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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Field Results from New Tensor Borehole Optical Fiber Strainmeter Installations in Oklahoma and Utah

The time evolving strain field contains a wealth of information that can be used to interpret subsurface behavior. For example, injecting or removing fluids from reservoirs or aquifers causes deformation that can be used as a diagnostic signal in some cases, while it can interfere with geodetic interpretations in other cases. We've previously demonstrated the feasibility of measuring the strain tensor at a depth of 30m caused by injection into a reservoir at 530m. The observed strain signals were interpreted using four independent analytic and numerical methods that resulted in estimates of the poroelastic properties and geometry of the reservoir that was consistent with data from well logs. However, studies like these are only possible if these deformations can be reliably measured. Years of lab and field work has culminated in the development of a novel borehole strainmeter capable of resolving multiple components of strain using embedded optical fibers configured as Michelson interferometers. It features four horizontal gauges separated by 45° to resolve the horizontal strain tensor as well as a vertical strain gauge and a sixth null component for state-of-health monitoring. The downhole sensing package also includes an open pipe through its center for grout circulation during single-trip deployments and a fully welded stainless steel exterior for robustness and longevity. These instruments have a resolution of 2x10-13 strain that can easily measure the solid earth tides. Preliminary data are available from four strainmeters in shale at our Oklahoma site and four in compacted sand and gravel in Utah. These are deployed from 40-60m, except one of the strainmeters in Oklahoma is deployed at 500m. The data include strains from the initial grout curing, comparisons to predicted earth tide models and in-situ calibration results, barometric pressure admittances and spectral analyses as well as signals from underground injections and surface waves from teleseismic events. Preliminary analyses indicate behavior consistent with other strainmeter deployments, and comparison to data from a Gladwin strainmeter at the Oklahoma site validate the performance of the new design. Analyses from a suite of six well tests at the Oklahoma site show for the first time how the strain tensor field varies with location during well testing.

DeWolf, Scott↗
DOE OSTI2020

Demand Driven Cycamore Archetypes

Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

ComStock Measure Documentation: Thermostat and Lighting Control for Load Shedding + Photovoltaics With 40% Rooftop Coverage

This report describes the modeling methodology for an upgrade package of two end-use savings shape measures - Thermostat Control for Load Shedding and Lighting Control for Load Shedding - and briefly introduces key results. The package combines thermostat control for load shedding, lighting control for load shedding, and PV with 40% rooftop coverage measures to reduce the net building load during the building's electricity peak window every weekday. The measure takes daily peak load schedule inputs generated by the method "Dispatch Schedule Generation" described in the "Supplemental Documentation: Dispatch Schedule Generation for Demand Flexibility Measures" to determine the start and end times of the predicted peak window, and then relaxes the thermostat setpoints and dims the lighting level from the original schedules during the peak window to reduce the peak demand, while applying the fixed rooftop PV application for onsite electricity generation. The measure is applicable to (large, medium and small) offices, warehouses, and primary and secondary schools, which correspond to approximately 68% of the stock floor area of commercial buildings in ComStock analysis. The measure demonstrates 5%-15% daily peak demand reduction performance for applicable buildings, and around 1% total site energy savings (0 trillion British thermal units [TBtu]) for the U.S. commercial building stock modeled in ComStock.

