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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 1,423 records · Page 79
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Lightning Charge Retrievals: Dimensional Reduction, LDAR Constraints, and a First Comparison with LIS Satellite Data

A "dimensional reduction" (DR) method is introduced for analyzing lightning field changes (DELTAEs) whereby the number of unknowns in a discrete two-charge model is reduced from the standard eight (x, y, z, Q, x', y', z', Q') to just four (x, y, z, Q). The four unknowns (x, y, z, Q) are found by performing a numerical minimization of a chi-square function. At each step of the minimization, an Overdetermined Fixed Matrix (OFM) method is used to immediately retrieve the best "residual source" (x', y', z', Q'), given the values of (x, y, z, Q). In this way, all 8 parameters (x, y, z, Q, x', y', z', Q') are found, yet a numerical search of only 4 parameters (x, y, z, Q) is required. The DR method has been used to analyze lightning-caused DeltaEs derived from multiple ground-based electric field measurements at the NASA Kennedy Space Center (KSC) and USAF Eastern Range (ER). The accuracy of the DR method has been assessed by comparing retrievals with data provided by the Lightning Detection And Ranging (LDAR) system at the KSC-ER, and from least squares error estimation theory, and the method is shown to be a useful "stand-alone" charge retrieval tool. Since more than one charge distribution describes a finite set of DELTAEs (i.e., solutions are non-unique), and since there can exist appreciable differences in the physical characteristics of these solutions, not all DR solutions are physically acceptable. Hence, an alternative and more accurate method of analysis is introduced that uses LDAR data to constrain the geometry of the charge solutions, thereby removing physically unacceptable retrievals. The charge solutions derived from this method are shown to compare well with independent satellite- and ground-based observations of lightning in several Florida storms.

Koshak, W. J.↗
DOE OSTI2020

Using Machine Learning to Predict Future Temperature Outputs in Geothermal Systems

Optimizing the power output, and economic value, of geothermal power plants over decades of operation is a major challenge in renewable energy. Optimizing the output requires the ability to predict the mass flow rates and the output temperatures of production wells based on the inputs of injection wells, as well as the time history of the system. Machine Learning (ML) that incorporates the known physics of geothermal systems is one possible solution to this challenge. In this work, we explore the ability of ML algorithms to predict future temperature outputs based on historical data. Considering the challenges with obtaining an empirical dataset from field data that is large enough to enable reliable ML, we propose an alternate approach: developing a high-fidelity reservoir model and using computational resources to build a dataset that enables ML. As a first step towards achieving this goal, we present preliminary results from applying ML to predict the temperature timeseries of simple modeled geothermal systems. We describe the application of relevant state-of-the-art ML approaches, such as the Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), to extract temporal structures in the model data. We assess the accuracy of the forecasts we obtain, compare the selected approaches, and share the lessons learned that would inform the process of training and utilizing ML algorithms for larger and more complex geothermal systems.

GEOTHERMAL ENERGY↗
NASA NTRS1991

Analysis of navigation performance for the Earth Observing System (EOS) using the TDRSS Onboard Navigation System (TONS)

Use of the Tracking and Data Relay Satellite System (TDRSS) Onboard Navigation System (TONS) was proposed as an alternative to the Global Positioning System (GPS) for supporting the Earth Observing System (EOS) mission. The results are presented of EOS navigation performance evaluation with respect to TONS based orbit, time, and frequency determination (OD/TD/FD). Two TONS modes are considered: one uses scheduled TDRSS forward link service to derive one way Doppler tracking data for OD/FD support (TONS-I); the other uses an unscheduled navigation beacon service (proposed for Advanced TDRSS) to obtain pseudorange and Doppler data for OD/TD/FD support (TONS-II). Key objectives of the analysis were to evaluate nominal performance and potential sensitivities, such as suboptimal tracking geometry, tracking contact scheduling, and modeling parameter selection. OD/TD/FD performance predictions are presented based on covariance and simulation analyses. EOS navigation scenarios and the contributions of principal error sources impacting performance are also described. The results indicate that a TONS mode can be configured to meet current and proposed EOS position accuracy requirements of 100 and 50 m, respectively.

