Search NASA⌕ Search

SEARCH · Search NASA

Results for “Richmond”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Demonstration of acoustic monitoring for structural health of microreactors: Through use of neural networks and resonant ultrasound spectroscopy

Nuclear microreactors prioritize modularity and portability and are intended to be a cost-effective technology for non-conventional nuclear markets. As such, the development of microreactors into a safe and feasible solution for energy security applications will necessitate the development of non-destructive technologies to monitor the integrity of inaccessible reactors components during operation. This demonstration applies linear and nonlinear acoustic techniques, in combination with machine learning, to detect and classify mechanical changes (stress and damage) in a test article which are broadly representative of potential operating challenges within a functioning microreactor. All necessary data has been collected for this demonstration, with minor experimental issues identified that can be addressed in follow-on work. Motivated by the expected conditions within a functioning microreactor, we have demonstrated our monitoring techniques on a core-block-like test article using an unstructured excitation source that approximates the noisy acoustic environment expected during reactor operation. At all stress states mechanically applied to the test article, a machine learning model using an artificial neural network was able to classify with 100% accuracy whether a 3D laser vibrometry point measurement was made on an intact or artificially defective test article. Further, model predictions about whether the defect interface was rough or smooth were 95% accurate, indicating the ability of acoustic techniques to recover defect characteristics. Resonant ultrasound spectroscopy (RUS) was also applied to the dataset to provide further quantitative insights about material properties. RUS analysis was ultimately hampered by several minor experimental and data issues, limiting results to certain cases for this demonstration. Last, analysis using nonlinear RUS exhibited sensitivity to changing levels of applied stresses for each intact and defective state. As presented in this demonstration, acoustic monitoring exhibits sensitivity to stress changes, which are of concern due to high thermal gradients expected during startup and operation. Further, our techniques distinguish between measurements made on intact and damaged test articles.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Sensitivity of Spent Fuel Temperatures to Variable Canister Backfills

This report describes thermal modeling done to understand the effects of a hypothetical loss of backfill to a spent fuel storage canister. A NAC MAGNASTOR system was analyzed using STAR-CCM+ and COBRA-SFS at various heat loads, helium pressures, and air environments. When helium was lost later in life the results showed no safety impact to the thermal performance of the cask system and fuel cladding. However, there were other results of interest from this study. The potential to detect helium leaks using external temperature measurement was considered and found to be a potential area for future technology development. The other major result of interest was the significant change in flow velocities with an air environment. This data will be useful in understanding the potential impacts of a breached canister.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Surface Charge at an Oil/Water Interface Promoting the Enhancement and Assignment of Water Combination Bands

Given the different polarities and distinct chemical structures, it is no wonder that oil and water are incompatible neighbors. They can be forced to cooperate if provided sufficient energy or a suitable intermediary. Emulsions are the result of such a cooperation, where an emulsifying agent (the intermediary) in the form of a surfactant or a cocktail of surface stabilizing compounds allows for the ‘mixing’ of oil and water. Surfactant stabilized emulsions that are nanoscale in size (nanoemulsions) have caught the attention of many because of their newly discovered use in the synthesis of nanomaterials, oil recovery, drug delivery, and food science. Unlike smaller emulsions (e.g. microemulsions with 2-20 nm diameters), very little is known about the molecular level properties of nanoemulsions (NEs) whose diameters are in the 100-500 nm range. One thing that is well known is the macroscopic properties of droplets from these two size regimes are dramatically different, often for reasons not yet fully understood. Nanoemulsions do not form spontaneously and require an external shear beyond ordinary procedures used to create micelles. This shear is required to overcome the effects of surface tension and create droplets in the nanoscale regime. Once formed, however, they can be stable for long periods of time, making them very attractive for commercial and pharmaceutical products requiring a long shelf life. What is not well known is the interfacial molecular processes that lead to the formation and stability of nanoemulsions including those stabilized by surfactants and/or polymers. Such knowledge is important in understanding the molecular structure-function relationship that assists in predicting what additives and solvents are necessary for a specific application. Key to advancing nanoemulsion utility is to develop the needed molecular-level understanding of the structure and bonding interactions present at the surface of these soft nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

