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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2012

IAEA CRP on HTGR Uncertainty Analysis: Benchmark Definition and Test Cases

Uncertainty and sensitivity studies are essential elements of the reactor simulation code verification and validation process. Although several international uncertainty quantification activities have been launched in recent years in the LWR, BWR and VVER domains (e.g. the OECD/NEA BEMUSE program, from which the current OECD/NEA LWR Uncertainty Analysis in Modelling (UAM) benchmark effort was derived), the systematic propagation of uncertainties in cross-section, manufacturing and model parameters for High Temperature Reactor (HTGR) designs has not been attempted yet. This paper summarises the scope, objectives and exercise definitions of the IAEA Coordinated Research Project (CRP) on HTGR UAM. Note that no results will be included here, as the HTGR UAM benchmark was only launched formally in April 2012, and the specification is currently still under development.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2014

Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case

Dynamic Probabilistic Risk Assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). While system simulator codes accurately model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by: 1) sampling values of a set of parameters from the uncertainty space of interest (using the simulation controller codes), and 2) simulating the system behavior for that specific set of parameter values (using the system simulator codes). For complex systems, one of the major challenges in using DPRA methodologies is to analyze the large amount of information (i.e., large number of scenarios ) generated, where clustering techniques are typically employed to allow users to better organize and interpret the data. In this paper, we focus on the analysis of a nuclear simulation dataset that is part of the Risk Informed Safety Margin Characterization (RISMC) Boiling Water Reactor (BWR) station blackout (SBO) case study. We apply a software tool that provides the domain experts with an interactive analysis and visualization environment for understanding the structures of such high-dimensional nuclear simulation datasets. Our tool encodes traditional and topology-based clustering techniques, where the latter partitions the data points into clusters based on their uniform gradient flow behavior. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

AOT_data_repo

Data repo for ocean test case testing new data assimilation algorithm in an idealized setting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Time-Resolved PIV Measurements of Flow Field at the Exit of a Diffuser Mounted on a Rotating Detonation Combustor

Time-resolved particle image velocimetry at 30 kHz is applied to the exhaust flow field of a RDC system configured with a diffuser geometry and a converging-diverging (CD) nozzle. The RDC is operated with methane fuel and oxygen-enriched air, motivated by power generation applications. The measured flow-field is discretized into axial and non-axial velocity components. Results are shown to characterize the flow-field across a cord length at the exit plane. Instantaneous contours of the two velocity components, and 1D histograms of velocity components, and 2D histograms of total velocity and tilt angle are shown at multiple locations in the flow. Repeatability of the data is evaluated by comparing tests conducted at similar flow rates. Results are presented for a baseline test case, and test cases showing the effect of reactant higher flow rate and CD nozzle geometry. Finally, the results are compared to a previous RDC study without the diffuser to compare the effectiveness of the diffuser and CD nozzle designs in mitigating the flow periodicity at the combustor exit.

particle image velocimetry, pressure gain combusti↗
DOE OSTI2019

Time-Resolved PIV Measurements of Flow Field at Diffuser Exit Downstream of a Rotating Detonation Combustor

Time-resolved particle image velocimetry at 30 kHz is applied to the exhaust flow field of a RDC system configured with a diffuser geometry and a converging-diverging (CD) nozzle. The RDC is operated with methane fuel and oxygen-enriched air, motivated by power generation applications. The measured flow-field is discretized into axial and non-axial velocity components. Results are shown to characterize the flow-field across a cord length at the exit plane. Instantaneous contours of the two velocity components, and 1D histograms of velocity components, and 2D histograms of total velocity and tilt angle are shown at multiple locations in the flow. Repeatability of the data is evaluated by comparing tests conducted at similar flow rates. Results are presented for a baseline test case, and test cases showing the effect of reactant higher flow rate and CD nozzle geometry. Finally, the results are compared to a previous RDC study without the diffuser to compare the effectiveness of the diffuser and CD nozzle designs in mitigating the flow periodicity at the combustor exit.

