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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 NTRS2019

Determining Appropriate Risk Remediation Thresholds from Empirical Conjunction Data Using Survival Probability Methods

Satellites sometimes maneuver before conjunctions to remediate the risk of an on-orbit collision. Many missions use probability of collision (P(sub c)) thresholds to decide when such maneuvers should be performed. These thresholds tend to be conservative because of policies that require satellites survive their lifetimes without collision with high confidence (e.g., 99.9%). This study presents a semi-empirical method to estimate remediation P(sub c) thresholds that satisfy such lifetime risk requirements. The formulation combines survival probability analysis with empirical conjunction histories to estimate remediation thresholds as a function of satellite size, remaining on-orbit duration, lifetime collision probability limit, collision consequence, and other parameters.

Risk remediation thresholds↗
NASA NTRS2010

Risk Assessment Using the Three Dimensions of Probability (Likelihood), Severity, and Level of Control

Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the twodimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and threedimensional charting gives a visual confirmation of the relationship between causes and their controls

Watson, Clifford↗
NASA NTRS2011

Risk Assessment Using the Three Dimensions of Probability (Likelihood), Severity, and Level of Control

Traditional hazard analysis techniques utilize a two-dimensional representation of the results determined by relative likelihood and severity of the residual risk. These matrices present a quick-look at the Likelihood (Y-axis) and Severity (X-axis) of the probable outcome of a hazardous event. A three-dimensional method, described herein, utilizes the traditional X and Y axes, while adding a new, third dimension, shown as the Z-axis, and referred to as the Level of Control. The elements of the Z-axis are modifications of the Hazard Elimination and Control steps (also known as the Hazard Reduction Precedence Sequence). These steps are: 1. Eliminate risk through design. 2. Substitute less risky materials for more hazardous materials. 3. Install safety devices. 4. Install caution and warning devices. 5. Develop administrative controls (to include special procedures and training.) 6. Provide protective clothing and equipment. When added to the two-dimensional models, the level of control adds a visual representation of the risk associated with the hazardous condition, creating a tall-pole for the least-well-controlled failure while establishing the relative likelihood and severity of all causes and effects for an identified hazard. Computer modeling of the analytical results, using spreadsheets and three-dimensional charting gives a visual confirmation of the relationship between causes and their controls.

Watson, Clifford C.↗
NASA NTRS2015

Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis

Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

Operations↗
NASA NTRSDate not supplied

Accounting for Point Estimate Uncertainty in Space Systems Reliability and Risk Analysis

Understanding and accounting for uncertainty in risk analysis is a critical step in the management and communication of risk in engineered systems. The component and system-level analysis to determine the probability of a negative outcome and its consequence is often quantified by a point estimate. Many Program and Enterprise decisions involving technical concerns and issues rely on reliability engineering activities to produce quantified risk analysis to inform the decision making process. At NASA, it is common to use a Probabilistic Risk Analysis (PRA) to inform the overall risk to Loss of Mission or Loss of Crew that involves integration across all spacecraft subsystem fault trees to produce an overall probability of mission failure. The point estimate is an estimate of this overall probability and is an immediate result of a fault tree model. It is the result of a model where the probability of each event is taken to be equal to its mean. The value provides an approximation of the overall mean without running any uncertainty calculations (e.g., no sampling). Using only the point estimate can lead to a false sense of precision and the point estimate may not match the resulting mean when uncertainty is taken into consideration. This paper will explore five conditions that can cause the PRA model mean to diverge from the point estimate and will provide engineers and managers insight into the importance of understanding uncertainty in the elements of PRA models.

Paul J Collier↗
NASA NTRS1989

Probability of failure and risk assessment of propulsion structural components

The probabilistic structural analysis method (PSAM) was developed to analyze the effects of fluctuating loads, variable material properties, and uncertain analytical models especially for high performance structures such as the Space Shuttle Main Engine turbopump blades. Risk is calculated after expensive service experience. However, probabilistic structural analysis provides a rational alternative method to quantify uncertainties in the structural performance and durability. NESSUS (Numerical Evaluation of Stochastic Structures Under Stress) was developed as a probabilistic structural analysis computer code which integrates finite element methods and reliability algorithms, capable to predicting the probability distributions of structural response variables such as stress, displacement, natural frequencies, and buckling loads. This computer code is detailed.

