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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Constellation Probabilistic Risk Assessment (PRA): Design Consideration for the Crew Exploration Vehicle

Managed by NASA's Office of Safety and Mission Assurance, a pilot probabilistic risk analysis (PRA) of the NASA Crew Exploration Vehicle (CEV) was performed in early 2006. The PRA methods used follow the general guidance provided in the NASA PRA Procedures Guide for NASA Managers and Practitioners'. Phased-mission based event trees and fault trees are used to model a lunar sortie mission of the CEV - involving the following phases: launch of a cargo vessel and a crew vessel; rendezvous of these two vessels in low Earth orbit; transit to th$: moon; lunar surface activities; ascension &om the lunar surface; and return to Earth. The analysis is based upon assumptions, preliminary system diagrams, and failure data that may involve large uncertainties or may lack formal validation. Furthermore, some of the data used were based upon expert judgment or extrapolated from similar components~systemsT. his paper includes a discussion of the system-level models and provides an overview of the analysis results used to identify insights into CEV risk drivers, and trade and sensitivity studies. Lastly, the PRA model was used to determine changes in risk as the system configurations or key parameters are modified.

Prassinos, Peter G.↗
NASA NTRS2010

Reliability Quantification of Advanced Stirling Convertor (ASC) Components

The Advanced Stirling Convertor, is intended to provide power for an unmanned planetary spacecraft and has an operational life requirement of 17 years. Over this 17 year mission, the ASC must provide power with desired performance and efficiency and require no corrective maintenance. Reliability demonstration testing for the ASC was found to be very limited due to schedule and resource constraints. Reliability demonstration must involve the application of analysis, system and component level testing, and simulation models, taken collectively. Therefore, computer simulation with limited test data verification is a viable approach to assess the reliability of ASC components. This approach is based on physics-of-failure mechanisms and involves the relationship among the design variables based on physics, mechanics, material behavior models, interaction of different components and their respective disciplines such as structures, materials, fluid, thermal, mechanical, electrical, etc. In addition, these models are based on the available test data, which can be updated, and analysis refined as more data and information becomes available. The failure mechanisms and causes of failure are included in the analysis, especially in light of the new information, in order to develop guidelines to improve design reliability and better operating controls to reduce the probability of failure. Quantified reliability assessment based on fundamental physical behavior of components and their relationship with other components has demonstrated itself to be a superior technique to conventional reliability approaches based on utilizing failure rates derived from similar equipment or simply expert judgment.

Shah, Ashwin R.↗
NASA NTRS2011

Towards Measurement of Confidence in Safety Cases

Arguments in safety cases are predominantly qualitative. This is partly attributed to the lack of sufficient design and operational data necessary to measure the achievement of high-dependability targets, particularly for safety-critical functions implemented in software. The subjective nature of many forms of evidence, such as expert judgment and process maturity, also contributes to the overwhelming dependence on qualitative arguments. However, where data for quantitative measurements is systematically collected, quantitative arguments provide far more benefits over qualitative arguments, in assessing confidence in the safety case. In this paper, we propose a basis for developing and evaluating integrated qualitative and quantitative safety arguments based on the Goal Structuring Notation (GSN) and Bayesian Networks (BN). The approach we propose identifies structures within GSN-based arguments where uncertainties can be quantified. BN are then used to provide a means to reason about confidence in a probabilistic way. We illustrate our approach using a fragment of a safety case for an unmanned aerial system and conclude with some preliminary observations

Denney, Ewen↗
NASA NTRS2015

New York City Panel on Climate Change 2015 Report: Climate Observations and Projections - Chapter 1

During 2013 and 2014, numerous international (IPCC, 2013) and national (Melillo et al., 2014; Gordon, 2014) reports have concluded that human activities are changing the climate, leading to increased vulnerability and risk. Since the industrial revolution, fossil fuel burning, industrial activity, and land use changes have led to a 40% increase in heat-trapping carbon dioxide (CO2), and an approximately 150% increase in methane (CH4), another powerful greenhouse gas (GHG), has been observed. Global temperatures have increased by close to 1 C since 1880 as the upper oceans have warmed and polar ice has retreated. These and other climate changes are projected to accelerate as greenhouse gas concentrations continue to rise. In the coming decades, climate change is extremely likely to bring warmer temperatures in the New York metropolitan region (see Box. 1.1 and Fig.1.1 for key definitions and terms). Heat waves are very likely to increase; total annual precipitation will likely increase and brief, intense rainstorms are very likely to increase. Because of incomplete knowledge about exactly how much climate change will occur, choosing among policies for reducing future damages requires prudent risk management (Yohe and Leichenko, 2010; Kunreuther et al., 2013). Given differing risk tolerances among stakeholders, a risk management approach allows for a range of possible climate change outcomes to be examined with associated uncertainties surrounding their likelihoods. The New York City Panel on Climate Change 2 (NPCC2) projections can be used to inform planning across multiple governmental scales (e.g., city, county, state) in the New York metropolitan region. Such coordinated efforts can serve as test cases for successful local, state, and federal coordination for integrated climate adaptation initiatives. This chapter describes the global climate system, and presents observed temperature and precipitation trends and projections for the region. Chapter 2 (NPCC, 2015) focuses on sea level rise and possible changes in coastal storms. Chapter 3 and Chapter 4 (NPCC, 2015) describe efforts to better understand the region's vulnerability to coastal flooding during coastal storms. The treatment of likelihood related to the NPCC projections is similar to that developed by the Intergovernmental Panel on Climate Change Fourth and Fifth Assessment Reports (IPCC, 2007; 2013), with six likelihood categories (Box 1.1 and Fig. 1.1). The assignment of climate hazards to these categories is based on observed data, global climate model simulations, published literature, and expert judgment.

