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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 1,153 records · Page 64

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NASA NTRS2010

Space Shuttle Probabilistic Risk Assessment (SPRA) Iteration 3.2

The Shuttle is a very reliable vehicle in comparison with other launch systems. Much of the risk posed by Shuttle operations is related to fundamental aspects of the spacecraft design and the environments in which it operates. It is unlikely that significant design improvements can be implemented to address these risks prior to the end of the Shuttle program. The model will continue to be used to identify possible emerging risk drivers and allow management to make risk-informed decisions on future missions. Potential uses of the SPRA in the future include: - Calculate risk impact of various mission contingencies (e.g. late inspection, crew rescue, etc.). - Assessing the risk impact of various trade studies (e.g. flow control valves). - Support risk analysis on mission specific events, such as in flight anomalies. - Serve as a guiding star and data source for future NASA programs.

Boyer, Roger L.↗

NASA NTRS2008

Effects of Moist Convection on Hurricane Predictability

This study exemplifies inherent uncertainties in deterministic prediction of hurricane formation and intensity. Such uncertainties could ultimately limit the predictability of hurricanes at all time scales. In particular, this study highlights the predictability limit due to the effects on moist convection of initial-condition errors with amplitudes far smaller than those of any observation or analysis system. Not only can small and arguably unobservable differences in the initial conditions result in different routes to tropical cyclogenesis, but they can also determine whether or not a tropical disturbance will significantly develop. The details of how the initial vortex is built can depend on chaotic interactions of mesoscale features, such as cold pools from moist convection, whose timing and placement may significantly vary with minute initial differences. Inherent uncertainties in hurricane forecasts illustrate the need for developing advanced ensemble prediction systems to provide event-dependent probabilistic forecasts and risk assessment.

Zhang, Fuqing↗

NASA NTRS2014

Qualitative Event-Based Fault Isolation under Uncertain Observations

For many systems, automatic fault diagnosis is critical to ensuring safe and efficient operation. Fault isolation is performed by analyzing measured signals from the system, and reasoning over the system behavior to determine which faults have occurred, based on models of predicted faulty behavior. For dynamic systems, reasoning may be performed using qualitative analysis of the differences between measured signals and their predicted values, in which observations take the form of qualitative symbols. Such an approach is quick to isolate faults, but depends critically on correct generation of the qualitative symbols from the signals. In this paper, we develop an approach to qualitative event-based fault isolation for dynamic systems that is robust to incorrect qualitative observations. Observations are treated as uncertain, where multiple interpretations of an observation, each with its own probability, are considered. By interpreting observed symbols in a probabilistic manner, the approach degrades gracefully as the number of incorrectly-generated symbols increases. The approach is demonstrated on an electrical power system testbed, and experiments using real data obtained from the hardware demonstrate the improved fault isolation performance in the presence of incorrect symbol generation.

Daigle, Matthew↗

DOE OSTI2021

Developing Generic Prior Distributions for Common Cause Failure Alpha Factors and Causal Alpha Factors

This report is a revision of the original report, INL/LTD-17-43723. Distribution of the original report was to the Nuclear Regulatory Commission (NRC) only, and the report was not made available to the public. The original report was revised as this report for public distribution. This report presents the latest update of generic prior distributions for common cause failure (CCF) alpha factors, as well as the development of new generic prior distributions for CCF causal alpha factors. The history of CCF treatment and parameter estimations is reviewed. The existing process for developing generic prior distributions is reviewed and used to develop new priors for CCF alpha factors and causal alpha factors. For causal alpha factors, different priors are developed for the five different CCF cause groups: Component (GC), Design (GD), Environment (GE), Human (GH), and Other (GO). These generic prior distributions could be used in the Standardized Plant Analysis Risk (SPAR) models for CCF parameter estimation. The issues and preliminary thoughts regarding prior distribution development are documented. Potential future work is then proposed for improving the process of developing priors.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS2004

Integration of Propulsion-Airframe-Aeroacoustic Technologies and Design Concepts for a Quiet Blended-Wing-Body Transport

