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DOE OSTI2023

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Application of a Physics-Informed Convolutional Neural Network for Monitoring the Temperature Fields in High-Temperature Gas Reactors

Here, this work presents current advances in applying a physics-informed convolutional neural network (CNN) to evaluate temperature distributions in advanced reactors. Our goal is to demonstrate that the CNN can reconstruct temperature fields within the solid region of a prismatic fuel assembly in a high-temperature gas reactor (HTGR) with sensor data available in only a few cooling channels. Before that, we showcase the superior performance of the physics-informed CNN in comparison to a purely data-driven multilayer perceptron (MLP), considering a canonical heated channel setup. This analysis shows the advantages of our approach and justifies its choice. The datasets employed here are obtained upon numerical simulations performed with codes under the Nuclear Energy Advanced Modeling and Simulation program. This work is important, as industry experience indicates that the assembly material in HTGR concepts is prone to large thermal-mechanical loads nearing operational limits. This makes it crucial to characterize peak temperatures and their distributions near hot spots. Modern thermocouples are unreliable in these types of harsh environments because of the high neutron fluxes and elevated temperatures involved. The CNN-based field reconstruction represents an attractive solution, enabling sensor arrays in less aggressive locations and augmenting indirect predictions for less accessible regions. The results show that the CNN reduces prediction errors by orders of magnitude in comparison to the MLP, considering the simple yet well-representative heated channel case. In the case of the HTGR fuel assembly, the CNN can successfully reconstruct temperature fields over various cooling regimes. Furthermore, we also explore the algorithm’s ability to detect abnormalities. Interestingly, the CNN proves it has the capacity to detect blockage in one of the noninstrumented cooling channels.

Machine learning↗

DOE OSTI2023

Nonthermal Bonding Origin of a Novel Photoexcited Lattice Instability in SnSe

Lattice dynamics measurements are often crucial tools for understanding how materials transform between different structures. Here, we report time-resolved x-ray scattering-based measurements of the nonequilibrium lattice dynamics in SnSe, a monochalcogenide reported to host a novel photoinduced lattice instability. By fitting interatomic force models to the fluence dependent excited-state dispersion, we determine the nonthermal origin of the lattice instability to be dominated by changes of interatomic interactions along a bilayer-connecting bond, rather than of an intralayer bonding network that is of primary importance to the lattice instability in thermal equilibrium.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Evolution of a Geological Model for Co-Producing Electricity at the Blackburn Oil Field, Nevada: Preprint

Oil fields characterized by significant proportions of hot water in the reservoir offer a unique opportunity for geothermal energy co-production and utilization. Development of an accurate geological model is essential in understanding the subsurface. However, this is a challenge at the preliminary stages of resource assessment when data on the subsurface parameters and subterranean fault network is scarce. Our focus in this paper is to explore a methodology for developing an accurate conceptual geological model of the complexly faulted reservoir at the Blackburn Field, Nevada, to create a numerical simulation that will eventually be used to assess the potential of co-producing electricity. There are four steps to the evolution of this model: (1) collection of public data and research on the geological setting of the system; (2) digitization of seismic time-interpretation and cross-sections to create the most representative model of the subsurface; and (3) creation of a natural-state numerical model to simulate the thermal gradient through the subsurface. Utilizing publicly available data of the geological system and Leapfrog conceptual modeling software, we refined a preliminary model to be more representative of the subsurface geology of an active geothermal exploration stage project. The conceptual geological model developed has been imported into VOLSUNG reservoir modeling software to create a natural-state model, further characterizing the Blackburn Field reservoir's thermal gradient. In the future, new seismic data will be utilized to further evolve the conceptual geological model for numerical simulations.

