Search NASA⌕ Search

SEARCH · Search NASA

Results for “probabilistic programming”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

Probabilistic structural analysis methods of hot engine structures

Development of probabilistic structural analysis methods for hot engine structures at Lewis Research Center is presented. Three elements of the research program are: (1) composite load spectra methodology; (2) probabilistic structural analysis methodology; and (3) probabilistic structural analysis application. Recent progress includes: (1) quantification of the effects of uncertainties for several variables on high pressure fuel turbopump (HPFT) turbine blade temperature, pressure, and torque of the space shuttle main engine (SSME); (2) the evaluation of the cumulative distribution function for various structural response variables based on assumed uncertainties in primitive structural variables; and (3) evaluation of the failure probability. Collectively, the results demonstrate that the structural durability of hot engine structural components can be effectively evaluated in a formal probabilistic/reliability framework.

Chamis, C. C.↗
NASA NTRS1991

Probabilistic lifetime strength of aerospace materials via computational simulation

The results of a second year effort of a research program are presented. The research included development of methodology that provides probabilistic lifetime strength of aerospace materials via computational simulation. A probabilistic phenomenological constitutive relationship, in the form of a randomized multifactor interaction equation, is postulated for strength degradation of structural components of aerospace propulsion systems subjected to a number of effects of primitive variables. These primitive variables often originate in the environment and may include stress from loading, temperature, chemical, or radiation attack. This multifactor interaction constitutive equation is included in the computer program, PROMISS. Also included in the research is the development of methodology to calibrate the constitutive equation using actual experimental materials data together with the multiple linear regression of that data.

Boyce, Lola↗
NASA NTRS2014

Aviation Safety Risk Modeling: Lessons Learned From Multiple Knowledge Elicitation Sessions

Aviation safety risk modeling has elements of both art and science. In a complex domain, such as the National Airspace System (NAS), it is essential that knowledge elicitation (KE) sessions with domain experts be performed to facilitate the making of plausible inferences about the possible impacts of future technologies and procedures. This study discusses lessons learned throughout the multiple KE sessions held with domain experts to construct probabilistic safety risk models for a Loss of Control Accident Framework (LOCAF), FLightdeck Automation Problems (FLAP), and Runway Incursion (RI) mishap scenarios. The intent of these safety risk models is to support a portfolio analysis of NASA's Aviation Safety Program (AvSP). These models use the flexible, probabilistic approach of Bayesian Belief Networks (BBNs) and influence diagrams to model the complex interactions of aviation system risk factors. Each KE session had a different set of experts with diverse expertise, such as pilot, air traffic controller, certification, and/or human factors knowledge that was elicited to construct a composite, systems-level risk model. There were numerous "lessons learned" from these KE sessions that deal with behavioral aggregation, conditional probability modeling, object-oriented construction, interpretation of the safety risk results, and model verification/validation that are presented in this paper.

Luxhoj, J. T.↗
NASA NTRS1986

Management of SSME hardware life utilization

Statistical and probabilistic reliability methodologies were developed for the determination of hardware life limits for the Space Shuttle Main Engine (SSME). Both methodologies require that a mathematical reliability model of the engine (system) performance be developed as a function of the reliabilities of the components and parts. The system reliability model should be developed from the Failute Modes and Effects Analysis/Critical Items List. The statistical reliability methodology establishes hardware life limits directly from the failure distributions of the components and parts obtained from statistically-designed testing. The probabilistic reliability methodology establishes hardware life limits from a decision analysis methodology which incorporates the component/part reliabilities obtained from a probabilistic structural analysis, a calibrated maintenance program, inspection techniques, and fabrication procedures. Probilistic structural analysis is recommended as a tool to prioritize upgrading of the components and parts. The Weibull probability distribution is presently being investigated by NASA/MSFC to characterize the failure distribution of the SSME hardware from a limited data base of failures.

Pauschke, J. M.↗
NASA NTRS2009

Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis

This document, Bayesian Inference for NASA Probabilistic Risk and Reliability Analysis, is intended to provide guidelines for the collection and evaluation of risk and reliability-related data. It is aimed at scientists and engineers familiar with risk and reliability methods and provides a hands-on approach to the investigation and application of a variety of risk and reliability data assessment methods, tools, and techniques. This document provides both: A broad perspective on data analysis collection and evaluation issues. A narrow focus on the methods to implement a comprehensive information repository. The topics addressed herein cover the fundamentals of how data and information are to be used in risk and reliability analysis models and their potential role in decision making. Understanding these topics is essential to attaining a risk informed decision making environment that is being sought by NASA requirements and procedures such as 8000.4 (Agency Risk Management Procedural Requirements), NPR 8705.05 (Probabilistic Risk Assessment Procedures for NASA Programs and Projects), and the System Safety requirements of NPR 8715.3 (NASA General Safety Program Requirements).

