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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

M3SF-24LL010301062-Surface Complexation/Ion Exchange Data Integration for Radionuclide Sorption to Clay Minerals

This progress report (Level 3 Milestone Number M3SF-24LL010301062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Argillite International Collaborations Work Package SF-24LL01030106. The activity is focused on our long-term commitment to engaging our partners in international nuclear waste repository research. The focus of this milestone is the establishment of international collaborations for sorption modeling and the associated impacts of unlocking larger, community-based datasets. More specifically, we are developing a database framework for Spent Fuel and Waste and Science Technology (SFWST) that is aligned with the Helmholtz Zentrum Dresden Rossendorf (HZDR) and other international sorption database development groups in support of the database needs of the SFWST program.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2025

Uncertainty Analysis of Inhalation Dose Coefficients for Nuclear Incident Response

This study addressed the need to characterize variability in inhalation dose coefficients due to uncertainties in respiratory tract deposition, systemic biokinetics, and physiological parameters. A Python-based implementation of the International Commission on Radiological Protection Publication 66 Human Respiratory Tract Model was developed called the Radiological Exposure Dose Calculator (REDCAL) to propagate parameter uncertainty.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Artificial Intelligence/Deep Learning FRNN Software for Prediction & Real-Time Control of DIII-D Plasma Control System (PCS)

This collaborative project integrated an improved version of the Artificial Intelligence/Deep Learning FRNN prediction and control software into the real-time DIII-D PCS (plasma control system). A key AI/DL software challenge is to build a modern high-performance computing (HPC) enabled “synthetic plasma simulator” capable of carrying out HPC-driven real-time plasma control applications. This involves development of a deep learning framework to train the surrogate model for a first-principles-based instability analysis simulator (“SGTC”) derived from the global gyrokinetic code GTC. The role of SGTC is to provide accurate and detailed plasma instability information from a real-time AI-based simulator capability to complement the deep learning prediction and control from experimentally-measured signals, such as ECE Imaging, supplemented by synthetic SGTC-ECEI.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Dark Sector and Gravitational Waves Searches with SRF Cavities at SQMS

The Superconducting Quantum Materials and Systems Center, led by Fermi National Accelerator Laboratory, is one of five research centers funded by the U.S. Department of Energy as part of a national initiative to develop and deploy the world’s most powerful quantum computers and sensors. The SQMS Center uses world-record quality-factor superconducting radio-frequency, or SRF, cavities as ultra-sensitive quantum probes. Within the quantum sensing thrust, researchers are developing experiments based on cavities and novel quantum devices to search for particles beyond the Standard Model, dark matter candidates, gravitational waves and fundamental material properties. The seminar will focus on Dark SRF and MAGO activities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A study on the scale dependence of mixing indices for Eulerian multiphase models

Abstract Mixing can vary based on the scale at which the system is observed, and a mixing index that can capture the features at different length scales is desirable. In this article, we analyze the scale dependence of the mixing indices developed for Eulerian multiphase models. Relevant length scales are distinguished by filtering solid fraction fields. The scale‐dependence study is first done on manufactured fields of solid fraction to assess the performance of the mixing indices. The study is extended to a two‐dimensional CFD simulation of the segregation of a bidisperse gas–solid mixture. The local mixing index performs well in capturing the spatial variation of mixing at different scales. The scale dependence of two global mixing indices is considered in the study, where the state of mixing is defined based on statistical measures. We demonstrate that the choice of measures influences the sensitivity of mixing indices to mixing at different scales.

Nagawkar, Barlev R.↗
DOE OSTI2025

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗
DOE OSTI2026

Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing

Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.

