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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cybersecurity Workforce Training for SMR Integration into Distribution Grids: A Competency Framework and Containerized Hands-On Lab for the SMR/DER/Microgrid Boundary

Small modular reactors (SMRs) and microreactors are entering the U.S. distribution grid as synchronous generation on feeders designed for loads and inverter-based distributed energy resources (DERs). No existing cybersecurity training program addresses this intersection of nuclear operations, DER management, and operational technology security. As subcontractor to Iowa State University on the CyDERMS Center, Argonne analyzed the relevant standards and training landscape, translated the resulting gaps into a twelve-objective competency framework across distribution-operator and graduate-analyst role tracks, and built a containerized training lab using a ∼400-bus composite grid model behind a realistically simulated Modbus TCP SCADA stack. The analysis isolates the balance-of-plant / energy-management-system (BOP/EMS) boundary as the critical jurisdictional seam where, as of March 2026, neither NRC nor NERC CIP cleanly claims cybersecurity responsibility for distribution-connected SMRs. The framework maps each objective across NIST CSF 2.0, ISA/IEC 62443, NIST NICE Task–Knowledge–Skill statements, and NRC RG 5.71 awareness-and-training controls. The training lab implements operator-recognition assessment scenarios spanning grid-side disturbances and telemetry-layer anomalies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Pre-trained network-based transfer learning: A small-sample machine learning approach to nuclear power plant classification problem

Some research topics belonging to classification problems in the nuclear industry, such as fault diagnosis and accident identification, can be solved by feature extraction and subsequent application of statistical machine learning classifiers. Recently, deep neural network-based methods with automatic feature extraction and high accuracy have gained wide attention. They usually require large-scale training data, however, plant fault or accident data are scarce or difficult to obtain. Here this paper proposes a convolutional network (CNN)-based transfer learning method to solve this problem. The network's shallow layer is derived from a pre-trained CNN based on the ImageNet database to automatically extract features, and the deep layer is customized to match the classification problem. Data in non-image formats are converted to image formats and subsequently used to train the network. Case studies of rotating machines fault diagnosis show that the proposed method requires only limited training data to achieve high accuracy.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

IER-484 CED4a Report: AFRRI ER1 Dosimetry Characterization

This report presents an analytical function as the reference neutron and photon dose (normalized to an ion chamber integral) for future nuclear accident dosimeter (NAD) exercise at the Armed Forces Radiobiology Research Institute (AFRRI). Various dosimeters and equipment were positioned within exposure room 1 (ER1) and irradiated using a 1.1 MW Training, Research, Isotope, General Atomics (TRIGA) Mark-F nuclear reactor. An ion chamber located in ER1 was used as a normalization factor in Equation 2. This allows for a quick calculation of neutron and photon dose based on the ion chamber data. Currently, AFRRI is in preparation to host a NAD exercise this summer.

61 RADIATION PROTECTION AND DOSIMETRY↗
NASA NTRS2022

Electrodeionization Salt Removal from Water

This project developed a method to selectively extract sodium (Na) from complex, mission-relevant wastewaters using electricity as a driving force. The team investigated capacitive deionization (CDI), which has been used for general desalination but not for selective removal of Na. Three tasks supported this overall aim: designing and building a reactor and electrodes, engineering absorption and desorption parameters, and investigating varying water compositions and sources. Potential benefits of this approach include integrating into existing Environmental Control & Life Support Systems (ECLSS) treatment trains, reducing salinity for hydroponic installations, and producing food preservatives. Significant accomplishments, including building new reactors and characterizing their optimal operations and applications, advance the frontier of in-situ resource utilization for planetary habitation.

Luke Roberson↗
DOE OSTI2022

Analysis of human performance differences between students and operators when using the Rancor Microworld simulator

Here, from within the umbrella of the Simplified Human Error Experimental Program (SHEEP) framework, this paper analyzes human performance differences between professional and student operators when using a simplified simulator (i.e., Rancor Microworld). This paper represents a crucial step in understanding the fidelity of the simplified simulators and student operators within the SHEEP study. This paper explores a randomized factorial experimental design that features two independent variables: participant type and event class. Six human performance measurements are considered in the experiment. The experiment is conducted using 20 professional reactor operators employed at actual nuclear power plants (NPPs), along with 20 trained students. The experimental data are analyzed via statistical analysis methods. Finally, this paper examines the differences in human performance between actual operators and students when using Rancor Microworld.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Subcritical Assembly at ORNL [Slides]