Dezfuli, Homayoon↗
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 NTRS2010

Probabilistic Design Analysis (PDA) Approach to Determine the Probability of Cross-System Failures for a Space Launch Vehicle

Quantifying the probability of significant launch vehicle failure scenarios for a given design, while still in the design process, is critical to mission success and to the safety of the astronauts. Probabilistic risk assessment (PRA) is chosen from many system safety and reliability tools to verify the loss of mission (LOM) and loss of crew (LOC) requirements set by the NASA Program Office. To support the integrated vehicle PRA, probabilistic design analysis (PDA) models are developed by using vehicle design and operation data to better quantify failure probabilities and to better understand the characteristics of a failure and its outcome. This PDA approach uses a physics-based model to describe the system behavior and response for a given failure scenario. Each driving parameter in the model is treated as a random variable with a distribution function. Monte Carlo simulation is used to perform probabilistic calculations to statistically obtain the failure probability. Sensitivity analyses are performed to show how input parameters affect the predicted failure probability, providing insight for potential design improvements to mitigate the risk. The paper discusses the application of the PDA approach in determining the probability of failure for two scenarios from the NASA Ares I project

Shih, Ann T.↗
NASA NTRS1983

A probabilistic analysis of silicon cost

Silicon materials costs represent both a cost driver and an area where improvement can be made in the manufacture of photovoltaic modules. The cost from three processes for the production of low-cost silicon being developed under the U.S. Department of Energy's (DOE) National Photovoltaic Program is analyzed. The approach is based on probabilistic inputs and makes use of two models developed at the Jet Propulsion Laboratory: SIMRAND (SIMulation of Research ANd Development) and IPEG (Improved Price Estimating Guidelines). The approach, assumptions, and limitations are detailed along with a verification of the cost analyses methodology. Results, presented in the form of cumulative probability distributions for silicon cost, indicate that there is a 55% chance of reaching the DOE target of $16/kg for silicon material. This is a technically achievable cost based on expert forecasts of the results of ongoing research and development and do not imply any market prices for a given year.

Reiter, L. J.↗
NASA NTRS2018

Probabilistic Sensitivity Analyses Incorporating Surrogate Models for the Structural Response of a Representative Inflatable Space Structure

This study concentrates on results from probabilistic analysis and numerical simulation tools to identify parameter sensitivities and assess surrogate model suitability for a novel inflatable airlock concept, specifically the Non-Axisymmetric Inflatable Pressure Structure (NAIPS) under NASA's Minimalistic Advanced Softgoods Hatch (MASH) Program. The current studies extended the demonstration of probabilistic analysis tools to identify parameter sensitivities by: incorporating nonlinear material load-strain property curves and orthotropic representation of the fabric; and exploring the use of surrogate models to facilitate rapid evaluations as new information becomes available, as well as enable optimizations. The airlock model has been simplified to focus on the end dome section. Brief overviews of the finite element model and associated surrogate models are provided along with the probabilistic sensitivity analysis approaches. Responses of interest include the sensitivities of various loads in the structural elements of the NAIPS to material properties, cord lengths, inflation pressure and friction between softgoods components. The use of surrogate models was explored to facilitate rapid sensitivity evaluations as parameter ranges change. The completion of the sensitivity studies improved understanding of the dependence of load responses to several uncertain parameters and confidence in the ability to use surrogate models to represent the finite element simulations.

