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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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DOE OSTI2023

A data-driven framework for permeability prediction of natural porous rocks via microstructural characterization and pore-scale simulation

Understanding the microstructure–property relationships of porous media is of great practical significance, based on which macroscopic physical properties can be directly derived from measurable microstructural informatics. However, establishing reliable microstructure–property mappings in an explicit manner is difficult, due to the intricacy, stochasticity, and heterogeneity of porous microstructures. In this paper, a data-driven computational framework is presented to investigate the inherent microstructure–permeability linkage for natural porous rocks, where multiple techniques are integrated together, including microscopy imaging, stochastic reconstruction, microstructural characterization, pore-scale simulation, feature selection, and data-driven modeling. A large number of 3D digital rocks with a wide porosity range are acquired from microscopy imaging and stochastic reconstruction techniques. A broad variety of morphological descriptors are used to quantitatively characterize pore microstructures from different perspectives, and they compose the raw feature pool for feature selection. Here high-fidelity lattice Boltzmann simulations are conducted to resolve fluid flow passing through porous media, from which reliable permeability references are obtained. The optimal feature set that best represents permeability is identified through a performance-oriented feature selection process, upon which a cost-effective surrogate model is rapidly fitted to approximate the microstructure-permeability mapping via data-driven modeling. This surrogate model exhibits great advantages over empirical/analytical formulas in terms of prediction accuracy and generalization capacity, which can predict reliable permeability values spanning four orders of magnitude. Besides, feature selection also greatly enhances the interpretability of the data-driven prediction model, from which new insights into the mechanism of how microstructural characteristics determine intrinsic permeability are obtained.

58 GEOSCIENCES↗
DOE OSTI2023

Non-linear Wire Rope Isolator Model to Enhance Transportation Simulation of Fragile Equipment

Abstract Introduction Wire rope isolators are often used as means of vibration isolation for the transportation of fragile machine equipment. When deployed as suspension systems in a transportation scenario, a simple spring–damper model is often used to predict the dynamic loads that act on the structure being transported. However, this simple model has proved to be too simplistic to be used in the development of an accurate numerical model that can predict the vibration levels experienced by the inner components of fragile equipment during its transportation. This paper describes the experimental tests conducted on a wire rope isolator used for the transportation of the prototype SSR1 cryomodule. Materials and methods A hysteretic Bouc–Wen model has been used to analytically describe the force–deformation relationship of the wire rope isolators. The developed model of the isolator has been implemented in a larger model to simulate the actual transportation of the prototype SSR1 cryomodule, a section of the new PIP-II linear accelerator under construction at Fermilab. A series of multibody dynamic simulations with rigid and flexible components was used to numerically determine the acceleration of some critical components. Results and conclusions An actual experimental transportation was simulated using two numerical models: the developed Bouc–Wen model and a conventional spring–damper model. It is shown how the Bouc–Wen formulation of the isolator characteristics drastically improves the correspondence between experimental and simulated results if compared to a spring–damper model, especially in the range of 0–30 Hz which is the most critical for transportation problems.

42 ENGINEERING↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Active learning with multifidelity modeling for efficient rare event simulation

Here, while multifidelity modeling provides a cost-effective way to conduct uncertainty quantification with computationally expensive models, much greater efficiency can be achieved by adaptively deciding the number of required high-fidelity (HF) simulations, depending on the type and complexity of the problem and the desired accuracy in the results. We propose a framework for active learning with multifidelity modeling emphasizing the efficient estimation of rare events. Our framework works by fusing a low-fidelity (LF) prediction with an HF-inferred correction, filtering the corrected LF prediction to decide whether to call the high-fidelity model, and for enhanced subsequent accuracy, adapting the correction for the LF prediction after every HF model call. The framework does not make any assumptions as to the LF model type or its correlations with the HF model. In addition, for improved robustness when estimating smaller failure probabilities, we propose using dynamic active learning functions that decide when to call the HF model. We demonstrate our framework using several academic case studies (including some high-dimensional problems) and two finite element model case studies: estimating Navier-Stokes velocities using the Stokes approximation and estimating stresses in a transversely isotropic model subjected to displacements via a coarsely meshed isotropic model. Across these case studies, not only did the proposed framework estimate the failure probabilities accurately, but compared with either Monte Carlo or a standard variance reduction method, it also required only a small fraction of the calls to the HF model.

