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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

PARTITIONING AND TRANSMUTATION OF USED NUCLEAR FUEL IN SUPPORT OF GEOLOGICAL WASTE DISPOSAL

Nuclear energy is a low-carbon technology that generates bulk baseload electricity and supports long-lasting eco-friendly sustainability goals. However, it still faces challenges, such as the securing and managing of long-lived nuclear actinides and fission products—most notably the used fuel, which is considered high-level waste (HLW). Direct geological waste disposal would be the most economical option for HLW but requires highly qualified sites whose geophysical behavior will remain stable for 10,000 years. Partitioning and transmutation (P&T) of used fuel reduces radioactivity and decay heat generation, enabling efficient geological waste disposal. P&T research and development, along with the prospects of P&T techniques such as wet/dry separation and accelerator-driven systems, were reviewed to identify critical needs that must be met to foster their successful implementation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

A Predictive Transient Model of the TREAT-SIRIUS Experiments

To support experiments modeling and transient analysis of the NASA-sponsored SIRIUS experiments in the Transient Reactor Test Facility (TREAT), an innovative predictive transient model has been developed to simulate various transient experiments. The developed model is based on a new approach that utilizes steady-state Monte Carlo calculations along with a surrogate model based on polynomial regression to determine the core reactivity. The reactivity is supplied to an exact point kinetics model that determines the reactor power, and it is coupled to an adiabatic feedback model to determine the fuel average temperature. In this work, the newly developed model is introduced along with validation test results considering SIRIUS-1 experiments at different power levels. The initial test results show a very good agreement with experimental data at different power levels, which indicates that the model can be used to predict the power of other transient tests.

42 ENGINEERING↗
DOE OSTI2020

Panel Session 37: The Wants and Needs of Graduating Students and New Engineers: Are Companies Even Listening? (R8.1)

This panel focused on new hires, graduating scientists and engineers having open lines of communication with employers. Considering the projected shortfalls in the workforce, effective communication of wants-and-needs of both the employer and employee must exist. Do these needs differ between industries and/or generations? Currently, it seems that both sides must work harder to achieve this level of communication. With this new approach, both sides can express their wants and needs for a more satisfied workforce and a better work environment. Panelists with presentations: The Future of Work: What Employers Seek From Recent Graduates (Melody Bell); Graduating Students and New Engineers - Wants and Needs - Are Companies Even Listening? (Joseph Aylor); Industry and Education: Bridging the Gap (Arthur Baranovskiy)

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

Initial exploration of a novel transient arrest system involving fuel heating

A preliminary analysis on a novel accident response system to diminish the severity of super- critical transients was conducted. The novel accident response system, called the instant shock arrest system, involves using electricity to heat the nuclear fuel at the onset of a large accidental reactivity insertion. This system is specifically designed for reactors with metallic fuel, such that the fuel is capable of conducting electricity, and being resistively heated. A reactor dynamics model of the advanced test reactor was created using the point kinetics equations and a linear reactivity feedback model to simulate how the system would effect the maximum fuel temperatures experienced during the transient. Transients with the instant shock arrest system were compared to those without it. It was found that the instant shock arrest system initially heated the fuel more than the unaffected transient but the negative reactivity inserted from such heating was enough to lower the maximum fuel temperature experienced during the transient. After simulating six different accident scenarios with reactivity insertions ranging from 0.5 to 1.3 dollar, it was found that an optimal system response could reduce peak fuel temperatures during the transient by 3.5% to 5%. Furthermore, discussion was given on how the optimal system response could be obtained using relatively simple numerical optimization algorithms due to the smoothness of the optimization problem. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is that the amount of equipment reliability (ER) data being continuously generated are extremely large. These data elements come in different forms: textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) and they provide system engineers with valuable insights and information regarding the discovery of anomalous behaviors or degradation trends, the identification of the possible causes behind such behaviors and trends, and the prediction of their direct consequences. This paper directly targets the generation of knowledge from ER data by putting “data into context”. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by identifying first which elements of the developed MBSE elements they are referring to. This task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process “knowledge extraction” where our methods to extract knowledge from textual data. Lastly, once numeric and textual ER data elements have been processed and “understood”, we discover possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if there is a temporal relation among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Reduce-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which usually consists of a database of tabulated values, used to calculate the cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of micro cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. To address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multi-group cross section data across isotopes, reaction types and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs for have been trained for all isotopes in this work and systematic Griffin testing is ongoing at this moment to ensure the feasibility of this ROM technique for cross section predictions.