Elrod, B.↗
DOE OSTI2019

Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques

Optimization of simulation-based or data-driven systems is a challenging task, which has attracted significant attention in the recent literature. A very efficient approach for optimizing systems without analytical expressions is through fitting surrogate models. Due to their increased flexibility, nonlinear interpolating functions, such as radial basis functions and Kriging, have been predominantly used as surrogates for data-driven optimization; however, these methods lead to complex nonconvex formulations. Alternatively, commonly used regression-based surrogates lead to simpler formulations, but they are less flexible and inaccurate if the form is not known a priori. In this work, we investigate the efficiency of subset selection regression techniques for developing surrogate functions that balance both accuracy and complexity. Subset selection creates sparse regression models by selecting only a subset of original features, which are linearly combined to generate a diverse set of surrogate models. Five different subset selection techniques are compared with commonly used nonlinear interpolating surrogate functions with respect to optimization solution accuracy, computation time, sampling requirements, and model sparsity. Furthermore, our results indicate that subset selection-based regression functions exhibit promising performance when the dimensionality is low, while interpolation performs better for higher dimensional problems.

42 ENGINEERING↗
NASA NTRSDate not supplied

Multilevel Probit Regression for 3-Alternative Forced Choice Audibility Testing

A recent psychoacoustic test at NASA Langley generated a dataset of 3-alternative forced-choice responses for 40 subjects that measured the audibility of a tone complex in a shaped broadband masker. The task was completed by 4 subjects at a time in a small theatre-like environment using predetermined stimuli levels. These data were subject to 4 forms of probit regression: a “complete pooling” analysis in which all data from the test was fit with one curve, two forms of “no pooling” analyses in which subjects’ data were treated individually (using both packaged and custom software), and a “partial pooling” analysis in which multilevel-regression software fit both individual curves as well as population-level parameters at the same time. The results of the analyses are compared in terms of both individual- and population-level parameters. Partial pooling appears to give the most consistent results at both levels, as well as provide the most robustness among the packaged approaches (albeit with added complexity over single-level regression). These results are also contained in a recent NASA technical memorandum entitled “Comparisons of Analysis Methods Applied to Alternative Forced Choice Audibility Data.”

Psychoacoustics↗
DOE OSTI2016

Advanced Computational Framework for Environmental Management ZEM, Version 1.x

Typically environmental management problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly integration into a series of models with different complexity for various types of model analyses where the data are applied as soft and hard model constraints. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions). To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian- Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org). The ZEM framework is open-source and can be applied to any environmental management site. The framework will be open-source and released under GPL V3 license.

Vesselinov, VelimirV.↗
DOE OSTI2016

ZEM: Integrated Framework for Real-Time Data and Model Analyses for Robust Environmental Management Decision Making - 16438

Typically environmental management (EM) problems require analysis of large and complex data sets originating from concurrent data streams with different data collection frequencies and pedigree. These big data sets require on-the-fly analysis, assimilation and integration into a series of models with different complexity for various types of model-based analyses. This is needed to provide fast iterative model analyses based on the latest available data to guide decision-making. Furthermore, the data and model are associated with uncertainties. The uncertainties are probabilistic (e.g. measurement errors) and non-probabilistic (unknowns, e.g. alternative conceptual models characterizing site conditions). To address all of these issues, we have developed an integrated framework for real-time data and model analyses for environmental decision-making called ZEM. The framework allows for seamless and on-the-fly integration of data and modeling results for robust and scientifically-defensible decision-making applying advanced decision analyses tools such as Bayesian-Information-Gap Decision Theory (BIG-DT). The framework also includes advanced methods for optimization that are capable of dealing with a large number of unknown model parameters, and surrogate (reduced order) modeling capabilities based on support vector regression techniques. The framework is predominantly coded in Julia, a state-of-the-art high-performance programing language (http://julialang.org) and incorporates the MADS computational tools (Model Analysis and Decision Support; http://mads.lanl.gov). We demonstrate the application of ZEM for data and model analyses at the LANL Chromium site. The ZEM framework will be released as open-source code and can be applied to any environmental management site. (authors)

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

Efficient data-driven models for prediction and optimization of geothermal power plant operations

