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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Development of a probabilistic analysis methodology for structural reliability estimation

The novel probabilistic analysis method for assessment of structural reliability presented, which combines fast-convolution with an efficient structural reliability analysis, can after identifying the most important point of a limit state proceed to establish a quadratic-performance function. It then transforms the quadratic function into a linear one, and applies fast convolution. The method is applicable to problems requiring computer-intensive structural analysis. Five illustrative examples of the method's application are given.

Torng, T. Y.↗
NASA NTRS1996

Concurrent Probabilistic Simulation of High Temperature Composite Structural Response

A computational structural/material analysis and design tool which would meet industry's future demand for expedience and reduced cost is presented. This unique software 'GENOA' is dedicated to parallel and high speed analysis to perform probabilistic evaluation of high temperature composite response of aerospace systems. The development is based on detailed integration and modification of diverse fields of specialized analysis techniques and mathematical models to combine their latest innovative capabilities into a commercially viable software package. The technique is specifically designed to exploit the availability of processors to perform computationally intense probabilistic analysis assessing uncertainties in structural reliability analysis and composite micromechanics. The primary objectives which were achieved in performing the development were: (1) Utilization of the power of parallel processing and static/dynamic load balancing optimization to make the complex simulation of structure, material and processing of high temperature composite affordable; (2) Computational integration and synchronization of probabilistic mathematics, structural/material mechanics and parallel computing; (3) Implementation of an innovative multi-level domain decomposition technique to identify the inherent parallelism, and increasing convergence rates through high- and low-level processor assignment; (4) Creating the framework for Portable Paralleled architecture for the machine independent Multi Instruction Multi Data, (MIMD), Single Instruction Multi Data (SIMD), hybrid and distributed workstation type of computers; and (5) Market evaluation. The results of Phase-2 effort provides a good basis for continuation and warrants Phase-3 government, and industry partnership.

Abdi, Frank↗
NASA NTRS1997

CARES/Life Software for Designing More Reliable Ceramic Parts

Products made from advanced ceramics show great promise for revolutionizing aerospace and terrestrial propulsion, and power generation. However, ceramic components are difficult to design because brittle materials in general have widely varying strength values. The CAPES/Life software eases this task by providing a tool to optimize the design and manufacture of brittle material components using probabilistic reliability analysis techniques. Probabilistic component design involves predicting the probability of failure for a thermomechanically loaded component from specimen rupture data. Typically, these experiments are performed using many simple geometry flexural or tensile test specimens. A static, dynamic, or cyclic load is applied to each specimen until fracture. Statistical strength and SCG (fatigue) parameters are then determined from these data. Using these parameters and the results obtained from a finite element analysis, the time-dependent reliability for a complex component geometry and loading is then predicted. Appropriate design changes are made until an acceptable probability of failure has been reached.

Nemeth, Noel N.↗
DOE OSTI2015

Challenges in leveraging existing human performance data for quantifying the IDHEAS HRA method

Our article documents an exploratory study for collecting and using human performance data to inform human error probability (HEP) estimates for a new human reliability analysis (HRA) method, the IntegrateD Human Event Analysis System (IDHEAS). The method was based on cognitive models and mechanisms underlying human behaviour and employs a framework of 14 crew failure modes (CFMs) to represent human failures typical for human performance in nuclear power plant (NPP) internal, at-power events [1]. A decision tree (DT) was constructed for each CFM to assess the probability of the CFM occurring in different contexts. Data needs for IDHEAS quantification are discussed. Then, the data collection framework and process is described and how the collected data were used to inform HEP estimation is illustrated with two examples. Next, five major technical challenges are identified for leveraging human performance data for IDHEAS quantification. Furthermore, these challenges reflect the data needs specific to IDHEAS. More importantly, they also represent the general issues with current human performance data and can provide insight for a path forward to support HRA data collection, use, and exchange for HRA method development, implementation, and validation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Reliability Prediction using FMEA, FTA, and Related Techniques [Slides]

Summary: We presented a reliability analysis framework. We made point estimates of reliability using reliability block diagrams, fault trees, success trees and estimates with uncertainty using expert elicitation, Monte Carlo simulation, Bayesian analysis. Expert elicitation of failure modes and probabilities is labor-intensive, but critical. Bayesian analysis updates information from expert elicitation with data from reliability and aging tests (aging/compatibility data are needed to estimate lower-bound reliabilities at end of life). Estimation by more than one method helps insure consistency and accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