Shiao, Michael C.↗
NASA NTRSDate not supplied

The Challenger Tradgedy was Caused by an Apollo Mistake, Terminating Risk Analysis

NASA’s attitude toward risk changed drastically between the Apollo and Space Shuttle design. [1]Apollo engineers were seriously alarmed about riskbecause of the fatal Apollo 1 fire and the fact that the estimated probability of another fatal accident was extremely high. The predictions were so appalling thatmanagement terminated risk analysis to avoid public apprehension. Because risk analysis was not done, the Space Shuttle design accepted excessively high andunknown risk. The immediate cause of the Challengertragedy was the mistaken decision to launch in coldweather that impaired the O-ring seals, but the fundamental cause was the high risk of the shuttle design. Before Challenger, management asserted that the probability of a fatal accident was 1 in 100,000.Probabilistic Risk Analysis (PRA) found a roughly 1 in 100 chance of a shuttle failure.

Harry W Jones↗
NASA NTRS1983

Application of advanced reliability methods to local strain fatigue analysis

When design factors are considered as random variables and the failure condition cannot be expressed by a closed form algebraic inequality, computations of risk (or probability of failure) might become extremely difficult or very inefficient. This study suggests using a simple, and easily constructed, second degree polynomial to approximate the complicated limit state in the neighborhood of the design point; a computer analysis relates the design variables at selected points. Then a fast probability integration technique (i.e., the Rackwitz-Fiessler algorithm) can be used to estimate risk. The capability of the proposed method is demonstrated in an example of a low cycle fatigue problem for which a computer analysis is required to perform local strain analysis to relate the design variables. A comparison of the performance of this method is made with a far more costly Monte Carlo solution. Agreement of the proposed method with Monte Carlo is considered to be good.

Wu, T. T.↗
NASA NTRS1984

Advanced reliability method for fatigue analysis

When design factors are considered as random variables and the failure condition cannot be expressed by a closed form algebraic inequality, computations of risk (or probability of failure) may become extremely difficult or very inefficient. This study suggests using a simple and easily constructed second degree polynomial to approximate the complicated limit state in the neighborhood of the design point; a computer analysis relates the design variables at selected points. Then a fast probability integration technique (i.e., the Rackwitz-Fiessler algorithm) can be used to estimate risk. The capability of the proposed method is demonstrated in an example of a low cycle fatigue problem for which a computer analysis is required to perform local strain analysis to relate the design variables. A comparison of the performance of this method is made with a far more costly Monte Carlo solution. Agreement of the proposed method with Monte Carlo is considered to be good.

Wu, Y.-T.↗
NASA NTRS2014

Probability of Loss of Crew Achievability Studies for NASA's Exploration Systems Development

Over the last few years, NASA has been evaluating various vehicle designs for multiple proposed design reference missions (DRM) beyond low Earth orbit in support of its Exploration Systems Development (ESD) programs. This paper addresses several of the proposed missions and the analysis techniques used to assess the key risk metric, probability of loss of crew (LOC). Probability of LOC is a metric used to assess the safety risk as well as a design requirement. These risk assessments typically cover the concept phase of a DRM, i.e. when little more than a general idea of the mission is known and are used to help establish "best estimates" for proposed program and agency level risk requirements. These assessments or studies were categorized as LOC achievability studies to help inform NASA management as to what "ball park" estimates of probability of LOC could be achieved for each DRM and were eventually used to establish the corresponding LOC requirements. Given that details of the vehicles and mission are not well known at this time, the ground rules, assumptions, and consistency across the programs become the important basis of the assessments as well as for the decision makers to understand.