14 SOLAR ENERGY↗
NASA NTRS2004

Eccentric loading of microtensile specimens

Ceramic materials have a lower density than most metals and are capable of performing at extremely high temperatures. The utility of these materials is obvious; however, the fracture strength of brittle materials is not easily predicted and often varies greatly. Characteristically, brittle materials lack ductility and do not yield as other materials. Ceramics materials are naturally populated with microscopic cracks due to fabrication techniques. Upon application of a load, stress concentration occurs at the root of these cracks and fracture will eventually occur at some not easily predicted strength. In order to use ceramics in any application some design methodology must exist from which a component can be placed into service. This design methodology is CARES/LIFE (Ceramics Analysis and Reliability Evaluation of Structures) which has been developed and refined at NASA over the last several decades. The CARES/LIFE computer program predicts the probability of failure of a ceramic component over its service life. CARES combines finite element results from a commercial FE (finite element) package such as ANSYS and experimental results to compute the abovementioned probability of failure. Over the course of several tests CARES has had great success in predicting the life of various ceramic components and has been used throughout industry. The latest challenge is to verify that CARES is valid for MEMS (Micro-Electro Mechanical Systems). To investigate a series of microtensile specimens were fractured in the laboratory. From this data, material parameters were determined and used to predict a distribution of strength for other specimens that exhibit a known stress concentration. If the prediction matches the experimental results then these parameters can be applied to a desired component outside of the laboratory. During testing nearly half of the tensile Specimens fractured at a location that was not expected and hence not captured in the FE model. It has been my duty to investigate the nature of this phenomenon in hopes of finding a better correlation between theory and empirical results. To investigate I built complete FE models of all of the tensile specimens using ANSYS. It is suspected that some misalignment naturally occurs during testing and thus additional bending stresses are present in the specimens. I modeled this eccentric loading and ran several FE trials using ANSYS/PDS (a probabilistic design system in ANSYS). My objective this summer has been familiarize myself with the CARES/LIFE program in hopes of using it in conjunction with ANSYS to help verify that CARES is applicable to MEMS-scale (greater that 1 micron, less than 1 millimeter) components.

Trapp, Mark A.↗
DOE OSTI2022

FutureTense

Protective vaccines and reliable diagnostics are essential tools for controlling viral diseases. However, the efficacy of these tools can be diminished by mutations in viral genomes. The delay between the emergence of new viral strains and the redesign of vaccines and diagnostics allows for continued viral transmission. Is it possible to address this challenge by computationally predicting viral genome sequence evolution? Can we “future-proof” vaccines and diagnostics by targeting both current and anticipated future sequence variants? While predicting viral evolution is still an unsolved, “grand challenge” problem in biology, the large, and rapidly growing, number of SARS-CoV-2 genome sequences provide an opportunity to quantify the ability of machine learning to predict viral genome sequence evolution. Towards this end, we have developed a simple computational model for predicting viral evolution at the level of individual nucleotides. The key metric for quantifying the per-base, prediction accuracy for viral evolution is the Mann-Whitney U statistic (or, equivalently, the area under the receiver operator curve). Since the Mann-Whitney U statistic is not a differentiable function, existing deep leaning packages (like Pytorch and Keras/TensorFlow) are not useful, as they require that the accuracy metric/objective function be analytically differentiable with respect to the model parameters. To overcome this challenge, we have implemented custom software, “FutureTense”, that can train a machine learning model by maximizing the non-differentiable Mann-Whitney U statistic. This software trains a machine learning model by exploring along the direction of the discrete gradient of the Mann-Whitney U statistic in the model parameter space. Parallel computing and genome sequence-specific optimizations are used to accelerate model training. The resulting machine learning model learns the observed high C->U mutation rates in the SARS-CoV-2 genome (which are potentially induced by host defenses) and provides prediction accuracies that are significantly better than one would expect from random chance. While predicting viral evolution is still quite far from a solved problem, the surprising performance of this simple model gives hope that the accuracy of predicting viral genome evolution can be further increased by more sophisticated approaches.

Gans, Jason↗
DOE OSTI2017

Model 9975 Life Extension Test Package 3 - Interim Report - January 2017

Life extension package LE3 (9975-03203) has been instrumented and subjected to an elevated temperature environment for approximately 8 years. During this time, the cane fiberboard has been maintained at a maximum temperature of ~160 - 165 °F, which was established by a combination of internal (19 watts) and external heat sources. Several tests and parameters were used to characterize the package components. Results from these tests generally indicate agreement between this full-scale shipping package and small-scale laboratory tests on fiberboard samples, including the degradation models based on the laboratory tests. These areas of agreement include the rate of change of fiberboard weight, dimensions and density, and change in fiberboard thermal conductivity. Corrosion of the lead shield occurred at a high rate during the first several weeks of aging, but dropped significantly after most of the moisture in the fiberboard migrated away from the lead shield. Dimensional measurements of the lead shield indicate that no significant creep deformation has occurred. This is consistent with literature data that predict a very small creep deformation for the time at temperature experienced by this package. The SCV O-rings were verified to remain leak-tight after ~5 years aging at an average temperature of ~170 °F. This package provides an example of the extent to which moisture within a typical fiberboard assembly can redistribute in the presence of a temperature gradient such as might be created by a 19 watt internal heat load. The majority of water within the fiberboard migrated to the bottom layers of fiberboard, with approximately 2 kg of water (2 liters) eventually escaping from the package. Two conditions have developed that are not consistent with package certification requirements. The axial gap at the top of the package increased to a maximum value of 1.549 inches, exceeding the 1 inch criterion. In addition, staining and/or corrosion have formed in a few spots on the drum. However, the package remains capable of performing its function. Aging of this package continues.

