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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Probabilistic Risk Assessment for Astronaut Post Flight Bone Fracture

Introduction: Space flight potentially reduces the loading that bone can resist before fracture. This reduction in bone integrity may result from a combination of factors, the most common reported as reduction in astronaut BMD. Although evaluating the condition of bones continues to be a critical aspect of understanding space flight fracture risk, defining the loading regime, whether on earth, in microgravity, or in reduced gravity on a planetary surface, remains a significant component of estimating the fracture risks to astronauts. This presentation summarizes the concepts, development, and application of NASA's Bone Fracture Risk Module (BFxRM) to understanding pre-, post, and in mission astronaut bone fracture risk. The overview includes an assessment of contributing factors utilized in the BFxRM and illustrates how new information, such as biomechanics of space suit design or better understanding of post flight activities may influence astronaut fracture risk. Opportunities for the bone mineral research community to contribute to future model development are also discussed. Methods: To investigate the conditions in which spaceflight induced changes to bone plays a critical role in post-flight fracture probability, we implement a modified version of the NASA Bone Fracture Risk Model (BFxRM). Modifications included incorporation of variations in physiological characteristics, post-flight recovery rate, and variations in lateral fall conditions within the probabilistic simulation parameter space. The modeled fracture probability estimates for different loading scenarios at preflight and at 0 and 365 days post-flight time periods are compared. Results: For simple lateral side falls, mean post-flight fracture probability is elevated over mean preflight fracture probability due to spaceflight induced BMD loss and is not fully recovered at 365 days post-flight. In the case of more energetic falls, such as from elevated heights or with the addition of lateral movement, the contribution of space flight quality changes is much less clear, indicating more granular assessments, such as Finite Element modeling, may be needed to further assess the risks in these scenarios.

Risk Analysis↗
NASA NTRS1973

Cluster Seeking Techniques in Pattern Recognition

A cluster seeking technique is defined as a method of dividing data into subsets, called clusters. These clusters contain data points that are similar to each other and different from the elements of other clusters. Various cluster seeking techniques were broken down into seven categories: (1) probabilistic, (2) signal detection, (3) clustering, (4) clumping, (5) eigenvalue, (6) minimal mode seeking, and (7) miscellaneous. Each category is described and one or more algorithms of that type are presented.

Barr, B. J.↗
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↗
NASA NTRS2011

Methodology for Developing a Probabilistic Risk Assessment Model of Spacecraft Rendezvous and Dockings

In 2007 NASA was preparing to send two new visiting vehicles carrying logistics and propellant to the International Space Station (ISS). These new vehicles were the European Space Agency s (ESA) Automated Transfer Vehicle (ATV), the Jules Verne, and the Japanese Aerospace and Explorations Agency s (JAXA) H-II Transfer Vehicle (HTV). The ISS Program wanted to quantify the increased risk to the ISS from these visiting vehicles. At the time, only the Shuttle, the Soyuz, and the Progress vehicles rendezvoused and docked to the ISS. The increased risk to the ISS was from an increase in vehicle traffic, thereby, increasing the potential catastrophic collision during the rendezvous and the docking or berthing of the spacecraft to the ISS. A universal method of evaluating the risk of rendezvous and docking or berthing was created by the ISS s Risk Team to accommodate the increasing number of rendezvous and docking or berthing operations due to the increasing number of different spacecraft, as well as the future arrival of commercial spacecraft. Before the first docking attempt of ESA's ATV and JAXA's HTV to the ISS, a probabilistic risk model was developed to quantitatively calculate the risk of collision of each spacecraft with the ISS. The 5 rendezvous and docking risk models (Soyuz, Progress, Shuttle, ATV, and HTV) have been used to build and refine the modeling methodology for rendezvous and docking of spacecrafts. This risk modeling methodology will be NASA s basis for evaluating the addition of future ISS visiting spacecrafts hazards, including SpaceX s Dragon, Orbital Science s Cygnus, and NASA s own Orion spacecraft. This paper will describe the methodology used for developing a visiting vehicle risk model.

