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DOE OSTI2021

Constitutive Model Development for Aging Polymer Encapsulants (ASC P&EM FY2021 L2 Milestone 7836)

This SAND report fulfills the completion requirements for the ASC Physics and Engineering Modeling Level 2 Milestone 7836 during Fiscal Year 2021. The Sandia Simplified potential energy clock (SPEC) non-linear viscoelastic constitutive model was developed to predict a whole host of polymer glass physical behaviors in order to provide a tool to assess the effects of stress on these materials over their lifecycle. Polymer glasses are used extensively in applications such as electronics packaging, where encapsulants and adhesives can be critical to device performance. In this work, the focus is on assessing the performance of the model in predicting material evolution associated with long-term physical aging, an area that the model has not been fully vetted in. These predictions are key to utilizing models to help demonstrate electronics packaging component reliability over decades long service lives, a task that is very costly and time consuming to execute experimentally. The initiating hypothesis for the work was that a model calibration process can be defined that enables confidence in physical aging predictions under ND relevant environments and timescales without sacrificing other predictive capabilities. To test the hypothesis, an extensive suite of calibration and aging data was assembled from a combination of prior work and collaborating projects (Aging and Lifetimes as well as the DoD Joint Munitions Program) for two mission relevant epoxy encapsulants, 828DGEBA/DEA and 828DGEBA/T403. Multiple model calibration processes were developed and evaluated against the entire set of data for each material. A qualitative assessment of each calibration's ability to predict the wide range of aging responses was key to ranking the calibrations against each other. During this evaluation, predictions that were identified as non-physical, i.e., demonstrated something that was qualitatively different than known material behavior, were heavily weighted against the calibration performance. Thus, unphysical predictions for one aspect of aging response could generate a lower overall rating for a calibration process even if that process generated better quantitative predictions for another aspect of aging response. This insurance that all predictions are qualitatively correct is important to the overall aim of utilizing the model to predict residual stress evolution, which will depend on the interplay amongst the different material aging responses. The DSC-focused calibration procedure generated the best all-around aging predictions for both materials, demonstrating material models that can qualitatively predict the whole host of different physical aging responses that have been measured. This step forward in predictive capability comes from an unanticipated source, utilization of calorimetry measurements to specify model parameters. The DSC-focused calibration technique performed better than compression-focused techniques that more heavily weigh measurements more closely related to the structural responses to be predicted. Indeed, the DSC-focused calibration procedure was only possible due to recent incorporation of the enthalpy and heat capacity features into SPEC that was newly verified during this L2 milestone. Fundamentally similar aspects of the two material model calibrations as well as parametric studies to assess sensitives of the aging predictions are discussed within the report. A perspective on the next steps to the overall goal of residual stress evolution predictions under stockpile conditions closes the report.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Models and Strategies for Optimal Demand Side Management in the Chemical Industries

Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

From Fluid Flow to Coupled Processes in Fractured Rock: Recent Advances and New Frontiers

Abstract Quantitative predictions of natural and induced phenomena in fractured rock is one of the great challenges in the Earth and Energy Sciences with far‐reaching economic and environmental impacts. Fractures occupy a very small volume of a subsurface formation but often dominate fluid flow, solute transport and mechanical deformation behavior. They play a central role in CO 2 sequestration, nuclear waste disposal, hydrogen storage, geothermal energy production, nuclear nonproliferation, and hydrocarbon extraction. These applications require predictions of fracture‐dependent quantities of interest such as CO 2 leakage rate, hydrocarbon production, radionuclide plume migration, and seismicity; to be useful, these predictions must account for uncertainty inherent in subsurface systems. Here, we review recent advances in fractured rock research covering field‐ and laboratory‐scale experimentation, numerical simulations, and uncertainty quantification. We discuss how these have greatly improved the fundamental understanding of fractures and one's ability to predict flow and transport in fractured systems. Dedicated field sites provide quantitative measurements of fracture flow that can be used to identify dominant coupled processes and to validate models. Laboratory‐scale experiments fill critical knowledge gaps by providing direct observations and measurements of fracture geometry and flow under controlled conditions that cannot be obtained in the field. Physics‐based simulation of flow and transport provide a bridge in understanding between controlled simple laboratory experiments and the massively complex field‐scale fracture systems. Finally, we review the use of machine learning‐based emulators to rapidly investigate different fracture property scenarios and accelerate physics‐based models by orders of magnitude to enable uncertainty quantification and near real‐time analysis.