42 ENGINEERING↗
DOE OSTI2024

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone. Mathematical modeling plays a crucial role in delineating the underlying mechanisms to explain sources of heterogeneity using patient-specific data. Intra-tumoral diversity necessitates the development of precision oncology therapies utilizing multiphysics, multiscale mathematical models for cancer. This review discusses recent advancements in computational methodologies for precision oncology, highlighting the potential of cancer digital twins to enhance patient-specific decision-making in clinical settings. We review computational efforts in building patient-informed cellular and tissue-level models for cancer and propose a computational framework that utilizes agent-based modeling as an effective conduit to integrate cancer systems models that encode signaling at the cellular scale with digital twin models that predict tissue-level response in a tumor microenvironment customized to patient information. Furthermore, we discuss machine learning approaches to building surrogates for these complex mathematical models. These surrogates can potentially be used to conduct sensitivity analysis, verification, validation, and uncertainty quantification, which is especially important for tumor studies due to their dynamic nature.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Analysis of Postulated Accident Scenarios for the Transformational Challenge Reactor

The Transformational Challenge Reactor (TCR) is a helium-cooled, yttrium-hydride-moderated reactor that was designed for the U.S. Department of Energy Office of Nuclear Energy. A key objective of the TCR was to employ advanced manufacturing techniques in a nuclear system and demonstrate their potential for revolutionizing the nuclear reactor design process. One purpose of the present work is to demonstrate the safety of the TCR under postulated accidents. Based on RELAP5-3D and COMSOL analyses, the TCR remained below all current safety limits and far below the expected failure limits for the core materials. Another purpose of this work is to provide useful insights and recommendations regarding the application of RELAP5-3D to gas-cooled or other advanced reactors. A novel approach was implemented for simultaneously modeling conduction and radiation in RELAP5-3D, which was found to provide reasonable predictions of radial core, vessel, and ex-vessel heat transfer during postulated events. A multicode approach was also applied, in which high-fidelity COMSOL calculations were used to tune the radial heat transfer parameters in RELAP5-3D. The tuned RELAP5-3D model demonstrated comparable peak temperature predictions as COMSOL, despite a coarse treatment of the core in RELAP5-3D consisting of only two lumped heat structures. This high-fidelity tuning approach enabled enhanced accuracy as well as minimal complexity within the RELAP5-3D model, even for complex fuel geometric designs as in the TCR. Finally, investigations were made into the potential for flow reversal during a pressurized loss-of-forced-flow event in the TCR. The TCR is designed with downward helium flow through the core during normal operation. The RELAP5-3D model predicted that this downward flow would persist, without flow reversal, up to several days after the circulator trip. This was attributed to natural circulation hysteresis effects as have been noted in similar thermofluidic systems. Although flow stagnation and eventual reversal did not lead to unsafe TCR conditions, interesting spatial effects were observed which may have safety relevance for other reactor system designs and coolant types that are designed for downward core flow during normal operation, warranting closer investigation of the flow reversal phenomenon.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Cytochrome Nanowire XRD

Microbial metabolism plays an important role in the global cycling of carbon, nutrients, and metals. Recent discovery of microorganisms sharing energy using direct electrical connections might represent a prominent strategy by which anaerobic microorganisms interact with the diversity of environments. The purpose of the proposed research is to apply these newly developed principles of electron exchange in microbial communities to the study of biogeochemical cycling in climate and subsurface systems. Previous studies revealed that soil bacteria Geobacter sulfurreducens produce conductive pili nanofilaments that facilitate long-range electron transport to extracellular electron acceptors and other cells. This discovery has already transformed our understanding of the function of microbial communities in diverse environments, making basic contributions to microbial ecology and helping to improve practical applications such as increasing current output of microbial fuel cells and enhancing the conversion of organic waste to methane. Microbial electron exchange via pili may also contribute to methane production in terrestrial environments that are major sources of atmospheric methane. However, the mechanism of electron transport through pili is not well understood. Initial studies have suggested that pili exhibit metallic-like conductivity similar to conducting polymer polyaniline. However, these studies were limited to electrical measurements. X-ray diffraction studies have suggested that aromatic amino acids tyrosine and phenylalanine are closely packed in pili. But these studies used homology modeling to predict the pilus assembly and information about the number of monomers in each pilus assembly unit and their organization are still lacking. The central hypothesis of this proposal is aromatics in pili enable intermolecular electron delocalization due to pi-stacking that can give rise to metallic-like conductivity. By analyzing pili structure using infrared scattering-scanning near field optical microscope (IR s-SNOM) and combining with solution NMR, Focused Ion Beam (FIB and computational capabilities, this work will provide comprehensive understanding of the conduction mechanism from a structure-function perspective. The specific aims are: 1) With infrared scattering scanning near field optical microscope (IR s-SNOM), image the organization and density of pilin monomers and pH-induced conformational changes in a pilus filament. 2) With solution NMR, resolve pH-induced structural changes in the pilin monomer. 3) Using Focused Ion Beam (FIB), draw electrodes on single pili to measure their conductivity. 4) With computational modeling using the NWChem, simulate the effect of environment on the electronic properties of pili as a function of pH. The proposed work will gain the nanoscale insight into the structure of the pili, as the basis for understanding interspecies electrical communications. The improved understanding will be harnessed to accelerate bioenergy production and bioremediation using pili as well as for predictive modeling of carbon cycling in diverse environments to reach the central goals of DOE-BER to develop sustainable energy sources, regulate the contaminants in the subsurface and understand the effects of greenhouse gas emissions on biosphere. The proposed studies will provide design principles for engineering of electrical interactions among these communities by manipulating the amino acid composition of pili. Moreover, these studies will identify the forces that maintain the function of these communities in the face of environmental perturbation. Therefore, these studies will help to address the need "to incorporate process understanding of biogeochemical cycling into a scalable hierarchy of predictive capabilities" specified in EMSL's Science Themes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Computational design of isotropic and anisotropic ultralow thermal conductivity polymer foams