42 ENGINEERING↗
NASA NTRS2004

A Study of Cirrus Clouds and Aerosols in the Upper Troposphere using Models and Satellite Data

This report is the final report for the Cooperative Agreement NCC2-1213. It is a compilation of publications produced under this Cooperative Agreement and conference presentations. The tasks for the Aerosol Physical Chemistry Model for the Upper Troposphere include: Task 1: To compare APCM predictions against the SUCCESS data and other aircraft campaigns and to investigate the role of aerosol composition on cirrus cloud nucleation; Task 2: To study the seasonal evolution and spatial distribution of upper-tropospheric tropical and polar cirrus; Task 3: To investigate CLAES cirrus data with other complementary (TOGA-COARE and CEPEX) data. Tasks for Upper Tropospheric Cirrus Clouds include: Task 1: Assemble 3-hourly (or more frequent) meteorological satellite data fiom geostationary satellites to obtain a global, or nearly global, dataset of infiared brightness temperatures as a function of time for airborne experimental periods; Task 2: Explore methods to improve the cloud top altitude distributions calculated fiom meteorological satellite data. This will focus on linlung the 6.5 micron channel geostationary brightness temperatures and the 10.5 micron brightness temperatures; Task 3: Explore methods to differentiate convective fiom stratiform cloudiness; Task 4: Perform trajectory analyses using an existing trajectory modeling package that links the cloud data with air mass histories; Task 5: Apply techniques from tasks 1 through 4 to provide meteorological support to the CRYSTAL-FACE mission, both in its preparation and deployment phases. The report include four published articles and two slide presentations.

Bergstrom, Robert W.↗
NASA NTRS2006

The Impact of Microphysics on Intensity and Structure of Hurricanes

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WFW is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WFW model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WW to examine the impact of six different cloud microphysical schemes on hurricane track, intensity and rainfall forecast. We are also performing the inline tracer calculation to comprehend the physical processes @e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes.

Tao, Wei-Kuo↗
DOE OSTI2021

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation

In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks, Frontiers in Earth Science: Dataset

This data package contains watershed modeling inputs and outputs as well as deep neural networks training and testing results used in "Estimating Watershed Subsurface Permeability From Stream Discharge Data using Deep Neural Networks" (Cromwell et al., 2021). We train various deep neural network models with different architectures to predict subsurface permeability from stream discharge hydrograph at the watershed outlet. The training data are obtained from ensemble simulations of hydrographs corresponding to an permeability ensemble using a fully-distributed, integrated surface-subsurface hydrologic model. The trained model is then applied to estimate the permeability of the real watershed using its observed hydrograph at the outlet. Our study demonstrates that the permeabilities of the soil and geologic facies that make significant contributions to the outlet discharge can be more accurately estimated from the discharge data. Their estimations are also more robust with observation errors. Compared to the traditional ensemble smoother method, DNNs show stronger performance in capturing the nonlinear relationship between permeability and stream hydrograph to accurately estimate permeability. Our study sheds new light on the value of the emerging deep learning methods in assisting integrated watershed modeling by improving parameter estimation, which will eventually reduce the uncertainty in predictive watershed models. For detailed information regarding watershed model description and DNNs setup, please refer to Cromwell et al., 2021. The deep_learning.zip file contains the inputs and outputs for the different deep neural networks. The watershed_model.zip file contains the inputs and outputs for watershed simulation using ATS. The figures.zip file contains raw figures and their corresponding scripts. For a more detailed description, please see the README.md file within each compressed file.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2007

The Impact of Microphysics on Intensity and Structure of Hurricanes and Mesoscale Convective Systems

