A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models

Echard, Benjamin; Gayton, Nicolas; Lemaire, Maurice; Relun, Nicolas

doi:10.1016/j.ress.2012.10.008

Cited by 526 publications

(215 citation statements)

References 32 publications

Supporting

Mentioning

213

Contrasting

Unclassified

Order By: Relevance

“…The second class of methods relies on the substitution of the original performance function by a surrogate model within a sampling-based scheme; a metamodel is in general orders of magnitude faster to be evaluated. In such metamodel-based approaches, Monte-Carlo sampling (AK-MCS [17]) or Importance Sampling (IS) techniques (AK-IS [13], MetaAK-IS 2 [14], KAIS [15], AK-SS [16]) are then used directly on the surrogate. Here, we propose a new method for the metamodel construction.…”

Section: 41mentioning

confidence: 99%

Computation of tail probabilities for non-classical gasdynamic phenomena

Razaaly

Congedo

2017

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Abstract. This paper presents a novel method for computing the tail probability of a given quantity of interest by using only a low number of evaluations of the computer model, representing the problem of interest under uncertainties. This method is then applied to the study of rarefaction shock waves (RSW) in a dense-gas shock tube. It is well-known in literature that the prediction of a RSW is highly sensitive to uncertainties on the initial flow conditions. The objective of this work is to compute a very accurate estimation of the RSW probability of occurrence in a shock tube configuration.

show abstract

Section: 41mentioning

confidence: 99%

Computation of tail probabilities for non-classical gasdynamic phenomena

Razaaly

Congedo

2017

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

show abstract

“…Consider the non-linear undamped single-degree-of-freedom (SDOF) oscillator sketched in Figure 6 (Echard et al, 2011(Echard et al, , 2013Schueremans and Van Gemert, 2005). The corresponding limit-state function reads:…”

Section: Problem Setupmentioning

confidence: 99%

Structural reliability analysis for p-boxes using multi-level meta-models

Schöbi

Sudret

2017

Probabilistic Engineering Mechanics

View full text Add to dashboard Cite

In modern engineering, computer simulations are a popular tool to analyse, design, and optimize systems. Furthermore, concepts of uncertainty and the related reliability analysis and robust design are of increasing importance. Hence, an efficient quantification of uncertainty is an important aspect of the engineer's workflow. In this context, the characterization of uncertainty in the input variables is crucial. In this paper, input variables are modelled by probability-boxes, which account for both aleatory and epistemic uncertainty. Two types of probability-boxes are distinguished: free and parametric (also called distributional) p-boxes. The use of probability-boxes generally increases the complexity of structural reliability analyses compared to traditional probabilistic input models. In this paper, the complexity is handled by two-level approaches which use Kriging meta-models with adaptive experimental designs at different levels of the structural reliability analysis. For both types of probability-boxes, the extensive use of meta-models allows for an efficient estimation of the failure probability at a limited number of runs of the performance function. The capabilities of the proposed approaches are illustrated through a benchmark analytical function and two realistic engineering problems.

show abstract

“…On the other hand, the potential shown for its application is high. Echard et al (2013) reduced the computational time of a fatigue reliability analysis from more than 100 days to approximately 9 hours by using a surrogate model and adaptive sampling methodologies. Showing therefore the huge benefits in reduction of the computational cost from the application of a Kriging interpolation scheme without compromising accuracy in the results.…”

Section: Kriging Model In Reliability Analysis Of Owtmentioning

confidence: 99%

Structural probabilistic assessment of offshore wind turbine operation fatigue based on Kriging interpolation

Teixeira

O’Connor

Nogal

et al. 2017

Safety and Reliability – Theory and Applications

View full text Add to dashboard Cite

The probabilistic analysis of Offshore Wind Turbines (OWT) is not a new practice. The standards for designing OWT (IEC 61400 class) emphasizes that assessing uncertainty is of major importance inside the design chain. Still, major challenges related to the uncertainty and the probabilistic assessment pose to the sector and its development. The analysis of operational loads is one them. The problem of analyzing extreme responses or cumulated damage in operation during the design phase is significantly related to its high computational cost. As we progressively add complexity to the system to account for its uncertainties, the computational effort increases and a perceptive design becomes a heavy task. If an optimization process is then sought, the designing effort grows even further. In the particular case of fatigue analysis, it is frequent to not be able to cover a full lifetime of simulations due to computational cost restrictions. The mentioned difficulties fomented the utilization of surrogate models in the reliability analysis of OWT. From these surrogate approximations the ones based on Kriging models gained a special emphasis recently for structural reliability. It was shown that, for several applications, these models can be efficient and accurate to approximate the response of the system or the limit state surfaces. The presented paper tackles some of the issues related to their applicability to OWT, in a case specific scenario of the tower component subjected to operational fatigue loads. A methodology to assess the reliability of the tower component to fatigue damage is presented. This methodology combines a Kriging model with the theory of extreme values. A one-dimensional Kriging case using the state of art NREL's monopile turbine is presented.The reliability of the OWT tower is calculated for 20 years. The results show that the usage of a Kriging model to calculate the long term damage variation shows a high potential to assess the reliability of OWT towers to fatigue failure.

show abstract

A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models

Cited by 526 publications

References 32 publications

Computation of tail probabilities for non-classical gasdynamic phenomena

Computation of tail probabilities for non-classical gasdynamic phenomena

Structural reliability analysis for p-boxes using multi-level meta-models

Structural probabilistic assessment of offshore wind turbine operation fatigue based on Kriging interpolation

Contact Info

Product

Resources

About