Basin and Range province↗

DOE OSTI2020

A scalable crosslinked fiberglass-aerogel thermal insulation composite

Ceramic aerogels are attractive for thermal insulation due to its lightweight and low thermal conductivity, while it is indispensable to address the obstacle of its brittleness nature and large-scale manufacturing for building efficiency applications. The economical manufacturing of large-scale elastic thermal insulation aerogels has remained challenging. Herein, we report a universal manufacturing of the fiberglass reinforced aerogel composites where the architectured three-dimensional fiber networks with the cross-linked silica aerogel exhibit superinsulation and mechanical elasticity. These remarkable performances are obtained through in-situ cross-linking reaction between molecular assembly induced aerogel and fiberglass under ambient pressure drying environment, in which the interfacial interactions are verified from its infrared spectra. The resulting ultralight aerogel composite sheet exhibits a density of 0.1 g cm -3 , large strain recovery (>50%), and low thermal conductivity (0.0253 W m –1 K –1 ). Here, the thermal insulation aerogel composites shed light on the super-elastic aerogel manufacturing with scalability for energy saving building applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Grey-box and ANN-based building models for multistep-ahead prediction of indoor temperature to implement model predictive control

Model-based predictive control (MPC) strategies for heating, ventilation, and air-conditioning (HVAC) systems present an opportunity to lower building energy consumption and operational costs. Such approaches rely on the development of a model to precisely forecast building thermal dynamics, such as room air temperature or heating/cooling rate, and make control-related decisions. The control-oriented modeling of building energy systems should be accurate in predicting indoor conditions and present low computational complexity. These features are the key challenge of implementing advanced control methods such as MPC. Extant studies on building modeling for MPC have focused on step-ahead forecasting techniques to forecast building thermal dynamics, while multistep-ahead forecasting is essential. Moreover, machine learning model suitable in case of the domain-based engineering expertise are also not available. To this aim, we perform a comparative analysis of the grey-box model based on a resistance-capacitance (RC) thermal network and a machine learning model composed of an artificial neural network (ANN) for multistep-ahead prediction of building thermal dynamics using current and historical data. Actual experimental data obtained from the Flexible Research Platform (FRP) in Oak Ridge National Laboratory (US) are used for estimation and validation purposes. The average root mean squared error (RMSE) of the grey-box and ANN models are 0.89 °C and 1.02°C, respectively. Finally, the results indicate that the grey-box model outperforms the ANN model in the considered validation periods in terms of accuracy and prediction stability.

42 ENGINEERING↗

DOE OSTI2025

Anion and Cation Size Effects on Viscoelasticity and Ion Transport of Imine Vitrimer Electrolytes

Vitrimers are a subclass of covalent adaptable networks where bond exchange occurs without breaking, thereby offering polymer materials with enhanced mechanical strength, thermal stability, and reprocessability compared to conventional electrolytes. Despite recent progress, we lack a complete understanding of the role of ions in controlling the physical and chemical properties of vitrimers. In this work, we study how different salts affect the viscoelasticity, morphology, and ionic conductivity of imine vitrimers. Our results show that addition of salt decreases relaxation times at elevated temperatures due to the catalytic effect of the cations, with smaller cations leading to faster relaxation. However, the activation energy for terminal relaxation increases with smaller cation size. This apparent discrepancy is attributed to the complex interplay among bond exchange kinetics, chain diffusion, and salt dissociation. Anions act as plasticizers by reducing the shear modulus, except lithium bromide. Ionic conductivity increases with larger anions due to smaller salt dissociation energies, whereas the cation type has a minor impact as polymer segmental dynamics dominate ionic transport. Imine-based vitrimers are reprocessable and recyclable, maintaining original mechanical properties and ionic conductivity after recovery. Mixed salt vitrimers exhibited tunable viscoelasticity and ionic conductivity intermediate to the analogous pure salt systems. Altogether, this work highlights the role of salt in the dynamic and conductive properties of imine vitrimers.

Anions↗

DOE OSTI2021

Ag-doped As–S–Se chalcogenide glasses: a correlative study of structural and dielectrical properties

This work is focused on investigating the effect of Ag doping on the dielectric and structural properties of the As–S–Se glass system and their suitability for potential applications in optoelectronics, such as electronic and switching components. A series of glasses from the system Agx(As40S30Se30)100-x (x = 5 at.% Ag) were prepared with melt-quenching technique. Morphological and compositional analysis was performed by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Raman spectroscopy measurements have shown structural modifications of the glass network upon Ag doping, with creation of new Ag–(S,Se)–As structures, resulting in increased number of defect states. Variations in dielectric permittivity (e') and dielectric loss (e") were investigated in a wide frequency and temperature range, and explained by dominant dipolar and electronic polarization, as well as correlated with the observed structural changes. Improvement in dielectric properties is observed upon doping with Ag. Furthermore, the low dielectric tangent loss at high frequencies for all glassy samples points to a beneficial ability of the material to absorb energy of the external electric field. Good thermal stability of these glasses is confirmed by the calculated temperature coefficient of the dielectric permittivity. Density of localized states in all glasses was determined from AC conductivity and the dielectric tangent loss using a correlated barrier hopping model, and found to increase with Ag concentration. The overall results suggest that Ag doping enhances the electrical and optical quality of this type of glasses, making them suitable as nonlinear optical materials and their applications in devices.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Numerically Testing Conceptual Models of the Utah FORGE Reservoir Using July 2024 Circulation Test Data

