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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Plant Reload Optimization (prlo)

The PRLO framework is built on a modular and extensible architecture that tightly couples advanced evolutionary optimization algorithms with nuclear fuel depletion solvers (i.e., nuclear physics neutronics code). It supports exploring complex, high-dimensional design spaces constrained by user-specified operational, safety, and economic constraints. Objectives such as minimizing fresh fuel enrichment, flattening radial and axial power distributions, and maximizing discharge burnup are evaluated. PRLO’s equilibrium cycle optimization capability enables the identification of core configurations that maintain fuel cycle sustainability over extended planning horizons. Its integration with the RAVEN platform facilitates optimization of loading patterns or fuel shuffling schemes across multiple cycles. The interface with SIMULATE, a licensed industry-standard nodal code developed by Studsvik, ensures accurate neutronic and thermal-hydraulic feedback for reactor core design. PRLO’s automated workflow engine supports iterative design refinement, enabling utilities to streamline core design processes and meet evolving performance and regulatory targets.

Kim, Junyung [Idaho National Laboratory] (00090005↗
DOE OSTI2024

Integrated Steady-State System Package for Nuclear Thermal Propulsion Analysis Using Multi-Dimensional Thermal Hydraulics and Dimensionless Turbopump Treatment

Nuclear thermal propulsion is an evolving technology that can be utilized for long-distance space travel. This technology yields the advantage of a high thrust and specific impulse, but requires an examination of the potential design adjustments necessary to enhance its feasibility. The development of nuclear thermal propulsion requires a comprehensive understanding of the system-level behavior during transient and steady-state operation. This paper extends our previous research by including the proper handling of turbomachinery with multi-channel thermal hydraulic simulations only for steady-state solutions. The system-level approach presented here enables the treatment of the turbopump components through non-dimensional analysis that eliminates the assumption of constant efficiencies. All the other components within the system (e.g., reflector and core) can be discretized to multiple channels and layers, in which the full thermal hydraulic solution is established. The approach chosen here enables the realistic modeling of the propellant flow within the expander cycle by capturing the pressure losses, mass flow rate splits, and enthalpy gain for various operational conditions. The verification of the package is completed through point comparisons of previous investigations into similar system designs. Furthermore, sensitivity studies are used to benchmark the capabilities of the package and investigate solution variations due to the perturbation of operational conditions and regimes. The sensitivity studies performed here are important to capture variation in flow characteristics (e.g., temperature, pressure, mass flow rates) for different design objectives such as the thrust and specific impulse. This work demonstrates that system-level simulations lacking multi-channel capability and proper turbomachinery treatment may yield higher uncertainties in understanding the engine’s response and characteristics to changing various requirements. This is extremely important when screening the design space of such propulsion systems and when transient simulations are required.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Why the US Needs an Enduring Plutonium Critical Assembly

Jezebel (operated from 1954-1977) has been the primary experiment for fast 239 Pu nuclear data validation for the last 70 years. Validation has included not only k eff , but also spectral indicies, Rossi-α, reactivity coefficients, and neutron leakage spectra. While this has been incredibly valuable to the community, there are three major issues. The first issue is that documentation on many of these experiments were lacking, leading to large uncertainties or (even worse) incorrect assumptions. The second issue is that while there have been many advances in research, there is no way to test those new advances today. The last issue is that since the assembly only operated for 23 years (and at time when 30 other critical assemblies were operating at the same facility and nuclear weapons testing was occurring), there were limited opportunities to observe how any system parameters changed as a function of time. Note that this work is not suggesting that a "Jezebel re placement" used for a limited experiment campaign would have great value. A new enduring (100 year target) plutonium (Pu) assembly with simple geometry and low uncertainties, however, would be extremely valuable. Such a capability would have a transformative impact on many research areas including nuclear data validation, analytical methods validation, dosimetry, reactor kinetics, and materials. The need for a new plutonium assembly is not new: it has been in the DOE Nuclear Criticality Safety Program (NCSP) Mission and Vision for over 10 years and was also the top priority established at the 2022 National Criticality Experiments Research Center (NCERC) Futures meeting. This work will discuss how such a new capability would help ensure that the US retains international leadership in plutonium research. Last, a brief overview of Lilith, a project aimed to design a new enduring plutonium assembly for operation at the NCERC will be given.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Nuclear microreactor transient and load-following control with deep reinforcement learning