Capacity allocation↗
DOE OSTI2025

Leveraging Large Language Models for Real-World Data Evidence: A Framework for Automated Treatment Extraction and Data Harmonization

Background: The ability to comprehensively collect treatment information from cancer patient medical records would enable studies to evaluate real-world benefits and risks tied to specific treatments. Currently, it is difficult to system- atically collect high-quality treatment information because it is often stored in unstructured text. Manually extracting and standardizing drug and regimen data is time-intensive. Recent advances in large language models (LLMs) offer a potential solution for automated extraction of structured treatment information from clinical text. Objective: This study systematically evaluates the utility of four LLMs from the Llama family for automated extraction of oncology treatment information from clinical text. This information can guide researchers using cancer registry data to provide insights into cancer care and outcomes beyond clinical trials. Methods: Four instruction-tuned Llama models with varying parameter counts (1B, 3B, 8B, and 70B) were evaluated for their ability to extract treatment information from clinical documents. A unified oncology knowledge base integrating seven major public data sources was developed to standardize and normalize extracted entities—a critical step for harmonizing data from diverse sources. Extracted treatment data were compared against expert-annotated ground truth. Model performance was assessed using accuracy metrics (Precision, Recall, F1-Score) and opera- tional feasibility metrics, including processing speed and structural compliance of the output. Results: A strong positive correlation was observed between model size and extraction accuracy. F1-score improved from 0.609 for the 1B model to 0.710 (3B), 0.807 (8B), and 0.828 (70B). While larger models demonstrated superior accuracy and compliance, they incurred higher computational costs. The modest performance difference between 8B and 70B suggests diminishing returns with increasing model size. Conclusions: LLMs represent a viable technology for automating oncology treatment extraction. The 8B-parameter model emerged as a highly effective option, balancing high accuracy and computational efficiency. Selecting an appropriate LLM for deployment in cancer registries involves a trade-off between desired accuracy and available operational resources. Harmonizing extracted entities with the oncology knowledge base facilitates standardized integration into common data models, enhancing data quality for real-world evidence analyses.

artificial intelligence↗
DOE OSTI2025

High-fidelity forced convection simulations of the University of Wisconsin–Madison air-cooled reactor cavity cooling system

Among the various types of advanced nuclear technologies that exist, high-temperature gas-cooled reactors (HTGRs) stand out for their co-generation capabilities and exceptional passive safety systems. HTGRs rely on a reactor cavity cooling system (RCCS) to remove decay heat from the reactor pressure vessel (RPV) during the course of long-term transients. Several studies have focused on experimental and numerical modeling of the RCCS; however, the literature reveals a notable gap when it comes to high-fidelity simulations such as direct numerical simulations (DNS) or large eddy simulation (LES) models of the RCCS. An LES model was developed for forced convection inside the RCCS so as to obtain a high-fidelity representation of the RCCS at a scale never before attempted. Furthermore, the LES model was compared against three different Reynolds-averaged Navier–Stokes (RANS) models (Realizable k − ϵ, k − ω SST, and Reynolds stress model [RSM]). This comparison verified the robustness and accuracy of the turbulence modeling approach of the RANS models for the RCCS. Finally, based on the comparison between the LES and the RANS models, the k − ω SST RANS model of the experimental facility was developed and compared against the available experimental data.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Machine Learning-Driven Optimization of Building Enclosures for Moisture Durability and Thermal Performance

The design of moisture-durable building enclosures with low embodied carbon often involves an iterative process of selecting the materials for the specific exposure conditions to meet the performance requirements. While hygrothermal simulations are commonly used to evaluate moisture durability, they often require advanced expertise for proper implementation. Machine learning (ML) provides a promising alternative by streamlining the design process and minimizing the reliance on complex simulations. This study presents a machine learning-based approach for predicting moisture durability in residential wall assemblies. The ML model was trained to estimate the mold index and maximum moisture content of various layers under typical exposure conditions. The model achieved a high predictive accuracy, with a coefficient of determination (R²) exceeding 0.90 when compared to traditional hygrothermal simulations on materials that were not part of training the ML model. Building on these results, the ML model was developed into a practical tool for optimizing wall assembly designs. This tool allows users to automatically optimize material selections based on energy, moisture, and carbon performance criteria. By incorporating multi-objective optimization, the tool identifies configurations that minimize embodied carbon while maintaining moisture safety and code-compliant thermal performance. Additionally, it provides insights into how material choices influence assembly durability, energy efficiency, and carbon reduction. The tool will be implemented in the Building Science Advisor (BSA) to enhance its performance and provide more granularity on the results. This research highlights the potential for ML-driven tools to simplify the design of high-performance building enclosures, offering architects and engineers a faster, more efficient way to balance critical performance factors.