During the past decade, both research and operational numerical weather prediction models, e.g. Weather Research and Forecast (WRF) model, have started using more complex microphysical schemes originally developed for high-resolution cloud resolving models (CRMs) with a 1-2 km or less horizontal resolutions. WRF is a next-generation mesoscale forecast model and assimilation system that has incorporated modern software framework, advanced dynamics, numeric and data assimilation techniques, a multiple moveable nesting capability, and improved physical packages. WRF model can be used for a wide range of applications, from idealized research to operational forecasting, with an emphasis on horizontal grid sizes in the range of 1-10 km. The current WRF includes several different microphysics options such as Purdue Lin et al. (1983), WSM 6-class and Thompson microphysics schemes. We have recently implemented three sophisticated cloud microphysics schemes into WRF. The cloud microphysics schemes have been extensively tested and applied for different mesoscale systems in different geographical locations. The performances of these schemes have been compared to those from other WRF microphysics options. We are performing sensitivity tests in using WRF to examine the impact of six different cloud microphysical schemes on precipitation processes associated hurricanes and mesoscale convective systems developed at different geographic locations [Oklahoma (IHOP), Louisiana (Hurricane Katrina), Canada (C3VP - snow events), Washington (fire storm), India (Monsoon), Taiwan (TiMREX - terrain)]. We will determine the microphysical schemes for good simulated convective systems in these geographic locations. We are also performing the inline tracer calculation to comprehend the physical processes (i.e., boundary layer and each quadrant in the boundary layer) related to the development and structure of hurricanes and mesoscale convective systems.

Tao, Wei-Kuo↗
DOE OSTI2021

RouteE: A Vehicle Energy Consumption Prediction Engine

The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL's Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.

47 OTHER INSTRUMENTATION↗
NASA NTRS2015

Climate Model Diagnostic Analyzer

The comprehensive and innovative evaluation of climate models with newly available global observations is critically needed for the improvement of climate model current-state representation and future-state predictability. A climate model diagnostic evaluation process requires physics-based multi-variable analyses that typically involve large-volume and heterogeneous datasets, making them both computation- and data-intensive. With an exploratory nature of climate data analyses and an explosive growth of datasets and service tools, scientists are struggling to keep track of their datasets, tools, and execution/study history, let alone sharing them with others. In response, we have developed a cloud-enabled, provenance-supported, web-service system called Climate Model Diagnostic Analyzer (CMDA). CMDA enables the physics-based, multivariable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. At the same time, CMDA provides a crowd-sourcing space where scientists can organize their work efficiently and share their work with others. CMDA is empowered by many current state-of-the-art software packages in web service, provenance, and semantic search.