climate change↗
DOE OSTI2024

An Approach to Dynamic Human Reliability Analysis and Its Data Collection Framework

Human reliability analysis (HRA) is a method for evaluating human errors in a variety of complex systems such as nuclear power plants, military systems, aircraft, and chemical plants. Most HRA methods currently used by regulatory institutes or utilities are called static HRA and are carried out by simple worksheets or simple calculators. To date, there are many unsolved or intrinsic challenges in static HRA. For example, existing static HRA does not realistically model and evaluate human actions as they would be performed at actual systems. There is no method with HRA to objectively estimate the time required for human actions despite being essential to HRA processes. In addition, many HRA methods still rely on a dataset generated prior to the 1980s, from unrelated industry experience or simply from expert judgment. Accordingly, this study attempted to research how to overcome the challenges of existing HRA via dynamic risk assessment (a.k.a., simulation-based or computation-based risk assessment) techniques. First, this study developed a dynamic HRA method, named as PRocedure-based Investigation Method of EMRALD Risk Assessment – HRA (PRIMERA-HRA). The PRIMERA-HRA mainly concentrates on providing HRA analysts with specific guidelines on how to reasonably model human actions, assign human reliability data and evaluate output of simulation within a dynamic probabilistic risk assessment tool, called as Event Modeling Risk Assessment using Linked Diagrams (EMRALD). Second, this study also developed a module for performance shaping factors (i.e., the key concept in HRA quantification) applicable to dynamic HRA, then implemented it based on PRIMERA-HRA within the EMRALD tool. Third, this study developed an HRA data collection framework to support dynamic HRA, called as Simplified Human Error Experimental Program (SHEEP). Originally, the SHEEP study aimed to support static HRA and its data collection, but recently extended the scope to the new technologies such as dynamic HRA or HRA for advanced reactors. SHEEP focuses on the use of data collected from simplified simulators to complement—but not replace—data collection studies using full-scope simulators and actual operators. To date, many experiments were conducted under the SHEEP framework. Multiple analyses, such as human performance analysis, human error analysis, task complexity analysis, learning effect analysis and time distribution analysis, were also carried out using the collected data. Then, based on the major insights, an approach to inferring full-scope data based on simplified simulator data was proposed. The PRIMERA-HRA and SHEEP research are expected to evaluate human actions more realistically than existing static HRA, provide an opportunity to collect more HRA data with reasonable cost and labor, then contribute to enhance the quality of HRA.

99 - GENERAL AND MISCELLANEOUS↗
NASA NTRS1989

Expert systems should be more accurate than human experts - Evaluation procedures from human judgment and decisionmaking

Two procedures for the evaluation of the performance of expert systems are illustrated: one procedure evaluates predictive accuracy; the other procedure is complementary in that it uncovers the factors that contribute to predictive accuracy. Using these procedures, it is argued that expert systems should be more accurate than human experts in two senses. One sense is that expert systems must be more accurate to be cost-effective. Previous research is reviewed and original results are presented which show that simple statistical models typically perform better than human experts for the task of combining evidence from a given set of information sources. The results also suggest the second sense in which expert systems should be more accurate than human experts. They reveal that expert systems should share factors that contribute to human accuracy, but not factors that detract from human accuracy. Thus the thesis is that one should both require and expect systems to be more accurate than humans.