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in microreactors, exploring performance in regard to load-following scenarios. By leveraging a point kinetics model with thermal and xenon feedback, we first establish a baseline using a single-output RL agent, then compare it against a traditional proportional–integral–derivative (PID) controller. This study demonstrates that RL controllers, including both single- and multi-agent RL (MARL) frameworks, can achieve similar or even superior load-following performance as traditional PID control across a range of load-following scenarios. In short transients, the RL agent was able to reduce the tracking error rate in comparison to PID by one half to one third. Over extended 300-minute load-following scenarios in which xenon feedback becomes a dominant factor, PID maintained better accuracy, but RL still remained within a 1% error margin despite being trained only on short-duration scenarios. This highlights RL’s strong ability to generalize and extrapolate to longer, more complex transients, affording substantial reductions in training costs and reduced overfitting. Furthermore, when control was extended to multiple drums, MARL enabled independent drum control as well as maintained reactor symmetry constraints without sacrificing performance---an objective that standard single-agent RL could not learn. We also found that, as increasing levels of Gaussian noise were added to the power measurements, the RL controllers were able to maintain lower error rates than PID, and to do so with at least 10% and upwards of 150% less control effort. These findings illustrate RL's potential for autonomous nuclear reactor control, laying the groundwork for future integration into high-fidelity simulations and experimental validation efforts.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

FORCE Regression Testing

Via programs including the Light Water Reactor Sustainability and Integrated Energy Systems, the U.S. Department of Energy has invested in the Framework for Optimization of ResourCes and Economics (FORCE) software framework (Idaho National Laboratory 2024a) for the technical and economic analysis of nuclear-integrated energy systems (IES). Nuclear IES expand the use of nuclear from traditional baseload electricity generation to a flexible and adaptive source of combined heat and power. Nuclear heat can be used in the production of a variety of energy currencies such as hydrogen and ammonia as well as other heat applications including water desalination and district heating. FORCE is designed with the intent to provide interconnected analysis tools that enable the accurate technical and economic assessment of specific nuclear IES configurations for individual energy markets. FORCE consists of three main analysis pathways: HYBRID (Idaho National Laboratory 2024b), which contains high-resolution physical models for IES; Holistic Energy Resource Optimization Network (HERON) (Idaho National Laboratory 2024c), which analyzes IES long-term economic viability; and Optimization of Real-time Capacity Allocation (ORCA) (Idaho National Laboratory 2024d), designed for real-time control of IES via digital twins and optimal decision making, including autonomous and remote operation research. Development of the FORCE ecosystem is guided by three pillars: capability, which assures that the computational requirements of IES analysis are met by the software tools; reliability, which provides for consistent code performance and expected behaviors; and accessibility, which lowers the barrier to entry for using the software and accelerates analysis by users beyond the FORCE primary developers. Reliability of the FORCE ecosystem is established according to the American Nuclear Society?s Nuclear Quality Assurance (NQA-1) program [American Society of Mechanical Engineers 1982], with specific levels of software quality assurance (SQA) within NQA-1 applied to each software tool in FORCE. As the tools within FORCE have matured, some integration algorithms to accurately connect the software tools for holistic analysis have been developed and deployed within the FORCE software repository. In accordance with NQA-1 standards, regression tests are required to guarantee the software performs consistently even when new capabilities are added to the software. In this report, we document the deployment of both unit tests, which test the consistent behavior of small pieces of the FORCE code base, as well as integration tests, which test the consistent performance of full use cases for the FORCE integration algorithms. We further document the encapsulation of these tests within a test harness, which collectively checks for each successful test completion on demand. Finally, we document the automation of the test harness using GitHub Actions [GitHub 2024], which require all tests succeed before any new capability or other changes can be added to the FORCE integration software

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Physical and thermal property changes under uniform oxidation in nuclear graphite

Material property changes of fine- and medium-grain nuclear graphite grades were measured after subjection to uniform oxidation. The cores of high temperature reactors are composed of large nuclear graphite block components. Here, these large core components are designed to provide neutron moderation and reflection, create a large thermal sink to assist in operational control, and form the solid core structure containing the nuclear fuel, coolant channels, and the safety critical channels for control rod insertion. Oxidation is a principle degradation mechanism affecting all aspects of the nuclear graphite component functions. This study addresses the underlying physical property changes of nuclear-graphite components for oxidized mass loss ranges beyond the current recommended ASME code rule limits to ensure structural integrity within the graphite components (a maximum mass loss = 10%).