Farnham, Steven J., II↗
NASA NTRS2010

Control of Risks Through the Use of Procedures: A Method for Evaluating the Change in Risk

This paper considers how procedures can be used to control risks faced by an organization and proposes a means of recognizing if a particular procedure reduces risk or contributes to the organization's exposure. The proposed method was developed out of the review of work documents and the governing procedures performed in the wake of the Columbia accident by NASA and the Space Shuttle prime contractor, United Space Alliance, LLC. A technique was needed to understand the rules, or procedural controls, in place at the time in the context of how important the role of each rule was. The proposed method assesses procedural risks, the residual risk associated with a hazard after a procedure's influence is accounted for, by considering each clause of a procedure as a unique procedural control that may be beneficial or harmful. For procedural risks with consequences severe enough to threaten the survival of the organization, the method measures the characteristics of each risk on a scale that is an alternative to the traditional consequence/likelihood couple. The dual benefits of the substitute scales are that they eliminate both the need to quantify a relationship between different consequence types and the need for the extensive history a probabilistic risk assessment would require. Control Value is used as an analog for the consequence, where the value of a rule is based on how well the control reduces the severity of the consequence when operating successfully. This value is composed of two parts: the inevitability of the consequence in the absence of the control, and the opportunity to intervene before the consequence is realized. High value controls will be ones where there is minimal need for intervention but maximum opportunity to actively prevent the outcome. Failure Likelihood is used as the substitute for the conventional likelihood of the outcome. For procedural controls, a failure is considered to be any non-malicious violation of the rule, whether intended or not. The model used for describing the Failure Likelihood considers how well a task was established by evaluating that task on five components. The components selected to define a well established task are: that it be defined, assigned to someone capable, that they be trained appropriately, that the actions be organized to enable proper completion and that some form of independent monitoring be performed. Validation of the method was based on the information provided by a group of experts in Space Shuttle ground processing when they were presented with 5 scenarios that identified a clause from a procedure. For each scenario, they recorded their perception of how important the associated rule was and how likely it was to fail. They then rated the components of Control Value and Failure Likelihood for all the scenarios. The order in which each reviewer ranked the scenarios Control Value and Failure Likelihood was compared to the order in which they ranked the scenarios for each of the associated components; inevitability and opportunity for Control Value and definition, assignment, training, organization and monitoring for Failure Likelihood. This order comparison showed how the components contributed to a relative relationship to the substitute risk element. With the relationship established for Space Shuttle ground processing, this method can be used to gauge if the introduction or removal of a particular rule will increase or decrease the .risk associated with the hazard it is intended to control.

Praino, Gregory T.↗
NASA NTRS1990

Probabilistic low cycle fatigue failure analysis with application to liquid propellant rocket engines

A probabilistic Low Cycle Fatigue (LCF) failure analysis of a candidate turbine disk for use in a turbopump of a rocket engine of the Space Shuttle Main Engine class is described. A state-of-the-art LCF failure prediction method was used in a Monte Carlo simulation to generate a distribution of failure lives. A stochastic Stress/Life (S/N) model was used for materials characterization. The LCF failure model expresses fatigue life as a function of stochastic parameters including environmental parameters, loads, material properties, structural parameters, and model specification errors. The rationale for the particular characterization of each stochastic input parameter is described. The results and interpretation of the failure analysis are given.

Newlin, L.↗
NASA NTRS1991

Quantitative knowledge acquisition for expert systems

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a direct task. A statistical method is presented for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development is described of a Navigation Sensor Management (NSM) Expert System from Kalman Filter convariance data. The method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 Algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithms identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. Both are detailed.

Belkin, Brenda L.↗
NASA NTRS1994

Data Analysis with Graphical Models: Software Tools

Probabilistic graphical models (directed and undirected Markov fields, and combined in chain graphs) are used widely in expert systems, image processing and other areas as a framework for representing and reasoning with probabilities. They come with corresponding algorithms for performing probabilistic inference. This paper discusses an extension to these models by Spiegelhalter and Gilks, plates, used to graphically model the notion of a sample. This offers a graphical specification language for representing data analysis problems. When combined with general methods for statistical inference, this also offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper outlines the framework and then presents some basic tools for the task: a graphical version of the Pitman-Koopman Theorem for the exponential family, problem decomposition, and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Buntine, Wray L.↗
NASA NTRS1991

Knowledge acquisition for expert systems using statistical methods

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.