42 - ENGINEERING↗
DOE OSTI2024

Reduced-Order Modeling of Multigroup Neutron Cross Sections for High-Temperature Gas-cooled Reactors

Abstract – Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Advanced Cross Section Library Generation using Reduced Order Models

Deterministic neutronics calculations rely on multigroup neutron cross section libraries, which consist of databases of tabulated values, used to calculate the neutron cross sections through multivariate linear interpolation. However, interpolation of the multidimensional cross section data becomes memory inefficient and time consuming as the number of tabulations increases, significantly slowing down the neutronics calculation, especially in the case of microscopic cross section libraries where every isotope (on the order of hundreds) has its own set of specific reactions and cross sections. In order to address this challenge, this work constructs efficient and robust reduced-order models (ROMs) of the multi-group cross sections to support the Griffin simulation of high-temperature gas-cooled reactors (HTGRs). The first part of the study investigates the linearity of the multigroup cross section data across isotopes, reaction types, and energy groups on pre-generated datasets for the purpose of dimensionality reduction. Secondly, a down-selection of ROM techniques is presented on representative classical machine learning (ML) techniques, including variants of linear regression, kernel-based methods, tree-based algorithms, and artificial neural networks. The selection criteria jointly consider the memory efficiency, predictive accuracy, prediction speed, and scalability in comparison to the multidimensional interpolation. Among all the ML techniques, deep neural networks (DNNs) have proven to be the best selection with sufficient accuracy, high robustness, good memory efficiency, great scalability, and superior flexibility. DNNs have been trained for all isotopes in this work and systematic Griffin testing is ongoing to ensure the feasibility of this ROM technique for predicting cross section and reducing memory requirements without a significant sacrifice in computational performance.

42 - ENGINEERING↗
DOE OSTI2024

Model-Based Approaches to Generate Knowledge from Data in a Plant Reliability Context

One challenge that nuclear power plant system engineers are facing is continuous generation of an extremely large amount of equipment reliability (ER) data. These data elements come in textual (e.g., condition reports) and numeric (e.g., generated by monitoring systems) forms. They provide system engineers with valuable insights and information by discovering anomalous behaviors or degradation trends, identifying possible causes behind such behaviors and trends, and predicting their direct consequences. This paper directly targets the knowledge generation from ER data by putting “data into context.” We employ model-based system engineering (MBSE) of systems and assets to represent and capture their architecture and functional (i.e., cause-effect) relations. ER data elements are processed by first identifying which of the developed MBSE elements they are referring to. This task is harder for textual data since the information contained in issue or maintenance reports needs to be “understood” by a computational tool. We called this process “knowledge extraction” since our methods extract knowledge from textual data. Last, once numeric and textual ER data elements have been processed and “understood,” we discover possible cause-effect relations among them. This is performed by observing whether a logical connection through the MBSE models exists, and if there is a temporal relationship among them. The logic and temporal are the two main ingredients to perform “machine reasoning” from ER data.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Advanced Test Reactor Safety Basis Update for Gas-Cooled Experiments

