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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 523 records · Page 29

VERAView

In order to support engineering analysis of Virtual Environment for Reactor Analysis (VERA) model results, the Consortium for Advanced Simulation of Light Water Reactors (CASL) needs a tool that provides visualizations of HDF5 files that adhere to the VERAOUT specification. VERAView provides an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The Python-based software provides instantaneous 2D and 3D images, 1D plots, and alphanumeric data from VERA multi-physics simulations.

Lee, Ronald W.↗

Reactor simulations with nuclear data uncertainties

This work demonstrates the influence of uncertainties in microscopic nuclear data on the results of reactor simulations, both for stationary and transient states. It gives an overview on the methods in use for uncertainty and sensitivity analyses with respect to nuclear data, and discusses the pros and cons. For full-scale reactor simulations, in particular with coupled neutron transport and thermo-hydraulics, random sampling provides a powerful means to propagate nuclear data uncertainties through the complete calculation sequence. Results of uncertainty analyses performed with the GRS XSUSA – “cross section (XS) Uncertainty and Sensitivity Analysis” methodology are shown for radial power distributions from steady-state PWR calculations and for the time evolution of the reactor power in the course of a control rod withdrawal from a PWR mini-core. In all cases, the output uncertainties are considerable. For the radial power distributions, relative 1σ uncertainties of up to 10% are observed, and for the power peak during the transient, the relative 1σ uncertainty reaches 20%. These large uncertainties strongly suggest to routinely accompany best-estimate simulations by uncertainty analyses with respect to nuclear data, in particular for systems beyond LWR for which much less operation experience is available.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Inverse Uncertainty Quantification of Reactor Simulation with Polynomial Chaos Surrogate Model

Reliable uncertainty quantification (UQ) and sensitivity analysis (SA) is significant part of modelling and simulation. This is particularly important in the nuclear community due to the fact that the licensing of nuclear installations has shifted from conservative to Best Estimate plus Uncertainty (BEPU) methodologies. UQ can be broadly classified as forward UQ and inverse UQ. Forward UQ is the process of quantifying the uncertainties in simulation outputs by propagating the uncertainties in input parameters. It requires knowledge in the input uncertainty, which is often specified by ad-hoc expert judgment which lacks mathematical rigor. The inverse UQ is the process to determine the uncertainty in input parameters given observations of the output. The purpose of this paper is to replace such ad-hoc expert judgment of the statistical properties of input model parameters in nuclear reactor simulation. This summary presents work on inverse UQ of a nonlinear Point Reactor Kinetics Equations - lumped thermal-hydraulics parameter coupled problem. Bayesian inference and Markov Chain Monte Carlo (MCMC) are used for the inverse UQ. Because MCMC requires a large number of simulations which is impractical for models that are computationally expensive, surrogate models is constructed by generalized Polynomial Chaos (PC) expansion technique. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Floods of Egypt’s Nile in the 21st century

Extreme precipitation and flooding events are rising globally, necessitating a thorough understanding and sustainable management of water resources. One such setting is the Nile River’s source areas, where high precipitation has led to the filling of Lake Nasser (LN) twice (1998–2003; 2019–2022) in the last two decades and the diversion of overflow to depressions west of the Nile, where it is lost mainly to evaporation. Using temporal satellite-based data, climate models, and continuous rainfall-runoff models, we identified the primary contributor to increased runoff that reached LN in the past two decades and assessed the impact of climate change on the LN’s runoff throughout the twenty-first century. Findings include: (1) the Blue Nile subbasin (BNS) is the primary contributor to increased downstream runoff, (2) the BNS runoff was simulated in the twenty-first century using a calibrated (1965–1992) rainfall-runoff model with global circulation models (GCMs), CCSM4, HadGEM3, and GFDL-CM4.0, projections as model inputs, (3) the extreme value analysis for projected runoff driven by GCMs’ output indicates extreme floods are more severe in the twenty-first century, (4) one adaptation for the projected twenty-first century increase in precipitation (25–39%) and flood (2%-20%) extremes is to recharge Egypt’s fossil aquifers during high flood years.