Lyle, Karen H.↗
NASA NTRS2006

Probabilistic Risk Assessment (PRA): A Practical and Cost Effective Approach

The Lunar Reconnaissance Orbiter (LRO) is the first mission of the Robotic Lunar Exploration Program (RLEP), a space exploration venture to the Moon, Mars and beyond. The LRO mission includes spacecraft developed by NASA Goddard Space Flight Center (GSFC) and seven instruments built by GSFC, Russia, and contractors across the nation. LRO is defined as a measurement mission, not a science mission. It emphasizes the overall objectives of obtaining data to facilitate returning mankind safely to the Moon in preparation for an eventual manned mission to Mars. As the first mission in response to the President's commitment of the journey of exploring the solar system and beyond: returning to the Moon in the next decade, then venturing further into the solar system, ultimately sending humans to Mars and beyond, LRO has high-visibility to the public but limited resources and a tight schedule. This paper demonstrates how NASA's Lunar Reconnaissance Orbiter Mission project office incorporated reliability analyses in assessing risks and performing design tradeoffs to ensure mission success. Risk assessment is performed using NASA Procedural Requirements (NPR) 8705.5 - Probabilistic Risk Assessment (PRA) Procedures for NASA Programs and Projects to formulate probabilistic risk assessment (PRA). As required, a limited scope PRA is being performed for the LRO project. The PRA is used to optimize the mission design within mandated budget, manpower, and schedule constraints. The technique that LRO project office uses to perform PRA relies on the application of a component failure database to quantify the potential mission success risks. To ensure mission success in an efficient manner, low cost and tight schedule, the traditional reliability analyses, such as reliability predictions, Failure Modes and Effects Analysis (FMEA), and Fault Tree Analysis (FTA), are used to perform PRA for the large system of LRO with more than 14,000 piece parts and over 120 purchased or contractor built components.

Lee, Lydia L.↗
NASA NTRS1994

Probabilistic inspection strategies for minimizing service failures

The INSIM computer program is described which simulates the 'limited fatigue life' environment in which aircraft structures generally operate. The use of INSIM to develop inspection strategies which aim to minimize service failures is demonstrated. Damage-tolerance methodology, inspection thresholds and customized inspections are simulated using the probability of failure as the driving parameter.

Brot, Abraham↗
NASA NTRS1994

NASA/CARES dual-use ceramic technology spinoff applications

NASA has developed software that enables American industry to establish the reliability and life of ceramic structures in a wide variety of 21st Century applications. Designing ceramic components to survive at higher temperatures than the capability of most metals and in severe loading environments involves the disciplines of statistics and fracture mechanics. Successful application of advanced ceramics material properties and the use of a probabilistic brittle material design methodology. The NASA program, known as CARES (Ceramics Analysis and Reliability Evaluation of Structures), is a comprehensive general purpose design tool that predicts the probability of failure of a ceramic component as a function of its time in service. The latest version of this software, CARESALIFE, is coupled to several commercially available finite element analysis programs (ANSYS, MSC/NASTRAN, ABAQUS, COSMOS/N4, MARC), resulting in an advanced integrated design tool which is adapted to the computing environment of the user. The NASA-developed CARES software has been successfully used by industrial, government, and academic organizations to design and optimize ceramic components for many demanding applications. Industrial sectors impacted by this program include aerospace, automotive, electronic, medical, and energy applications. Dual-use applications include engine components, graphite and ceramic high temperature valves, TV picture tubes, ceramic bearings, electronic chips, glass building panels, infrared windows, radiant heater tubes, heat exchangers, and artificial hips, knee caps, and teeth.

Powers, Lynn M.↗
NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Marvis, Dimitri N.↗
NASA NTRS2003

Exploration of Advanced Probabilistic and Stochastic Design Methods

The primary objective of the three year research effort was to explore advanced, non-deterministic aerospace system design methods that may have relevance to designers and analysts. The research pursued emerging areas in design methodology and leverage current fundamental research in the area of design decision-making, probabilistic modeling, and optimization. The specific focus of the three year investigation was oriented toward methods to identify and analyze emerging aircraft technologies in a consistent and complete manner, and to explore means to make optimal decisions based on this knowledge in a probabilistic environment. The research efforts were classified into two main areas. First, Task A of the grant has had the objective of conducting research into the relative merits of possible approaches that account for both multiple criteria and uncertainty in design decision-making. In particular, in the final year of research, the focus was on the comparison and contrasting between three methods researched. Specifically, these three are the Joint Probabilistic Decision-Making (JPDM) technique, Physical Programming, and Dempster-Shafer (D-S) theory. The next element of the research, as contained in Task B, was focused upon exploration of the Technology Identification, Evaluation, and Selection (TIES) methodology developed at ASDL, especially with regards to identification of research needs in the baseline method through implementation exercises. The end result of Task B was the documentation of the evolution of the method with time and a technology transfer to the sponsor regarding the method, such that an initial capability for execution could be obtained by the sponsor. Specifically, the results of year 3 efforts were the creation of a detailed tutorial for implementing the TIES method. Within the tutorial package, templates and detailed examples were created for learning and understanding the details of each step. For both research tasks, sample files and tutorials are attached in electronic form with the enclosed CD.