Boyer, Roger L.↗
NASA NTRS2015

Probability of Loss of Crew Achievability Studies for NASA's Exploration Systems Development

Over the last few years, NASA has been evaluating various vehicle designs for multiple proposed design reference missions (DRM) beyond low Earth orbit in support of its Exploration Systems Development (ESD) programs. This paper addresses several of the proposed missions and the analysis techniques used to assess the key risk metric, probability of loss of crew (LOC). Probability of LOC is a metric used to assess the safety risk as well as a design requirement. These risk assessments typically cover the concept phase of a DRM, i.e. when little more than a general idea of the mission is known and are used to help establish "best estimates" for proposed program and agency level risk requirements. These assessments or studies were categorized as LOC achievability studies to help inform NASA management as to what "ball park" estimates of probability of LOC could be achieved for each DRM and were eventually used to establish the corresponding LOC requirements. Given that details of the vehicles and mission are not well known at this time, the ground rules, assumptions, and consistency across the programs become the important basis of the assessments as well as for the decision makers to understand.

Boyer, Roger L.↗
NASA NTRS2015

Probability of Loss of Crew Achievability Studies for NASA's Exploration Systems Development

Over the last few years, NASA has been evaluating various vehicle designs for multiple proposed design reference missions (DRM) beyond low Earth orbit in support of its Exploration Systems Development (ESD) programs. This paper addresses several of the proposed missions and the analysis techniques used to assess the key risk metric, probability of loss of crew (LOC). Probability of LOC is a metric used to assess the safety risk as well as a design requirement. These risk assessments typically cover the concept phase of a DRM, i.e. when little more than a general idea of the mission is known and are used to help establish "best estimates" for proposed program and agency level risk requirements. These assessments or studies were categorized as LOC achievability studies to help inform NASA management as to what "ball park" estimates of probability of LOC could be achieved for each DRM and were eventually used to establish the corresponding LOC requirements. Given that details of the vehicles and mission are not well known at this time, the ground rules, assumptions, and consistency across the programs become the important basis of the assessments as well as for the decision makers to understand.

Boyer, Roger L.↗
NASA NTRS2015

Probability of Loss of Crew Achievability Studies for NASA's Exploration Systems Development

Over the last few years, NASA has been evaluating various vehicle designs for multiple proposed design reference missions (DRM) beyond low Earth orbit in support of its Exploration Systems Development (ESD) programs. This paper addresses several of the proposed missions and the analysis techniques used to assess the key risk metric, probability of loss of crew (LOC). Probability of LOC is a metric used to assess the safety risk as well as a design requirement. These assessments or studies were categorized as LOC achievability studies to help inform NASA management as to what "ball park" estimates of probability of LOC could be achieved for each DRM and were eventually used to establish the corresponding LOC requirements. Given that details of the vehicles and mission are not well known at this time, the ground rules, assumptions, and consistency across the programs become the important basis of the assessments as well as for the decision makers to understand.

Boyer, Roger L.↗
NASA NTRS1986

Cost Risk Analysis Based on Perception of the Engineering Process

In most cost estimating applications at the NASA Langley Research Center (LaRC), it is desirable to present predicted cost as a range of possible costs rather than a single predicted cost. A cost risk analysis generates a range of cost for a project and assigns a probability level to each cost value in the range. Constructing a cost risk curve requires a good estimate of the expected cost of a project. It must also include a good estimate of expected variance of the cost. Many cost risk analyses are based upon an expert's knowledge of the cost of similar projects in the past. In a common scenario, a manager or engineer, asked to estimate the cost of a project in his area of expertise, will gather historical cost data from a similar completed project. The cost of the completed project is adjusted using the perceived technical and economic differences between the two projects. This allows errors from at least three sources. The historical cost data may be in error by some unknown amount. The managers' evaluation of the new project and its similarity to the old project may be in error. The factors used to adjust the cost of the old project may not correctly reflect the differences. Some risk analyses are based on untested hypotheses about the form of the statistical distribution that underlies the distribution of possible cost. The usual problem is not just to come up with an estimate of the cost of a project, but to predict the range of values into which the cost may fall and with what level of confidence the prediction is made. Risk analysis techniques that assume the shape of the underlying cost distribution and derive the risk curve from a single estimate plus and minus some amount usually fail to take into account the actual magnitude of the uncertainty in cost due to technical factors in the project itself. This paper addresses a cost risk method that is based on parametric estimates of the technical factors involved in the project being costed. The engineering process parameters are elicited from the engineer/expert on the project and are based on that expert's technical knowledge. These are converted by a parametric cost model into a cost estimate. The method discussed makes no assumptions about the distribution underlying the distribution of possible costs, and is not tied to the analysis of previous projects, except through the expert calibrations performed by the parametric cost analyst.