Increasing the capacity of geothermal energy as a renewable resource calls for development and deployment of efficient control and optimization technologies for geothermal power plants. A data-driven prediction and optimization model is presented as a cost-effective and efficient alternative to physics-based approach. The model predicts power output and operational cost by propagating the influence of control and disturbance variables within an artificial neural network (ANN). Numerical experiments with simulated and field data from a real geothermal power plant are first used to demonstrate the prediction performance of the ANN model. The model is then adopted to maximize the net predicted power production by automatically adjusting the working fluid circulation rate. The optimization performance of the model in evaluated using a thermodynamic flowsheet simulation model. The workflow is applied to model and control the effect of ambient temperature on an air-cooled binary cycle power plant, which is complex and costly to perform using a physics-based predictive model. As a result, the performance of the method is demonstrated by applying it to both simulated and field datasets from a binary cycle geothermal power plant.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hydrogen-Based Energy Storage Systems for Large-Scale Data Center Applications

Global demand for data and data access has spurred the rapid growth of the data center industry. To meet demands, data centers must provide uninterrupted service even during the loss of primary power. Service providers seeking ways to eliminate their carbon footprint are increasingly looking to clean and sustainable energy solutions, such as hydrogen technologies, as alternatives to traditional backup generators. In this viewpoint, a survey of the current state of data centers and hydrogen-based technologies is provided along with a discussion of the hydrogen storage and infrastructure requirements needed for large-scale backup power applications at data centers.

08 HYDROGEN↗
DOE OSTI2013

Nuclear Energy Return on Energy Investment

This report provides a methodology and requisite data to assess the potential Energy Return on (Energy) Investment (EROI) for nuclear fuel cycle alternatives, and applies that methodology to an example “once-through” fuel cycle using low enrichment uranium (LEU) in conventional light water cooled reactors (LWRs). Within the DOE Office of Nuclear Energy Science and Technology - Fuel Cycle Technologies program, the Fuel Cycle Options Campaign “performs integrating analyses of nuclear energy and fuel cycle systems to inform fuel cycle R&D, programmatic decisions, strategy formulation, and policy development”. Campaign objectives include development of relevant fuel cycle metrics, and development of tools and associated data for analysis of fuel cycle systems. This study represents an extension of a prior evaluation of EROI as a metric for fuel cycle facilities, processes and technologies. That prior study [Simon 2011] addressed the energy return on the addition of fuel recycle to an existing nuclear energy system. Limited to just the addition of fuel recycle, that study did not include all the energy investments required to create, operate and decommission the underlying nuclear fuel cycle, such as uranium mining, fuel fabrication, reactor construction, and used fuel disposition. This extension of the prior work adds these remaining pieces of the fuel cycle to provide a basic evaluation framework and initial data to enable evaluation of EROI for nuclear energy in general. It is intended as a basis for evaluation of alternative fuel cycle options in the future with the addition of pertinent details for other fuel cycle scenarios. In the prior work, a spreadsheet tool was constructed for the specific purpose of this energy input/output analysis of nuclear fuel cycle facilities. This tool allows the user to enter the parameters of the nuclear fuel cycle and assumptions about energy use in reprocessing. In the current analysis, energy consumption for an entire nuclear energy enterprise is considered, including facility construction, materials, facility operation, and decommissioning. The analysis tool has been extended to include the entire fuel cycle for a representative scenario of a once-through LWR nuclear energy system. Energy content data have been developed for an initial representation. In many cases where there is a valid range of plausible energy content, the range is discussed and a representative value selected for demonstration of the evaluation methodology. In this analysis, energy produced is the energy output of power reactors within the fuel cycle. The spreadsheet calculates both Primary EROI and Final EROI for the fuel cycle entered. The EROI is the ratio of the output energy divided by the consumed energy. The intent for this study was to develop the methodology and analysis tool for a complete fuel cycle. It is not the intent of the study to fully explore the wide range of potential energy intensities, or to reconcile the disparate values found in the literature. Representative numbers were used in this demonstration. The methodology and tool do provide a framework for future exploration of the key energy intensity values and for conducting sensitivity studies on specific values, either to assess improved understanding of the values, or to explore the potential for alternative technologies to impact EROI. The Primary and Final Energy EROI values calculated for the representative scenario were ~ 52 and 24, respectively.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Oak Ridge Response to Versatile Test Reactor Environmental Impact Statement Data Request