58 GEOSCIENCES↗

DOE OSTI2022

Experimental calibration of an Fe3+/Fe2+-in-amphibole oxybarometer and its application to shallow magmatic processes at Shiveluch Volcano, Kamchatka

Abstract Oxygen fugacity is an important but difficult parameter to constrain for primitive arc magmas. In this study, the partitioning behavior of Fe3+/Fe2+ between amphibole and glass synthesized in piston-cylinder and cold-seal apparatus experiments is developed as an oxybarometer, applicable to magmas ranging from basaltic to dacitic composition. The partitioning of Fe2+ is strongly dependent on melt polymerization; the relative compatibility of Fe2+ in amphibole decreases with increasing polymerization. The Fe2+/Mg distribution coefficient between amphibole and melt is a relatively constant value across all compositions and is, on average, 0.27. The amphibole oxybarometer is applied to amphibole in mafic enclaves, cumulates, and basaltic tephra erupted from Shiveluch volcano in Kamchatka with measured Fe3+/FeTotal. An average Fe3+/Fe2+ amphibole-glass distribution coefficient for basalt is used to convert the Fe3+/FeTotal of amphibole in samples from Shiveluch to magmatic oxygen fugacity relative to NNO. The fO2 of primitive melts at the volcano is approximately NNO+2 and is faithfully recorded in amphibole from an amphibole-rich cumulate and the basaltic tephra. Apparently, higher fO2 recorded by amphibole in mafic enclaves likely results from partial dehydrogenation of amphibole during residence in a shallow andesite storage region. We identify three pulses of mafic magma recharge within two weeks of, a month before, and two to three months before the eruption and find that, at each of these times, the host andesite was recharged by at least two magmas at varying stages of differentiation. Application of the amphibole oxybarometer not only gives insight into magmatic fO2 but also potentially details of shallow magmatic processes.

Geochemistry & Geophysics↗

DOE OSTI2021

Assessment of Updraft Modeling Bias Using Computational Fluid Dynamics

Golden Eagle (Aquila chrysaetos) habitats may overlap with wind energy development in some regions of the US. Eagles, and similar soaring bird species, are therefore at risk of collision with wind turbines when flying through wind farms. Recently developed behavioral modeling approaches can predict the presence of eagles near turbines within the rotor-swept layer but require reliable prediction of atmospheric flowfield conditions. In particular, the vertical component of the wind speed dictates a soaring bird's ability to maintain or gain altitude, since they rely on updrafts to subsidize their flight. In this work, we investigate the atmospheric conditions around a wind farm in complex terrain and compare methods for atmospheric characterization. We use computational fluid dynamics (specifically, large-eddy simulations, or LES) to simulate the atmospheric boundary layer over a region encompassing multiple wind farms with high temporal and spatial resolution (seconds and 10's of meters, respectively). We compare traditional non-simulation-based methods of determining the orographic updraft potential based on wind direction, terrain slope and aspect, with the flowfields from LES that include both orographic updrafts alone and combined thermal and orographic updrafts. Preliminary analysis suggests that although the model captures the horizontal pattern of vertical updrafts, their magnitude can be improved with information about the surface heat flux, which is usually correlated with time of the day. Within our study region, we found that the low-fidelity model may over- or underestimate updraft potential by up to 400% at 80 m AGL, depending on local orographic features. This can result in an inaccurate representation of eagle presence and, consequently, risk. Another important finding is that flowfield time-averaging can hide important details about the flight environment, including how thermally generated flow structures within the atmospheric boundary layer (e.g., convective rolls and/or cells) may be important drivers of eagle flight.

