An island particle algorithm for rare event analysis

Vergé, Christelle; Mitard, J.; Moral, Pierre Del

doi:10.1016/j.ress.2015.11.017

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…This is best demonstrated in Figure 5. The source [30] presents the sequential Monte Carlo Square algorithm, describing the basic principles of its function and applicability. The proposed algorithm was adapted for the rare-events analysis to be sampled from the target distribution.…”

Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

“…The meteorological data, mass, and angle between the vertical axis and velocity vector direction of the LVs' SPs were used as varying parameters. As a result, a conclusion was attained, according to The source [30] presents the sequential Monte Carlo Square algorithm, describing the basic principles of its function and applicability. The proposed algorithm was adapted for the rare-events analysis to be sampled from the target distribution.…”

Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

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Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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The article aims at reviewing the drop regions (DR) of the launch vehicles (LV) separating parts (SP) and methods of their determination. The DRs include sea and land areas; going beyond them is associated with a number of environmental, economic, and political factors. Their combination dictates the need to ensure the safety of the people, transport, infrastructure, and environment from the negative impact of LV SPs and fuel residues. The Monte Carlo method is mostly used to determine the impact areas. It enables an estimation of the probability of the SPs of LVs falling in certain areas, constituting the DRs. These points are varied according to a set of different initial parameters. The methods of controlling the impact areas are contingently divided into engineering (based on a change in the design appearance of the LV), mathematical (which includes the changes in or optimization of the LV’s trajectory or its SP), and “soft landing” (implying the return of the LV’s to the spaceport or to a certain prepared area). The present analysis can be used as a starting point when choosing a method for determining and controlling the projected LVs and the SPs’ area of impact.

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Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

Section: Monte Carlo Methods and Alternative Optionsmentioning

confidence: 99%

Methods for the Calculation and Control of Launch Vehicle Drop Regions

et al. 2023

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“…Consequently, without any loss of generality, one can assume the existence of a joint pdf f = k j=1 f j as a product of the marginal pdfs of the j [34]. Note here that one could also consider a dependence structure between the distribution parameters.…”

Section: Reliability Analysis Under Distribution Parameter Uncertaintymentioning

confidence: 99%

“…[34] and another approach to estimate the probability of collision can be found in [57]. Assuming that the position and the speed of the satellite are perfectly known, the input vector of basic variables gathers the three components of the space debris position vector and the three components of its speed vector [58].…”

Section: Tablementioning

confidence: 99%

Evaluation of failure probability under parameter epistemic uncertainty: application to aerospace system reliability assessment

Chabridon

Balesdent

Bourinet

et al. 2017

Aerospace Science and Technology

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uation of failure probability under parameter epistemic uncertainty: application to aerospace system reliability assessment. Aerospace Science and Technology, Elsevier, 2017, 69, pp.526-537. 10.1016/j.ast.2017 v1.221; Prn:25/07/2017; 9:27] This paper aims at comparing two different approaches to perform a reliability analysis in a context of uncertainties affecting probability distribution parameters. The first approach called "nested reliability approach" (NRA) is a classical double-loop-approach involving a sampling phase of the parameters and then a reliability analysis for each sampled parameter value. A second approach, called "augmented reliability approach" (ARA), requires to sample both distribution parameters and basic random variables conditional to them at the same phase and then integrate simultaneously over both domains. In this article, a numerical comparison is led. Possibilities offered by both approaches are investigated and the advantages of the ARA are illustrated through the application on two academic test-cases illustrating several numerical difficulties (low failure probability, nonlinearity of the limit-state function, correlation between input basic variables) and two real space system characterization (a launch vehicle stage fallback zone estimation and a collision probability between a space debris and a satellite estimation) for which only the ARA is tractable.P.1 (1-12) Aerospace Science and Technology

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