This presentation discusses the US Department of Energy (DOE) Nuclear Criticality Safety Program (NCSP), specifically regarding the training and education courses. Also discussed is the AGN-201M reactor and fuel disk. Additionally, the ORSA conceptual design and design changes are presented. In conclusion, the ORSA design has moved from conceptual to implementable. ORSA hands-on experiments will remain subcritical under normal and credible abnormal conditions. The ORSA location will have minimal security and safety basis impact and is feasible for a diversity of students. The implementation of ORSA will improve upon NCSP T&E’s vision to remain adaptable and responsive.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

Advanced nuclear reactors often exhibit complex thermal-fluid phenomena during transients. To accurately capture such phenomena, a coarse-mesh three-dimensional (3-D) modeling capability is desired for modern nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor transients that involve flow and heat transfer, accurately predicting the turbulent viscosity is a challenging task that requires an accurate and computationally efficient model to capture the unresolved fine-scale turbulence. In this work, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor. The model has a coarse mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data to ensure accuracy. A novel neural network architecture, combining a densely connected convolutional network and a long-short-term-memory network, is developed that can efficiently learn from the spatial temporal CFD transient simulation results. The neural network model was trained and optimized on a loss-of flow transient and demonstrated high accuracy in predicting the turbulent viscosity field during the whole transient. The trained model's generalization capability was also investigated on two other transients with different inlet conditions. The study demonstrates the potential of applying the proposed data-driven approach to support the coarse-mesh multi-dimensional modeling of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Neutron Source Facility of the National Science Center “Kharkiv Institute of Physics and Technology” at Kharkiv, Ukraine

Argonne National Laboratory developed, designed, and supported the construction of a state-of-the-art Neutron Source Facility (NSF) at the Kharkov Institute of Physics and Technology (KIPT) in Kharkov Ukraine, under the U.S. DOE NNSA Russian Research Reactor Fuel Return (RRRFR) program. Ukraine approved the return of all highly enriched uranium in the country to Russia prior to the 2012 Nuclear Security Summit in return for the RRRFR program to fund the development, the design, and the construction of the NSF. The facility is designed to produce medical isotopes, train nuclear professionals, support the Ukrainian nuclear industry, and provide experimental capabilities for performing reactor physics, materials, and basic science research. The NSF was successfully started and operated in August 2021. The NSF is the first facility of this type in the world, and it will be used to understand the physics of driven systems for energy production and the disposal of spent nuclear fuels. The NSF consists of a subcritical assembly using low enriched uranium (LEU) fuel driven with an electron accelerator. The NSF target design utilizes tungsten or natural uranium for producing neutrons through photonuclear reactions using 100-MeV electrons. The accelerator power is 100 KW, which produces 3x10 14 neutrons per second from the uranium target. The subcritical assembly is designed to obtain the highest possible neutron flux intensity with an effective neutron multiplication factor of less than 0.98. Passive safety, reliability, and environmental considerations were included in the NSF design. The NSF utilization study shows that this neutron source has the capability of producing different medical isotopes. Several horizontal neutron channels are incorporated for performing applied and basic research. The NSF is configured to accommodate future design upgrades and new missions. The facility design was approved by the Ukraine Academy of Sciences before starting the NSF construction. The construction, the equipment installation, and the operation of the different systems of the NSF were completed and approved by the Ukrainian regulators. In July 2020, the State Nuclear Regulatory Inspectorate of Ukraine (SNRIU) issued a license for the physical startup of the NSF. The construction of facility was completed in early 2021.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Feasibility Study for a Proposed Subcritical Assembly at Oak Ridge National Laboratory

Nuclear Criticality Safety (NCS) staff use the guidelines in the ANSI/ANS-8.26 standard to establish and maintain their training and qualifications. Section 7.4 of the standard requires NCS staff to participate in hands-on experiments meant to “…demonstrate how varying the properties of a fissionable material system can affect neutron multiplication.” This training is performed to ensure NCS and operations staff are aware of the risks involved with conducting operations with fissionable materials outside reactors. A new, inherently safe, subcritical assembly, to be housed at Oak Ridge National Laboratory (ORNL) is being considered to augment the current training capabilities of the US Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP), which conducts hands-on subcritical and critical experiments to support the training and qualifications of NCS staff. This paper reports the results of a feasibility study performed by ORNL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Sensor Drift Estimation for Reactor Systems by Fusing Multiple Sensor Measurements

A nuclear power plant is instrumented with a variety of sensors that monitor its variables to estimate the state and initiate safety actions, if needed. We address the problem of estimating drifts or errors in sensor measurements due to factors such as calibration changes. We propose an information fusion method that uses measurements from other sensors to generate an estimate of a sensor measurement, and its difference from an actual measurement provides an error estimate. We present two fusers based on the ensemble of trees and support vector machine that are trained using sensor measurements collected at an emulated test loop of a pressurized water reactor under no-drift conditions. We present error estimates for a differential pressure sensor of the heat exchanger of the primary coolant system, under twenty controlled scenarios using the test loop. Both positive and negative errors are captured by both methods in scenarios involving calibration drifts, blocking, air gap and electromagnetic interference. The root mean square errors of estimated drifts are typically within 2% percent of the maximum.