This paper summarizes the results of studies undertaken to investigate revolutionary propulsion-airframe configurations that have the potential to achieve significant noise reductions over present-day commercial transport aircraft. Using a 300 passenger Blended-Wing-Body (BWB) as a baseline, several alternative low-noise propulsion-airframe-aeroacoustic (PAA) technologies and design concepts were investigated both for their potential to reduce the overall BWB noise levels, and for their impact on the weight, performance, and cost of the vehicle. Two evaluation frameworks were implemented for the assessments. The first was a Multi-Attribute Decision Making (MADM) process that used a Pugh Evaluation Matrix coupled with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This process provided a qualitative evaluation of the PAA technologies and design concepts and ranked them based on how well they satisfied chosen design requirements. From the results of the evaluation, it was observed that almost all of the PAA concepts gave the BWB a noise benefit, but degraded its performance. The second evaluation framework involved both deterministic and probabilistic systems analyses that were performed on a down-selected number of BWB propulsion configurations incorporating the PAA technologies and design concepts. These configurations included embedded engines with Boundary Layer Ingesting Inlets, Distributed Exhaust Nozzles installed on podded engines, a High Aspect Ratio Rectangular Nozzle, Distributed Propulsion, and a fixed and retractable aft airframe extension. The systems analyses focused on the BWB performance impacts of each concept using the mission range as a measure of merit. Noise effects were also investigated when enough information was available for a tractable analysis. Some tentative conclusions were drawn from the results. One was that the Boundary Layer Ingesting Inlets provided improvements to the BWB's mission range, by increasing the propulsive efficiency at cruise, and therefore offered a means to offset performance penalties imposed by some of the advanced PAA configurations. It was also found that the podded Distributed Exhaust Nozzle configuration imposed high penalties on the mission range and the need for substantial synergistic performance enhancements from an advanced integration scheme was identified. The High Aspect Ratio Nozzle showed inconclusive noise results and posed significant integration difficulties. Distributed Propulsion, in general, imposed performance penalties but may offer some promise for noise reduction from jet-to-jet shielding effects. Finally, a retractable aft airframe extension provided excellent noise reduction for a modest decrease in range.

Hill, G. A.↗

NASA NTRS2016

Estimating Orion Heat Shield Failure Due To Ablator Cracking During The EFT-1 Mission

The Orion EFT-1 heatshield suffered from two major certification challenges: First, the mechanical properties used in design were not evident in the flight hardware and second, the flight article itself cracked during fabrication. The combination of these events motivated the Orion Program to pursue an engineering-level Probabilistic Risk Assessment (PRA) as part of heatshield certification rationale. The PRA provided loss of Mission (LOM) likelihoods considering the probability of a crack occurring during the mission and the likelihood of subsequent structure over-temperature. The methods and input data for the PRA are presented along with a discussion of the test data used to anchor the results. The Orion program accepted an EFT-1 Loss of Vehicle (LOV) risk of 1-in-160,000 due to in-mission Avcoat cracking based on the results of this analysis. Conservatisms in the result, along with future considerations for Exploration Missions (EM) are also addressed.

Avcoat↗

DOE OSTI2021

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Forecasting Day-Ahead Solar Irradiance for Puerto Rico Using the WRF Model and NSRDB

Accurately predicting solar energy resources is a major challenge in integrating photovoltaics generation on the electric grid. Numerical weather prediction has been recognized by the solar energy community as a major approach to provide solar resource forecasts at various locations and for a variety of timescales. In this study, as a part of the Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100), we develop day-head solar irradiance forecast data using the Weather Research and Forecasting (WRF) model at 3 km and hourly/5-minute. The global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts simulated from the WRF model are postprocessed by a simple optimization method using satellite-derived gridded observations from the National Solar Radiation Data Base (NSRDB) to reduce error and bias of the solar irradiance forecasts covering 2018-2020. The NSRDB contributes to improving the GHI and DNI forecasts and also offers the opportunity for an in-depth analysis to evaluate their accuracy over a wide range of Puerto Rico regions. Preliminary results show overall improvements of GHI forecasts up to 37% (DNI: 15%) for mean absolute error and 97% (DNI: 76%) for mean bias error by applying a postprocessing technique to WRF model output.