COBRA-SFS Version 6.2.1 User Guide: A Thermal Hydraulic Analysis Code for Spent Fuel Storage and Transportation Casks

This document is an updated and reduced version of the complete documentation of the COBRA-SFS thermal analysis code (COolant Boiling in Rod Arrays – Spent Fuel Storage) (Michener et al. 1995). This manual provides the analyst with input instructions and fundamental guidelines for contrasting models using COBRA-SFS. COBRA-SFS is designed to analyze the dry storage and transportation casks that house spent or used nuclear fuel. The primary focus and application is on square light water reactor fuel assemblies, however the code also has the capability to model hexagonal assemblies and is will suited to any geometry that can be defined as an extruded 2-D map.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

FAST-1.0: Integral Assessment: Developed under NQA-1-2017

An integral assessment has been performed to quantify the predictive capabilities of 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 (NRC). FAST code calculations are shown to compare satisfactorily to a preselected set of experimental data with both steady-state, anticipated operating occurrence (AOO) and design basis transient operating conditions. This document describes the assessment of FAST-1.0, which is the latest version of FAST, released February 2020.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Uncertainty in Thermal Modeling of Spent Nuclear Fuel Casks

Uncertainty is a key metric in computational modeling that must be evaluated for results to have wide ranging applicability. A well characterized uncertainty range is ideal with clear error bars on results that can be presented to stakeholders. In the field of spent fuel cask modeling, this ideal has been historically difficult to achieve in practice because of the computationally intensive nature of the models used and the difficulty assigning reasonable uncertainties to quantities in as-built systems. The work in this report has been conducted to evaluate the overall state of uncertainty and sensitivity in spent fuel cask models and develop methodologies for evaluating these uncertainties. These methodologies must be practical for engineering applications. They should not require excessive computational resources or calendar time to achieve results. In engineering, the model must be on a scale such that it can be changed and adapted throughout a project as new information is discovered and project goals evolve. This report covers three major modeling task areas that provide an overview of the types of sensitivity and uncertainty present in a spent fuel storage and transportation system. Section 3 discusses sensitivity and uncertainty analysis in the effective thermal conductivity model for the fuel region and applies these results to a single assembly model. Section 4 shows sensitivity analysis of a full cask model in the TN-32B and Section 5 demonstrates the overall uncertainty workflow using Coolant Boiling in Rod Arrays – Spent Fuel Storage and STAR-CCM+ developed from the sensitivity work in the preceding sections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Assessment of Technical Information Needs and Considerations for Front-End Activities for Molten Salt Fuel Types

This report details an assessment of the hazards and safety impacts related to front-end operations of molten salt reactors (MSR) as well as an assessment of existing safety guidance. Publicly available information was evaluated to make recommendations to the U.S. Nuclear Regulatory Commission (NRC) for guidance development and information gathering needs. Five different MSR technologies were evaluated along with information from 1960’s about the Atomic Energy Commission programs for the Aircraft Reactor Experiment and Molten Salt Reactor Experiment. Primary areas of evaluation include fuel cycle facilities, transportation and at reactor fuel mixing operations up to reactor startup. In general, there was difficulty synthesizing publicly available information due to the early stage of the MSR development and proprietary information associated with the reactor technology companies analyzed. There are a few specific recommendations from this report. One overarching paradigm shift that the NRC needs to evaluate is the possibility of collocating fuel cycle facilities with operating MSRs. Although there is no specific prohibition to this in the NRC regulations, the collocation of the fuel cycle facilities with the operating MSR presents a novel approach compared to the light water reactor baseline. More guidance and research into thorium operations and regulation is needed. The hazards associated with the stability of stored fuel constituents need to be evaluated. Some reactor fuel cycles use beryllium, and its use and the associated hazards need further analysis. NRC-approved designs for transportation packages for shipping molten salt fuels and factory-built and fueled reactors are not available.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Technical Information Needs and Regulatory Considerations for Front-End Transportation Activities of HALEU Fuel Feed Material