atmospheric turbulence↗

DOE OSTI2024

A RELAP5-3D Model of the Lobo Lead Loop

The goal of our research is to build upon the capability of RELAP5-3D to model molten lead systems. Molten lead has several potential uses in future advanced reactors like the lead fast reactor or fusion reactors that utilize dual-coolant lead lithium blankets. This potential for use in future generations of reactors highlights the necessity to develop molten lead models to ensure that they can accurately predict the thermohydraulic behavior. We have developed a RELAP5-3D model of the Lobo Lead Loop facility, located at the University of New Mexico, to verify the accuracy of RELAP5-3D via comparison to existing computational fluid dynamics results and analytical calculations. It was found that RELAP5-3D accurately calculated radiative heat transfer (within < 1%) when compared to theoretical calculations. In addition, pressure drop calculations done in RELAP5-3D demonstrated reasonable agreement within 20 kPa, mostly within ~7-15%, when compared to the computational fluid dynamics model of the facility developed by the University of New Mexico, and captured the dependence of pressure drop on flow velocity accurately. Finally, a hypothetical loss of flow transient was imposed on the RELAP5-3D model to determine the feasibility of performing a similar experiment with the Lobo Lead Loop. It was found that such an experiment could be possible as the RELAP5-3D model indicated that the temperatures of the fluid would not exceed limiting temperatures of the structure (1658 K) nor the maximum temperature of the electromagnetic pump inlet (823 K). Although there is not experimental data to begin validation, the model will be readily available for future validation studies when the experimental data is generated, especially as the model continues to evolve over time. Furthermore, the results so far demonstrate a promising first step in the verification/validation of the RELAP5-3D model of the Lobo Lead Loop.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2020

Shaker Table Test Plan

Currently, spent nuclear fuel (SNF) is stored in onsite independent spent fuel storage facilities (ISFSIs), which is a dry storage facility, at 55 nuclear power plant sites. The majority of SNF in dry storage is in welded metal canisters (2,917 canisters at the end of 2019). The canisters are loaded for storage in storage overpacks (vertical casks or horizontal storage modules) and placed on outdoor concrete pads. Because the SNF will be stored at ISFSIs for an extended period of time, there is growing concern with regards to the behavior of the SNF within these dry storage systems during earthquakes. To address these concerns, the SFWST program is considering conducting an earthquake shaker table test. The goal of this test is to determine the strains and accelerations on fuel assembly hardware and cladding during earthquakes of different magnitudes to better quantify the potential damage an earthquake could inflict on spent nuclear fuel rods. The seismic integrity of the storage system has been addressed in the past by the US Nuclear Regulatory Commission and is not the focus of this potential test. Instead the DOE would benefit from knowing the condition of the fuel cladding from storage, transportation, to disposal so that it can ascertain repository performance for the fuel and packaging in its final state. A seismic event is part of the possible loading events that the fuel could experience in its lifetime. This report proposes several earthquake shaker table tests with different degrees of complexity. Alternative 1 was defined in the FY20 work scope. Alternatives 2 and 3 were recently developed to take advantage of the NUHOMS 32PTH dry storage canister that may be available in FY21 for this test at a minimum cost to the project. The selection of the alternative(s) will depend on the available budget and the SFWST program priorities for the near future.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2020

Nonlinear dynamics and dissipation of a curvilinear vortex driven by a strong time-dependent Meissner current