data models↗

DOE OSTI2024

Generative AI for Power Grid Operations

Generative artificial intelligence (AI) has captured into the mainstream, demonstrating capabilities that once belonged solely to the realm of human cognition. From defeating world champions in complex games to generating human-quality text and images, Generative AI has proven its potential to revolutionize countless industries. The electric power grid is no exception. Generative AI's ability to process vast amounts of data rapidly, assist decision support and identify patterns could significantly enhance power grid operations. For example, Generative AI could improve state estimation where measurements are not available or integrate renewable energy sources more efficiently with probabilistic forecasting. The key contributions of this whitepaper are outlined below: (1) Comprehensive overview of Generative AI's applications in power grid operations: It highlights the opportunities in areas such as forecasting, state estimation, and demonstrating the potential for enhancing efficiency, reliability, and resilience. (2) Expanding Generative AI's impact through synergies with emerging technologies: The paper introduce NREL developed eGridGPT and explores how AI orchestration, multi-agent systems, and Digital Twins can collaborate to optimize grid operations, addressing the complexities of a decarbonized and electrified future. (3) In-depth analysis of challenges in implementing Generative AI: This includes considerations like data availability and quality, model validation, certification, and ethical concerns, ensuring responsible AI deployment. (4) Emphasizing human-AI collaboration: The whitepaper underscores the importance of trustworthy, transparency, and explainability in AI systems to promote seamless interaction between human operators and AI, ultimately improving decision-making. (5) Exploring future research and development: It identifies critical areas for further advancement to fully realize Generative AI's potential in power grid operations. This whitepaper serves as a valuable resource for researchers, practitioners, and policymakers looking to harness Generative AI for a more reliable, stable, and cost-effective power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2005

Semantic Analysis of Email Using Domain Ontologies and WordNet

The problem of capturing and accessing knowledge in paper form has been supplanted by a problem of providing structure to vast amounts of electronic information. Systems that can construct semantic links for natural language documents like email messages automatically will be a crucial element of semantic email tools. We have designed an information extraction process that can leverage the knowledge already contained in an existing semantic web, recognizing references in email to existing nodes in a network of ontology instances by using linguistic knowledge and knowledge of the structure of the semantic web. We developed a heuristic score that uses several forms of evidence to detect references in email to existing nodes in the Semanticorganizer repository's network. While these scores cannot directly support automated probabilistic inference, they can be used to rank nodes by relevance and link those deemed most relevant to email messages.

Berrios, Daniel C.↗

NASA NTRS2021

Online NASA Software Estimating Tools (ONSET): A Suite of Web-Based Cost Analysis Tools

This paper provides an overview of ONSET theOnline NASA Software Estimating Tools suite of web-basedcosts analysis tools. ONSET is comprised of the AnalogySoftware Cost Tool (ASCoT) [1], and the CubeSat Or MicrosatProbabilistic and Analogies Cost Tool (COMPACT) [2]. TheOnline NASA Space Estimation Tool (ONSET) has beendeveloped to provide a standardized platform for hosting webbasedNASA cost estimation tools on NASA ONCE (OneNASA Cost Engineering). It evolved from the BETA version ofASCoT (The NASA Analogy Software Cost Tool) which wasreleased in 2017 and was presented at the 2017 IEEEAerospace Conference. This first release of ONSET contains anew version of ASCoT as well as the first official release ofCOMPACT (CubeSat Or Microsat Probabilistic + AnalogiesCost Tool). In this paper we will provide an overview ofONSET, all its features and a high-level summary of bothASCoT and COMPACT. The heart of both tools arealgorithms for analogy-based estimation based on system levelinputs.

Johnson, James↗

DOE OSTI1989

Nuclear Safety [Vol. 30, No. 3, July-September 1989]

