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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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Ensuring Safe Decision-Making on the Moon and Mars: Cognitive Performance Assessment for Exploration Class Mission EVA

Extravehicular activity (EVA) is one of the most dangerous and cognitively demanding actions that astronauts can execute, and the cognitive demands associated with future partial gravity EVAs on the Moon and Mars are expected to be higher compared to microgravity EVAs currently conducted from the International Space Station. Decrements in cognitive performance present an important risk to crew safety during exploration mission class EVA. Yet there is currently insufficient data to characterize cognitive performance prior to, during, and following EVA. Furthermore, it is still unclear which cognitive domains are most important for conducting mission critical decisions with crew safety implications. To address this gap, we conducted a cognitive task analysis (CTA) of EVA to characterize the procedures, the cognitive demands required, and the critical safety decisions associated with decrements in cognitive performance. We conducted a cognitive task analysis with 15 astronauts and subject matter experts in EVA operations and research. Interviews focused on surface exploration EVA and elicited feedback from experts on the cognitive skills required for specific EVA tasks, including cognitive strategies, critical cues, and decision-making strategies. A cognitive demands table was assembled to consolidate and synthesize the information from all interviews. The information from this cognitive task analysis informs ongoing exploration EVA for Moon to Mars. This work identifies the specific cognitive challenges that astronauts are likely to encounter during surface exploration EVA, and provides the foundation for: (1) prioritized and targeted cognitive performance measurement and functional performance tests, (2) EVA simulation design at varying levels of cognitive workload, and (3) the development of training and other technologies that can improve safe decision-making and inform EVA planning on future spaceflight missions to the Moon and Mars.

Steven R Anderson↗
DOE OSTI2013

Method and apparatus for distributed intrusion protection system for ultra high bandwidth networks

A method for providing security to a network having a data stream with a plurality of portions of data, each having differing levels of sensitivity. The data stream is interrogated to determine the presence of predetermined characteristics associated with at least one of the portions of data within the data stream. At least one of the portions of data is then characterized, based upon the portion of data exhibiting a predetermined combination of characteristics, wherein the predetermined combination of characteristics is related to the sensitivity of the portion of data. The portions of the data stream are then distributed into a plurality of different channels, each of the channels associated with different level of sensitivity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Server-side Log Data Analytics for I/O Workload Characterization and Coordination on Large Shared Storage Systems

Inter-application I/O contention and performance interference have been recognized as severe problems. In this work, we demonstrate, through measurement from Titan (world s No. 3 supercomputer), that high I/O variance co-exists with the fact that individual storage units remain under-utilized for the majority of the time. This motivates us to propose AID, a system that performs automatic application I/O characterization and I/O-aware job scheduling. AID analyzes existing I/O traffic and batch job history logs, without any prior knowledge on applications or user/developer involvement. It identifies the small set of I/O-intensive candidates among all applications running on a supercomputer and subsequently mines their I/O patterns, using more detailed per-I/O-node traffic logs. Based on such auto- extracted information, AID provides online I/O-aware scheduling recommendations to steer I/O-intensive applications away from heavy ongoing I/O activities. We evaluate AID on Titan, using both real applications (with extracted I/O patterns validated by contacting users) and our own pseudo-applications. Our results confirm that AID is able to (1) identify I/O-intensive applications and their detailed I/O characteristics, and (2) significantly reduce these applications I/O performance degradation/variance by jointly evaluating out- standing applications I/O pattern and real-time system l/O load.