One Size Does Not Fit All: Human Failure Event Decomposition and Task Analysis

In the probabilistic safety assessments (PSAs) used in the nuclear industry, human failure events (HFEs) are determined as a subset of hardware failures, namely those hardware failures that could be triggered or exacerbated by human action or inaction. This approach is top-down, starting with hardware faults and deducing human contributions to those faults. Elsewhere, more traditionally human factors driven approaches would tend to look at opportunities for human errors first in a task analysis and then identify which of those errors is risk significant. The intersection of top-down and bottom-up approaches to defining HFEs has not been carefully studied. Ideally, both approaches should arrive at the same set of HFEs. This question remains central as human reliability analysis (HRA) methods are generalized to new domains like oil and gas. The HFEs used in nuclear PSAs tend to be top-down—defined as a subset of the PSA—whereas the HFEs used in petroleum quantitative risk assessments (QRAs) are more likely to be bottom-up—derived from a task analysis conducted by human factors experts. The marriage of these approaches is necessary in order to ensure that HRA methods developed for top-down HFEs are also sufficient for bottom-up applications. In this paper, I first review top-down and bottom-up approaches for defining HFEs and then present a seven-step guideline to ensure a task analysis completed as part of human error identification decomposes to a level suitable for use as HFEs. This guideline illustrates an effective way to bridge the bottom-up approach with top-down requirements.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

Differential Power Processing for Increased Energy Production and Reliability of Photovoltaic Systems

Conventional energy conversion architectures in photovoltaic (PV) systems are often forced to tradeoff conversion efficiency and power production. This paper introduces an energy conversion approach that enables each PV element to operate at its maximum power point (MPP) while processing only a small fraction of the total power produced. This is accomplished by providing only the mismatch in the MPP current of a set of series-connected PV elements. Differential power processing increases overall conversion efficiency and overcomes the challenges associated with unmatched MPPs (due to partial shading, damage, manufacturing tolerances, etc.). Several differential power processing architectures are analyzed and compared with Monte Carlo simulations. Local control of the differential converters enables distributed protection and monitoring. Reliability analysis shows significantly increased overall system reliability. Simulation and experimental results are included to demonstrate the benefits of this approach at both the panel and subpanel level.

Shenoy, PS↗
NASA NTRS1985

Front-End Program for SYSGEN

SYSGEN, Power Generation System Production costing and Reliability Analysis program, simulates production costs and reliability of electric utility with and without time-dependent generating units. FEPS transforms inputs into proper formats, builds data files and adds Job Control Language necessary to running SYSGEN program.

Carlson, C. R.↗
NASA NTRS1993

Tutorial: Advanced fault tree applications using HARP

Reliability analysis of fault tolerant computer systems for critical applications is complicated by several factors. These modeling difficulties are discussed and dynamic fault tree modeling techniques for handling them are described and demonstrated. Several advanced fault tolerant computer systems are described, and fault tree models for their analysis are presented. HARP (Hybrid Automated Reliability Predictor) is a software package developed at Duke University and NASA Langley Research Center that is capable of solving the fault tree models presented.

Dugan, Joanne Bechta↗
NASA NTRS2016

VIIRS Reflective Solar Band Radiometric and Stability Evaluation Using Deep Convective Clouds

This work takes advantage of the stable distribution of deep convective cloud (DCC) reflectance measurements to assess the calibration stability and detector difference in Visible Infrared Imaging Radiometer Suite (VIIRS) reflective bands. VIIRS Sensor Data Records (SDRs) from February 2012 to June 2015 are utilized to analyze the long-term trending, detector difference, and half angle mirror (HAM) side difference. VIIRS has two thermal emissive bands with coverage crossing 11 microns for DCC pixel identification. The comparison of the results of these two processing bands is one of the indicators of analysis reliability. The long-term stability analysis shows downward trends (up to approximately 0.4 per year) for the visible and near-infrared bands and upward trends (up to 0.5per year) for the short- and mid-wave infrared bands. The detector difference for each band is calculated as the difference relative to the average reflectance overall detectors. Except for the slightly greater than 1 difference in the two bands at 1610 nm, the detector difference is less than1 for other solar reflective bands. The detector differences show increasing trends for some short-wave bands with center wavelengths from 400 to 600 nm and remain unchanged for the bands with longer center wavelengths. The HAM side difference is insignificant and stable. Those short-wave bands from 400 to 600 nm also have relatively larger HAM side difference, up to 0.25.Comparing the striped images from SDR and the smooth images after the correction validates the analyses of detector difference and HAM side difference. These analyses are very helpful for VIIRS calibration improvement and thus enhance product quality

