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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development for Integrated System-Level Analysis Capabilities in SAM for Molten Salt Reactors

In recent years, there has been renewed interest in Molten Salt Reactors (MSRs) for their potential advantages compared to reactors that rely on solid fuel. In response to such interest, many methods and codes have been developed to capture the unique features of MSRs. Among them, the System Analysis Module (SAM) is a modern system analysis tool that provides fast-running, modest-fidelity, whole-plant transient analysis capabilities, essential for fast-turnaround design scoping and engineering analyses of advanced reactor concepts. For liquid-fuel MSRs, the complex physics and chemistry involved in MSR operation—such as reactor kinetics, fluid flow, heat transfer, and salt composition dynamics—pose significant challenges for system-level modeling. Specific modeling capabilities are needed for system-level transient simulation. This paper presents recent advancements in SAM capability enhancements for system-level modeling of MSRs, focusing on improved simulation fidelity, computational efficiency, and multi-physics integration. Key enhancements include the development of species transport, Delayed Neutron Precursor (DNP) drift, modified Point Kinetics Equations (PKE), decay heat modeling, key fission product behavior, salt corrosion, and thermal-hydraulic coupling, as well as code robustness and performance enhancements for MSR applications. The code enhancement allows for better predictive accuracy in safety analysis, transient behavior, and operational optimization, thus supporting the design and licensing of next-generation MSRs. Results from case studies are presented to demonstrate the benefits of these enhancements in accurately capturing key reactor transient behaviors.

Hu, Rui (ORCID:0000000237712920)↗
DOE OSTI2021

CUORE opens the door to tonne-scale cryogenics experiments

The past few decades have seen major developments in the design and operation of cryogenic particle detectors. This technology offers an extremely good energy resolution – comparable to semiconductor detectors – and a wide choice of target materials, making low temperature calorimetric detectors ideal for a variety of particle physics applications. Rare event searches have continued to require ever greater exposures, which has driven them to ever larger cryogenic detectors, with the CUORE experiment being the first to reach a tonne-scale, mK-cooled, experimental mass. CUORE, designed to search for neutrinoless double beta decay, has been operational since 2017 at a temperature of about 10 mK. This result has been attained by the use of an unprecedentedly large cryogenic infrastructure called the CUORE cryostat: conceived, designed and commissioned for this purpose. In this article the main characteristics and features of the cryogenic facility developed for the CUORE experiment are highlighted. In this work, a brief introduction of the evolution of the field and of the past cryogenic facilities are given. The motivation behind the design and development of the CUORE cryogenic facility is detailed as are the steps taken toward realization, commissioning, and operation of the CUORE cryostat. The major challenges overcome by the collaboration and the solutions implemented throughout the building of the cryogenic facility will be discussed along with the potential improvements for future facilities. The success of CUORE has opened the door to a new generation of large-scale cryogenic facilities in numerous fields of science. Broader implications of the incredible feat achieved by the CUORE collaboration on the future cryogenic facilities in various fields ranging from neutrino and dark matter experiments to quantum computing will be examined.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Theoretical background for a fast flow liquid metal divertor experiment

Tokamak Energy has developed a liquid metal experiment featuring a lithium loop that circulates lithium through the HIDRA stellarator at the University of Illinois at Urbana-Champaign. The loop includes a replaceable divertor module, designed to demonstrate fast, steady, open-surface liquid metal flow. The first-generation divertor module was deliberately kept simple—an open-surface chute—to enable modelling validation and establish a benchmark for future design iterations. This paper presents the theoretical foundations of the experimental design. Specifically, we identify module overflooding as the primary challenge and determine the limits of fluid velocity and magnetic field strength necessary to prevent it. The steady-state flow patterns are expected to exhibit relatively slow surface velocities, which do not fully align with the fast-flow concept. Nevertheless, the experiment is designed to achieve controllable, continuous open-surface flow within an operational fusion device, representing a significant step toward the development of liquid metal divertor technology.