The Advanced Test Reactor (ATR) supports neutron irradiation of several types of experiments. One such experiment type is referred to as a gas leadout. Gas leadout experiments actively flow gas through the experiment which allows for active temperature control. It also allows for in-situ data of the experiment. For example, fission gas migration through a fuel sample can be monitored via activity of the sweep gas. Historically, ex-pile equipment and fission product monitors were housed in shielded ATR cubicles. Due to other facility updates, cubicle space is no longer available for gas leadout experiment equipment. To support continued operation of gas leadout experiments, ATR completed a safety basis update that supports a new housing for leadout equipment that may process potentially contaminated gas. In addition to the structure and associated equipment, technical safety requirements regarding handling and storage of experiments needed to be revised to support fueled gas leadout experiments and associated outage configurations. The safety basis update addressed the full lifecycle of these experiments, including experiment movement and interim storage, and credible abnormal events such as failures or leaks in contaminated gas tubing in occupied areas. This paper discusses the completed analyses performed to support the safety basis update associated with gas leadout experiments, including thermal-hydraulic evaluation, probabilistic analysis, and dose consequence analyses.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Development of MOSCATO: A CFD-Level Electrochemistry and Corrosion Simulator for Molten Salt Systems

For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2015

Affordable Development and Demonstration of a Small NTR Engine and Stage: A Preliminary NASA, DOE, and Industry Assessment

The Nuclear Thermal Rocket (NTR) represents the next evolutionary step in cryogenic liquid rocket engines. Deriving its energy from fission of uranium-235 atoms contained within fuel elements that comprise the engine's reactor core, the NTR can generate high thrust at a specific impulse of approx. 900 seconds or more - twice that of today's best chemical rockets. In FY'11, as part of the AISP project, NASA proposed a Nuclear Thermal Propulsion (NTP) effort that envisioned two key activities - "Foundational Technology Development" followed by system-level "Technology Demonstrations". Five near-term NTP activities identified for Foundational Technology Development became the basis for the NCPS project started in FY'12 and funded by NASA's AES program. During Phase 1 (FY'12-14), the NCPS project was focused on (1) Recapturing fuel processing techniques and fabricating partial length "heritage" fuel elements for the two candidate fuel forms identified by NASA and the DOE - NERVA graphite "composite" and the uranium dioxide (UO2) in tungsten "cermet". The Phase 1 effort also included: (2) Engine Conceptual Design; (3) Mission Analysis and Requirements Definition; (4) Identification of Affordable Options for Ground Testing; and (5) Formulation of an Affordable and Sustainable NTP Development Strategy. During FY'14, a preliminary plan for DDT&E was outlined by GRC, the DOE and industry for NASA HQ that involved significant system-level demonstration projects that included GTD tests at the NNSS, followed by a FTD mission. To reduce development costs, the GTD and FTD tests use a small, low thrust (approx. 7.5 or 16.5 klbf) engine. Both engines use graphite composite fuel and a "common" fuel element design that is scalable to higher thrust (approx. 25 klbf) engines by increasing the number of elements in a larger diameter core that can produce greater thermal power output. To keep the FTD mission cost down, a simple "1-burn" lunar flyby mission was considered along with maximizing the use of existing and flight proven liquid rocket and stage hardware (e.g., from the RL10-B2 engine and Delta Cryogenic Second Stage) to further ensure affordability. This paper provides a preliminary NASA, DOE and industry assessment of what is required - the key DDT&E activities, development options, and the associated schedule - to affordably build, ground test and fly a small NTR engine and stage within a 10-year timeframe.

Nuclear rocket engines↗
DOE OSTI2023

Validation of Pronghorn’s Subchannel code using the EBR-II shutdown heat removal tests

A Subchannel application (Pronghorn-SC) is developed in MOOSE, which affords the required flow field resolution, while still preserving an engineering-scale approach. This new solver can be natively coupled to Pronghorn and other MOOSE objects to enable full-core, multi-physics, multi-scale engineering studies. This work utilizes the EBR-II SHRT tests to validate the subchannel capabilities. Multi-scale and multi-physics coupling is used to improve the fidelity of the subchannel code calculations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