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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Britton, Paul T.↗
NASA NTRS2018

Burden Versus Capability Statistical Analysis for Structural Probabilistic Risk Assessments

The purpose of the burden versus capability analysis is to analyze the ability of components to withstand the loads that they are subjected to. When designing a launch vehicle, there is always a trade-off for the strength of the components versus the weight of the vehicle. The vehicle needs to have some margin built in to the design, but this added margin should not add a significant amount of weight to the vehicle. When the material properties and limits are known, estimated loads can be used to ensure that the vehicle will survive launch loads. If the variation in the distributions can be quantified, the probability of failure can be estimated more accurately.

Green, Becky↗
NASA NTRS2014

Comparative Analysis of Static and Dynamic Probabilistic Risk Assessment

Implementation of risk-informed design allows the design team to thoroughly explore the risks of a system while iterating the operations concept, design, and requirements until the system meets mission objects and is achievable within constraints. To arrive at a space system design that is likely to meet all constraints placed upon mass, cost, performance and risk, the system requirements must be understood and traded against each other as early as the conceptual design phase. Depending on the project phase and the goals of the risk analysis, various PRA methodologies could be used to produce quantitative risk estimates to enable such a process. In order to better understand the applicability, advantages, and limitations of various PRA methodologies, a comparative analysis of three bottom-up, component-based PRA approaches was performed. The three methods examined are a traditional static fault tree, a fault tree hybrid, and a dynamic Monte Carlo simulation. Each approach was used to assess a generic reaction control system (RCS) thruster pod and mission. The methods are assessed in terms of the process of modeling a system, the actionable information produced for the design team, and the overall fidelity of the quantitative risk evaluation generated. The paper also discusses the applicability of each methodology to the different phases of system development.

Probablistic↗
NASA NTRSDate not supplied

SafeMAP: Safe Multi-Agent Planning Framework Based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗
NASA NTRSDate not supplied

SafeMAP: Safe Multi-Agent Planning framework based on Dynamic Probabilistic Risk Assessment

This paper proposes a risk-aware framework for Safe Multi-Agent Planning (SafeMAP) that unifies disparate models for multi-agent systems in a Markovian process that allows for simultaneous system health monitoring, decision making under uncertainty, and multi-agent system collaboration. As operations beyond low earth orbit mature, there is an increased need for autonomous cyber-physical systems with onboard decision making capabilities. Multi-agent cyber-physical systems in particular offer the potential of increased efficiency, resiliency, and mission capabilities for future applications such as multi-rover terrain operations, distributed satellite operations, and management of smart lunar habitats. SafeMAP utilizes physics-based models of each agent and the relevant components, probability models of the environment and component operational states, and reward models for mission-specific objectives such as scientific task completion or resource consumption. The output of SafeMAP is a set of mission plans that satisfy the mission objective under specified risk/reward constraints. A readable interpretation of each of these generated mission plans is provided as an additional output. SafeMAP has been demonstrated on a simulated case study involving a four-rover system performing surface mapping operations and science tasks. Results of this paper demonstrate SafeMAP’s ability to generate explainable mission plans that satisfy the mission objective while minimizing risk under nominal and off-nominal conditions.

Mohammad Hejase↗