Dean, Edwin B.↗
NASA NTRS2014

An Example of Risk Informed Design

NASA Engineering requested a Probabilistic Risk Assessment (PRA) to compare the difference in the risk of Loss of Crew (LOC) and Loss of Mission (LOM) between different designs of a fluid assembly. They were concerned that the configuration favored by the design team was more susceptible to leakage than a second proposed design, but realized that a quantitative analysis to compare the risks between the two designs might strengthen their argument. The analysis showed that while the second design did help improve the probability of LOC, it did not help from a probability of LOM perspective. This drove the analysis team to propose a minor design change that would drive the probability of LOM down considerably. The analysis also demonstrated that there was another major risk driver that was not immediately obvious from a typical engineering study of the design and was therefore unexpected. None of the proposed alternatives were addressing this risk. This type of trade study demonstrates the importance of performing a PRA in order to completely understand a system's design. It allows managers to use risk as another one of the commodities (e.g., mass, cost, schedule, fault tolerance) that can be traded early in the design of a new system.

Banke, Rick↗
NASA NTRS2010

Effects of Increasing Drag on Conjunction Assessment

Conjunction Assessment Risk Analysis relies heavily on the computation of the Probability of Collision (Pc) and the understanding of the sensitivity of this calculation to the position errors as defined by the covariance. In Low Earth Orbit (LEO), covariance is predominantly driven by perturbations due to atmospheric drag. This paper describes the effects of increasing atmospheric drag through Solar Cycle 24 on Pc calculations. The process of determining these effects is found through analyzing solar flux predictions on Energy Dissipation Rate (EDR), historical relationship between EDR and covariance, and the sensitivity of Pc to covariance. It is discovered that while all LEO satellites will be affected by the increase in solar activity, the relative effect is more significant in the LEO regime around 700 kilometers in altitude compared to 400 kilometers. Furthermore, it is shown that higher Pc values can be expected at larger close approach miss distances. Understanding these counter-intuitive results is important to setting Owner/Operator expectations concerning conjunctions as solar maximum approaches.

Frigm, Ryan Clayton↗
NASA NTRS2019

Constraint-Based Off-Nominal Behavior Modeling for Europa Clipper

The risk analysis for the Europa Clipper mission evaluates the probability of mission failure based on the failure rates of individual components and dependencies among them. The probabilities are calculated by integrating over the intervals of time within which a fault occurs, accounting for an infinite number of cases. The response of the spacecraft to different faults can result in different schedules of activities, changing the intervals of integration. Europa currently uses models of spacecraft systems and components to simulate individual flight scenarios. The goal is to develop a framework for integrating, automating, and improving this modeling process. We describe an approach to generating the schedules for the different fault cases and determining the intervals for faults. It is not enough to just simulate individual cases because we are working with continuous variables that generate an infinite number of possible futures. Instead, we determine time windows within which certain faults can occur and use these time windows as bounds for integration. We found that determining these time windows is a constraint optimization problem. In order to represent these problems, we employ a language based on ontologies of behavior and scenarios. The language enables us to specify constraints in a simple, declarative syntax. A constraint-based analysis engine uses the declarative specification to identify bounds on system parameters and fill in details of behavior. For example, we created a detailed model of power generation, power use, and the corresponding effects on the battery in order to determine when an undervoltage fault can occur. An undervoltage during a trajectory correction maneuver requires that thrusting be interrupted for just enough time to recharge the battery such that the maneuver can be completed within battery limits. This behavior is generated based on the model to minimize the interruption time. For certain scenarios the constraint optimization problems were simple enough to be solved by hand, but the framework made the process substantially faster. It also produced solutions to other problems that we could not solve by hand or with existing tools and allowed us to generate and run many scenarios at once. The scenario language and engine greatly simplified the process of identifying time bounds and separating cases.

Everline, Chester J.↗