Levi, Keith↗
NASA NTRS2005

CoMET: Cost and Mass Evaluation Tool for Spacecraft and Mission Design

New technology in space exploration is often developed without a complete knowledge of its impact. While the immediate benefits of a new technology are obvious, it is harder to understand its indirect consequences, which ripple through the entire system. COMET is a technology evaluation tool designed to illuminate how specific technology choices affect a mission at each system level. COMET uses simplified models for mass, power, and cost to analyze performance parameters of technologies of interest. The sensitivity analysis that CoMET provides shows whether developing a certain technology will greatly benefit the project or not. CoMET is an ongoing project approaching a web-based implementation phase. This year, development focused on the models for planetary daughter craft, such as atmospheric probes, blimps and balloons, and landers. These models are developed through research into historical data, well established rules of thumb, and engineering judgment of experts at JPL. The model is validated by corroboration with JpL advanced mission studies. Other enhancements to COMET include adding launch vehicle analysis and integrating an updated cost model. When completed, COMET will allow technological development to be focused on areas that will most drastically improve spacecraft performance.

Bieber, Ben S.↗
DOE OSTI1969

Application of Nuclear Technology to Art Identification Problems: First Annual Report

Throughout the centuries, as man has become increasingly affluent, his interest in antiquities and objects of art, and the price he has been willing to pay for them, has increased. Inevitably, one result has been to attract the unscrupulous to the production and sale of counterfeit paintings, sculptures, and other works of art. The skill of forgers varies but often is highly sophisticated. However, dealers, museum curators, and other experts have also become more skillful in the detection of forgeries. Scientific tools of increasing sensitivity and sophistication have gradually been applied to the examination of the materials of art and archaeology. Such tools frequently support and render more reliable the judgment of the experts. The quality of forgeries may improve further as their makers become acquainted with the new methods of detection and in turn learn to circumvent or confound the methods. Therefore, the development of still more advanced methods of examination has great utility in the art world. This is particularly true if the ultimate effect is to make circumvention of the methods of examination so costly that the economic incentives for producing forgeries may be substantially reduced, if not eliminated.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

A new machine classification method applied to human peripheral blood leukocytes

Human beings judge images by complex mental processes, whereas computing machines extract features. By reducing scaled human judgments and machine extracted features to a common metric space and fitting them by regression, the judgments of human experts rendered on a sample of images may be imposed on an image population to provide automatic classification.

Rorvig, Mark E.↗
NASA NTRS2005

Visualization support for risk-informed decision making when planning and managing software developments

Key decisions are made in the early stages of planning and management of software developments. The information basis for these decisions is often a mix of analogy with past developments, and the best judgments of domain experts. Visualization of this information can support to such decision making by clarifying the status of the information and yielding insights into the ramifications of that information vis-a-vis decision alternatives.

technology infusion↗
NASA NTRS2006

Scheduling Software for Complex Scenarios

Preparing a vehicle and its payload for a single launch is a complex process that involves thousands of operations. Because the equipment and facilities required to carry out these operations are extremely expensive and limited in number, optimal assignment and efficient use are critically important. Overlapping missions that compete for the same resources, ground rules, safety requirements, and the unique needs of processing vehicles and payloads destined for space impose numerous constraints that, when combined, require advanced scheduling. Traditional scheduling systems use simple algorithms and criteria when selecting activities and assigning resources and times to each activity. Schedules generated by these simple decision rules are, however, frequently far from optimal. To resolve mission-critical scheduling issues and predict possible problem areas, NASA historically relied upon expert human schedulers who used their judgment and experience to determine where things should happen, whether they will happen on time, and whether the requested resources are truly necessary.

Source record↗
NASA NTRS1985

Perspectives on knowledge in engineering design

Various perspectives are given of the knowledge currently used in engineering design, specifically dealing with knowledge-based expert systems (KBES). Constructing an expert system often reveals inconsistencies in domain knowledge while formalizing it. The types of domain knowledge (facts, procedures, judgments, and control) differ from the classes of that knowledge (creative, innovative, and routine). The feasible tasks for expert systems can be determined based on these types and classes of knowledge. Interpretive tasks require reasoning about a task in light of the knowledge available, where generative tasks create potential solutions to be tested against constraints. Only after classifying the domain by type and level can the engineer select a knowledge-engineering tool for the domain being considered. The critical features to be weighed after classification are knowledge representation techniques, control strategies, interface requirements, compatibility with traditional systems, and economic considerations.

Rasdorf, W. J.↗
NASA NTRS1976

Cost estimation: An expert-opinion approach

A methodology is outlined which can be used to estimate the costs of research and development projects. The approach uses the Delphi technique a method developed by the Rand Corporation for systematically eliciting and evaluating group judgments in an objective manner. The use of the Delphi allows for the integration of expert opinion into the cost-estimating process in a consistent and rigorous fashion. This approach can also signal potential cost-problem areas. This result can be a useful tool in planning additional cost analysis or in estimating contingency funds. A Monte Carlo approach is also examined.