Experiment↗
DOE OSTI2023

HLA-Clus: HLA class I clustering based on 3D structure

In a previous paper, we classified populated HLA class I alleles into supertypes and subtypes based on the similarity of 3D landscape of peptide binding grooves, using newly defined structure distance metric and hierarchical clustering approach. Compared to other approaches, our method achieves higher correlation with peptide binding specificity, intra-cluster similarity (cohesion), and robustness. Here we introduce HLA-Clus, a Python package for clustering HLA Class I alleles using the method we developed recently and describe additional features including a new nearest neighbor clustering method that facilitates clustering based on user-defined criteria. The HLA-Clus pipeline includes three stages: First, HLA Class I structural models are coarse grained and transformed into clouds of labeled points. Second, similarities between alleles are determined using a newly defined structure distance metric that accounts for spatial and physicochemical similarities. Finally, alleles are clustered via hierarchical or nearest-neighbor approaches. We also interfaced HLA-Clus with the peptide:HLA affinity predictor MHCnuggets. By using the nearest neighbor clustering method to select optimal allele-specific deep learning models in MHCnuggets, the average accuracy of peptide binding prediction of rare alleles was improved. The HLA-Clus package offers a solution for characterizing the peptide binding specificities of a large number of HLA alleles. This method can be applied in HLA functional studies, such as the development of peptide affinity predictors, disease association studies, and HLA matching for grafting. HLA-Clus is freely available at our GitHub repository (https://github.com/yshen25/HLA-Clus).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Secure Route: Roadway Risk Mapping for Transportation Planners

The secure transport of sensitive materials across U.S. road networks pose unique challenges for local, state, and federal agencies. Threats range from random events (e.g., accidents, medical emergencies, mechanical failures) to opportunistic or organized tactical assaults. Although the probability of such attacks is very low, the consequences of material loss to foreign states or terrorists can be catastrophic, qualifying these scenarios as “grey swan” events—low-probability, high-impact occurrences that are predictable but difficult to quantify. Traditional risk assessments struggle in these contexts, necessitating a shift toward subjective risk perception to inform planning. Risk perception in transport planning is shaped by various factors, including knowledge of adversarial capabilities, vehicle defenses, manifest details, and geographic features along the route. Geographic features such as bridges, tunnels, roadside elevation, and gaps in cellular coverage introduce vulnerabilities, while mitigative features include safe havens, police stations, and medical services. Temporal variables such as congestion, accidents, and weather further complicate route planning. Despite their importance, existing routing tools like Google Maps and commercial software do not explicitly account for geographic risk features, requiring planners to rely on personal familiarity with routes—a time-intensive, non-scalable approach. This work addresses these gaps by: (1) developing datasets that catalog geographic risk features along U.S. roadways, (2) eliciting risk perceptions from experienced transportation security experts, and (3) linking these perceptions to roadway conditions and geographic data. We implement these capabilities within Secure Route a novel mapping tool for classifying route segment risks associated with roadway conditions. This system provides transportation planners with an intuitive interface to assess and contextualize risk along potential routes, improving decision-making for secure transport. We present current progress in this effort and identify next steps.

Stewart, Robert [ORNL] (ORCID:0000000281867559)↗
DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗
DOE OSTI2020

Computationally efficient CFD prediction of bubbly flow using physics-guided deep learning

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. In this work, by defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

SARS-CoV-2 RBD antibodies that maximize breadth and resistance to escape

An ideal therapeutic anti-SARS-CoV-2 antibody would resist viral escape 1,2,3 , have activity against diverse sarbecoviruses 4,5,6,7 , and be highly protective through viral neutralization 8,9,10,11 and effector functions 12,13 . Understanding how these properties relate to each other and vary across epitopes would aid the development of therapeutic antibodies and guide vaccine design. Here we comprehensively characterize escape, breadth and potency across a panel of SARS-CoV-2 antibodies targeting the receptor-binding domain (RBD). Despite a trade-off between in vitro neutralization potency and breadth of sarbecovirus binding, we identify neutralizing antibodies with exceptional sarbecovirus breadth and a corresponding resistance to SARS-CoV-2 escape. One of these antibodies, S2H97, binds with high affinity across all sarbecovirus clades to a cryptic epitope and prophylactically protects hamsters from viral challenge. Antibodies that target the angiotensin-converting enzyme 2 (ACE2) receptor-binding motif (RBM) typically have poor breadth and are readily escaped by mutations despite high neutralization potency. Nevertheless, we also characterize a potent RBM antibody (S2E12 8 ) with breadth across sarbecoviruses related to SARS-CoV-2 and a high barrier to viral escape. These data highlight principles underlying variation in escape, breadth and potency among antibodies that target the RBD, and identify epitopes and features to prioritize for therapeutic development against the current and potential future pandemics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Overview of Base Model in Parametric Studies Specific to Performance of U-Mo Plates