Over the past several years, many new data sets have become available regarding the characterization of the Utah FORGE reservoir. These include, but are not limited to, the stimulation of Well 16A, the drilling and completion of Well 16B, and interwell circulation confirmatory testing. As part of the characterization efforts, conceptual models of the reservoir are re-examined as new data become available. As part of the planning for FORGE activities, numerical models are often used to predict the reservoir response to the planned testing. Stochastic methods are often employed to bound uncertainty and allow for evaluation of comprehensive ranges of key reservoir parameters. For the most recent interwell circulation confirmatory testing (July 2023), a priori numerical model predictions did bound the observed behavior (Xinj et al., 2023), but key deviations from expected behavior prompted the FORGE team to reevaluate our conceptual model of the reservoir. In early October 2023, key members of the development, testing, and monitoring teams met for 2 days to review newly collected data and discuss ‘interesting’ or ‘key’ observations. From these discussions, 15 Key Observations were documented, with several significant ones being that the discrete fracture network developed from the 16A stimulation data may not be appropriate and that the early time pressure data obtained during the summer 2023 reservoir testing were best described using radial solutions. In July 2023, two campaigns of interwell confirmatory testing were conducted, the first set of tests occurred on July 4-5 and the second set on July 18-19. The second set of circulation tests conducted at the Utah FORGE site between the injection well 16A(78)-32 and production well 16B(78)-32 on July 18 and 19, 2023 are used to calibrate material properties in a thermal-hydraulic-mechanical (THM) simulation of the discrete fracture network connecting the wells. The spatially and temporally varying reservoir properties are calibrated to match the time dependent pressure and production profiles from the circulation tests. In future work, this calibrated model will be coupled to the native state THM model of the FORGE reservoir to predict surface deformation and strains resulting from pumping schedules.

15 GEOTHERMAL ENERGY↗

DOE OSTI2025

Modification of Fractured Rock Permeability Field Using Polymer Foam to Improve Geothermal System Efficiency

The project aims at developing novel methods for blocking high-permeability pathways in geothermal reservoirs to improve heat exchange efficiency. While the project was at DRI, and now when it has been transferred to UNR, the goal of blocking high-permeability pathways is achieved through targeted injection of heat-sensitive polymer foams, particularly foamed epoxy resins, whose activation and curing are triggered by the ambient thermal field.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Ab-Initio molecular dynamics simulations of binary NaCl-ThCl 4 and ternary NaCl-ThCl 4 -UCl 3 molten salts

Molten salt reactor (MSR) with Thorium (Th) fuel cycle has attracted growing attention due to its merits such as safety, low radioactive waste production, and non-proliferation. The design and safe operation of MSR rely on a thorough understanding of the thermophysical properties of molten salts over a wide range of composition and temperature. However, experimental data of Th-based molten salts are limited due to the inherent challenges of dealing with corrosive molten salts and the radioactive nature of actinides. Here, in this work, thermophysical properties including density, heat capacity, thermal expansion coefficient, and mixing energy of binary NaCl-ThCl 4 and ternary NaCl-ThCl 4 -UCl 3 molten salts are explored using ab-initio molecular dynamic simulations (AIMD) with the dDsC dispersion correction. The calculated mixing energy of binary NaCl-ThCl 4 exhibits a minimum close to the eutectic composition. The heat capacity of the mixtures is linearly dependent on the mole fraction of each component. 7-fold and 6-fold coordinated Th complexes are dominant in the mixtures. 8-fold coordinated Th complex increases with ThCl 4 fraction due to the formation of network structures. The average coordination number of Th exhibits a minimum near the eutectic composition. The minimum mixing energy for the ternary mixtures is observed in systems with a composition close to [NaCl] 0.5 [ThCl 4 ] 0.25 [UCl 3 ] 0.25 . The density positively deviates from the ideal solution in mixtures near this composition. These results are important to fill the data and knowledge gap of ThCl 4 molten salts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Overlapping Domain Coupling of Multidimensional and System Codes in NEAMS - Pronghorn and SAM