Current state-of-the-art commercial polymer thermal insulation foam exhibits a thermal conductivity of 24 mW·m -1 ·K -1 (equivalently thermal resistivity of R-6/in.), similar to that of static air. To further optimize building energy efficiency, achieving even lower thermal conductivity is needed, which is, however, highly challenging. This paper presents computational evidence that demonstrates the feasibility of achieving an ultra-low thermal conductivity of less than 14.4 mW·m -1 ·K -1 (equivalently R-10/in.) using isotropic and anisotropic foam cell designs. For the isotropic design, we have identified analytical effective medium approximation (EMA) models within the accuracy of ±5% as finite element analysis (FEA) in predicting the effective thermal conductivity of foams with various porosities and filler gases. For the anisotropic design, we have developed and validated new EMA models against FEA in predicting the effective thermal conductivity of general anisotropic cuboids and Voronoi foams. For both isotropic and anisotropic designs, the design spaces for 18, 16, and 14.4 mW·m -1 ·K -1 (equivalently R-8, R-9 and R-10/in.) using various filler gases are obtained. It is found that polymer foams can be improved to achieve ultralow thermal conductivity by reducing CO 2 concentration, reducing radiation, increasing porosity, and using anisotropic pore geometry. In conclusion, these findings contribute to the development of highly efficient thermal insulation materials, enhancing building energy efficiency and promoting sustainable construction practices.

42 ENGINEERING↗
DOE OSTI2022

Soil physical and thermal properties from soil samples at multiple locations at Teller Road Site, Seward Peninsula, Alaska, 2019

This dataset contains results of the laboratory analysis of soil samples collected across the watershed located at miles #27 along the Teller road, near Nome, AK. The dataset includes 124 soil samples retrieved from 52 locations during the first week of August 2019. The soil samples were collected in the top 1.2 m of soil with volume known samples, analyzed with a thermal analyser to infer thermal parameters (thermal conductivity, volumetric heat capacity and thermal diffusivity), and then processed in a lab for estimation of their bulk density, water content, carbon and nitrogen content, soil texture, and elemental fractions. This dataset has one csv file and one pdf file.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

High-Temperature Gas-Cooled Reactors Multiphysics Simulation Demonstration and Code Validation

This study presents a comprehensive benchmarking and verification effort of several thermal-hydraulic and multiphysics capabilities for high-temperature gas-cooled reactor applications. The first part of this effort focuses on the running-in verification of Griffin’s multiphysics capabilities, specifically for simulating the evolution of pebble-bed reactor cores from startup to equilibrium. Since Fiscal Year 2024, improvements and enhancements have been implemented in Griffin, including simplifying the process to specify streamlines and developing the online cross-section generation capability. In the absence of validation data, code-to-code comparisons are conducted with kugelpy, showing good agreement for integral quantities like k-eff predictions and predictions for maximum power density. However, accuracy issues are noted for more detailed quantities like the spatial distribution of fission rate densities which will require further work to address. The second part of this report presents an improved System Analysis Module (SAM) core channel model where the effects of cross flow are considered during the pressurized loss of forced cooling transient, resulting in an improved agreement of the predicted pebble temperature with respect to the predictions from the SAM 2D porous media model. Additionally, the wall channeling effect due to variable porosity at the near wall region of the core is also investigated. Furthermore, to demonstrate Griffin’s online cross-section generation capability, a Multiphysics simulation is performed by coupling Griffin to the SAM core channel model. In the third part of the report, as a part of the Organisation for Economic Co-operation and Development/Nuclear Energy Agency (OECD/NEA) thermal-hydraulic code validation benchmark activity for a high-temperature gas-cooled reactor, the High Temperature Test Facility (HTTF) is investigated first using the NekRS computational fluid dynamics (CFD) code to study the flow mixing phenomenon in the lower plenum of the facility. Then, code-to-code and code-to-data comparisons are performed for Test PG27, which is a pressurized conduction cooldown (PCC) test, using five different codes by six organizations from five countries. The different simulations show good agreements in terms of the general trend but there are differences in some results such as the peak temperatures of different regions and heat removal rate.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Boosting Barlow Twins Reduced Order Modeling for Machine Learning‐Based Surrogate Models in Multiphase Flow Problems