cloud computing↗
NASA NTRS2000

Power Systems Evaluated for Solar Electric Propulsion Vehicles

Solar electric propulsion (SEP) mission architectures are applicable to a wide range NASA missions including the robotic exploration of the outer planets in the next decade and the human exploration of Mars within the next 2 decades. SEP enables architectures that are very mass efficient with reasonable power levels (1-MW class) aerobrake and cryogenic upper-stage transportation technologies are utilized. In this architecture, the efficient SEP stage transfers the payload from low Earth orbit (LEO) High Energy Elliptical Parking Orbit (HEEPO) within a period of 6 to 12 months. highthrust, cryogenic upper stage and payload then separate from the SEP vehicle for injection to the planetary target, allowing for fast heliocentric trip times. This mission architecture offers a potential reduction in mass to LEO in comparison to alternative all-chemical nuclear propulsion schemes. Mass reductions may allow launch vehicle downsizing enable missions that would have been grounded because of cost constraints. The preceding figure illustrates a conceptual SEP stage design for a human Mars mission. Researchers at the NASA Glenn Research Center at Lewis Field designed conceptual SEP vehicle, conceived the mission architecture to use this vehicle, and analyzed the vehicle s performance. This SEP stage has a dry mass of 35 metric tons (MT), 40 MT of xenon propellant, and a photovoltaic array that spans 110 m, providing power to a cluster of eight 100-kW Hall thrusters. The stage can transfer an 80-MT payload and upper stage to the desired HEEPO. Preliminary packaging studies show this space-station-class SEP vehicle meets the proposed "Magnum" launch vehicle and volume requirements with considerable margin. An SEP vehicle for outer planetary missions, such as the Europa Mapper Mission, would be dramatically smaller than human Mars mission SEP stage. In this mission architecture, the SEP power system with the payload to provide spacecraft power throughout the mission. Several photovoltaic array design concepts were considered for the SEP vehicle power system for the human mission to Mars. These include a space station derivative, a SCARLET (Solar Concentrator Arrays with Refractive Linear Element Technology) derivative, and a hybrid inflatable-deployable thin polymer membrane array with thin-film solar cells (as shown in the concept illustration). This concept is based on a design developed for the Next Generation Space Telescope Sun shield. The array is divided into 16 independent electrical sections with 500-V, negative-grounded solar cell strings. The power system employs a channelized, 500-Vdc power management and distribution (PMAD) architecture with lithium ion batteries for energy storage for vehicle and payload secondary loads (the high-power Hall thrusters do not operate in eclipse periods). The 500-V PMAD voltage permits "direct-drive" thruster operation, greatly reducing the power processing unit size, complexity, and power loss. Similar power system architecture, designs, and technology are assumed for the Europa Mapper Mission SEP vehicle. The primary exceptions are that the photovoltaic array is assumed to consist of two rectangular wings and that the power system rating is 15 kW in Earth orbit and 200 W at Europa. To size the SEP vehicle power system, a dedicated Fortran code was developed to predict detailed power system performance, mass, and thermal control requirements. This code also modeled all the relevant Earth orbit environments; that is, the particulate radiation, plasma, meteoroids and debris, ultraviolet radiation, contamination, and thermal conditions. Analysis results for the Human Mars Mission SEP vehicle show a power system mass of 9-MT and photovoltaic array area of 5800-square meters for the thin-membrane design concept with CuInS2 thin-film cells. Power processing unit input power for a thin-membrane array design with three-junction, amorphous SiGe solar cells is shown in the graph. Power falls off rapidly inhe first weeks of the mission because of light-induced (Staebler-Wronksi) solar cell losses. During the next 200 days, power decreases steadily as the SEP stage spirals through the proton belts and sustains the bulk of the mission radiation damage. Once the vehicle apogee is above approximately four Earth radii, little additional degradation is incurred. From 400 to 800 days, a 1100-km "parking" orbit is maintained to await the next payload transfer opportunity. This orbit is below the main proton belt, and thus, little radiation dose is accumulated during this time period. During the second LEO-to-HEEPO transfer, power degrades somewhat further, but power requirements are still met. In comparison, the Europa Mapper SEP vehicle power system had a mass of 150 kg and a thin membrane array area of 100 square meters.

Kerslake, Thomas W.↗
DOE OSTI2015

System-Level Heat Transfer Analysis, Thermal- Mechanical Cyclic Stress Analysis, and Environmental Fatigue Modeling of a Two-Loop Pressurized Water Reactor. A Preliminary Study

This report provides an update on an assessment of environmentally assisted fatigue for light water reactor components under extended service conditions. This report is a deliverable in April 2015 under the work package for environmentally assisted fatigue under DOE's Light Water Reactor Sustainability program. In this report, updates are discussed related to a system level preliminary finite element model of a two-loop pressurized water reactor (PWR). Based on this model, system-level heat transfer analysis and subsequent thermal-mechanical stress analysis were performed for typical design-basis thermal-mechanical fatigue cycles. The in-air fatigue lives of components, such as the hot and cold legs, were estimated on the basis of stress analysis results, ASME in-air fatigue life estimation criteria, and fatigue design curves. Furthermore, environmental correction factors and associated PWR environment fatigue lives for the hot and cold legs were estimated by using estimated stress and strain histories and the approach described in NUREG-6909. The discussed models and results are very preliminary. Further advancement of the discussed model is required for more accurate life prediction of reactor components. This report only presents the work related to finite element modelling activities. However, in between multiple tensile and fatigue tests were conducted. The related experimental results will be presented in the year-end report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Web-based, GPU-accelerated, Monte Carlo simulation and visualization of indirect radiation imaging detector performance