This report describes the implementation and testing of domain-overlapping coupling of the Pronghorn and SAM codes. Both Pronghorn and SAM are codes developed by the DOE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program for analysis of advanced nuclear reactors. Pronghorn focuses on analyzing multi-dimensional core flow conditions, while SAM focuses on analysis of the entire system including, among other components, piping, pumps, and heat exchangers. It is desirable for many advanced reactor thermal-hydraulics simulations to couple Pronghorn and SAM to obtain self-consistent solutions in the core and the system. The domain-overlapping coupling approach provides a robust and numerically efficient candidate for this coupling. We describe the algorithm for obtaining self-consistent solutions between a SAM network of flow channels and an overlapped multi-dimensional Pronghorn domain. The algorithm is tested for a variety of simple canonical test problems and a model of the Molten Salt Reactor Experiment (MSRE). Results demonstrate that the domain overlapping coupling approach provides consistent pressure, mass flow rate, enthalpy, and passive scalar distributions between Pronghorn and SAM and converges reliably and efficiently (at most 15 iterations, usually much less than 10) for the considered test problems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Coupled Investigation of Fracture Permeability Impact on Reservoir Stress and Seismic Slip Behavior (Final Technical Report)

Enhanced Geothermal Systems (EGS) produce clean energy by circulating fluid through hot rock deep underground and bringing that heat to the surface to generate electricity. For this process to work reliably, fluids must be able to move efficiently through networks of natural or engineered fractures in the rock. Enhancing and maintaining subsurface permeability over time is essential for sustainable energy production. However, fluid injection changes the underground temperature, pressure, rock stress, and chemistry, which can alter permeability and sometimes trigger earthquakes. Predicting these interconnected processes remains a key challenge. To address this, we combined high-temperature laboratory experiments with high-fidelity simulations to better understand how fractures in geothermal reservoirs evolve over time. Our experiments measured how fractures respond to stress, slip, slip rate, and chemical reactions under geothermal conditions. These data were integrated into coupled thermal-hydrological-mechanical-chemical and earthquake (THMC+E) models tailored to the Utah FORGE site. The validated modeling framework improves predictions of reservoir performance and seismic response and helps guide operational decisions. This work reduces technical risk and strengthens the scientific foundation needed to make geothermal energy a reliable and scalable clean energy resource.

15 GEOTHERMAL ENERGY↗

DOE OSTI2026

Frontier Job-Centric Telemetry Dataset

Comprehensive analysis of high-performance computing (HPC) systems requires linking workload execution to system behavior. This kind of analysis is vital for diagnosing performance issues, managing capacity, detecting anomalous workloads, and understanding how applications interact with system hardware. This job-centric telemetry dataset unifies scheduler job records with node-level measurements, enabling direct association between workloads and their corresponding power, thermal, and performance characteristics. It contains sanitized, scheduler related metadata for 152,400 individual jobs that ran on the Frontier supercomputer and ended on selected days throughout 2024 and 2025, a subpopulation of ~6.8% of the total number of allocated jobs with non-zero run time on the system over that same period. Each is linked with files that contain telemetry time series records of the power utilization and temperature behavior of its allocated nodes and their processors during the run time of the job. Where available, a portion of the job files also contain network performance time series. Jobs are sampled from select days that reflect normal levels of user activity and possess job size distributions with large numbers of leadership class jobs (>20% of Frontier nodes). Jobs in this dataset attempt to best represent successful user workflows.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

PurpleAir Sensors as Effective Indicators of PM Exposure in Urban Areas

Particulate matter that is 2.5 microns or less in diameter (PM 2.5 ) is a biproduct of combustion reactions used for energy production. Populations that are exposed to consistently high levels of aerosolized PM 2.5 face serious health risks. This project compared low-cost PM 2.5 sensors with federally recognized methods to look for a cost-effective way to expand the air quality monitor network. Within metropolitan areas that face inconsistent spatial distribution of PM 2.5 , there may not be the necessary network density to indicate neighborhood-levels of PM 2.5 . This project aimed to examine the sensitivity of low-cost PM 2.5 sensor measurements on a neighborhood scale (< 4 km diameter) in an urban area to prevent citizens from being exposed to unsafe levels of PM 2.5 without their knowledge. Using publicly available sensor data from Livermore, CA and Bakersfield, CA, it was determined, based on the revealed patterns, that the analyzed low-cost sensors were able to display representative PM 2.5 levels for neighborhoodscale areas exposed to pollution from PM 2.5 sources.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2023