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

FAST-1.2.2: Integral Assessment

This integral assessment quantifies the predictive capabilities of Fuel Analysis under Steady-state and Transients (FAST), a thermal-mechanical nuclear fuel performance code designed to analyze fuel behavior from beginning of life to burnup levels allowed by the U.S. Nuclear Regulatory Commission. FAST code calculations are shown to compare satisfactorily to a pre-selected set of experimental data with both steady-state and anticipated operating occurrence conditions and design basis accident transient operating conditions. This document describes the assessment of FAST-1.2.2, the latest version of FAST, released April 2026.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

pyTriBeam

SAND2025-01899O pyTriBeam is a software tool that creates automated processes for a scanning electron microscope including workflows for 3D serial sectioning dataset collection, high-res image montaging, and support for custom script use. This includes integration for 3D chemical mapping (EDS) and crystallographic (EBSD) data collection with select supported detectors. The application allows end users to setup and run customizable data collection workflows without requiring expertise in programming. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hovey, Chad↗
DOE OSTI2024

Longitudinal Solid P Longitudinal Solid Polarized Target for CL get for CLAS12 and Study of Spin AS12 and Study of Spin Structure of Nucleons

A suite of experiments measuring target-spin observables in electron-nucleon scattering (dubbed Run Group C) was conducted at Jefferson Lab's Hall B in Newport News, VA with a new polarized nuclear target known as 'APOLLO' (Ammonia POLarized LOngitudinally). This innovative target is engineered to seamlessly integrate with the advanced 12GeV CEBAF (Continuous Electron Beam Accelerator Facility) accelerator and the Hall B CLAS12 (12 GeV CEBAF Large Acceptance Spectrometer) detector array. The 'APOLLO' target harnesses the power of Dynamic Nuclear Polarization (DNP) to achieve longitudinal polarization of solid ammonia, thereby creating a net polarization in both protons (NH3) and deuterons (ND3). These samples are subjected to a 5 Tesla magnetic field produced by the CLAS12 spectrometer central solenoid, cooled to 1 K using helium evaporation, and subsequently exposed to microwave radiation at a frequency of 140 GHz. This project was made possible through a collaborative partnership between the Jefferson Lab Target Group, Old Dominion University, University of Virginia, Christopher Newport University, and the CLAS Collaboration. Integrating the APOLLO target into CLAS12 presented unique challenges because of its specific spatial and dimensional constraints. This dissertation will highlight the innovative solutions developed to address these challenges, including the creation of a new target material transport system, the incorporation of superconducting magnetic correction coils, and the development of a customized Nuclear Magnetic Resonance (NMR) system. In addition to a detailed description of the development and operation of APOLLO, and an overview of RGC, this document also describes the global data fitting of proton asymmetries in the Deep Inelastic scattering (DIS) and the Resonance regions, and it offers insights into the comprehensive analysis of target polarization data.

Pandey, Pushpa↗
DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2026

Evaluation of AI-enabled Digital Documented Safety Analysis

The National Reactor Innovation Center (NRIC) is leading a transformative initiative to accelerate advanced reactor deployment by fundamentally reimagining how nuclear safety basis documentation is developed, reviewed, and maintained. Traditional Documented Safety Analysis (DSA) processes for DOE-authorized facilities rely on static, document-centric workflows that consume significant time and resources, exemplified by recent major licensing efforts requiring hundreds of thousands of staff hours and millions of pages of documentation review. These conventional approaches create barriers to the rapid, cost-effective deployment of advanced reactors that America's future energy needs demand. NRIC's DOE Authorization Digital Transformation Project addresses these challenges through an innovative framework that integrates artificial intelligence (AI), digital engineering, and systems-based data management into a cohesive digital ecosystem. This white paper presents NRIC's methodology for evaluating AI-enabled document generation capabilities within this broader digital infrastructure, using the Demonstration of Microreactor Experiments (DOME) facility as a pilot case study. The evaluation will assess an AI tool's ability to generate a Preliminary Documented Safety Analysis (PDSA) through progressive integration stages—from standalone document processing to full digital thread connectivity—while maintaining rigorous verification, validation, and regulatory acceptance standards. By establishing dynamic, traceable connections between design data and safety documentation, NRIC's approach has the potential to reduce both document development time and regulatory review cycles by as much as 50%, while simultaneously improving accuracy, consistency, and traceability. This initiative represents a critical step toward establishing reusable digital infrastructure that reactor developers can leverage to accelerate their path from concept to commercial operation, directly supporting NRIC's mission to demonstrate and deploy advanced nuclear energy technologies.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Preliminary Design of Engineering-Scale Salt Accident Analysis Facility to Support Molten Salt Reactor Licensing