Buffalano, C.↗
NASA NTRS2005

Developing a Methodology for Eliciting Subjective Probability Estimates During Expert Evaluations of Safety Interventions: Application for Bayesian Belief Networks

The NASA Aviation Safety Program (AvSP) has defined several products that will potentially modify airline and/or ATC operations, enhance aircraft systems, and improve the identification of potential hazardous situations within the National Airspace System (NAS). Consequently, there is a need to develop methods for evaluating the potential safety benefit of each of these intervention products so that resources can be effectively invested to produce the judgments to develop Bayesian Belief Networks (BBN's) that model the potential impact that specific interventions may have. Specifically, the present report summarizes methodologies for improving the elicitation of probability estimates during expert evaluations of AvSP products for use in BBN's. The work involved joint efforts between Professor James Luxhoj from Rutgers University and researchers at the University of Illinois. The Rutgers' project to develop BBN's received funding by NASA entitled "Probabilistic Decision Support for Evaluating Technology Insertion and Assessing Aviation Safety System Risk." The proposed project was funded separately but supported the existing Rutgers' program.

Wiegmann, Douglas A.a↗
NASA NTRS2015

Effects of Acute Stress on Aircrew Performance: Literature Review and Analysis of Operational Aspects

Situational stress can adversely affect the cognition and skilled performance of pilots, as well as experts in other domains. Emergencies and other threatening situations require pilots to execute infrequently practiced procedures correctly and to use their skills and judgment to select an appropriate course of action, often under high workload, time pressure, and ambiguous indications, all of which can be stressful. Our current study, consited of three parts, starting with a critical review of the research literature on the effects of stress on skilled performance, going back to World War II and continuing to recent and more sophisticated studies of the cognitive effects of anxiety. In the second part we analyzed the specific ways stress may have impaired the performance of airline crews in twelve major accidents, selected for diversity of the situations the crews encountered. The third part examined the operational significance and practical implications of the findings from the first two parts, suggested specific ways to reduce the harmful effects of stress on flight crews, and identified aspects requiring further research. Even thought this study focused on flight crews, the findings apply to the effects of stress on the skilled performance of experts in almost any domain.

stress↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
NASA NTRS1978

Satellite Power System (SPS): an Overview of Prospective Organizational Structures in the Solar Satellite Field

A literature survey, interviews with acknowledged experts in the fields of organizational entities, space, solar energy, and the SPS concept, and an analysis of these inputs to identify the organizational alternatives and make judgments as to their feasibility to serve as patterns for a future SPS entity are presented. Selection and evaluation criteria were determined to include timeliness, reliability, and adequacy to contribute meaningfully to the U.S. supply; political feasibility (both national and international) and cost-effectiveness (including environmental and other external costs). Based on these criteria, four organizational alternatives are discussed which offer reasonable promise as potential options for SPS. These included three domestic alternatives and one international alternative.

Edler, H. G.↗
NASA NTRS1992

An expert system for integrated structural analysis and design optimization for aerospace structures

The results of a research study on the development of an expert system for integrated structural analysis and design optimization is presented. An Object Representation Language (ORL) was developed first in conjunction with a rule-based system. This ORL/AI shell was then used to develop expert systems to provide assistance with a variety of structural analysis and design optimization tasks, in conjunction with procedural modules for finite element structural analysis and design optimization. The main goal of the research study was to provide expertise, judgment, and reasoning capabilities in the aerospace structural design process. This will allow engineers performing structural analysis and design, even without extensive experience in the field, to develop error-free, efficient and reliable structural designs very rapidly and cost-effectively. This would not only improve the productivity of design engineers and analysts, but also significantly reduce time to completion of structural design. An extensive literature survey in the field of structural analysis, design optimization, artificial intelligence, and database management systems and their application to the structural design process was first performed. A feasibility study was then performed, and the architecture and the conceptual design for the integrated 'intelligent' structural analysis and design optimization software was then developed. An Object Representation Language (ORL), in conjunction with a rule-based system, was then developed using C++. Such an approach would improve the expressiveness for knowledge representation (especially for structural analysis and design applications), provide ability to build very large and practical expert systems, and provide an efficient way for storing knowledge. Functional specifications for the expert systems were then developed. The ORL/AI shell was then used to develop a variety of modules of expert systems for a variety of modeling, finite element analysis, and design optimization tasks in the integrated aerospace structural design process. These expert systems were developed to work in conjunction with procedural finite element structural analysis and design optimization modules (developed in-house at SAT, Inc.). The complete software, AutoDesign, so developed, can be used for integrated 'intelligent' structural analysis and design optimization. The software was beta-tested at a variety of companies, used by a range of engineers with different levels of background and expertise. Based on the feedback obtained by such users, conclusions were developed and are provided.

Source record↗