Here, we report numerical simulations of large-amplitude oscillations of a trapped vortex line under a strong ac magnetic field $\textit{H(t) = H}$ sin$\textit{ωt}$ parallel to the surface. The power dissipated by an oscillating vortex segment driven by the surface ac Meissner currents was calculated by taking into account the nonlinear vortex line tension, vortex mass, and a nonlinear Larkin-Ovchinnikov (LO) viscous drag coefficient $\textit{η(v)}$. We show that the LO decrease of $\textit{η(v)}$ with the vortex velocity $\textit{v}$ can radically change the field dependence of the surface resistance R i ( H ) caused by trapped vortices. At low frequencies, $R_i(H)$ exhibits a conventional increase with $\textit{H}$, but as $\textit{ω}$ increases, the surface resistance becomes a nonmonotonic function of $\textit{H}$ which decreases with $\textit{H}$ at higher fields. The effects of frequency, pin spacing, and the mean-free path $l_i$ on the field dependence of $R_i(H)$ were calculated. It is shown that, as the surface gets dirtier and $l_i$ decreases, the anomalous drop of $R_i(H)$ with $\textit{H}$ shifts to lower fields which can be much smaller than the lower critical magnetic field. Our numerical simulations also show that the LO decrease of $\textit{η(v)}$ with $\textit{v}$ can cause a vortex bending instability at high field amplitudes and frequencies, giving rise to the formation of dynamic kinks along the vortex. Measurements of $R_i(H)$ caused by sparse vortices trapped perpendicular to the surface can offer opportunities to investigate an extreme nonlinear dynamics of vortices driven by strong current densities up to the depairing limit at low temperatures. The behavior of $R_i(H)$, which can be tuned by varying the rf frequency or concentration of nonmagnetic impurities, is not masked by strong heating effects characteristic of dc or pulse transport measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Reliable extrapolation of deep neural operators informed by physics or sparse observations

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity, via the 2-Wasserstein distance between two function spaces and propose a new strategy of bias–variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Furthermore, our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

42 ENGINEERING↗

DOE OSTI2022

Influence of temporal variations in plasma conditions on the electric potential near self-organized dust chains

Self-organization of dust grains into stable filamentary dust structures (or “chains”) largely depends on dynamic interactions between individual charged dust grains and complex electric potential arising from the distribution of charges within a local plasma environment. Recent studies have shown that the positive column of the gas discharge plasma in the Plasmakristall-4 (PK-4) experiment at the International Space Station supports the presence of fast-moving ionization waves, which lead to variations of plasma parameters by up to an order of magnitude from the average background values. The highly variable environment resulting from ionization waves may have interesting implications for the dynamics and self-organization of dust particles, particularly concerning the formation and stability of dust chains. Here, we investigate the electric potential surrounding dust chains in the PK-4 experiment by employing a molecular dynamics model of the dust and ions with boundary conditions supplied by a particle-in-cell with Monte Carlo collision simulation of the ionization waves. The model is used to examine the effects of the plasma conditions within different regions of the ionization wave and compare the resulting dust structure to that obtained by employing the time-averaged plasma conditions. Here, the comparison between simulated dust chains and experimental data from the PK-4 experiment shows that the time-averaged plasma conditions do not accurately reproduce observed results for dust behavior, indicating that more careful treatment of plasma conditions in the presence of ionization waves is required. It is further shown that commonly used analytic forms of the electric potential do not accurately describe the electric potential near charged dust grains under these plasma conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Maximum bound principle preserving integrating factor Runge–Kutta methods for semilinear parabolic equations