Systems-level nuclear accident analysis codes for reactor licensing must be validated using experimental data that represent behaviors expected during actual full-scale accidents. Some behaviors may arise from coupled processes and only manifest at large scales. This report presents the preliminary design of the Salt Accident Analysis Facility (SAAF, pronounced “safe”), which is an experimental test facility to be constructed at Argonne that can be used to conduct integrated salt accident tests at an engineering scale. The measurement capabilities of the SAAF are based on previously developed methods and will provide the representative datasets that are needed to support molten salt reactor (MSR) licensing. Details of the design, the processes to be quantified, the measurement techniques for quantifying the processes, the variables that can be adjusted to simulate different accident scenarios, and operational considerations are presented herein. This report provides stakeholders the opportunity to give feedback on the test facility capabilities and planned analyses before it is constructed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Development of Predictive Models for Advanced Reactor Autonomous Control

Advanced reactor designs including microreactors and small modular reactors will contribute to the clean production of cheap energy, and autonomous control for advanced reactors is an appealing option for reducing cost. However, there is a lack of industry experience applying autonomous control for advanced nuclear reactors. To accelerate the development and industry acceptance of autonomous control software for nuclear reactors, we aim to demonstrate autonomous control of the Purdue University research reactor (PUR-1) using INL-developed model predictive control (MPC) methods. To prepare for this demonstration, data-driven predictive models based on process data collected from PUR-1 have been developed and integrated with MPC and used to control a physics-based model of PUR-1. A data-driven dynamics model and a gated recurrent unit (GRU) network were both trained on process data from PUR-1. The dynamics model was shown to effectively control the reactor model with MPC when provided reactivity as a control variable but failed to control the model through the control rod positions. The GRU network produced more accurate predictions than the dynamics model when evaluated on operational data, and future work will include the evaluation of the GRU network in the controller.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Status of the International Criticality Safety Benchmark Evaluation Project

The International Criticality Safety Benchmark Evaluation Project (ICSBEP) has continued its work generating evaluations of new and historical benchmark experiments since the last update to the nuclear criticality safety (NCS) community at the 12th International Conference on Nuclear Criticality Conference held in 2023. One additional version of the ICSBEP Handbook has been published since that update, and the Technical Review Group (TRG) held two in-person meetings to review and approve additional benchmarks. The 2022 and 2023 editions of the handbook were combined into one release (published in November 2024) and contained 13 new evaluations with 46 different configurations and two major revisions to existing evaluations. The 2024 version of the handbook, currently under publication review, will contain two new evaluations with 15 new configurations and one major revision to HEU-MET-FAST-028, the evaluation of Flattop with a uranium core. The ICSBEP TRG met again in person in April 2025 to review benchmarks for the 2025 ICSBEP Handbook and final comment resolution is currently ongoing. Many of the new benchmarks represent contemporaneous experiments that have been specifically optimized to provide validation cases relevant to the NCS community. One major area of focus for new critical experiments is to target the sparsely populated intermediate energy (or resonance) region. Another focus of many of the new benchmarks is to provide experiments sensitive to different materials, such as chlorine, hafnium, tantalum, titanium, molybdenum, chromium, and polymethyl methacrylate (PMMA, or Lucite). The ICSBEP continues to deliver high-quality, peer reviewed evaluations of integral experiments relevant to the nuclear data community.