This paper provides an overview of the base model specifically developed to perform parametric sensitivity studies on the U-10Mo monolithic fuel system. U-Mo monolithic fuels are being considered for the conversion of test reactors into high-performance research reactors that operate using proliferation-resistant, low-enriched uranium (LEU) fuels. These plate-type fuels contain a high-density, low-enrichment fuel sandwiched between zirconium diffusion barriers and encapsulated in aluminum claddings. All U.S. high-performance research reactors have released the designs of their LEU monolithic fuel reactor cores. These designs include nearly 50 distinct fuel plate geometries with different operational parameters. Consequently, a single generic plate geometry representing all the extreme points in this design matrix is unrealistic. To evaluate the performance for various parameters, a set of sensitivity studies was performed. These studies considered various input parameters (i.e., geometric, operational, and material property-related). The results revealed valuable information about plate performance and the sensitivity of this performance to various modeling inputs. To establish a reference state for comparing these result, base model featuring representative irradiation conditions was developed. To capture in-reactor behavior accurately, incorporation of representative constitutive models capable of evolving properties with respect to temperature, irradiation time, and burnup was needed. The behavioral models considered burnup-dependent properties, swelling, creep, and degradation. This paper introduces the base model created for the parametric sensitivity studies. The detailed description of the procedure includes the model geometry, model discretization, thermo-mechanical coupling, material properties and behavioral models. This paper also provides selected results and assesses the performance of the base model.

42 ENGINEERING↗
DOE OSTI2026

Evaluation of a Reduced-Order Model for IBR Fault Response Representation via OEM Blackbox Models

This paper presents a fully implemented inverter reduced-order-model (ROM) in an EMT simulation (PSCAD) library component for direct user utilization in protection studies. The developed inverter ROM has the following features: Equivalent to a full inverter-based resource (IBR) inverter model with positive- and negative-sequence current formulation and representation. A Python script is developed to fully automate this process, including training data generation, ROM parameter training, updating parameters, and model verification and validation. The ROM is validated using both IEEE 2800-compliant and non-compliant OEM modes in a real-world system, building confidence of its usability by protection engineers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species

Abstract Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI’s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Clinical knowledge extraction via sparse embedding regression (KESER) with multi-center large scale electronic health record data

The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. However, it is difficult to know all the relevant codes related to a phenotype due to the large number of codes available. Traditional data mining approaches often require the use of patient-level data, which hinders the ability to share data across institutions. In this project, we demonstrate that multi-center large-scale code embeddings can be used to efficiently identify relevant features related to a disease of interest. We constructed large-scale code embeddings for a wide range of codified concepts from EHRs from two large medical centers. We developed knowledge extraction via sparse embedding regression (KESER) for feature selection and integrative network analysis. We evaluated the quality of the code embeddings and assessed the performance of KESER in feature selection for eight diseases. Besides, we developed an integrated clinical knowledge map combining embedding data from both institutions. The features selected by KESER were comprehensive compared to lists of codified data generated by domain experts. Features identified via KESER resulted in comparable performance to those built upon features selected manually or with patient-level data. The knowledge map created using an integrative analysis identified disease-disease and disease-drug pairs more accurately compared to those identified using single institution data. Analysis of code embeddings via KESER can effectively reveal clinical knowledge and infer relatedness among codified concepts. KESER bypasses the need for patient-level data in individual analyses providing a significant advance in enabling multi-center studies using EHR data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Convoluted filtering for process cycle modeling

Principles of materials science and engineering, physics, mathematics, and information science are used to extract knowledge and insights from the process-structure–property-performance relationships hidden in materials data. The process-structure modeling can be accelerated without loss of interpretability, with artificial intelligence tools that mimic the salient features of the process and process-structure relations. In this work, a novel convoluted model-filtering technique was exploited to build and successfully train the Convoluted Filter (CoFi) artifacts for Fe-based alloy heat treatment cycles. The artifacts were pre-trained to filter out deep models that change the surrogate microstructure state after the heat treatment at ambient conditions. Direct representation of the thermal cycle features within knowledge Graph facilitated development of meaningful data models for microstructure evolution, which reduce overfitting to limited datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Investigating the FLASH Effect in a Rat Brain Organotypic Model With a Novel High-Energy Electron Beam

Ultrahigh dose rate (FLASH) radiation therapy is reported to reduce normal tissue toxicity while maintaining tumor control; however, mechanism(s) remain obscure. To study FLASH mechanisms in brain tissue, we developed a novel experimental platform featuring a specialized high-energy electron linear accelerator, High Intensity Gamma Ray Source (HIGS), paired with an organotypic ex vivo brain metastasis model. We varied interpulse spacing to modulate the mean dose rate (MDR) of our unique 35 MeV electron beam, while maintaining extremely high instantaneous dose rate (IDR). We characterized dosimetry and targeting accuracy of the FLASH beam with film dosimetry. We combined this FLASH beam with an organotypic rat brain slice/breast carcinoma coculture model of brain metastasis to assess effects on normal and neoplastic tissues. Live-cell and bioluminescence imaging demonstrated cancer cell growth effects, whereas normal tissue responses and immune activation were assessed using live-cell imaging, cytokine profiles, and confocal microscopy. Here, we performed comparison experiments with 20 MeV electrons from a Varian clinical linear accelerator (VCLA) using conventional dose rates. The highest IDR of the FLASH beam to date was 20.7 ± 0.6 MGy/s, with maximum MDR of 20.7 MGy/s delivered in 1 pulse of 1 µs duration. Beam targeting was accurate to <1 mm and reproducible. HIGS-FLASH and VCLA dose rates equivalently decreased cancer cell growth. HIGS-FLASH irradiation significantly increased tumor necrosis factor α and fractalkine levels and confocal microscopy revealed distinct changes in microglial morphology slices suggesting microglia activation. Our novel experimental platform produces extremely high dose rates and rapid normal/neoplastic tissue readouts for mechanistic research into the effects of FLASH radiation in the brain. HIGS-FLASH irradiation induces comparable cancer cell growth inhibition but differential effects on cytokines and microglial morphology, suggesting that acute innate immune responses may be involved in FLASH normal tissue effects in the brain.