A large class of semilinear parabolic equations satisfy the maximum bound principle (MBP) in the sense that the time-dependent solution preserves for any time a uniform pointwise bound imposed by its initial and boundary conditions. Here, the MBP plays a crucial role in understanding the physical meaning and the well-posedness of the mathematical model. Investigation on numerical algorithms with preservation of the MBP has attracted increasingly attentions in recent years, especially for the temporal discretizations, since the violation of MBP may lead to nonphysical solutions or even blow-ups of the algorithms. In this paper, we study high-order MBP-preserving time integration schemes by means of the integrating factor Runge-Kutta (IFRK) method. Beginning with the space-discrete system of semilinear parabolic equations, we present the IFRK method in general form and derive the sufficient conditions for the method to preserve the MBP. In particular, we show that the classic four-stage, fourth-order IFRK scheme is MBP preserving for some typical semilinear systems although not strong stability preserving, which can be instantly applied to the Allen-Cahn type of equations. To our best knowledge, this is the first time to present a fourth-order linear numerical method preserving the MBP. In addition, convergence of these numerical schemes is proved theoretically and verified numerically, as well as their efficiency by simulations of 2D and 3D long-time evolutional behaviors. Numerical experiments are also carried out for a model which is not a typical gradient flow as the Allen-Cahn type of equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Synthesis, Crystal Structure, and Cooperative 3d–5d Magnetism in Rock Salt Type Li 4 NiOsO 6 and Li 3 Ni 2 OsO 6

Two new transition metal oxides with the nominal chemical compositions of Li 4 NiOsO 6 and Li 3 Ni 2 OsO 6 were successfully synthesized. Both compounds crystallize in an ordered rock salt structure type in the monoclinic C2/m space group. The crystal structures were determined using both synchrotron X-ray and time-of-flight neutron, powder diffraction data. In both phases, Ni2+ ions are present while oxidation states of osmium are +6 and +5 in Li 4 NiOsO 6 and Li 3 Ni 2 OsO 6 , respectively. Ni2+ ions in the hypothetical fully ordered phase form a honeycomb arrangement in the ab crystallographic plane and these hexagons are centered by osmium ions. Furthermore, the magnetic layers are separated along the c axis by the octahedra, which are centered by Li+ (or Li+/Ni 2+ , depending on the chemical compositions). Crystal structure refinements reveal that there is some degree of mixed occupancy in cationic positions. Temperature dependent magnetic susceptibility data for both phases show ferrimagnetic transitions with predominant antiferromagnetic (AFM) interactions among 3d electrons of nickel and Sd electrons of osmium. Iso-thermal magnetization loops as a function of the applied magnetic field below the transition temperatures confirm the ferrimagnetic nature in magnetic transitions. Temperature dependent heat capacity data, however, did not exhibit any anomaly in either phase, indicating the absence of long-range magnetic ordering. The lack of long-range order for both Os 5+ and Os 6+ -based compounds was also confirmed by low temperature neutron diffraction data down to 10 K. Temperature dependent AC magnetic susceptibility data in various frequencies for both samples indicate that Li 4 NiOsO 6 exhibits spin-glass-like behavior, while the transition temperature for Li 3 Ni 2 OsO 6 is nearly frequency independent.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Proceedings of RIKEN BNL Research Center Workshop: Predictions for sPHENIX [Slides]

To complete the RHIC mission, sPHENIX was specifically designed to measure jet and heavy-flavor observables with a level of precision not previously achievable at RHIC. This will enhance our understanding of the quark-gluon plasma (QGP) properties and their temperature dependence beyond what is possible with existing and planned data from the LHC and other RHIC experiments. A major goal of the sPHENIX program is to address the question of the approach to thermalization of the quark-gluon plasma and its transport properties using hard probes such as jets and heavy flavor. The current three-year run plan includes Au+Au, p+Au and p+p collisions at 200 GeV. The Au+Au dataset provides a large QGP system to study the QGP properties. The p+Au dataset will allow for additional studies of the intriguing behavior observed in flow measurements from other RHIC experiments as well as transport properties of cold QCD matter and proton/nuclear structure. The p+p collisions provide a necessary reference for Au+Au and p+Au collisions and also allow for additional studies of proton structure. Anticipated measurements include but are not limited to, jet substructure observables, photon and heavy flavor tagged jets as well as comparisons of the production of the different upsilon states in all three collision systems. To maximize the rich physics sPHENIX is capable of accessing, this workshop will enhance the discussions between the experimentalists making the measurements and the theorists whose models will be tested and constrained by the new data. Since sPHENIX will start taking data in early 2023, this workshop is timely for theorists wishing to make final predictions of anticipated observables before data collection commences. In addition, it will provide an opportunity for theorists and experimentalists to propose and discuss new observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