Rao, Nageswara↗
DOE OSTI2023

Data-Driven RANS Turbulence Closures for Forced Convection Flow in Reactor Downcomer Geometry

Recent progress in data-driven turbulence modeling has shown its potential to enhance or replace traditional equation-based Reynolds-averaged Navier-Stokes (RANS) turbulence models. Here, this work utilizes invariant neural network (NN) architectures to model Reynolds stresses and turbulent heat fluxes in forced convection flows (when the models can be decoupled). As the considered flow is statistically one dimensional, the invariant NN architecture for the Reynolds stress model reduces to the linear eddy viscosity model. To develop the data-driven models, direct numerical and RANS simulations in vertical planar channel geometry mimicking a part of the reactor downcomer are performed. Different conditions and fluids relevant to advanced reactors (sodium, lead, unitary-Prandtl-number fluid, and molten salt) constitute the training database. The models enabled accurate predictions of velocity and temperature, and compared to the baseline k–τ turbulence model with the simple gradient diffusion hypothesis, do not require tuning of the turbulent Prandtl number. The data-driven framework is implemented in the open-source graphics processing unit–accelerated spectral element solver nekRS and has shown the potential for future developments and consideration of more complex mixed convection flows.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Nuclear Electric Propulsion Brayton Power Conversion Working Fluid Considerations

NASA has considered Nuclear Electric Propulsion (NEP) for high ∆V missions since the late 1950s, but the current technology readiness level of the requisite power-train needs to be raised for such a mission. The powertrain includes reactor, power conversion, power management, electric propulsion, and thermal management components that must be integrated to minimize system a (kg/kWe) and to finalize a propulsion system architecture within the 2025-2027 timeframe requires advancing these component technologies to technology readiness level (TRL) 5 and Advancement Degree of Difficulty (AD2) 3 in an expeditious manner with minimal risks. This paper will only address the power conversion (heat to electric) components of that system. Based on both internal and industry studies, a primary conclusion of this work is that a single-spool supercritical Xe-He radial flux Brayton heat engine combined with a permanent magnet synchronous alternator can potentially be developed with the least technical risk to meet the technology readiness schedule and required NEP system performance metrics.

Nuclear Electric Propulsion (NEP)↗
DOE OSTI2022

Machine learning predictions of irradiation embrittlement in reactor pressure vessel steels

Abstract Irradiation increases the yield stress and embrittles light water reactor (LWR) pressure vessel steels. In this study, we demonstrate some of the potential benefits and risks of using machine learning models to predict irradiation hardening extrapolated to low flux, high fluence, extended life conditions. The machine learning training data included the Irradiation Variable for lower flux irradiations up to an intermediate fluence, plus the Belgian Reactor 2 and Advanced Test Reactor 1 for very high flux irradiations, up to very high fluence. Notably, the machine learning model predictions for the high fluence, intermediate flux Advanced Test Reactor 2 irradiations are superior to extrapolations of existing hardening models. The successful extrapolations showed that machine learning models are capable of capturing key intermediate flux effects at high fluence. Similar approaches, applied to expanded databases, could be used to predict hardening in LWRs under life-extension conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Health Physics Research Reactor Criticality Accident Alarm System Benchmark Overview