Liu, Y.↗
DOE OSTI2018

Importance Sampling with Analog Scenarios for Stochastic Economic Dispatch

Increasing penetrations of renewable energy sources such as wind into power grids motivates the investigation of new approaches to 5-minute economic dispatch computation. In this poster, we investigate the application of importance sampling in the two-stage stochastic economic dispatch problem while using historical data to characterize uncertainty. In our numerical experiments, we observe that importance sampling performs better than the standard Monte Carlo sampling approach in preventing loss-of-load. While first-stage costs are comparable for both sampling methods, the second-stage costs are significantly reduced when the importance sampling method is employed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Single-object Imaging and Spectroscopy to Enhance Dark Energy Science from LSST

Single-object imaging and spectroscopy on telescopes with apertures ranging from ~4 m to 40 m have the potential to greatly enhance the cosmological constraints that can be obtained from LSST. Two major cosmological probes will benefit greatly from LSST follow-up: accurate spectrophotometry for nearby and distant Type Ia supernovae will expand the cosmological distance lever arm by unlocking the constraining power of high-z supernovae; and cosmology with time delays of strongly-lensed supernovae and quasars will require additional high-cadence imaging to supplement LSST, adaptive optics imaging or spectroscopy for accurate lens and source positions, and IFU or slit spectroscopy to measure detailed properties of lens systems. We highlight the scientific impact of these two science drivers, and discuss how additional resources will benefit them. For both science cases, LSST will deliver a large sample of objects over both the wide and deep fields in the LSST survey, but additional data to characterize both individual systems and overall systematics will be key to ensuring robust cosmological inference to high redshifts. Community access to large amounts of natural-seeing imaging on ~2-4 m telescopes, adaptive optics imaging and spectroscopy on 8-40 m telescopes, and high-throughput single-target spectroscopy on 4-40 m telescopes will be necessary for LSST time domain cosmology to reach its full potential. In two companion white papers we present the additional gains for LSST cosmology that will come from deep and from wide-field multi-object spectroscopy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Flexible and Adaptive Malware Identification Using Techniques from Biology

The holy grail in cyber analytics is to find new ways to understand the information we already have access to. One way to do that is to characterize the data into reasonable sizes and then leverage any known information to generate new insights. Biologists have been using a similar process for decades. This paper introduces the MLSTONES tool set that was developed by leveraging biology and bioinformatics, high performance computing, and statistical algorithms applied to cyber data and specifically to malware. Furthermore, the paper discusses the tool suite, its applications, and how it compares or can work with other related tools.

Peterson, Elena S.↗
DOE OSTI2023

AN OVERVIEW OF THE DESIGN AND PERFORMANCE TESTING OF A 275 BAR INTEGRALLY GEARED SUPERCRITICAL CO2 COMPRESSOR FOR POWER GENERATION

An integrally geared compressor-expander (compander) for supercritical CO2 (sCO2) was developed to convert thermal energy to electricity. With operating pressures of 275 bar, this turbomachine represents the state of the art in integrally geared machine architecture, hitting compressor inlet densities of 600 kg/m3 and discharge pressures of 275 bar. The product development was funded by the Department of Energy’s Energy Efficiency and Renewable Energy Office under EE0007114 to advance the state of the art in concentrated solar power applications; however, the technology itself is agnostic to heat source, and is predicted to achieve a thermal-to-electric conversion efficiency of 50% for any indirect heat source capable of providing turbine inlet temperatures of 700°C or greater. While the program encompassed the design and testing for the compressor and turbine elements, this paper will focus on the compressor design and operation. The paper will begin with an introduction to the cycle design and analysis of an indirectly heated integrally geared sCO2 compander, a discussion of the mechanical and aerodynamic design of the compressor that is a two stage radial compressor operating subcritically at 27,512 rpm, and will then proceed to describe the test facility and measured data to characterize the performance and robustness of the machine. The paper will conclude with a discussion on lessons learned throughout the course of commissioning and testing. Practical aspects of testing a compressor operating near the dome including complications relating to obtaining an accurate compressor flow map, efficiency calculations, flow unsteadiness, and associated measurement uncertainties will be discussed.

wilkes, jason↗
DOE OSTI2021

Characterizing Signatures of Geothermal Exploration Data with Machine Learning Techniques: An Application to the Nevada Play Fairway Analysis