Climate change↗

Utilizing GEOPHIRES-X Beyond Electricity

The GEOPHIRES tool is a techno-economic simulator for evaluating the thermal performance and cost-competitiveness of geothermal plants for electricity, heating, and/or cooling. The tool combines reservoir, wellbore, and surface plant cost and performance models to estimate overall techno-economic metrics such as net present value or levelized cost of electricity, heating, or cooling. We recently upgraded the tool to an object-oriented Python framework, presented in an accompanying paper. As part of the upgrade, we enhanced the capability to simulate the performance of geothermal plants for heating and cooling, which is the topic of this paper. Specifically, we (1) integrated absorption chillers to investigate the performance of utilizing geothermal heat for cooling, (2) integrated a heat pump module to boost the geothermal temperature and thermal output, (3) integrated a district heating module to estimate heating demand for a district based on local weather data, and simulated heat supply with geothermal energy and peaking boilers, and (4) integrated GEOPHIRES as an engine in the dGeo simulator to perform a geospatial analysis of geothermal district heating feasibility across a large region (e.g., a state or the entire United States) utilizing resource and thermal demand maps. This paper presents background information and case studies for several of these heating and cooling end-use options in GEOPHIRES.

absorption chiller↗

Status of the NEAMS and ARC fast reactor tools integration to the NEAMS Workbench

The Workbench initiative was launched in FY-2017 within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program to facilitate the transition from conventional tools to high-fidelity tools. The NEAMS Workbench provides a common user interface for model creation, real-time validation, execution, output processing, and visualization for integrated codes. The integration of the Argonne Reactor Computation (ARC) suite of codes into the NEAMS Workbench through the PyARC module was initiated in FY-2017. The ARC codes, which focus on fast reactor multiphysics analysis, contain both legacy codes like DIF3D and REBUS-3 that were developed with over 30 years of experience, and newer NEAMS additions like MC 2 -3, PERSENT, and PROTEUS. Recent work expended this integration to other tools to support the U.S. fast reactor community such as DASSH for sub-channel thermal-hydraulics, Griffin for high-fidelity deterministic transport calculations, and OpenMC for Monte Carlo simulations (with Shift integration planned for FY-2023). The integration of the “extended ARC” suite of codes into the NEAMS Workbench interface relies on the PyARC and PyGriffin modules to handles the pre- and post-processing of these codes input, and the runtime environment. The PyARC module together with the NEAMS Workbench interface are both released under Open Source Software licenses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Analysis of near-field and far-field aerosol dispersion for microreactors

The current paper presents a simulation-based analysis of aerosol dispersion in the near-field and far-field of a generic, conceptual microreactor operating at pressures close to the ambient pressure; therefore, in the event of an accident that causes radionuclide leakage from the microreactor containment, the radionuclide particles are less likely to travel too far from the reactor, as opposed to conventional reactors. Accordingly, the presented work provides estimates of average and 95-percentile values of the relative effluent concentration. A parametric study is then performed to narrow down the parameters which affect the aerosol dispersion characteristics most significantly. Simulations were performed in the computer code ARCON96, and the parameters found to affect aerosol dispersion characteristics are the atmospheric stability class, and the distance between the release point and the receptor. It is recommended that the computer code RADTRAD be used to calculate actual dosage over distance, using the outputs from ARCON96 as inputs, along with reactor-specific core term inventories. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗

Linearized spectrum correlation analysis for line emission measurements

A new spectral analysis method, Linearized Spectrum Correlation Analysis (LSCA), for charge exchange and passive ion Doppler spectroscopy is introduced to provide a means of measuring fast spectral line shape changes associated with ion-scale micro-instabilities. This analysis method is designed to resolve the fluctuations in the emission line shape from a stationary ion-scale wave. The method linearizes the fluctuations around a time-averaged line shape (e.g., Gaussian) and subdivides the spectral output channels into two sets to reduce contributions from uncorrelated fluctuations without averaging over the fast time dynamics. In principle, small fluctuations in the parameters used for a line shape model can be measured by evaluating the cross spectrum between different channel groupings to isolate a particular fluctuating quantity. High-frequency ion velocity measurements (100–200 kHz) were made by using this method. We also conducted simulations to compare LSCA with a moment analysis technique under a low photon count condition. Both experimental and synthetic measurements demonstrate the effectiveness of LSCA.

47 OTHER INSTRUMENTATION↗

Modeling and Steady-State Analysis of a Rotary Transformer-Based Field Excitation System for Wound Rotor Synchronous Machine

Wound rotor synchronous motors are gaining attention as an alternative to the permanent magnet-based synchronous motors for electric vehicle traction. Rotary transformers have been proposed for the field excitation of these motors to avoid the disadvantages of the slip rings and brushes arrangement. Conventional rotary transformers use small airgap and as a result can be limited in operating speed unless costly high precision manufacturing is used. This paper proposes a novel rotary transformer-based excitation system that overcomes these disadvantages. Modeling and analysis of the proposed system is provided. Simulation results using the developed model and PLECs shows very good agreement for fundamental frequency. Furthermore, a steady state study of the field excitation system using harmonic balance technique is carried out to show dependencies of the output controlled variables on the input variables in the presence of system parameter variations. With series resonant compensations, the open-loop control of the proposed excitation system is robust against variation of field winding resistance with temperature.