Abstract We present an innovative approach called boosting Barlow Twins reduced order modeling (BBT‐ROM) to enhance the reliability of machine learning surrogate models for multiphase flow problems. BBT‐ROM builds upon Barlow Twins reduced order modeling that leverages self‐supervised learning to effectively handle linear and nonlinear manifolds by constructing well‐structured latent spaces of input parameters and output quantities. To address the challenge of high contrast data in multiphase flow problems due to injection wells and faults, we employ a boosting algorithm within BBT‐ROM. This algorithm sequentially trains a set of weak models (i.e., inaccurate models), improving prediction accuracy through ensemble learning. To evaluate the performance of BBT‐ROM, we conduct three three‐dimensional multiphase flow problems, including waterflooding and geologic carbon storage (GCS), with varying numbers of input parameter cases and model domain features. The results demonstrate that BBT‐ROM excels at predicting non‐wetting phase saturation (e.g., oil or saturation) and fluid pressure, with average relative errors ranging from 0.5% to 3%. Importantly, BBT‐ROM showcases robustness when faced with limited input parameter space during GCS testing.

58 GEOSCIENCES↗
DOE OSTI2024

Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

data processing↗
DOE OSTI2026

Evaluation and optimization of flow boiling frictional pressure drop correlations using the data from traditional and next-generation refrigerants in a micro-fin tube

This study presents an experimental evaluation and optimization of flow boiling frictional pressure drop correlations for conventional and next-generation refrigerants in a horizontal micro-fin tube, with particular emphasis on the newly emerging refrigerant blends R-454C and R-455A, for which pressure-drop data in enhanced tubes remain limited. Experiments were conducted with R-410A, R-454C, R-455A, R-134a, R-1234yf, and R-1234ze(E) in a copper micro-fin tube with an inner diameter of 8.468 mm, over mass fluxes ranging from 100 to 300 kg/(m²·s) depending on the refrigerant, and evaporation temperatures of 7, 12, and 14 °C. Frictional pressure gradients were determined from measured total pressure drops after subtracting acceleration pressure drop, and the resulting database was used to assess four existing models: Kuo and Wang (1996), Cavallini et al. (1997), Goto et al. (2001), and Diani et al. (2014). The measured frictional pressure gradient increased with vapor quality and mass flux for all refrigerants and increased further at lower evaporation temperatures, with the overall trend strongly related to liquid viscosity. Among the four correlations, the Goto et al. (2001) model provided the best overall agreement with the measured data before optimization. To further improve prediction accuracy, the Kuo and Wang (1996) and Goto et al. (2001) models were optimized using the complete experimental database. After optimization, both models reduced the overall mean absolute deviation to below 15%, while the optimized Goto et al. (2001) model maintained the best and most consistent overall performance. The results provide new pressure-drop data for next-generation refrigerants and demonstrate that parameter optimization can significantly enhance the applicability of existing micro-fin-tube correlations.