Mavris, Dimitri N.↗
NASA NTRS2010

JSC Safety and Mission Assurance Data Analysis Overview

These slides describe the data analysis methods that are used to determine inputs for probabilistic risk models supporting the Space Shuttle Program. Other applications can follow a similar path probably using different data sources. Statistical approaches are different and not addressed here. Topics included here: 1) Prior Distribution; 2) Likelihood Data; 3) Bayesian Updating; and 4) Uncertainty and Error. Note: This is a high-level discussion and is not intended to be a tutorial.

Roelant, Henk↗
NASA NTRSDate not supplied

Use of Preliminary PRA to Inform Decisions During Initial NASA Gateway Development

How do you use Probabilistic Risk Assessment (PRA) to support a program in development before designs are even known? Traditional PRAs require detailed design information and early in a program life cycle such information is not available. National Aeronautics and Space Administration (NASA) Safety and Mission Assurance (SMA) developed a preliminary PRA for the Gateway Program based upon NASA reference designs utilizing data and models from pervious PRA studies as surrogates for the Gateway systems. Gateway will be a lunar outpost that supports missions to the moon and includes several elements/modules developed by NASA and International Partners. NASA SMA began supporting Gateway in fall 2017 during initial formulation and continues to support the Gateway Program today. This paper will explore how the NASA SMA developed preliminary PRA was used to inform Gateway Program decisions with specific examples provided.

PRA↗
NASA NTRSDate not supplied

Use of Preliminary PRA to Inform Decisions During Initial NASA Gateway Development

How do you use Probabilistic Risk Assessment (PRA) to support a program in development before designs are even known? Traditional PRAs require detailed design information and early in a program life cycle such information is not available. National Aeronautics and Space Administration (NASA) Safety and Mission Assurance (SMA) developed a preliminary PRA for the Gateway Program based upon NASA reference designs utilizing data and models from previous PRA studies as surrogates for the Gateway systems. Gateway will be a lunar outpost that supports missions to the moon and includes several elements/modules developed by NASA and International Partners. NASA SMA began supporting Gateway in fall 2017 during initial formulation and continues to support the Gateway Program today. This paper will explore how the NASA SMA developed preliminary PRA was used to inform Gateway Program decisions with specific examples provided.

PRA↗
NASA NTRSDate not supplied

Comparison of Artemis 2 and Artemis 5 Model Outcomes Using the Impact Probabilistic Risk Assessment Tool

BACKGROUND The Artemis campaign is a Moon exploration program with a series of six planned missions, five of which will be crewed. These five crewed missions will contain a single mission segment (space flight), or multiple mission segments involving space flight (Orion), lunar landing (LTV) and/or space habitat (Gateway). Each crewed segment faces the risk of unique medical conditions, necessitating medical sets/kits tailored to those specificities. To support and enable a data-driven and evidence-based decision-making process through out a mission’s life cycle, a software tool called IMPACT was developed. Using probabilistic risk assessment (PRA) methodologies, IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool built for analyzing the possibility of encountering complex medical risks during space flight, and for identifying the medical resources and capabilities needed to treat those potential at-risk medical conditions. This presentation will seek to compare IMPACT’s computational results upon potential complex space medical conditions (e.g., sprain/strain back or sleep disturbance) using IMPACT’s risk metrics and the associated optimized medical sets/kits between two Artemis missions: single segment Artemis 2 and multi-segmented Artemis 5. OVERVIEW By identifying potential medical conditions in space using input criteria such as crew quantity and composition, certain crew physical characteristics, mission duration and mission activities, IMPACT can produce analyses on the type of medical resources and capabilities needed to produce an optimized medical set/kit to address those medical conditions. IMPACT achieves this by performing hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. IMPACT’s risk metrics include loss of crew life (LOCL) – a measure of crew mortality due to medical conditions in space, return to definitive care (RTDC) – the need to perform crew evacuation, and task time lost (TTL) – a measure of the inability to perform activities due to crew disability. These risk metrics are applied to every medical condition identified by IMPACT’s computation analyses for every segment of the mission. Medical sets/kits are optimized to address these medical conditions but must fit within the stated Artemis Design Reference Mission (DRM) request for mass and volume physical size constraints. ANTICIPATED ANALYSIS AND CONCLUSION Using two Artemis missions, Artemis 2 and Artemis 5, IMPACT will provide the analyses for comparison of medical set/kit contents based upon mass and/or volume requirements and identify the at-risk medical conditions within both missions. This paper serves as an initial exploration of probabilistic risk assessment (PRA) medical risk calculations between two crewed Artemis missions and is not intended to be deemed the official medical response for the Artemis campaign.

probabilistic risk assessment↗