Purpose: Monte Carlo simulations play a vital role in the understanding of the fundamental limitations, design, and optimization of existing and emerging medical imaging systems. Efforts in this area have resulted in the development of a wide variety of open-source software packages. One such package, hybridMANTIS, uses a novel hybrid concept to model indirect scintillator detectors by balancing the computational load using dual CPU and graphics processing unit (GPU) processors, obtaining computational efficiency with reasonable accuracy. In this work, the authors describe two open-source visualization interfaces, webMANTIS and visualMANTIS to facilitate the setup of computational experiments via hybridMANTIS. Methods: The visualization tools visualMANTIS and webMANTIS enable the user to control simulation properties through a user interface. In the case of webMANTIS, control via a web browser allows access through mobile devices such as smartphones or tablets. webMANTIS acts as a server back-end and communicates with an NVIDIA GPU computing cluster that can support multiuser environments where users can execute different experiments in parallel. Results: The output consists of point response and pulse-height spectrum, and optical transport statistics generated by hybridMANTIS. The users can download the output images and statistics through a zip file for future reference. In addition, webMANTIS provides a visualization window that displays a few selected optical photon path as they get transported through the detector columns and allows the user to trace the history of the optical photons. Conclusions: The visualization tools visualMANTIS and webMANTIS provide features such as on the fly generation of pulse-height spectra and response functions for microcolumnar x-ray imagers while allowing users to save simulation parameters and results from prior experiments. The graphical interfaces simplify the simulation setup and allow the user to go directly from specifying input parameters to receiving visual feedback for the model predictions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Control And Optimization Modular Modeling Application For Nuclear Deployment

The purpose of the COMMAND code is to provide a flexible, scalable tool for use in developing, integrating, and testing the technologies necessary for achieving autonomous operations of advanced nuclear reactors. The code enables users to efficiently implement custom simulations and experiments by combining key methods from different software modules. These modules are focused on: modeling and simulation tools, such as nuclear simulation tools used for high-fidelity modeling (e.g., Reactor Excursion and Leak Analysis Program [RELAP5-3D] and Monte Carlo N-Particle [MCNP]); machine learning and optimization tools (e.g., anomaly detection and data-driven modeling techniques); advanced control in its digital, high-performance, and supervisory control forms (e.g., proportional integral derivative (PID) control and model predictive control (MPC); and integration with hardware through industrial communication protocols. To ensure flexibility and scalability, COMMAND was designed to be both modular—the software “pieces” all inherit from generic building blocks and can be combined and connected to create complicated simulations—and high performing—designed for parallel processing, enabling simulations and experiments to take advantage of multi-core computers, servers, and nodes. The code is written in the Python programming language due to the language's popularity, active community, and open-source and cross-platform nature. Maintaining consistency with other simulation tools used within the nuclear energy community, users implement simulations and experiments through text input files, which define components, parameters, connections, etc., through lines of text. Given that COMMAND is written in Python, these input files are native Python scripts, and so use the standard Python structure and formatting. This also enables users to take advantage of Python's extensive package library to develop custom capabilities for their specific use cases.

Faber, Jacob [Idaho National Laboratory (INL), Ida↗
DOE OSTI2016

Performance of U3Si2 Fuel in a Reactivity Insertion Accident

In this study we examined the performance of the U3Si2 fuel cladded with Zircaloy (Zr) in a reactivity insertion accident (RIA) in a PWR core. The power excursion as a result of a $1 reactivity insertion was calculated by a TRACE PWR plant model using point-kinetics, for alternative cores with UO2 and U3Si2 fuel assemblies. The point-kinetics parameters (feedback coefficients, prompt-neutron lifetime and group constants for six delayed-neutron groups) were obtained from beginning-of-cycle equilibrium full core calculations with PARCS. In the PARCS core calculations, the few-group parameters were developed utilizing the TRITON/NEWT tools in the SCALE package. In order to assess the fuel response in finer detail (e.g. the maximum fuel temperature) the power shape and thermal boundary conditions from the TRACE/PARCS calculations were used to drive a BISON model of a fuel pin with U3Si2 and UO2 respectively. For a $1 reactivity transient both TRACE and BISON predicted a higher maximum fuel temperature for the UO2 fuel than the U3Si2 fuel. Furthermore, BISON is noted to calculate a narrower gap and a higher gap heat transfer coefficient than TRACE. This resulted in BISON predicting consistently lower fuel temperatures than TRACE. This study also provides a systematic comparison between TRACE and BISON using consistent transient boundary conditions. The TRACE analysis of the RIA only reflects the core-wide response in power. A refinement to the analysis would be to predict the local peaking in a three-dimensional core as a result of control rod ejection.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