We are introducing machine learning methods to the play fairway analysis to generate geothermal potential maps to support the evaluation of geothermal resource potential and the exploration for undiscovered blind geothermal systems in the Nevada Great Basin region. Our project aims to identify new ways to combine the play fairway data and empirically organize relationships between feature weights and labels in an improved workflow. As a means of doing this, we introduce machine learning methods to evaluate the influence of certain geological and geophysical features/feature sets in predicting geothermal favorability. This report highlights promising approaches based on supervised and unsupervised learning methods. First, we demonstrate a filter method applied to supervised classification modeling. The supervised filter method is based on permutation analysis to evaluate every possible feature combination/drop out scenario and rank feature influence based on the performance variance of supervised classification models. Additionally, we present an unsupervised factor analysis based on principal component analysis coupled with a semi-supervised kmeans clustering algorithm. This analysis allows us to identify the optimal number of groups/clusters for training sites and structural settings to identify feature patterns including correlation, variance, and latent and dominant feature relationships. The results from these methods offer a promising avenue for identifying favorable sources of predictive information to identify the locations of blind geothermal systems and furthering our understanding of complex geothermal feature and label relationships in the Great Basin region and beyond.

15 GEOTHERMAL ENERGY↗
DOE OSTI2016

Further Investigation of Employing Binomial Small-Sample Estimator in M-LROM

Reduced Order Modeling (ROM) is essential to enable the identification of the so-called active parameter subspace for high-fidelity models that are employed in computationally intensive analyses such as uncertainty characterization and data assimilation. Given the complexity of models for real-world application, brute force methods to identify the active subspace may not be computationally efficient to realize the value of reduction, since in realistic problems, the full scale model must be executed few hundred times, which may be computationally taxing. Earlier work has introduced a new approach, which represents a further development of our previous work on ROM, referred to as multi-level ROM (M-LROM). In M-LROM, approximate models are employed to identify the active subspace instead of the full scale model. In this summary, we present our recent results on M-LROM, specifically in regard to the construction of reliable error metrics that can bound the errors resulting from the reduction. The idea is to employ a lower fidelity model, i.e., an approximation of the full scale or high fidelity model, to build the active subspace, and via the proposed error bound, the quality of the active subspace can be determined by calculating an upper bound on the errors resulting from the reduction. For this approach to be practical, the error bound must be realistic, implying that it should be close to the actual errors expected, and not much higher. Therefore, an important goal of this summary is to compare different error bounds in terms of the relation between their estimated bounds to the actual errors. In particular, we compare the proposed error bound which numerically construct an estimator using binomial distribution to the widely used small-sample norm estimators. Unlike most probabilistic error estimation methods, the proposed bound employs a binomial distribution to construct the estimator whereas in other conventional methods, the distribution employed is typically the standard Gaussian, normalized over a unit hypersphere as initially proposed by the seminal paper of J. Dixon on random matrix theory. Previous publications provided evidence that the binomial distribution calculates more realistic bounds as compared to the Gaussian distribution, which tends to be conservative, implying that the bound is much bigger than the actual errors, which diminishes the value of the reduction, as now must increase the size of the active subspace to reach the desired upper bound on the reduction errors. This work has provided a rigorous approach by which error bounds on the reduction errors can be established without having to execute the high fidelity model during the construction of the active subspace. The high fidelity model is only need to test the error bound using small number of executions. This methodology paves the way for a more efficient application of ROM to complex engineering models, where standard ROM techniques may be too taxing to optimally realize their value in practical situations. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Sensitivity studies to Support TEX critical experiments design