A study on the creep behavior of alloy 709 using in-situ scanning electron microscopy

In this research, an experimental evaluation of creep properties of Alloy 709 in the temperature range of 750–850 °C was undertaken. Alloy 709 is a novel austenitic stainless steel with 20% Cr and 25% Ni by wt% that was developed for application in structural components of nuclear power plants. Creep rupture tests were conducted in an in-situ heating-loading and Scanning Electron Microscope (SEM) unit equipped with Electron Backscatter Diffraction (EBSD) detector and Energy Dispersive Spectroscopy (EDS). “Real-time” creep damage mechanisms of Alloy 709 at various stresses and temperatures using a flat, un-notched sample with continuously reducing cross-section is studied so that the failure and maximum creep damage occurred at the center of the sample where the in-situ SEM imaging could be focused. Accelerated creep tests at temperatures and stresses above service conditions were performed by employing multiple blocks of constant loads where the loads were increased once the sample attained constant creep rate, indicating a secondary creep regime. This technique ensures multiple data points can be obtained from the same test, saves the time required for an otherwise long-term creep test and usage of SEM. Further, Coincident Site Lattice (CSL) boundary maps were collected as control maps before testing, and the grain boundaries were observed during the creep test to understand the effect of grain boundary character on the creep damage mechanism. Void growth, grain boundary separation, and sliding were found to be the main creep mechanisms whose rate is dependent on stress and temperature. Failure mechanisms studied on the fracture surface using SEM fractography were correlated to the sample surface observations to create complementary information to better understand the underline creep mechanism of Alloy 709.

36 MATERIALS SCIENCE↗

DOE OSTI2021

In Situ Inference for Earth System Predictability

An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

In Situ Inference for Earth System Predictability

Focal Area: Focal Area 3: Insight gleaned from complex simulated data using AI, big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI. Science Challenge: An understanding of future evolution in precipitation extremes is critical to numerous DOE mission questions. Extreme events are by nature short time-scale events that are difficult to diagnose in available model data. Accurate modeling of extreme events necessarily requires high spatial resolution at the storm scale locally. However, the environment in which storms grow is dependent on global, remote, processes. These complex spatiotemporal relationships are impossible to diagnose at resolutions required to accurately model storms responsible for extreme precipitation. At exascale, climate simulations will produce results at fine enough resolution to investigate these relationships. However, the resulting data from these simulations will be far too large to save for post-simulation analysis. We advocate for fitting statistical models inside the simulations as they run, a context known as in situ, which will facilitate scientific investigations using the full fine-scale data stream. Figure 1 shows an example of the type of model we could consider, a Bayesian hierarchical spatial regression model. Precipitation extremes at each grid cell are modeled using extreme value distributions. Since extremes are rare, fitting models to individual grid cells can result in high variance and poor estimates. Instead, the model can be made more robust by smoothing the parameters of the extreme value model across space. Additionally, the parameters themselves can be functionally linked to other variables elsewhere in the simulation. Thus, we can use the fine-scale data to build more robust models for extremes that link extreme behavior to other climate patterns.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Computationally Accelerated Discovery and Experimental Demonstration of High-Performance Materials for Advanced Solar Thermochemical Hydrogen Production