Nuclear Safety is a review journal that covers significant developments in the field of nuclear safety. Its scope includes the analysis and control of hazards associated with nuclear energy, operations involving fissionable materials, and the products of nuclear fission and their effects on the environment. Primary emphasis is on safety in reactor design, construction, and operation; however, the safety aspects of the entire fuel cycle, including fuel fabrication, spent-fuel processing, nuclear waste disposal, handling of radioisotopes, and environmental effects of these operations, are also treated. Table of Contents for this issue follows. GENERAL SAFETY CONSIDERATIONS: 325 Safety of Framatome Advanced Nuclear Steam Supply Systems Designs by J. A. Charles and D. Lange, 333 Book Review of Nuclear Accidents: Intervention Levels for the Protection of the Public by H. B. Piper; ACCIDENT ANALYSIS: 335 Living PRA Computer Systems by S. C. Dinsmore and H.-P. Balfanz, 343 Summary of ICAP Assessments of RELAP5/MOD2 by W. E. Driskell and R. G. Hanson; CONTROL AND INSTRUMENTATION: 352 Thermal Performance Monitoring System at Maanshan Nuclear Power Plant by H.-J. Chao, Y.-P. Lin, G.-H. Jou, L.-Y. Liao, and Y.-B. Chen; DESIGN FEATURES: 358 Warning Systems for Nuclear Power Plant Emergencies by J. H. Sorensen and D. S. Mileti; WASTE AND SPENT FUEL MANAGEMENT: 371 Activities Related to Waste Management Compiled by E G. Silver; OPERATING EXPERIENCES: 382 Steam Generator Tube Performance: Experience with Water-Cooled Nuclear Power Reactors During 1985 by O. S. Tatone and R. L. Tapping, 400 Systems Interaction Analyses: Concepts and Techniques (Part II) by M. D. Muhlheim and G. A. Murphy, 413 Reactor Shutdown Experience Compiled by J. W. Cletcher, 416 Operating U.S. Power Reactors Compiled by E G. Silver; RECENT DEVELOPMENTS: 440 General Administrative Activities Compiled by E G. Silver, 460 Reports, Standards, and Safety Guides by D. S. Queener, 466 Status of Power-Reactor Licensing Activities Compiled by E G. Silver, 470 Proposed Rule Changes as of Mar. 31, 1989; ANNOUNCEMENTS: 334 Proceedings Published, 351 CEC Seminar on Methods and Codes for Assessing the Off-Site Consequences of Nuclear Accidents, 357 Short Course on Multiphase Flow and Heat Transfer: Bases and Applications in A: The Nuclear Power Industry B: The Process Industries, 357 International Conference on Probabilistic Safety Assessment and Management, 478 International Topical Meeting on the Safety, Status, and Future of Non-Commercial Reactors and Irradiation Facilities, 475 The Authors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

CHP-PRA Proof-of-Concept [Simulation] Sensitivity Assessment

An effort is underway to establish a Crew Health and Performance system (CHP) tradespace tool using a Probabilistic Risk Assessment (PRA) modeling and simulation system. The goal of the CHP-PRA effort is to provide a means of quantifying the integrated influence of CHP functions and capabilities on risk outcome metrics associated with health, performance, and long-term health. These metrics can then be used to establish potential risk-based trades on CHP system designed functionality and capabilities. Previously, our team demonstrated a proof-of-concept PRA approach that estimated the integrated influence of exercise countermeasures on 8 human system risks (Figure 1a) with outcomes associated with health and medical risk metrics. We reported that the change in the integrated relative health risk was small (Figure 1b) and that the small change in overall risk resulted from compounding and competing contribution levels of the individual risks. This interesting observation illustrates the emergent complexity of even straightforward representations of the human health and performance risk space and the ability of PRA models to capture this balance of global risk concerns. A key question that is not addressed in the initial analysis is “even though the global risk is relatively nominal, do any of the local risks become unacceptable?” In essence, we seek to determine what relative change in the human system risks are contributing to the relatively muted sensitivity of the proof-of-concept model combined risk assessments. Evaluations at the component risk level should elucidate if any individual risk reaches a high level that is subsequentially balanced by reductions in other areas. To further understand the relative changes in the component risks in the proof-of-concept model, and to elucidate how future refinements can be targeted, a means of establishing the contributions of the robustness of the proof-of-concept approach will be demonstrated.

astronaut health↗

DOE OSTI2023

Physics-informed machine learning for fault-leakage reduced-order modeling

Geologic carbon storage (GCS) is a promising technology for mitigating CO 2 emissions. The overall success of GCS depends on safe operations that are informed by risk assessment and have proper mitigation plans in place. Performing quantitative probabilistic risk assessment for a GCS site using traditional reservoir simulators can be challenging due to the high computational costs. To overcome this challenge, the US Department of Energy’s National Risk Assessment Partnership (NRAP) project has developed an integrated assessment modeling approach that utilizes computationally efficient reduced-order models (ROM) for simulating various parts of a GCS storage site to quantify uncertainty. Here, in this study, we develop a reduced-order model for fault leakage risk assessment. We use a deep learning approach to build the reduced-order model. We perform a sensitivity analysis and find that the deep learning model yields high accuracy with a much smaller computational cost than full-physics simulation. We also evaluate the performance of the model in scenarios where simulations are not possible to run, providing analysis not previously performed in fault-leakage ROM analyses. Based on a sensitivity analysis of the model, we suggest a simplified conceptual model for fault leakage and site monitoring.