In the frame of the ongoing Thermal/Epithermal experiments program, supported by the DOE-NCSP, we are characterizing expected data with analysis of sensitivity coefficients to nuclear data. The computations were performed for detailed and simplified models of the experimental configurations in order to demonstrate their invariance with respect to small geometry variations. The sensitivity coefficients were computed with recently developed precise methodologies using Monte Carlo tools (MCNP-6.1, SCALE 6.1 and MORET 5.C.1) with the following nuclear data libraries: ENDF/B-VII.0, ENDF/B-VII.1, and JEFF3.1. Since the considered experiments are devoted to tantalum data validation, their neutronic characteristics are compared with the relevant integral experiment data collected in the OECD/NEA ICSBEP Handbook. Although analysis of sensitivity coefficients taken alone is insufficient for an entire assessment, we can conclude that TEX experimental results can fill gaps in the available benchmarks to facilitate quantification of uncertainty for plutonium containing systems with epithermal neutron spectra (e.g. wet plutonium-based powders). However, analysis of sensitivity profiles demonstrates that an advanced statistical methodology will be needed to fit tantalum nuclear data or to evaluate the associated uncertainties. (authors)

42 ENGINEERING↗
DOE OSTI2024

Bayesian Attack Model (BAM)

The Bayesian Attack Model (BAM) is an analytical tool designed to enhance the comprehension of adversarial activity in OT environments. BAM leverages both expert cybersecurity insights and historical data to characterize the likelihood of adversarial behavior given anomalous observable events.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Visualization and Quantification of Wind Induced Variability in Hydrogen Clouds Following Releases of Liquid Hydrogen: Preprint

Well characterized experimental data for consequence model validation is important in progressing the use of liquid hydrogen as an energy carrier. In 2019, the Health and Safety Executive (HSE) undertook a series of liquid hydrogen dispersion and combustion experiments as a part of the Pre-normative Research into the Safe Use of Liquid Hydrogen (PRESLHY) project. In partnership between the National Renewable Energy Laboratory (NREL) and HSE, time and spatially varying hydrogen concentration measurements were made in 25 dispersion experiments and 23 congested ignition experiments associated with PRESLHY WP3 and WP5, respectively. These measurements were undertaken using the hydrogen wide area monitoring system developed by NREL. During the 23 congested ignition experiments, high variability was observed in the measured explosion severity during experiments with similar initial conditions. This led to the conclusion that wind, including localized gusts, had a large influence on the dispersion of the hydrogen, and therefore the quantity of hydrogen that was present in the congested region of the explosions. Using the hydrogen concentration measurements taken immediately prior to ignition, the hydrogen clouds were visualized in an attempt to rationalize the variability in overpressure between the tests. Gaussian process regression was applied to quantify the variability of the measured hydrogen concentrations. This analysis could also be used to guide modifications in experimental designs for future research on hydrogen combustion behavior.

HSR&D↗
DOE OSTI2025

Assessment of Buffer-IPyC Thermomechanical Debonding Behavior Using New Experiment Strength Data in BISON

TRIstructural ISOtropic (TRISO) single sub-millimeter-diameter nuclear fuel particle consists of a spherical fuel kernel surrounded by four coating layers. During reactor operation, the TRISO coating layers are subjected to irradiation-induced dimensional changes and the associated thermomechanical behavior of each layer. One of the observed behaviors is gap formation between the buffer and IPyC layer due to the porous buffer’s irradiation-induced shrinkage exceeding that of the IPyC layer. To predict TRISO thermomechanical performance, multi-physics models have been built that are continually updated and refined. The BISON code is a finite element-based nuclear fuel performance code that may be used for TRISO particle simulations. One of the recent additions to the BISON code is the ability to model the process of layer debonding. This paper will focus on the simulation results of the improved BISON debonding model that will utilize updated strengths measured from irradiated TRISO fuel particles. The new experimental strength data from micromechanical tests of irradiated TRISO fuel samples were exercised in the BISON simulations and compared to baseline strength data to assess their applicability in the models. This also includes updated buffer-IPyC bond strengths to simulate layer delamination. Based on current experimental observations it is noted that the buffer-IPyC separation occurs not exactly at the junction of these two layers, but more on the side of the buffer layer. This improved modeling approach using experimental strength data to characterize buffer-IPyC debonding and its potential subsequent cracking will be presented in the paper along with comparisons to available experimental observations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Assessment of buffer-IPyC thermomechanical debonding behavior using new experimental strength data in BISON

