Dynamic cost risk estimation and budget misspecification
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Engineering topics
Publications and source records attributed to Ebbeler, D. H..
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Cost risk for new technology development is estimated by explicit stochastic processes. Monte Carlo simulation is used to propagate technology development activity budget changes during the technology development cycle.
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The Space Interferometry Mission in NASA's Origins Program is a 10m-baseline space-based Michelson interferometer scheduled for launch in 2009. This large instrument will measure the angles between stars to an accuracy of about one billionth of a degree of arc. This is an about two orders of magnitude over current astrometric instruments.
As a result of the concern for the growing number of air-bag-induced injuries and fatalities, the administrators of the National Highway Traffic Safety Administration (NHTSA) and the National Aeronautics and Space Administration (NASA) agreed to a cooperative effort that "leverages NHTSA's expertise in motor vehicle safety restraint systems and biomechanics with NASAs position as one of the leaders in advanced technology development... to enable the state of air bag safety technology to advance at a faster pace..." They signed a NASA/NHTSA memorandum of understanding for NASA to "evaluate air bag to assess advanced air bag performance, establish the technological potential for improved technology (smart) air bag systems, and identify key expertise and technology within the agency (i.e., NASA) that can potentially contribute significantly to the improved effectiveness of air bags." NASA is committed to contributing to NHTSAs effort to: (1) understand and define critical parameters affecting air bag performance; (2) systematically assess air bag technology state of the art and its future potential; and (3) identify new concepts for air bag systems. The Jet Propulsion Laboratory (JPL) was selected by NASA to respond to the memorandum of understanding by conducting an advanced air bag technology assessment. JPL analyzed the nature of the need for occupant restraint, how air bags operate alone and with safety belts to provide restraint, and the potential hazards introduced by the technology. This analysis yielded a set of critical parameters for restraint systems. The researchers examined data on the performance of current air bag technology, and searched for and assessed how new technologies could reduce the hazards introduced by air bags while providing the restraint protection that is their primary purpose. The critical parameters which were derived are: (1) the crash severity; (2) the use of seat belts; (3) the physical characteristics of the occupants; (4) the proximity of the occupants to the airbag module; (5) the deployment time, which includes the time to sense the need for deployment, the inflator response parameters, the air bag response, and the reliability of the air bag. The requirements for an advanced air bag technology is discussed. These requirements includes that the system use information related to: (1) the crash severity; (2) the status of belt usage; (3) the occupant category; and (4) the proximity to the air bag to adjust air bag deployment. The parameters for the response of the air bag are: (1) deployment time; (2) inflator parameters; and (3) air bag response and reliability. The state of occupant protection advanced technology is reviewed. This review includes: the current safety restraint systems, and advanced technology characteristics. These characteristics are summarized in a table, which has information regarding the technology item, the potential, and an date of expected utilization. The use of technology and expertise at NASA centers is discussed. NASA expertise relating to sensors, computing, simulation, propellants, propulsion, inflatable systems, systems analysis and engineering is considered most useful. Specific NASA technology developments, which were included in the study are: (1) a capacitive detector; (2) stereoscopic vision system; (3) improved crash sensors; (4) the use of the acoustic signature of the crash to determine crash severity; and (5) the use of radar antenna for pre-crash sensing. Information relating to injury risk assessment is included, as is a summary of the areas of the technology which requires further development.
The feasibility is discussed for alternative methods of direct Monte Carlo simulation for failure probability computations. First and second order reliability methods are used for fatigue crack growth and low cycle fatigue structural failure modes to illustrate typical problems.
The accelerator grid in two-grid ion optics systems is subject to sputter erosion by charge-exchange ions created in the ion beam.
A probabilistic methodology for evaluating failure risk, assessing service life, and establishing design parameters for structures subject to fatigue failure has been developed.
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Report describes probabilistic failure assessment (PFA). Developed for application to spaceflight systems for sufficient testing of hardware to ensure reliability not feasible. However, must be ascertained that critical failure modes extremely unlikely to occur during service. PFA applied to any failure mode described by quantitative models of physics and mechanics of failure phenomena, such as fatigue crack in initiation or propagation in structures, leakage of seals, wear in bearings, and erosion of arcjet thrustor cathodes.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for designs failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflights systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with analytical modeling of failure phenomena to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in analytical modeling, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which analytical models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. State-of-the-art analytical models currently employed for design, failure prediction, or performance analysis are used in this methodology. The rationale for the statistical approach taken in the PFA methodology is discussed, the PFA methodology is described, and examples of its application to structural failure modes are presented. The engineering models and computer software used in fatigue crack growth and fatigue crack initiation applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes, These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk of spaceflight systems to assess flight readiness and identify risk control measures is presented. This methodology, called Probabilistic Failure Assessment (PFA), combines operating experience from tests and flights with engineering analysis to estimate failure risk. The PFA methodology is of particular value when information on which to base an assessment of failure risk, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is expensive or difficult to acquire. The PFA methodology is a prescribed statistical structure in which engineering analysis models that characterize failure phenomena are used conjointly with uncertainties about analysis parameters and/or modeling accuracy to estimate failure probability distributions for specific failure modes. These distributions can then be modified, by means of statistical procedures of the PFA methodology, to reflect any test or flight experience. Conventional engineering analysis models currently employed for design of failure prediction are used in this methodology. The PFA methodology is described and examples of its application are presented. Conventional approaches to failure risk evaluation for spaceflight systems are discussed, and the rationale for the approach taken in the PFA methodology is presented. The statistical methods, engineering models, and computer software used in fatigue failure mode applications are thoroughly documented.
An improved methodology for quantitatively evaluating failure risk for a spaceflight system in order to assess flight readiness is presented. This methodology is of particular value when information relevant to failure prediction, including test experience and knowledge of parameters used in engineering analyses of failure phenomena, is limited. In this approach, engineering analysis models that characterize specific failure modes based on the physics and mechanics of the failure phenomena are used in a prescribed probabilistic structure to generate a failure probability distribution that is modified by test and flight experience in a Bayesian statistical procedure. The probabilistic structure and statistical methodology are generally applicable to any failure mode for which quantitative engineering analysis can be employed to characterize the failure phenomenon and are particularly well suited for use under the constraints on information availability that are typical of such spaceflight systems as the Space Shuttle and planetary spacecraft.