The Versatile Test Reactor (VTR) is a fast-spectrum test reactor being developed in the United States under the direction of the US Department of Energy Office of Nuclear Energy (DOE-NE). The VTR mission is to enable accelerated testing of advanced reactor fuels and materials required for advanced reactor technologies. The conceptual design of the 300 MWth sodium-cooled metallic-fueled pool-type fast reactor has been led by the US National Laboratories in collaboration with General Electric–Hitachi and Bechtel National, Inc. In support of the VTR project, DOE issued a Notice of Intent (NOI) in the Federal Register on August 5, 2019, announcing the intent to prepare an Environmental Impact Statement (EIS) in accordance with the National Environmental Policy Act (NEPA) and its implementing regulations. The EIS will evaluate alternatives for a versatile reactor–based fast-neutron source facility and associated facilities for the preparation, irradiation, and post-irradiation examination (PIE) of test/experimental fuels and materials. Specifically, the NOI identified two siting alternatives for the VTR reactor facility: Idaho National Laboratory (INL) or Oak Ridge National Laboratory (ORNL). In addition, the NOI also specified two siting alternatives for VTR fuel fabrication: INL and the Savannah River Site (SRS). This report provides information in response to data requests made to ORNL to fill in site-specific knowledge gaps to develop a high-quality EIS. The responses provided are not required to provide full details in every aspect; instead, they adequately bound possible environmental impacts or provide sufficient information to adequately assess likely environmental impacts. This work is being performed under a subcontract from INL to ORNL using DOE-NE funds and is directed by DOE-NE and DOE-ID. Leidos has been contracted by DOE-NE to write the VTR EIS, so most data requests have come from Leidos but were often routed through INL or DOE-ID. DOE-ID is overseeing the NEPA and EIS processes for the VTR project. Leidos will use the information provided in this report to inform the VTR EIS and will also cite this document to establish a clear, publicly available source of the information. Section 2 of this report briefly describes the proposed ORNL VTR Alternative and illustrates the location of the proposed site for the ORNL VTR Alternative. Sections 3 through 7 provide direct responses to data requests received by ORNL. These sections use a tabular format in which data requests are divided into separate items to be addressed; the items are numbered, the data requests are restated with more topical information included, and then the responses are provided. Initial data requests and follow-on requests for additional information (RAIs) are combined under the original data request fields. Finally, Section 8 presents summarized conclusions and describes future work.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Leveraging Human Performance Data to Change the Narrative that People are the Safety Problem

The study of errors and failure has a long and productive history in the behavioral sciences. By studying how systems fail, we rule out various mechanisms for how those systems might work, thereby refining our theories of how they actually work. Human performance, however, includes more than errors; human performance comprises both failures and successes. A systematic bias to collect and analyze data only on error affects the decisions we make as a community by promoting the narrative that “people are the safety problem.” This narrative manifests in both obvious and subtle ways in the design of systems intended for human use. When the only safety data that are available are about human failure, then “data-driven” designs can only consider that humans fail. Changing this narrative will depend on new data and new ways to examine data – specifically, data on the processes by which human create and contribute to safety. An alternate narrative is that people represent a primary source of safety, through their capability to anticipate, monitor for, respond to, and learn from expected and unexpected change. This presentation will describe research efforts to expand the range of safety-relevant events to include not just rare safety failures but frequent safety successes. These efforts include use of data from both operations and simulations to develop methods and metrics for learning from structured observation, self-report, and system data.

Jon Holbrook↗
NASA NTRS1994

Reanalysis of X-ray emission from M87. 2: The multiphase medium

In a previous paper, we showed that a single-phase model for the gas around M87 simultaneously explained most available X-ray data. Total enclosed masses derived from the model, however, fell well below the determinations from optical measurements. In this paper, we consider possible solutions to the inconsistency, including two multiphase medium models for the gas and the consequences of systematic errors of the Einstein Focal Point Crystal Spectrometer (FPCS). First, we find that when constraints from optical mass determinations are not considered, the best-fit model to the X-ray data is always the single-phase model. Multiphase models or consideration of FPCS systematic errors are required only when optical mass constraints are included. We find that the cooling time model of White & Sarazin adequately explains the available X-ray data and predicts total masses which agree with optical measurements. An ad hoc power-law multiphase does not. This shows both that the existence of mass dropping out of the ambient phase is consistent with the data and that the cooling-time model gives a reasonable parameterization of the dropout rate. Our derived mass accretion rate is similar to previous determinations. The implications of this result for cluster mass determinations in general are discussed. We then consider 'self absorbing' models where we assume that material dropping out of the ambient medium goes completely into X-ray absorbing gas. The resulting internal absorption is small compared to Galactic absorption at most radii. The models are therefore indistinguishable from models with only Galactic absorption. We finally show that it is alternatively possible to simultaneously fit optical mass measurements and X-ray data with a single-phase model if some of the observed FPCS line fluxes are too high by the maximum systematic error. This possiblity can be checked with new data from satellites such as ASCA.