From the countless critical experiments performed in the world during the past century, high-quality integral benchmarks experiments have been collected and gathered into the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook), managed by the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Working Group. This information preservation and dissemination effort is crucial for reactor licensing as well as criticality and radiation transport modeling validation. This summary reports on the status of a tentative benchmark addition to the ICSBEP Handbook. The proposed benchmark arises from legacy operation data of the Oak Ridge National Laboratory (ORNL) Health Physics Research Reactor (HPRR). The HPRR was a small, unmoderated, unshielded fast burst reactor that was used for research in health physics and radiobiology as well as teaching and training. As part of a comprehensive investigation of the available HPRR operation data and characteristics, different possibilities for use of the valuable results were studied. A critical experiment benchmark evaluation was performed, analyzing data coming from sub-critical and critical operation of the HPRR during operator training, steady-state irradiation of samples and before critical bursts. The results of the evaluation do not satisfy for the ICSBEP standards as the benchmark relative standard uncertainty is of about 4% for k eff , and the relative difference between sample calculations and expected k eff results is of about 1.5%. Due to those unsatisfactory results, it was decided not to pursue critical experiments evaluation of the HPRR presently and to focus instead on shielding type data for the creation of a criticality accident alarm system (CAAS) and shielding category benchmark, which is currently very scarce in the ICSBEP handbook—especially concerning critical, pulsed assembly, or reactor operation data. Several dosimetry and shielding experiments from HPRR burst operation were evaluated, with different benchmark metrics as sulfur fluence, Element 57 dose, or neutron fluence at different distances and under different shield materials conditions. An evaluation focusing on the Element 57 neutron dose as a benchmark metric was submitted to the ICSBEP Technical Review Group (TRG) meeting in October 2021, and the inclusion of the evaluation in the ICSBEP Handbook was deferred. The main change proposed by the international experiment evaluation experts is to use the neutron fluence measured by Bonner spheres as a benchmark metric. This represents a quantity closer to that actually measured by the experimentalists of the HPRR compared to the Element 57 dose, which adds another step of data transformation, thus potentially adding uncertainty to the benchmark. The evaluation has been updated and will be submitted to the 2022 ICSBEP TRG meeting for inclusion in the 2023 edition of the ICSBEP Handbook. The evaluation is performed using the KENO and MAVRIC combination from the SCALE 6.2.4 code suite which was previously used in similar CAAS benchmarks to allow for the use of variance reduction techniques.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Physics-informed neural network with transfer learning (TL-PINN) based on domain similarity measure for prediction of nuclear reactor transients

Nuclear reactor safety and efficiency can be enhanced through the development of accurate and fast methods for prediction of reactor transient (RT) states. Physics informed neural networks (PINNs) leverage deep learning methods to provide an alternative approach to RT modeling. Applications of PINNs in monitoring of RTs for operator support requires near real-time model performance. However, as with all machine learning models, development of a PINN involves time-consuming model training. Here, we show that a transfer learning (TL-PINN) approach achieves significant performance gain, as measured by reduction of the number of iterations for model training. Using point kinetic equations (PKEs) model with six neutron precursor groups, constructed with experimental parameters of the Purdue University Reactor One (PUR-1) research reactor, we generated different RTs with experimentally relevant range of variables. The RTs were characterized using Hausdorff and Fréchet distance. We have demonstrated that pre-training TL-PINN on one RT results in up to two orders of magnitude acceleration in prediction of a different RT. The mean error for conventional PINN and TL-PINN models prediction of neutron densities is smaller than 1%. We have developed a correlation between TL-PINN performance acceleration and similarity measure of RTs, which can be used as a guide for application of TL-PINNs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

U.S. Indirect and Direct Potable Water Reuse Systems (2020)

Collection of proposed, operating, demonstration, and pilot study potable reuse systems across the U.S. as of 2020. Information includes project name, location, augmentation type, capacity, year operational, treatment train, and source water type. References are included in this submission. As potable reuse facilities undergo expansions or upgrades, treatment trains and capacities are subject to change. Operational status may also change. Treatment trains listed are reported to the detail that the source provides. Type and doses of chemical post-treatment or chlorination is often not specified. Please see README section in spreadsheet for definitions of acronyms used.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive and time-consuming than most other disciplines. One potential way to resolve the data scarcity issue is deep generative learning, which uses certain ML models to learn the underlying distribution of existing data and generate synthetic samples that resemble the real data. In this way, one can significantly expand the dataset to train more accurate predictive ML models. In this study, our objective is to evaluate the effectiveness of data augmentation using variational autoencoder (VAE)-based deep generative models. We investigated whether the data augmentation leads to improved accuracy in the predictions of a deep neural network (DNN) model trained using the augmented data. Additionally, the DNN prediction uncertainties are quantified using Bayesian Neural Networks (BNN) and conformal prediction (CP) to assess the impact on predictive uncertainty reduction. To test the proposed methodology, we used TRACE simulations of steady-state void fraction data based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle Test (BFBT) benchmark. Here, we found that augmenting the training dataset using VAEs has improved the DNN model’s predictive accuracy, improved the prediction confidence intervals, and reduced the prediction uncertainties.

Bayesian neural network↗
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↗