Using small angle x-ray scattering to examine the aggregation mechanism in silica nanoparticle-based ambigels for improved optical clarity

Silica-based aerogels are a promising low-cost solution for improving the insulation efficiency of single-pane windows and reducing the energy consumption required for space heating and cooling. Two key material properties required are high porosity and small pore sizes, which lead to low thermal conductivity and high optical transparency, respectively. However, porosity and pore size are generally directly linked, where high porosity materials also have large pore sizes. This is unfavorable as large pores scatter light, resulting in reduced transmittance in the visible regime. In this work, we utilized preformed silica colloids to explore methods for reducing pore size while maintaining high porosity. Further, the use of preformed colloids allows us to isolate the effect of solution conditions on porous gel network formation by eliminating simultaneous nanoparticle growth and aggregation found when using typical sol–gel molecular-based silica precursors. Specifically, we used in situ synchrotron-based small-angle x-ray scattering during gel formation to better understand how pH, concentration, and colloid size affect particle aggregation and pore structure. Ex situ characterization of dried gels demonstrates that peak pore widths can be reduced from 15 to 13 nm, accompanied by a narrowing of the overall pore size distribution, while maintaining porosities of 70%–80%. Optical transparency is found to increase with decreasing pore sizes while low thermal conductivities ranging from 95 +/− 13 mW/m K are maintained. Mechanical performance was found to depend primarily on effective density and did not show a significant dependence on solution conditions. Overall, our results provide insights into methods to preserve high porosity in nanoparticle-based aerogels while improving optical transparency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Dual modulation of the anion-driven thermodynamic properties of aqueous choline halide-based deep eutectic solvents

Deep eutectic solvents (DESs) are considered tunable solvents because their specific properties can be achieved based on the choice of components and their relative concentrations in a mixture. In this article, we investigate the influence of the variation in halide ions (F − , Cl − , Br − , I − ) of choline salts used on the thermodynamic and physicochemical properties of choline halide-based DESs. Our findings show that the density of choline halide-based DESs decreases nonlinearly with an increasing mole fraction of water, following a trend based on the size of the halides, with choline iodide showing the highest density. Temperature-dependent density data reveal that the thermal expansion coefficient decreases slightly with increasing water content, indicating a more stable volume at a higher mole fraction of water. The excess molar volume (V E ) of the DES mixtures exhibits complex behavior depending on the choline halide used, with both negative and positive V E values observed across different water mole fractions. These variations are linked to the hydrogen bonding interactions between the DES components and water molecules. In addition, viscosities decrease with increasing water content, suggesting the disruption of hydrogen bonding networks and enhanced mobility of the ions, which contributes to the observed increase in conductivity. The excess molar Gibbs energies, enthalpies, and entropies of activation have also been determined.

Choline halide↗

DOE OSTI2024

Complex dislocation loop networks as natural extensions of the sink efficiency of saturated grain boundaries in irradiated metals

The development of radiation-tolerant structural materials is an essential element for the success of advanced nuclear energy concepts. A proven strategy to increase radiation resistance is to create microstructures with a high density of internal defect sinks, such as grain boundaries (GBs). However, as GBs absorb defects, they undergo internal transformations that limit their ability to capture defects indefinitely. Here, we show that, as the sink efficiency of GBs becomes exhausted with increasing irradiation dose, networks of irradiation loops form in the vicinity of saturated or near-saturated GB, maintaining and even increasing their capacity to continue absorbing defects. The formation of these networks fundamentally changes the driving force for defect absorption at GB, from “chemical” to “elastic.” Using thermally-activated dislocation dynamics simulations, we show that these networks are consistent with experimental measurements of defect densities near GB. Our results point to these networks as a natural continuation of the GB once they exhaust their internal defect absorption capacity.

36 MATERIALS SCIENCE↗