TRIstructural ISOtropic (TRISO) fuel is a nuclear fuel commonly used in High Temperature Gas-cooled Reactors (HTGRs). A single sub-millimeter-diameter TRISO fuel particle consists of a spherical fuel kernel surrounded by four coating layers: a low-density pyrocarbon buffer layer, an inner pyrolytic carbon (IPyC) layer, a silicon carbide (SiC) layer, and an outer pyrolytic carbon (OPyC) layer. The kernel is commonly made of UO2 or a mixture of uranium carbide and uranium oxide (UCO). During reactor operation, the TRISO coating layers are subjected to irradiation-induced dimensional changes and the associated thermomechanical behavior of each layer. One of the observed behaviors is gap formation between the buffer and IPyC layer due to the porous buffer’s irradiation-induced shrinkage exceeding that of the IPyC layer. Not all irradiated particles will experience buffer-IPyC gap formation. The debonding may be partial, or it may be nearly total. However, from post-irradiation examination of UCO TRISO fuels irradiated as part of the Advanced Gas Reactor (AGR) Fuel Development and Qualification Program, it was concluded that partial buffer-IPyC debonding was the most common type of buffer-IPyC interaction. To predict TRISO thermomechanical performance, multi-physics models have been built that are being continually updated and refined. The BISON code is a finite element-based nuclear fuel performance code that may be used for 1D, 2D, and 3D TRISO particle simulations. This code is used to calculate fuel temperature, kernel swelling, buffer densification, thermal and irradiation creep, fracture, and fission gas production and release. One of the recent additions to the BISON code is the ability to model the process of layer debonding. This paper will focus on the simulation results of the improved BISON debonding model that will utilize updated strengths measured from irradiated AGR TRISO fuel particles. The new experimental strength data from micromechanical tests of irradiated TRISO fuel samples were exercised in the BISON simulations and compared to baseline strength data to assess their applicability in the models. This also includes updated buffer-IPyC bond strengths to simulate layer delamination. Based on current experimental observations it is noted that the buffer-IPyC separation occurs not exactly at the junction of these two layers, but more on the side of the buffer layer. That observation is also implemented in the TRISO interface debonding model. This improved modeling approach using experimental strength data to characterize buffer-IPyC debonding and its potential subsequent cracking will be presented in the paper along with comparisons to available experimental observations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Spatial and Temporal Characterization of Activity in Public Space, 2019–2020

The data reported here characterize spatial and temporal variation in the ratio of short-to-long-duration visits in public places (i.e., points of interest) in the United States for each week between January 2019 and December 2020. The underlying data on anonymized and aggregated foot traffic to public places is curated by SafeGraph, a geospatial data provider. In this work, we report the estimated number and duration of “short” (i.e., <4 hours) and “long” (i.e., >4 hours) visits to public places at the US census block group level. Long visits are shown to be a good proxy for workers based on formal economic data. We propose that short visits are more likely to represent nonobligate activities: people visiting a public place for leisure, shopping, entertainment, or civic or cultural engagement. Our work constructs a ratio of short to long visits, which can be used to inform population estimates for nonworker use of public space. These data may be useful for understanding how people’s use of public space has changed during the COVID-19 pandemic and, more generally, for understanding activity patterns in public.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2010

Characterizing DSN System Frequency Stability with Spacecraft Tracking Data

This paper describes a recent effort in characterizing frequency stability performance of the ground system in the NASA Deep Space Network (DSN). Unlike the traditional approach where performance is obtained from special calibration sessions that are both time consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This method significantly increases the amount of data available for analysis, roughly by two orders of magnitude; making it possible to conduct trend analysis with reasonable confidence. Since the system is monitored daily, any significant variation in performance can be detected timely. This helps the DSN maintain its performance commitment to customers.

performance analysis↗