Salonvaara, Mikael [ORNL] (ORCID:0000000318991554)↗
DOE OSTI2025

Portable Software Environment for Ultrahigh-Resolution ELM Development on GPUs

This paper presents our endeavors in developing the large-scale, ultra-high-resolution E3SM Land Model (uELM), specifically designed for exascale computers furnished with accelerators such as Nvidia GPUs. The uELM is a sophisticated code that substantially relies on High-Performance Computing (HPC) environments, necessitating particular machine and software configurations. To facilitate community-based uELM developments employing GPUs, we have created a portable, standalone software environment preconfigured with uELM input datasets, simulation cases, and source code. This environment, utilizing Docker, encompasses all essential code, libraries, and system software for uELM development on GPUs. It also features a functional unit test framework and an offline model testbed for comprehensive numerical experiments. From a technical perspective, the paper discusses GPU-ready container generations, uELM code management, and input data distribution across computational platforms. Lastly, the paper demonstrates the use of environment for functional unit testing, end-to-end simulation on CPUs and GPUs, and collaborative code development.

E3SM Land Model↗
DOE OSTI2026

Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model

Near-real-time (NRT) streamflow data are critical importance for timely water resources management. Here, we developed an open-source tool, FlowStats, for NRT streamflow analysis and visualization in Germany, based on NRT meteorological data from the German Weather Service and simulated streamflow from a long short-term memory neural network (LSTM). The LSTM model achieved very good overall performance, median NSE of 0.80 for the test period across 1,479 catchments. FlowStats provides options for deriving various streamflow statistics, from normal and abnormal streamflow detection to drought and flood analyses. An example analysis from FlowStats revealed widespread below-normal to extreme low-flow conditions across Germany from March to May 2025, which weakened from June to September 2025. Drought analysis for September 2025 highlighted severe to extreme drought conditions in northwestern Germany, while flood classifications indicated that high-flow events occurred in southwestern Germany. FlowStats can be used for various hydrological assessments to support water resources management.

Hydrological modeling↗
DOE OSTI2024

Model-Based Energy and Cost Analysis of Direct Air Capture Using ePTFE-Based Laminate-Structured Gas–Solid Contactors

Carbon dioxide removal (CDR) technologies will play a significant role in limiting global warming if implemented on a large scale. Direct air capture (DAC) is a scalable approach for removing atmospheric carbon, yet the true scope of its scalability remains unclear due to the early stage of technology development and high first plant costs. This study provides groundwork for understanding the technoeconomic trade-offs in developing DAC systems using laminate-structured gas–solid contactors, encompassing the analysis of both contactor and process design spaces. The robust mass transfer and process models outlined in this study provide tools for evaluating DAC processes and designing DAC plants based on cost and energy analysis. First, the key contactor geometrical parameters are identified to understand the CO 2 productivity–energy demand trade-offs, where geometries yielding higher mass transfer rates can achieve higher CO 2 productivities at the expense of energy consumption by fans and steam use. Next, a detailed process parametric study is conducted for DAC systems coupled with steam-assisted temperature-vacuum swing adsorption (S-TVSA) to visualize the trade-offs in the multidimensional design space. The main cost driver dramatically changes over different process conditions, but the operating cost prevailed on the Pareto front, with potential to operate as low as 150 $/tonne-CO 2 (within the cost range of 148–504 $/tonne-CO 2 in this study where the DAC system is coupled with industrial facilities for steam production).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.