Haruna, Josiah↗

An investigation of error characteristics and coding performance

The performance of forward error correcting coding schemes on errors anticipated for the Earth Observation System (EOS) Ku-band downlink are studied. The EOS transmits picture frame data to the ground via the Telemetry Data Relay Satellite System (TDRSS) to a ground-based receiver at White Sands. Due to unintentional RF interference from other systems operating in the Ku band, the noise at the receiver is non-Gaussian which may result in non-random errors output by the demodulator. That is, the downlink channel cannot be modeled by a simple memoryless Gaussian-noise channel. From previous experience, it is believed that those errors are bursty. The research proceeded by developing a computer based simulation, called Communication Link Error ANalysis (CLEAN), to model the downlink errors, forward error correcting schemes, and interleavers used with TDRSS. To date, the bulk of CLEAN was written, documented, debugged, and verified. The procedures for utilizing CLEAN to investigate code performance were established and are discussed.

Ebel, William J.↗

MARGInS: Model-Based Analysis of Realizable Goals in Systems

Under NASAs Constellation effort, the Exploration Technology Development Program funded research toward a system validation capability that applied machine learning and test-case generation techniques to the analysis of black-box system behavior. The behavior analysis capability scaled to spaces of hundreds of input parameters and tens of thousands of test cases. Aerospace systems at the vehicle level, especially those systems which contain some level of autonomy, are best described by hybrid and non-linear mathematics. Even simplified models of such systems need parameter dimensionalities in the hundreds or thousands of parameters in order to capture sufficient fidelity. The System Safety Assessments (such as those described in the SAE ARP 4761A Safety Assessment Process guidelines) for these systems are prone to errorinteractions between the vehicles subsystems are complex, and can display emergent behaviors. NASA captured this new analysis in the Model-based Analysis of Realizable Goals in Systems (MARGInS) tool and applied it to the Pad Abort 1 (PA-1) simulation as part of the independent validation and verification cycle before the PA-1 flight test in May of 2010. MARGInS evaluated the adherence of the high-fidelity simulation to its requirements, and deter- mined the margins to failure from the expected nominal input conditions. Following the PA-1 test, the capabilities within the MARGInS framework have been extended with sophisticated statistical and white-box test case generation techniques and applied to other NASA missions. The frame- work now includes a critical factors analysis that was applied to NASAs Orion simulation and design. NASAs Aeronautics Research Mission Directorate (ARMD) leveraged the existing MARGInS framework for work on aviation safety for civil transport vehicles and for research on autonomy issues. The NASA ARMD effort created a time series output prediction capability that has been used to characterize trajectories for a plane with an adaptive control system, and a safety boundary detection capability that has been applied to an air traffic control concept of operation for the Federal Aviation Administration. The statistical and machine- learning based techniques within MARGInS have been successfully combined with concolic execution to improve the coverage of a critical unit by driving system-level inputs. The use case driving the concolic execution and MARGInS integration was inspired by the Air France 447 disaster in which the loss of a critical functionality (the airspeed calculation from the pitot tubes) led to loss of the entire plane with the people aboard. To illustrate capabilities and limitations, we will highlight the analyses for the applications listed above. We will then discuss the future plans for MARGInS and its interfaces with other tools.

Validation↗

Overview of Micrometeoroid and Orbital Debris Analysis Process for Mars Sample Return Earth Entry System

