuels are essential for facing the double challenge of climate change and energy security. Computational materials science can make important contributions, guiding and supporting research activities through both materials search and discovery and through detailed studies that help to develop a mechanistic understanding of materials performance and bottlenecks. This presentation will highlight three recent computational projects with relevance for photovoltaics and solar fuels (1) Defect graph neural networks (dGNN) for materials discovery in solar thermochemical hydrogen (STCH) [1]. The dGNN approach facilitates broad and fast materials screening for defect properties. (2) Modeling highly off-stoichiometric systems by evaluating the free energy of defect interaction [2]. This approach allows quantitative prediction of H2 production in complex STCH oxides. (3) First-principles atomic structure prediction for interfaces [3]. This work showed how an atomically thin CdCl2 interlayer phase enables in principle ideal electron transport across the incommensurate SnO2/CdTe interface. [1] M.D. Witman, A. Goyal, T. Ogitsu, A.H. McDaniel, S. Lany, Nat. Comput. Sci. (2023). https://doi.org/10.1038/s43588-023-00495-2. [2] A. Goyal, M.D. Sanders, R.P. O'Hayre, S. Lany, PRX Energy 3, 013008 (2024). https://doi.org/10.1103/PRXEnergy.3.013008. [3] A. Sharan, M. Nardone, D. Krasikov, N. Singh, S. Lany, Appl. Phys. Rev. 9, 041411 (2022). https://doi.org/10.1063/5.0104008.

density functional theory↗
DOE OSTI2024

Physics-based modeling and data analytics [Slides]

This presentation contains a summary of ongoing work within the physics-based modeling and data analytics work package within the Nuclear Materials Discovery and Qualification initiative (NMDQi). Topics include work on MOOSE-based crystal plasticity, molecular dynamics modeling of recombination in metals and alloys, the MOOSE Stochastic Tools Module, and machine learning and atomistic modeling to predict thermo-kinetic properties of nuclear structural materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Influence of Irradiation-induced Microstructural Defects on Thermal Transport in Single Crystal Thorium Dioxide

Microstructural defects formed as a result of fission fragment damage are known to drastically alter thermal conductivity, a fuel property that governs the efficiency of a nuclear reactor. A fundamental understanding of radiation-induced effects on thermal transport is critical for the development of advanced fuels. Ion-irradiation has widely been used to simulate the effects of neutron-irradiation by seeding atomic-to-nanoscale defects of controllable size and density. In this work, we investigate the influence of microstructural defects, induced by ion-irradiation, on the thermal properties of single crystal thorium dioxide samples grown using the hydrothermal technique. The samples were irradiated using 2 MeV protons at room temperature. A laser-based modulated thermoreflectance technique was used to measure the thermal diffusivity and conductivity within the damaged ThO2 region. The experimentally measured thermal properties were compared with Boltzmann Transport Equation predictions, to determine the impact of various defect types on the thermal properties of ThO2.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Identification of Critical Process Parameters for Knife Milling and Alternative Communication Strategies

An assessment of knife milling operations was performed to identify parameters that require consideration to model wear and degradation of knife blades. A quality-by-design (QbD) paradigm was followed to identify material and feedstock attributes, and processing parameters that impact the quality attributes of the blades used in the comminution of biomass into forms compatible with downstream operations. Based on analytical and experimental observations of knife and tool wear, a QbD model is proposed that considers three wear mechanisms (erosion, abrasion, and gouging) to predict volumetric wear of knife blades and sharpness. These factors, if properly applied, provide a science-based approach to predict the mechanical efficiency of the mill as a function of feedstock properties, knife blade material properties, and processing parameters (feed rate, speed, and design parameters).

09 BIOMASS FUELS↗
DOE OSTI2022

Analytical-based simulation approach for an anion exchange membrane fuel cell

An analytical and empirical-based 1-D, non-isothermal, steady-state model for anion exchange membrane fuel cell capable of capturing two-phase phenomena is presented in this study. Coupled multi-physics including mass and charge transport, electrochemical reactions, heat transfer, and two-phase water transport are considered in the model and the simulated results are compared to experimental data. To better represent actual material properties and localized conditions, the model applies multilayer discretization in the gas diffusion electrode to enhance prediction accuracy. The model successfully predicts the baseline performance at 70 °C, 131 kPa abs., 92% RH with pure H 2 /O 2 gas as well as the limiting current at 10% H 2 . The robust simulation approach allows for simplistic and accurate estimation of cell performance without the complications of applying two-phase parameters and expensive computational need for numerical models. In addition, the results from the sensitivity studies of material properties and operating conditions provide valuable insights on water management strategies and optimal component design for advancing anion exchange membrane fuel cell technology.

1-D model↗
DOE OSTI2024

Complex Structure of Molten FLiBe (2 Li F – Be F 2 ) Examined by Experimental Neutron Scattering, X-Ray Scattering, and Deep-Neural-Network Based Molecular Dynamics

The use of molten salts as coolants, fuels, and tritium breeding blankets in the next generation of fission and fusion nuclear reactors benefits from furthering the characterization of the molecular structure of molten halide salts, paving the way to predictive capability of the chemical and thermophysical properties of molten salts. Due to its neutronic, chemical, and thermochemical properties, 2 Li F - Be F 2 is a candidate molten salt for several fusion- and fission-reactor designs. We performed neutron and x-ray total-scattering measurements to determine the atomic structure of liquid 2 Li F - Be F 2 . We also performed and neural-network molecular-dynamics simulations to predict the structure obtained by neutron- and x-ray-diffraction experiments. The use of machine learning provides improvements to the efficiency in predicting the structure at a longer length scales than is achievable with simulations at significantly lower computational expense while retaining near accuracy. We found that the NNMD simulations accurately predicted the Be F 4 2 − oligomer formations seen in the experimental first-structure-factor peak. Our combination of high-resolution measurements with large-scale molecular dynamics provided an avenue to explore and experimentally verify the intermediate-range ordering beyond the first-nearest neighbor that has posed too many experimental and computational challenges in previous works. With a deeper understanding of the salt structure and ion ordering, the evolution of salt chemistry over the lifetime of a reactor can be better predicted, which is crucial to the licensing and operation of advanced fission and fusion reactors that employ molten salts. To this end, this work will serve as a reference for future studies of salt structure and macroscopic properties with and without the addition of solutes. Published by the American Physical Society 2024

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