25 - ENERGY STORAGE↗
DOE OSTI2025

A GPU ‐Accelerated 3D Unstructured Mesh Based Particle Tracking Code for Multi‐Species Impurity Transport Simulation in Fusion Tokamaks

ABSTRACT This paper presents the multi‐species global impurity transport capability developed in a GPU‐accelerated fully 3D unstructured mesh‐based code, GITRm, to simultaneously track multiple impurity species and handle interactions of these impurities with mixed‐material surfaces. Different computational approaches to model particle‐surface interaction or surface response have been developed and compared. Sheath electric field is taken into account by employing a fast distance‐to‐boundary calculation, which is carried out in parallel on distributed or partitioned meshes on multiple GPUs without the need for any inter‐process communication during the simulation. Several example cases, including two for the DIII‐D tokamak, that is, one with the SAS‐V divertor and the other with the collector probes, are used to demonstrate the utility of the current multi‐species capability. For the DIII‐D probe case, the capability of GITRm to resolve the spatial distribution of particles in localized regions, such as diagnostic probes, within non‐axisymmetric tokamak geometries is demonstrated. These simulations involve up to 320 million particles and utilize up to 48 GPUs.

Nath, Dhyanjyoti D. [Scientific Computation Resear↗
DOE OSTI2024

Optimal control of the electron temperature profile in DIII-D using machine learning surrogate models

The viability of the tokamak as a potential fusion reactor depends on the ability to keep the plasma in a stable regime while achieving temperatures, densities, and confinement times that are as high as possible. Tokamak scenario development attempts to find plasma regimes that achieve all of these conditions and are accessible with a given set of hardware constraints. This requires the ability to control plasma properties such as the normalized beta, the internal inductance, safety factor, rotation, etc. One property that has received less attention than some of the others, but is no less critical to achieving high performance, is the electron temperature (T e ) profile. In this work, Linear Quadratic Integral (LQI) control is used to develop a controller for the electron temperature profile in DIII-D. The controller is based on a linearized model derived from the transport equation that describes the evolution of the electron temperature, and includes contributions from the neural network surrogate models NubeamNet and MMMnet. Furthermore, the controller is tested in simulation using COTSIM, and is proven capable of tracking a target T e profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Microreactor Automated Control System - Digital Twin Models and Advanced Control Systems Updates

Automation of control systems is expected to be important in the economic and safe operation of microreactors. Therefore, there is a need to develop and demonstrate automated control for microreactors, along with the development of testbeds for this purpose. This report provides updates on the status of a nonnuclear microreactor automated control system (MACS)—a real-time, hardware-in-the-loop testbed for non-nuclear testing of microreactor control system automation. A real-time hardware-in-the-loop testbed incorporates the realistic dynamics of physical systems into control system development and testing. The collaborative effort between Oak Ridge National Laboratory (ORNL) and Idaho National Laboratory (INL) resulted in the development of a prototypic microreactor plant-level digital twin that includes the reactor and a balance of plant system. Advanced control strategies were incorporated to demonstrate testing of control automation solutions. The gRPC communication protocol, which was implemented in the hardware-in-the-loop testbed by INL, was coupled to a digital twin model developed using the TRANsient Simulation Framework of Reconfigurable Models (TRANSFORM) library in Modelica. This digital twin simulation was tested with the ViBRANT hardware for realistic feedback and visual representation of control action in real time. A modular Python client structure was developed to manage functional mock-up unit-based simulation and real-time gRPC communication. Hardware-in-the-loop testing indicated that the modeled reactor—a natural-convection, molten-salt coolant loop configuration—responds well to control of drum positioning for modulation of reactor core power, as well as system-level control and downstream demand changes. Ongoing research is focused on integrating additional control algorithms that utilize data from newly included sensors within the MACS hardware testbed, as well as demonstrating and assessing the performance of the different automated control algorithms on multiple additional operational scenarios.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