03 NATURAL GAS↗
DOE OSTI2013

Computer Simulation of an Electrostatic Generator: Example Case

A test case of the LLNL Electrostatic Generator system was run on an in-house simulation code, the results of which can be used as a check on other E-S Generator simulation codes. This report describes the electrical circuit employed in the calculation, its parameters, and selected results. These results have not been checked against an independent code (such as the SPICE code) and therefor there is no implied guarantee of the validity of the data summarized here.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

SAS4A/SASSYS-1 Lead Verification Test Suite

SAS4A/SASSYS-1 is a simulation tool used to perform deterministic analyses of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. With its origin as SAS1A in the late 1960s, the SAS series of codes has been under continuous use and development for over forty-five years and represents a critical investment in safety analysis capabilities for the U.S. Department of Energy. Although SAS4A/SASSYS-1 was developed to support the analysis of any liquid-metal-cooled nuclear reactor, it has primarily been utilized to design and analyze Sodium Fast Reactors (SFRs). As a result, most of the verification basis for SAS4A/SASSYS-1 utilizes sodium as a coolant and geometry descriptions that are typical of a SFR facility. In this work, new verification problems are defined to extend the applicability of the SAS4A/SASSYS-1 verification basis into Lead Fast Reactor (LFR) design space. A review of the existing SAS4A/SASSYS-1 SFR verification test suite is performed to determine the number of test cases that are SFR specific. SFR specific test cases are replicated using lead as a coolant and system layouts that are representative of LFRs. This report provides definitions and reference solutions for seven new test cases that scope lead-specific features and capabilities of the software. For a verification test to be considered acceptable, comparisons of analytical solutions to SAS4A/SASSYS-1 predictions must produce negligible or justifiable errors. All LFR specific test cases are found to be acceptable.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Sustainable Data Evolution Technology (SDET) for Power Grid Optimization (Final Report)

This project develops an innovative sustainable data evolution technology (SDET) to create open-access power grid datasets and facilitate updates to these datasets by the power grid community. The lack of open-access, realistic large-scale datasets significantly limits the ability of researchers, developers and ultimately end users to develop, benchmark and compare new methods and tools for optimizing the operation and planning of the grid, which leads to slow adoption by end users. Available open datasets include the IEEE sponsored 14-300 bus transmission test cases and distribution test feeders, the Edinburgh Power Systems Test Case Archive (39-2224 buses), and the Polish cases (2383-3375 buses) available with MATPOWER. These datasets serve certain purposes, but for power grid optimization, they are insufficient in one or more of these aspects: 1) they are too small; 2) they are incomplete in both models and scenarios for optimization purposes; and 3) they are static and are not keeping up with grid needs. The SDET technology delivers large-scale realistic datasets and data-creation tools capable of generating new datasets. We work closely with the Category-2 data repository project awarded by the same GRID DATA program. The generated datasets and data creation tools are compatible with and available through the data repository. The objective is to make this a sustained effort within and beyond the ARPA-E GRID DATA program so that the datasets can evolve over time and meet the current and future needs for power grid optimization and potentially other applications in power grid operation and planning. The novel and disruptive features of SDET differentiate itself from prior work in the following aspects: 1) SDET derives features and metrics for both transmission and distribution (T&D) systems by analyzing many public and private datasets provided by our industry partners National Rural Electric Cooperative Association (NRECA) and PJM Interconnection (PJM). 2) SDET develops data-creation tools and use these tools to generate large-scale open-access realistic datasets that comply with the metrics for both T&D systems. 3) SDET validates the created datasets using industry tools provided by our vendor partner GE Energy Solution. The data creation tools, as well as the created datasets, are integrated into the data repository (DR. POWER) funded by ARPA-E GRID DATA program in Category 2. Users can use the datasets and access these data creation tools to create and update datasets, thus datasets can evolve per user requirements and power grid development. The SDET is a novel and disruptive technology compared to current ad hoc and static dataset generation. It is expected the SDET technology will fundamentally enhance the development, assessment, and adoption of new methods and tools for power grid optimization and other applications, which in turn would significantly improve the reliability, resiliency, and efficiency of the power grid – a major ARPA-E mission objective.

24 POWER TRANSMISSION AND DISTRIBUTION↗