Hu, Yifeng [ORNL] (ORCID:0000000242875185)↗
DOE OSTI2025

Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape

Arctic warming is altering vegetation and carbon dynamics with global implications, yet Earth System Model (ESM) predictions in the Arctic remain highly uncertain, in part due to historically limited data for model parameterization and validation. As such, ESMs typically represent Arctic ecosystems in an oversimplified manner. Recently, nine plant functional types (PFTs) designed to realistically represent tundra vegetation were integrated into the Energy Exascale Earth System Model (E3SM) Land Model (ELM) and parameterized using plot-scale observations from a single site. Additional evaluation was needed to determine their transferability across the Arctic. Here, in this study, we evaluated whether refined representation of tundra vegetation improved model accuracy by conducting spatially explicit 100 × 100 m resolution ELM simulations on Alaska's Seward Peninsula. Simulations with the default two-PFT configuration and with the nine Arctic-specific PFTs were benchmarked against observations of net ecosystem exchange, gross primary production, and aboveground biomass from multiple data streams including an eddy covariance flux tower, flux chambers, and aircraft and unoccupied aerial system hyperspectral remote sensing. Evaluation revealed that Arctic-specific PFT simulations produced more realistic landscape-level carbon exchanges, and better captured observed heterogeneity in biomass and productivity, explaining 60%–70% of spatial variance (R 2 = 0.6–0.7) compared to just 12%–18% (R 2 = 0.12–0.18) with the default configuration. However, the refined model failed to reproduce observed aboveground biomass for highly productive alder-willow communities, requiring further evaluation of carbon allocation parameterizations for tall shrubs that are increasingly expanding across tundra landscapes. Our results demonstrate that enhanced representation of vegetation heterogeneity boosts predictive understanding of tundra carbon dynamics, facilitating regional to pan-Arctic model and remote-sensing scaling.

Murphy, Bailey A. [Oak Ridge National Laboratory (↗
DOE OSTI2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗
DOE OSTI2025

Dynamics of hot QCD matter 2024 — Bulk properties

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark–gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. In conclusion, combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

EXPERIMENTAL INVESTIGATION OF IMPACT AND ABRASION EROSIONS AT LOW VELOCITIES AND COMPARISON TO CFD SIMULATIONS

Wear and erosion damage of materials due to interaction of the surface with solid particles is a major issue in various industries. This phenomenon is common in mining, and oil and gas production, causing damages worth millions of dollars annually. Erosion is becoming a critical issue in renewable technologies as well such as concentrated solar thermal power (CSP) systems. In some CSP systems, solid particles can be used to absorb solar energy and enhance the heat storage. However, these particles may cause significant amount of wear to system components while falling down the power tower at high temperatures. This damage can be costly, and therefore, investigating solid particle erosion in CSP systems is of utmost importance. In collaboration with Sandia National Laboratories, the erosion durability of particle-based Generation 3 (GEN3) CSP system are being investigated both experimentally and numerically. In order to develop models to predict the life time of such systems, erosion under low temperature and low particle impact velocity conditions are investigated. Impact erosion and abrasion erosion experiments are conducted for low velocity and low temperature conditions for various candidate particles and containment materials. The impact erosion testing is conducted for three different containment material including Stainless Steel 316, special refractory material, and Inconel 740H with CARBOBEAD HSP 40/70 ceramic particles and UNIWEST 430 silica quartz erodent particles. All this data will enable us to better model the erosion behavior of these materials and provides a data base to investigate erosion at high temperatures experienced in CSP systems. At low temperatures and low velocities the softer refractory material exhibited greater erosion by an order-of-magnitude when compared to the harder Inconel 740H and Stainless Steel 316 materials. The overall erosion ratio for all the candidate materials was of the order of 10 -7 kg/kg. We also investigated wear in containment materials from sliding of particles over the surfaces at very low angles, also referred to as abrasion. For the velocities expected in a typical 1MW CSP plant, visible surface wear was noted, but the overall mass loss per unit surface area was found to be dependent on the hardness of the containment material tested. In this manuscript, we also present Computational Fluid Dynamics (CFD) simulations and erosion prediction of low velocity impact erosion. These results are compared with the experimental data to evaluate the ability of available erosion models for predicting erosion under these conditions.

14 SOLAR ENERGY↗
DOE OSTI2020

Irradiation enhanced diffusion and diffusional creep in U₃Si₂

U₃Si₂ is a candidate as an advanced fuel for light water reactors (LWRs). Compared to UO₂, there are significant data gaps for the thermophysical and thermomechanical properties of U₃Si₂. Atomic scale simulations can help fill in those gaps, while also providing a more mechanistic understanding of the material properties. Creep is an important property for fuel performance as it influences the pellet-cladding gap, which in turn affects the temperature of the fuel (a key parameter in many other aspects of fuel behavior). In this work, we employed a DFT-informed cluster dynamics framework to predict point defect concentrations in U₃Si₂. The concentrations, diffusivities, and volumes of these defects were used to develop a diffusional creep model based on bulk (Nabarro-Herring) and grain boundary (Coble) diffusion processes. It was found that irradiation enhanced Nabarro-Herring creep is dominant at low temperatures, while Coble creep dominates for high temperatures. The model compares well against available experimental data and has been implemented in BISON. A demonstration case using simple power profiles has been carried out, showing that negligible creep occurs due to the low temperatures experienced by U₃Si₂, a consequence of its high thermal conductivity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