Introduction: Micrometeoroid and orbital debris (MMOD) risk analyses for the Mars Sample Return (MSR) Earth Entry System (EES) have been significantly more rigorous than previously flown missions because of its categorization as a Class V restricted return mission. This means the returned samples present significant concern for biogenic contamination. These analyses seek to determine if a micrometeoroid or orbital debris strike would result in loss of containment assurance and are summarized in the flowchart in Fig. 1. Methodology: The mission is considered in two MMOD phases: a pre-release phase where the EES is protected by a Micrometeoroid Protection System (MMPS) and a post-release phase called “free-flight” where the EES is exposed directly to the MMOD environment. These phases correspond to interplanetary cruise and imminent re-entry, respectively. To inform the MMPS design, a 30-shot high velocity impact testing (HVIT) series on candidate configurations at NASA White Sands Test Facility was completed in the summer of 2022. Sample post-shot images are shown in Fig 2 [1]. These data are used to baseline the MMPS design and to tune the hydrocode simulations. ALE3D, CTH, and SPHC are the hydrocodes that simulate physics of high-speed impacts [2]. Results generated with these populate a penetration depth versus energy space beyond the testable velocity regime of HVIT (~7 km/s). The penetration depth versus energy space data are used to define a critical projectile diameter function called a Ballistic Limit Equation (BLE), where the projectile “criticality” is determined by zone dependent failure criteria defined a-priori [3]. For example, the nose of the heatshield has a failure criterion of 50% TPS penetration, assigned because the landing loads are concentrated on that region and no substructure damage is permitted. The BLEs for each vehicle material zone are input into the BUMPER 3 code, along with the vehicle surface mesh and the corresponding space environment model, to calculate a probability of penetration or number of penetrations. The environment models, MEM3 for MM and ORDEM 3.2 for OD, simulate the meteoroid environment from 0.2 to 2 au based on the Grün flux equation, and the debris environment up to 40,000 km altitude from Earth surface, respectively [4,5]. Presentation Focus: The presentation or poster will present the results to-date focusing on the full risk analysis process flow seen in Fig. 1. Details on the derivation of the failure criteria will be discussed, along with HVIT results and how these influenced the MMPS configuration baseline decision. Further, results of hydrocode simulations will be presented and the tuning to HVIT outputs will be described. Finally, the strategies that direct the BLE formulation will be reviewed, specifically, for the EES elements that are most exposed to the MMOD environment.

mmod↗

High Performance Interactive System Dynamics Visualization

This brochure describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.

97 MATHEMATICS AND COMPUTING↗

High Performance Interactive System Dynamics Visualization

This presentation describes a system dynamics simulation (SD) framework that supports an end-to-end analysis workflow that is optimized for deployment on ESIF facilities(Peregrine and the Insight Center). It includes (I) parallel and distributed simulation of SD models, (ii) real-time 3D visualization of running simulations, and (iii) comprehensive database-oriented persistence of simulation metadata, inputs, and outputs.

97 MATHEMATICS AND COMPUTING↗

A New Integrated Analysis Suite for Fast-Ion Study in KSTAR

Here, an integrated workflow for fast-ion analysis was developed by adapting the One Modeling Framework for Integrated Task (OMFIT) workflow manager to support a standard and unified analysis platform for KSTAR users. The newly established analysis suite offers a graphical user interface–based workflow to enable users to readily access and handle experimental data archived in various data formats and servers. Further, users can analyze the data by importing modules designed for conducting certain tasks, such as profile fitting, equilibrium reconstruction, and postprocessing of tokamak data. The procedures for preparing the inputs for fast-ion simulations are streamlined by a common workflow manager, which enables the parallel processing of various tasks to efficiently analyze large fast-ion datasets. The OMFIT platform comprises a flexible Python-based application that enables users to freely manipulate the Python scripts for applications that are unavailable in the standard workflow. The framework also offers mapping tools to translate the output data into the Integrated Modeling and Analysis Suite format to maintain application compatibility for future ITER burning plasma experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Global Sensitivity Analysis for Statistical Model Parameters

Global sensitivity analysis (GSA) is typically used to analyze the influence of uncertain parameters in mathematical models and simulations. In principle, tools from GSA may be extended to analyze the influence of parameters in statistical models. Such analyses may enable reduced or parsimonious modeling and greater predictive capability. Yet, difficulties such as parameter correlation, model stochasticity, multivariate model output, and unknown parameter distributions prohibit a direct application of GSA tools to statistical models. By leveraging a loss function associated with the statistical model, we introduce a novel framework to address these difficulties and enable efficient GSA for statistical model parameters. Theoretical and computational properties are considered and hihglighted on a synthetic example. The framework is applied to a Gaussian process model from the literature, which depends on 95 parameters. Noninfluential parameters are discovered through GSA, and a reduced model with equal or stronger predictive capability is constructed by using only 79 parameters.

97 MATHEMATICS AND COMPUTING↗

Analysis and Optimization of the Characteristics of Superpower Virtode Generation

We report on the results of the 3D numerical simulation of perspective design of an oscillator based on an external-feedback virtual cathode (virtode), as well as optimization of characteristics of its generation. We have analyzed the effect of beam parameters on the virtode output characteristics. In particular, we have obtained the dependence of the efficiency on the feeding voltage and on the current being injected. We have studied the effect of beam velocity premodulation and electron velocity spread on virtode generation characteristics. It has been established that the introduction of premodulation sharply increases the generation efficiency up to 13% for optimal parameters. The possibility of stable generation in the virtode at relatively large spread in electron velocities has been demonstrated.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