Tsai, John C.↗
DOE OSTI2012

Review of comparative LCAs of food waste management systems - Current status and potential improvements

Highlights: Black-Right-Pointing-Pointer GHG-emissions from different treatment alternatives vary largely in 25 reviewed comparative LCAs of bio-waste management. Black-Right-Pointing-Pointer System-boundary settings often vary largely in reviewed studies. Black-Right-Pointing-Pointer Existing LCA guidelines give varying recommendations in relation to several key issues. - Abstract: Twenty-five comparative cycle assessments (LCAs) addressing food waste treatment were reviewed, including the treatment alternatives landfill, thermal treatment, compost (small and large scale) and anaerobic digestion. The global warming potential related to these treatment alternatives varies largely amongst the studies. Large differences in relation to setting of system boundaries, methodological choices and variations in used input data were seen between the studies. Also, a number of internal contradictions were identified, many times resulting in biased comparisons between alternatives. Thus, noticed differences in global warming potential are not found to be a result of actual differences in the environmental impacts from studied systems, but rather to differences in the performance of the study. A number of key issues with high impact on the overall global warming potential from different treatment alternatives for food waste were identified through the use of one-way sensitivity analyses in relation to a previously performed LCA of food waste management. Assumptions related to characteristics in treated waste, losses and emissions of carbon, nutrients and other compounds during the collection, storage and pretreatment, potential energy recovery through combustion, emissions from composting, emissions from storage and land use of bio-fertilizers and chemical fertilizers and eco-profiles of substituted goods were all identified as highly relevant for the outcomes of this type of comparisons. As the use of LCA in this area is likely to increase in coming years, it is highly relevant to establish more detailed guidelines within this field in order to increase both the general quality in assessments as well as the potentials for cross-study comparisons.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1981

Structure, composition and thermal state of the crust in Brazil

Aeromagnetic survey data of the Minas Gerais areas were analyzed and compared with MAGSAT anomaly data. Initially the data set was used to determine the thickness of the magnetized crust. The value obtained is in agreement with crustal models derived from deep seismic sounding and gravity data, indicating that the bottom of the magnetized layer occurs close to the Moho transition. An analysis of the long wavelength pattern indicates that the anomalies form a series of lineations along the magnetic E-W. These are shown to be the result of attenuation. On the MAGSAT total intensity map localized anomalies are superimposed onto an anomaly background formed by alternating positive and negative patches. Implementation and testing of analysis software for MAGSAT data are also discussed.

Pacca, I. I. G.↗
NASA NTRS1991

Some practical universal noiseless coding techniques, part 3, module PSl14,K+

The algorithmic definitions, performance characterizations, and application notes for a high-performance adaptive noiseless coding module are provided. Subsets of these algorithms are currently under development in custom very large scale integration (VLSI) at three NASA centers. The generality of coding algorithms recently reported is extended. The module incorporates a powerful adaptive noiseless coder for Standard Data Sources (i.e., sources whose symbols can be represented by uncorrelated non-negative integers, where smaller integers are more likely than the larger ones). Coders can be specified to provide performance close to the data entropy over any desired dynamic range (of entropy) above 0.75 bit/sample. This is accomplished by adaptively choosing the best of many efficient variable-length coding options to use on each short block of data (e.g., 16 samples) All code options used for entropies above 1.5 bits/sample are 'Huffman Equivalent', but they require no table lookups to implement. The coding can be performed directly on data that have been preprocessed to exhibit the characteristics of a standard source. Alternatively, a built-in predictive preprocessor can be used where applicable. This built-in preprocessor includes the familiar 1-D predictor followed by a function that maps the prediction error sequences into the desired standard form. Additionally, an external prediction can be substituted if desired. A broad range of issues dealing with the interface between the coding module and the data systems it might serve are further addressed. These issues include: multidimensional prediction, archival access, sensor noise, rate control, code rate improvements outside the module, and the optimality of certain internal code options.

Rice, Robert F.↗