HEU-MET-FAST-028↗
DOE OSTI2024

Coupled Neutronics - Thermal-Hydraulics - Thermomechanics modeling of Molten Chloride Reactor

Molten Salt Reactors (MSRs) are innovative Generation-IV nuclear reactors known for their enhanced safety features, fuel economy, and fuel cycle advancements. However, designing and studying these reactors present unique safety and modeling challenges. The Department of Energy (DOE)'s Nuclear Energy Advanced Modeling and Simulation program addresses these challenges through the development of flexible multi-fidelity, multiphysics simulation tools. This study exemplifies NEAMS tools' capabilities by modeling a chloride-based micro-MSR: Griffin for neutronics simulations, Pronghorn for thermal-hydraulics simulations, and BISON for thermomechanics simulations. These tools are integrated into a comprehensive 3D multiphysics model, successfully capturing steady-state and transient reactor operations. A loss of flow accident is examined as an illustrative example of transient behavior. Additionally, this paper contributes to demonstrating the theoretical feasibility of micro-MSR concepts, which have received less attention compared to other designs. The subsequent sections provide a brief description of the MSR concept studied, an overview of the computational tools used, and present results for both steady-state operation and a loss of flow accident.

42 - ENGINEERING↗
DOE OSTI2026

Reduced-Order CFD Modeling to Support Waste Loading Optimization in Hanford WTP Vitrification

The U.S. DOE Hanford Site stores over 56 million gallons of radioactive liquid tank waste that must be treated and immobilized for long-term disposal The Waste Treatment and Immobilization Plant (WTP) will vitrify this waste by feeding it into Joule-heated melters, where it is incorporated into a stable borosilicate glass Computational fluid dynamics (CFD) simulations of glass melters can provide insight into the maximum achievable waste loading under varying melter operating conditions Fully resolved VOF multiphase simulations were used as the reference model to capture bubble-driven convection in the melter, including bubble formation, rise behavior, and induced glass melt circulation Effective bubble column diameter and rise velocity were extracted from the resolved simulations, compared with empirical correlations, and refit across relevant viscosity and gas flow rate conditions Explicit gas–liquid interface tracking was replaced with a single-phase momentum source term model, enabling faster steady-state CFD simulations while preserving the dominant hydrodynamic effects of bubbling New empirical correlations were developed for effective bubble column diameter and bubble rise velocity by fitting resolved simulation data across expected melter viscosity and gas flow rate ranges, providing improved inputs for the momentum source term model compared with existing literature correlations The momentum source term model reduced fluid-domain mesh size by 89% and achieved an 8.4× computational speedup relative to resolved bubbling simulations The validated momentum source term approach enables prediction of process-relevant heat transfer behavior in the integrated melter model, including heat transfer from the molten glass to the cold cap, plenum, refractory walls, and surrounding structural regions under varying melter operating conditions

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2024

Towards High-Performance AI4NP Applications on Modern GPU Platforms

The evolution of modern heterogeneous accelerators, such as GPUs, has significantly advanced the landscape of artificial intelligence (AI). There is a notable surge to adopt AI within the nuclear physics domain (AI4NP). While most AI4NP studies focus on feasibility analysis, our attention is directed towards evaluating their performance on contemporary GPUs that integrate tensor cores. We first benchmark the throughput of hyperparameterized multi-layer perceptron (MLP) models. We then examine the performance of an AI4NP application: Hydra. We assess the performance gain and accuracy loss caused by the tensor cores for low-precision floating-point operations. Our experiments encompass the PyTorch and TensorFlow Keras frameworks on NVIDIA’s T4 and A100 GPUs. We explore the behavior of different GPU hardware platforms and AI software tools. This study can be a valuable resource for guiding the performance optimization of larger-scale deployments of AI4NP applications.

Mei, Xinxin↗
DOE OSTI2025

SECURED: Simulator-Enhanced Control and Understanding of Reactor systems for cyber-Event Defense

The study discusses a learning approach for analyzing cyber-events in reactor systems using integrated hardware and personal computer simulator models. Key points include the rise in cyber-attacks and their sophistication in industrial control systems (ICS), the necessity for awareness, understanding, resource allocation, and preparation to combat these threats, and the digital transformation of old and new nuclear plants, increasing their exposure to cyber threats. It highlights the cyber vulnerabilities of advanced reactor systems, which rely on digital instrumentation and control for operations and safety functions, making them susceptible to cyber-attacks. The approach involves demonstrating reactor system plant ICS cyber-attacks under various operational conditions utilizing tools like simulator models and hardware-based kits. A strategic solution approach tailored to critical infrastructure is emphasized, along with community engagement for public and government support, adopting effective learning approaches, and the preparation for anticipated future challenges. The presentation concludes with a call to action to address challenges, leverage opportunities, and advance through lesson learning in cybersecurity for nuclear energy systems.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