Kay, Tyler V. [Duke University, Durham, NC (United↗
DOE OSTI2025

Enhancing 2D hydrodynamic flood models through machine learning and urban drainage integration

Two-dimensional hydrodynamic flood models are commonly employed for simulating flood extent and inundation depth. However, the influence of urban drainage network (UDN) is frequently overlooked in these models, potentially compromising their accuracy. Furthermore, the expensive computational costs and longer processing times make them challenging for large-scale hydrodynamic simulation. To address these challenges, this paper develops a machine learning (ML)-driven emulator for an open-source flood model, the Two-dimensional Runoff Inundation Toolkit for Operational Needs (TRITON). A TRITON-ML Emulator (TR-Emulator) that utilizes Convolutional Long Short-Term Memory is developed to capture the spatiotemporal features of flood events based on the outputs from TRITON. We further enhance the emulator by integrating UDN parameters (TR-UDN), such as the flow capacity of drainage pipes, pipe size, and pipe length, via an ML stacking technique to improve the water surface elevation (WSE) simulation. Hurricane Harvey 2017 in Houston, TX is used as the case study. We compare WSE results from TRITON, TR-Emulator, TR-UDN, and the United States Geological Survey (USGS) observations to evaluate the performance of these models. The results indicate that the TR-Emulator effectively replicates the WSE simulated by TRITON. Additionally, TR-UDN performs well in capturing WSE patterns and peak flows, aligning more closely with USGS observations, except in areas with milder slopes where conveyance discrepancies are observed. We further test the generalizability of our ML-based models using another smaller event. This paper shows that the TR-Emulator is effective for users and engineers to emulate a 2D hydrodynamic model, and the enhanced version of the TR-Emulator, TR-UDN, can be an efficient tool for predicting WSEs during urban flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

3.0 - MOOSE: Enabling massively parallel multiphysics simulations

The development of MOOSE has kept accelerating since the last release, with over 2,100 pull requests merged over the last 30 months that involved nearly fifty contributors across close to a dozen institutions internationally. The growth in MOOSE's capabilities and downstream applications is reflected in the growth of the community. User support provided on the GitHub discussions forum has steadily increased to nearly 50 daily interactions. New simulation projects, notably to model advanced nuclear reactor and fusion devices, are driving a significant expansion of the capabilities. This paper reports on these developments, with several major released features, new physics modules, and key improvements to the user experience and simulation workflow.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Multiomic atlas with functional stratification and developmental dynamics of zebrafish cis -regulatory elements

Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and re-analyzed published genomic data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sustainable layered cathode with suppressed phase transition for long-life sodium-ion batteries

Sodium-ion batteries are among the most promising alternatives to lithium-based technologies for grid and other energy storage applications due to their cost benefits and sustainable resource supply. For the cathode—the component that largely determines the energy density of a sodium-ion battery cell—one major category of materials is P2-type layered oxides. Unfortunately, at high state-of-charge, such materials tend to undergo a phase transition with a very large volume change and consequent structural degradation during long-term cycling. Here we address this issue by introducing vacancies into the transition metal layer of P2-Na$_{0.7}$Fe$_{0.1}$Mn$_{0.75\square}$$_{0.15}$O2 (‘$_{\square}$’ represents a vacancy). The transition metal vacancy serves to suppress migration of neighbouring Na ions and therefore maintain structural and thermal stability in Na-depleted states. Moreover, the specific Na-O-$_{\square}$ configuration triggers a reversible anionic redox reaction and boosts the energy density. As a result, the cathode design here enables pouch cells with energy densities of 170 Wh kg -1 and 120 Wh kg -1 that can operate for over 600 and 1,000 cycles, respectively. In conclusion, our work not only suggests a feasible strategy for cathode design but also confirms the possibility of developing a battery chemistry that features a reduced need for critical raw materials.

25 ENERGY STORAGE↗