This report fulfills task order number 31310022F0033 Task 1 Assessment of technical information needs and considerations for front-end transportation activities for HALEU fuel feed material. This assessment primarily focuses on the transportation outlook and packaging requirements for transporting high-assay low-enriched uranium (HALEU) fuel feed material. The regulatory framework is described in section 3.0. In section 4.0 the specific issues associated with reviewing HALEU feed material packages are discussed. The rest of the report discusses the need for new packages and Department of Energy progress on HALEU production. Through this evaluation it is shown that the 5% enrichment barrier between low-enriched uranium and HALEU is a significant challenge to transportation packaging. However, there are licensed designs and concepts that can be used to transport HALEU feedstock. No significant issues were identified through the HALEU range up to 20% enriched.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Physical Testing of The PSEC5 ASIC

The PSEC5 ASIC is a high-speed waveform sampling chip designed for ultra-fast timing detectors, offering up to 40 GSPS sampling with 10-bit resolution. This makes it well-suited for applications requiring fine time resolution, such as MCPs and LGADs. This work focuses on the physical testing and validation of the chip s internal clocking and SPI-controlled registers. Testing began with inspection of schematics and the prototype to identify and resolve design issues. Then, custom firmware was developed for an Arduino controller to interface with the chip via SPI, enabling read and write access to key control registers. Results confirm that the VCO operates between ~3.2 4.0 GHz and remains stable under non-VCOVDD fluctuations. The Division Ratio register enables frequency division by known factors (256, 128, 64, etc.), indicating a pre-division frequency of 3.2 GHz when the digital band is unmodified. While most registers responded correctly, some issues were observed, including unexpected current draw and unstable discriminator behavior. Overall, the chip shows promising functionality, but further work is needed to understand the state of the read only registers and address the existing issues. Continued testing and firmware development will be critical to ensuring reliable integration into detector systems.

Fahey, Alexander [Unlisted, US]↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2022

FAST-1.1: Integral Assessment

An integral assessment has been performed to quantify the predictive capabilities of 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 (NRC). FAST code calculations are shown to compare satisfactorily to a preselected set of experimental data with both steady-state and anticipated operating occurrence (AOO) conditions and design basis accident (DBA) transient operating conditions. This document describes the assessment of FAST-1.1, the latest version of FAST, released April 2022. Abstract

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Ligated Cluster Building Blocks as Chemical Donors and Acceptors for Two-Dimensional Metal Chalcogenides and Phosphorene Nanoribbons

Final report for studies on ligated transition metal chalcogenide clusters building blocks for serving as dopants in two dimensional semiconductors. The primary result is extending the superatom concept to transition metal chalcogenide clusters including a similarity with the perioidic electronic structures that are found in the periodic table. These results have found both new magic numbers, and new magic clusters with large spin magnetic moments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Separation of Lithium and Magnesium from Brines Using Zwitterionic Simulated Moving Beds: Cooperative Research and Development (Final Report)

The purpose of this work was to separate lithium and magnesium from lithium-containing brines by applying a zwitterionic simulated moving bed (zSMB) system that NLR developed. This work was needed to validate lithium and magnesium separation for future development at scale. Below is a summary of the tasks outlined in the Statement of Work and the results that were generated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-frequency Data Integration for Landscape Model Calibration of Carbon Fluxes Across Diverse Tidal Marshes

Terrestrial Aquatic Interfaces (TAIs), and tidal wetlands in particular, store large amounts of carbon yet are not well represented in Earth System Models (ESMs). Predictions of carbon cycling and greenhouse gas (GHG) emissions in tidal wetlands are highly uncertain. Eddy covariance (EC) towers provide ecosystem-scale GHG flux data at a temporal resolution (every 30min) that is helpful for parameterizing and improving mechanistic realism in ESMs. We propose to use a network of eddy covariance towers and standardized ancillary data streams, along with mesocosm experiments and statistical analyses, across diverse tidal wetlands of North America to develop and improve biogeochemical modeling at the TAI. Our overarching objective is to improve understanding and process-based modeling of gross primary productivity (GPP) and CH4 emission responses, both non-linear and asynchronous, to stressors including plant inundation, disturbance, salinity and nitrogen loading.

54 ENVIRONMENTAL SCIENCES↗