Belkin, Brenda L.↗
NASA NTRS2008

Science Goals in Radiation Protection for Exploration

Space radiation presents major challenges to future missions to the Earth s moon or Mars. Health risks of concern include cancer, degenerative and performance risks to the central nervous system, heart and lens, and the acute radiation syndromes. The galactic cosmic rays (GCR) contain high energy and charge (HZE) nuclei, which have been shown to cause qualitatively distinct biological damage compared to terresterial radiation, such as X-rays or gamma-rays, causing risk estimates to be highly uncertain. The biological effects of solar particle events (SPE) are similar to terresterial radiation except for their biological dose-rate modifiers; however the onset and size of SPEs are difficult to predict. The high energies of GCR reduce the effectiveness of shielding, while SPE s can be shielded however the current gap in radiobiological knowledge hinders optimization. Methods used to project risks on Earth must be modified because of the large uncertainties in projecting health risks from space radiation, and thus impact mission requirements and costs. We describe NASA s unique approach to radiation safety that applies probabilistic risk assessments and uncertainty based criteria within the occupational health program for astronauts and to mission design. The two terrestrial criteria of a point estimate of maximum acceptable level of risk and application of the principle of As Low As Reasonably Achievable (ALARA) are supplemented by a third requirement that protects against risk projection uncertainties using the upper 95% confidence level (CL) in radiation risk projection models. Exploration science goals in radiation protection are centered on ground-based research to achieve the necessary biological knowledge, and in the development of new technologies to improve SPE monitoring and optimize shielding. Radiobiology research is centered on a ground based program investigating the radiobiology of high-energy protons and HZE nuclei at the NASA Space Radiation Laboratory (NSRL) located at DoE s Brookhaven National Laboratory in Upton, NY. We describe recent NSRL results that are closing the knowledge gap in HZE radiobiology and improving exploration risk estimates. Linking probabilistic risk assessment to research goals makes it possible to express risk management objectives in terms of quantitative metrics, which include the number of days in space without exceeding a given risk level within well defined confidence limits, and probabilistic assessments of the effectiveness of design trade spaces such as material type, mass, solar cycle, crew selection criteria, and biological countermeasures. New research in SPE alert and risk assessment, individual radiation sensitivity, and biological countermeasure development are described.

Cucinotta, Francs A.↗
NASA NTRS2023

A Pilot Project for Quantifying the Effect of Medical Provider Knowledge, Skills, and Abilities on Outcomes for Spaceflight Using a Probabilistic Risk Assessment Tool

In order to enable the future of long-duration deep space exploration we must confront the uncertainty in medical risk. Limitations of communication, resupply, and evacuation in deep space will require a high degree of crew autonomy and accurate risk assessment will be critical to ensure adequate crew training and medical system design. To address this, NASA’s Human Research Program Exploration Medical Capability Element has developed the Informing Mission Planning via Analysis of Complex Tradespaces Tool (IMPACT). IMPACT is a suite of tools that can provide evidence-based, data-driven trade space assessments between available medical resources in the mass- and volume-constrained environment of a deep space exploration vehicle. In the current model, medical conditions either can or cannot be treated based on the availability of medical system resources and equipment. However, medical outcomes often depend just as much on the knowledge, skills, and abilities (KSA) of the provider operating the system. This paper presents a method for modeling and quantifying the effect of medical officer KSA on medically relevant mission risk outcomes during spaceflight.

capabilities↗
NASA NTRS1993

A possibilistic approach to clustering

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Krishnapuram, Raghu↗
NASA NTRS2002

Probabilistic Design of a Mars Sample Return Earth Entry Vehicle Thermal Protection System

The driving requirement for design of a Mars Sample Return mission is to assure containment of the returned samples. Designing to, and demonstrating compliance with, such a requirement requires physics based tools that establish the relationship between engineer's sizing margins and probabilities of failure. The traditional method of determining margins on ablative thermal protection systems, while conservative, provides little insight into the actual probability of an over-temperature during flight. The objective of this paper is to describe a new methodology for establishing margins on sizing the thermal protection system (TPS). Results of this Monte Carlo approach are compared with traditional methods.