This project achieved its overarching goal of accelerating the discovery and validation of solar thermochemical hydrogen (STCH) materials through a tightly integrated approach that combined high-throughput computational screening, advanced machine learning (ML), and experimental testing. Guided by the objectives outlined in the Statement of Project Objectives (SOPO), our work fulfilled all major milestones across four technical tasks and delivered scientific breakthroughs and practical tools that significantly exceeded the original scope of the project. We began by addressing the challenge of predicting material phase stability through machine learning. A novel Python module was developed to generate thousands of meaningful features from composition, structure, and electronic properties, enabling rapid and reproducible ML model development. Using these tools, we trained a model to predict temperature-dependent Gibbs energies (G(T)) for inorganic crystalline materials with near-chemical accuracy—roughly 40 meV/atom—marking the first such descriptor of its kind. We also introduced a new machine-learned tolerance factor, τ, that accurately predicted perovskite formability with over 90% success, outperforming traditional heuristic models, such as the Goldschmidt tolerance factor. These capabilities allowed for rapid and accurate predictions of phase stability across a vast oxide composition space, setting the stage for high-throughput thermodynamic screening. Building on this foundation, we conducted an extensive computational screening of candidate STCH oxide materials. Over 1.1 million perovskite compositions were evaluated using the τ descriptor, leading to the identification of more than 27,000 predicted stable structures. Using density functional theory (DFT), we refined over 68,000 multinary perovskite structures and computed oxygen vacancy formation energies for over 1,300 ternary and double perovskites. These calculations enabled us to isolate compounds with redox behavior consistent with STCH requirements and resulted in a public dataset now hosted on the Materials Project. Recognizing that thermodynamic screening alone is insufficient, we addressed kinetic limitations by developing a suite of tools to estimate transition state (TS) energies for key redox reactions. We implemented a novel bounding approach that provides lower and upper estimates of TS energies with dramatically reduced computational cost, requiring less than 10% of the CPU time of a full nudged elastic band (NEB) calculation while maintaining high accuracy. This enabled rapid evaluation of over 200 reaction pathways across 90 materials. To further accelerate screening, we developed a SISSO-based ML model to predict diffusion barriers with a 96.7% success rate in classifying fast vs. slow materials, supporting a robust, data-driven framework for assessing redox kinetics. Experimental validation was critical to confirming the predictive power of our models. We synthesized and tested a wide array of candidate materials, including Mn-doped hercynite and several Gd- and La-based perovskites. Notably, Sr 0.4 Gd 0.6 Mn 0.6 Al 0.4 O 3 (SGMA) and Gd 0.5 La 0.5 Co 0.5 Fe 0.5 O 3 (GLCF) emerged as leading STCH materials, exhibiting robust redox cycling and high hydrogen yields exceeding 150 µmol H 2 /g per cycle. These materials also retained over 50% of their hydrogen productivity under high-conversion conditions (H 2 O:H 2 = 1333:1), demonstrating strong thermodynamic favorability and promising performance under industrially relevant scenarios. Additional candidates, such as La 2 MnNiO 6 (L2MN), were found to produce even higher yields than ceria under standard STCH conditions. Our collaborators at Sandia National Laboratories confirmed these findings using high-temperature X-ray diffraction and thermogravimetric analysis, observing stable phase evolution and reversible redox activity. In several respects, the project went beyond the goals initially outlined in the SOPO. We published 17 peer-reviewed articles, including a large dataset of over 66,000 theoretical perovskites and a new structure prediction method (SPuDS-DFT) that accurately identifies ground-state structures at a fraction of the cost of traditional DFT. We demonstrated that our machine-learned G(T) model offers accuracy rivaling quasiharmonic calculations while being orders of magnitude faster. In partnership with the Materials Project, we made our datasets openly available, providing a powerful new resource for the broader materials science community. The combined computational and experimental advances of this project represent a significant advance in STCH materials discovery. By creating a robust, generalizable, and open workflow for thermodynamic and kinetic screening, and validating key findings through synthesis and reactor testing, we have provided a practical and scalable pathway for the rapid identification of new redox-active materials. The tools, data, and materials developed under this project are already supporting ongoing research and have laid the groundwork for the next generation of solar fuel technologies.

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