VIIRS↗
DOE OSTI2016

MODIFICATION OF THE SPAR-H METHOD TO SUPPORT HRA FOR LEVEL 2 PSA

Currently, available Human Reliability Analysis (HRA) methods were generally developed to support Level 1 Probabilistic Safety Analysis (PSA) models. There has been an increased emphasis placed on Level 2 PSA in recent years; however, the currently used HRA methods are not ideal for this application, including the SPAR-H method. Challenges that will likely be present during a severe accident such as degraded or hazardous operating conditions, shift in control from the main control room to the technical support center, unavailability of instrumentation, and others are not routinely considered for Level 1 HRA analysis. These factors combine to create a much more uncertain condition to be accounted for in the HRA analysis. While the SPAR-H shaping factors were established to support Level 1 HRA, previous studies have shown it may be used for Level 2 HRA analysis as well. The Canadian Nuclear Safety Commission (CNSC) and Idaho National Laboratory (INL) in a joint project are investigating modifications to the SPAR-H method to create more consistency in applying the performance shaping factors used in the method for Level 2 analysis.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2005

Monte Carlo Simulation of Markov, Semi-Markov, and Generalized Semi- Markov Processes in Probabilistic Risk Assessment

A standard tool of reliability analysis used at NASA-JSC is the event tree. An event tree is simply a probability tree, with the probabilities determining the next step through the tree specified at each node. The nodal probabilities are determined by a reliability study of the physical system at work for a particular node. The reliability study performed at a node is typically referred to as a fault tree analysis, with the potential of a fault tree existing.for each node on the event tree. When examining an event tree it is obvious why the event tree/fault tree approach has been adopted. Typical event trees are quite complex in nature, and the event tree/fault tree approach provides a systematic and organized approach to reliability analysis. The purpose of this study was two fold. Firstly, we wanted to explore the possibility that a semi-Markov process can create dependencies between sojourn times (the times it takes to transition from one state to the next) that can decrease the uncertainty when estimating time to failures. Using a generalized semi-Markov model, we studied a four element reliability model and were able to demonstrate such sojourn time dependencies. Secondly, we wanted to study the use of semi-Markov processes to introduce a time variable into the event tree diagrams that are commonly developed in PRA (Probabilistic Risk Assessment) analyses. Event tree end states which change with time are more representative of failure scenarios than are the usual static probability-derived end states.

English, Thomas↗
DOE OSTI2015

Science-based HRA: experimental comparison of operator performance to IDAC (Information-Decision-Action Crew) simulations

Information-Decision-Action Crew (IDAC) operator model simulations of a Steam Generator Tube Rupture are compared to student operator performance in studies conducted in the Ohio State University’s Nuclear Power Plant Simulator Facility. This study is presented as a prototype for conducting simulator studies to validate key aspects of Human Reliability Analysis (HRA) methods. Seven student operator crews are compared to simulation results for crews designed to demonstrate three different decision-making strategies. The IDAC model used in the simulations is modified slightly to capture novice behavior rather that expert operators. Operator actions and scenario pacing are compared. A preliminary review of available performance shaping factors (PSFs) is presented. After the scenario in the NPP Simulator Facility, student operators review a video of the scenario and evaluate six PSFs at pre-determined points in the scenario. This provides a dynamic record of the PSFs experienced by the OSU student operators. In this preliminary analysis, Time Constraint Load (TCL) calculated in the IDAC simulations is compared to TCL reported by student operators. We identify potential modifications to the IDAC model to develop an “IDAC Student Operator Model.” This analysis provides insights into how similar experiments could be conducted using expert operators to improve the fidelity of IDAC simulations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1994

Software analysis handbook: Software complexity analysis and software reliability estimation and prediction

This handbook documents the three software analysis processes the Space Station Software Analysis team uses to assess space station software, including their backgrounds, theories, tools, and analysis procedures. Potential applications of these analysis results are also presented. The first section describes how software complexity analysis provides quantitative information on code, such as code structure and risk areas, throughout the software life cycle. Software complexity analysis allows an analyst to understand the software structure, identify critical software components, assess risk areas within a software system, identify testing deficiencies, and recommend program improvements. Performing this type of analysis during the early design phases of software development can positively affect the process, and may prevent later, much larger, difficulties. The second section describes how software reliability estimation and prediction analysis, or software reliability, provides a quantitative means to measure the probability of failure-free operation of a computer program, and describes the two tools used by JSC to determine failure rates and design tradeoffs between reliability, costs, performance, and schedule.

Computer systems design↗