Dec, John A.↗
NASA NTRS2004

Application of Non-Deterministic Methods to Assess Modeling Uncertainties for Reinforced Carbon-Carbon Debris Impacts

The Space Shuttle Columbia Accident Investigation Board (CAIB) made several recommendations for improving the NASA Space Shuttle Program. An extensive experimental and analytical program has been developed to address two recommendations related to structural impact analysis. The objective of the present work is to demonstrate the application of probabilistic analysis to assess the effect of uncertainties on debris impacts on Space Shuttle Reinforced Carbon-Carbon (RCC) panels. The probabilistic analysis is used to identify the material modeling parameters controlling the uncertainty. A comparison of the finite element results with limited experimental data provided confidence that the simulations were adequately representing the global response of the material. Five input parameters were identified as significantly controlling the response.

Lyle, Karen H.↗
NASA NTRS1973

Eigenvalue uncertainty in stressed structures.

A method is presented for calculating the statistics of the natural frequencies and mode shapes of vibration for a structure acted upon by an external static loading which results in the structure being stressed for eigenvalue analysis. The analytical tools presented apply to the probabilistic eigenvalue problem, and it is apparent that structural parameter uncertainty will significantly influence all aspects of the structure's response. The treatment of a sample problem serves the purpose of furthering understanding for the importance of considering structural parameters as random variables.

Hart, G. C.↗
NASA NTRS1988

Probabilistic structural analysis computer code (NESSUS)

Probabilistic structural analysis has been developed to analyze the effects of fluctuating loads, variable material properties, and uncertain analytical models especially for high performance structures such as SSME turbopump blades. The computer code NESSUS (Numerical Evaluation of Stochastic Structure Under Stress) was developed to serve as a primary computation tool for the characterization of the probabilistic structural response due to the stochastic environments by statistical description. The code consists of three major modules NESSUS/PRE, NESSUS/FEM, and NESSUS/FPI. NESSUS/PRE is a preprocessor which decomposes the spatially correlated random variables into a set of uncorrelated random variables using a modal analysis method. NESSUS/FEM is a finite element module which provides structural sensitivities to all the random variables considered. NESSUS/FPI is Fast Probability Integration method by which a cumulative distribution function or a probability density function is calculated.

Shiao, Michael C.↗
NASA NTRS2016

A Computationally-Efficient Inverse Approach to Probabilistic Strain-Based Damage Diagnosis

This work presents a computationally-efficient inverse approach to probabilistic damage diagnosis. Given strain data at a limited number of measurement locations, Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling are used to estimate probability distributions of the unknown location, size, and orientation of damage. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. The approach is experimentally validated on cracked test specimens where full field strains are determined using digital image correlation (DIC). Access to full field DIC data allows for testing of different hypothetical sensor arrangements, facilitating the study of strain-based diagnosis effectiveness as the distance between damage and measurement locations increases. The ability of the framework to effectively perform both probabilistic damage localization and characterization in cracked plates is demonstrated and the impact of measurement location on uncertainty in the predictions is shown. Furthermore, the analysis time to produce these predictions is orders of magnitude less than a baseline Bayesian approach with the FE method by utilizing surrogate modeling and effective numerical sampling approaches.

Warner, James E.↗
NASA NTRS1975

Reliability approach to rotating-component design

A probabilistic methodology for designing rotating mechanical components using reliability to relate stress to strength is explained. The experimental test machines and data obtained for steel to verify this methodology are described. A sample mechanical rotating component design problem is solved by comparing a deterministic design method with the new design-by reliability approach. The new method shows that a smaller size and weight can be obtained for specified rotating shaft life and reliability, and uses the statistical distortion-energy theory with statistical fatigue diagrams for optimum shaft design. Statistical methods are presented for (1) determining strength distributions for steel experimentally, (2) determining a failure theory for stress variations in a rotating shaft subjected to reversed bending and steady torque, and (3) relating strength to stress by reliability.

Kececioglu, D. B.↗