58 GEOSCIENCES↗

NASA NTRS2019

A Double Bootstrap Approach to Superposed Epoch Analysis to Evaluate Response Uncertainty

The association between climate variability and episodic events, such as the antecedent moisture conditions prior to wildfire or the cooling following volcanic eruptions, is commonly assessed using Superposed Epoch Analysis (SEA). In SEA the epochal response is typically calculated as the average climate conditions prior to and following all event years or their deviation from climatology. However, the magnitude and significance of the inferred climate association may be sensitive to the selection or omission of individual key years, potentially resulting in a biased assessment of the relationship between these events and climate. Here we describe and test a modified double-bootstrap SEA that generates multiple unique draws of the key years and evaluates the sign, magnitude, and significance of event-climate relationships within a probabilistic framework. This multiple resampling helps quantify multiple uncertainties inherent in conventional applications of SEA within dendrochronology and paleoclimatology. We demonstrate our modified SEA by evaluating the volcanic cooling signal in a Northern Hemisphere tree-ring temperature reconstruction and the link between drought and wildfire events in the western United States. Finally, we make our Matlab and R code available to be adapted for future SEA applications.

Dendroclimatology↗

DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris‐Flow Inundation

Increases in wildfire activity and rainfall intensification are driving more postfire debris flows (PFDF) in many regions around the world. PFDFs are most common in the first postfire year and may even occur before a fire is fully controlled. This underscores the importance of assessing postfire hazards before a fire starts. Evaluation of PFDF hazards prior to fire can help strategize interventions lessening the negative effects of future fires. However, debris-flow runout and inundation analyses are not routine in PFDF hazard assessments, partially due to time constraints and substantial uncertainties in boundary conditions. Here, we propose a prefire PFDF inundation assessment framework using a debris-flow runout model based on the Herschel-Bulkley (HB) rheology (HEC-RAS v6.1). We constrain model inputs and parameters using Bayesian posterior analysis, rainfall-runoff simulations, and a debris-flow volume model. We use observations from recent PFDF incidents in northern Arizona, USA, to calibrate model components and then apply our prefire inundation assessment framework in a nearby unburned area. Specifically, we (a) identify yield stress as the most influential factor on inundation extent and arrival time in a HB model, (b) establish posterior distributions for model parameters suitable for forward modeling by leveraging uncertainties in field observations, and (c) implement a predictive forward analysis in an area that has not burned recently to evaluate PFDF inundation under several future fire scenarios. This study improves our ability to assess postfire debris-flow hazards before a fire begins and provides guidance for future applications of single-phase rheological models when assessing PFDF hazards.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Deep Neural Network Assisted Distributed Strain and Temperature Fiber Sensor System for Natural Gas Pipeline Monitoring

Natural gas pipeline integrity monitoring is crucial to detect potential leaks, find structural issues, and prevent environmental damage. This article presents a system of natural gas pipeline monitoring that uses a specialized double Brillouin peak sensing fiber along with the Brillouin optical time domain analysis (BOTDAs) technique. The calibrated sensing fiber coefficients for strain and temperature are 41.8 kHz/ με and 0.9 MHz/°C for peak 1; and 47.2 kHz/ με , and 1.11 MHz/°C for peak 2, respectively. Initially, lab tests were performed by installing a short section of double Brillouin peak fiber (DBPF) on a 1-in steel pipe under pressure up to 1000 per square inch (psi) at elevated temperatures. Simultaneous distributed measurements of temperature and pressure-induced hoop strain were successfully measured. Considering the long processing speed to extract Brillouin frequency shift (BFS), we employ a novel probabilistic deep neural network (PDNN) framework for rapid BFS prediction. Additionally, using the Finite Element Method, the effects of the pipeline pressure on hoop strain were modeled and compared to the experimental hoop strain under the same set of pipeline conditions. Finally, an actual 4-in outer diameter steel natural gas pipeline was used for pilot-scale tests, where hoop strain was measured at various pressure levels. Leaks were simulated to demonstrate accurate pipeline integrity monitoring. At an internal pipe pressure of 1000 psi, hoop strain of approximately 300 με was observed, and the sensitivity was calculated as 0.28 με /psi. The results of this pilot-scale study demonstrated that the system is capable of performing distributed monitoring sufficient to detect pipeline pressure and the presence of leaks to ensure the safe operation of gas pipelines in the field.

03 NATURAL GAS↗

DOE OSTI2024

A Prefire Approach for Probabilistic Assessments of Postfire Debris-Flow Inundation

54 ENVIRONMENTAL SCIENCES↗