Uncertainty Quantification in Estimating Critical Spacecraft Component Temperatures

Thunnissen, Daniel P.; Au, Siu‐Kui; Tsuyuki, Glenn T.

doi:10.2514/1.23979

Cited by 26 publications

(11 citation statements)

References 7 publications

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“…Subset simulation was originally developed for seismic risk analysis of building structures subjected to stochastic earthquake motions [20], [23], where the problem involved a large number (theoretically infinite) number of random variables arising primarily from the time-domain stochastic description of ground motions. Applications of SS to different disciplines have appeared, e.g., in aerospace engineering [24], [25], fire engineering [26], geotechnical engineering [27], [28], nuclear engineering [29], [30], and meteorology [31]. Performance of subset simulation in a set of benchmark problems is presented in [32] and [33].…”

Section: A Overview Of Subset Simulationmentioning

confidence: 99%

Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation

Hua

Bie

et al. 2015

IEEE Trans. Power Syst.

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This paper proposes an efficient method for evaluating composite system reliability via subset simulation. The central idea is that a small failure probability can be expressed as a product of larger conditional probabilities, thereby turning the problem of simulating a rare failure event into several conditional simulations of more frequent intermediate failure events. In existing methods, system states are simply assessed in a binary secure/failure manner. To fit into the context of subset simulation, the adequacy of system states is parametrized with a metric based on linear programming, thus allowing for an adaptive choice of intermediate failure events. Samples conditional on these events are generated by Markov chain Monte Carlo simulation. The proposed method requires no prior information before imulation. Different models for renewable energy sources can also be accommodated. Numerical tests show that this method is significantly more efficient than standard Monte Carlo simulation, especially for simulating rare failure events.

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Section: A Overview Of Subset Simulationmentioning

confidence: 99%

Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation

Hua

Bie

et al. 2015

IEEE Trans. Power Syst.

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“…Yet, it is still necessary to precisely assess and demonstrate the reliability of passive safety systems and the capacity to perform and complete the expected functions. In simple and direct words, passive safety systems may contribute to improving the safety of Nuclear Power Plants (NPPs), provided that their performance-based design and operation are demonstrated by tailored deterministic and reliability assessment methods, approaches and data (e.g., experimental databases) available to industry and regulators [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

et al. 2021

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Passive systems are fundamental for the safe development of Nuclear Power Plant (NPP) technology. The accurate assessment of their reliability is crucial for their use in the nuclear industry. In this paper, we present a review of the approaches and procedures for the reliability assessment of passive systems. We complete the work by discussing the pending open issues, in particular with respect to the need of novel sensitivity analysis methods, the role of empirical modelling and the integration of passive safety systems assessment in the (static/dynamic) Probabilistic Safety Assessment (PSA) framework.

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“…Originally developed and applied in estimating small failure probabilities in high-dimension structural-engineering applications, Subset Simulation has been modified to handle more general situations. [8][9][10][11] The remainder of the paper summarizes the investigated method, its application to a spacecraft ACS, and discusses the results. Details of the actual spacecraft ACS model assumed are provided in [10].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

Thunnissen¹,

Swenka

2007

Journal of Aerospace Computing, Information, and Communication

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A method for quantifying uncertainty in conceptual-level design via a computationallyefficient probabilistic method is described. As an example application, the investigated method is applied to estimating the propellant mass required by a spacecraft to perform attitude control. The variables of the design are first classified and assigned appropriate probability density functions. To characterize the attitude control system a slightly-modified version of Subset Simulation, an efficient simulation technique originally developed for reliability analysis of civil engineering structures, is used. The proposal distribution aspect of Subset Simulation is modified vis-à-vis the original technique to account for the general characteristics of the variables involved in conceptual-level design. The results of Subset Simulation are compared with traditional Monte Carlo simulation. The investigated method allows uncertainty in the propellant required to be quantified based on the risk tolerance of the decision maker. For the attitude control example presented, Subset Simulation successfully replicated Monte Carlo simulation results yet required significantly less computational effort, in particular for risk-averse decision makers. Nomenclaturelognormal random variable with parameters μ and σ m number of Subset Simulation levels N number of Subset Simulation samples N c number of Markov chains developed at each simulation level; N c = p 0 N

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Uncertainty Quantification in Estimating Critical Spacecraft Component Temperatures

Cited by 26 publications

References 7 publications

Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation

Extracting Rare Failure Events in Composite System Reliability Evaluation Via Subset Simulation

Reliability Assessment of Passive Safety Systems for Nuclear Energy Applications: State-of-the-Art and Open Issues

Uncertainty Quantification in Conceptual Design via an Advanced Monte Carlo Method

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