Reliability analysis of spacecraft structures under static and dynamic loading

Pellissetti, Manuel; Schuëller, Gerhart I.; Pradlwarter, H. J.; Calvi, Adriano; Fransen, Sebastiaan; Klein, M.

doi:10.1016/j.compstruc.2006.03.009

Cited by 42 publications

(8 citation statements)

References 11 publications

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“…Eqs. (21)- (23) were treated as the collaborative TLSSVMs to accomplish the MOMD dynamic probabilistic analysis of BTRRC.…”

Section: Dynamic Probabilistic Analysis Of Blade-tip Radial Clearancementioning

confidence: 99%

“…Many attempts to solve the effects of the uncertainties have led to the development of the other probabilistic analysis method-response surface method (RSM, also called surrogate model), and it was widely employed in many fields [15][16][17][18][19][20]. Currently, RSM is effective to continuously improve the accuracy and efficiency in structural reliability by reducing the number of expensive finite element analysis [21][22][23][24][25]. The typical surrogate models include polynomial response surface method [15,16,21,22] and support vector machine (SVM) [25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Fei

Choy

et al. 2016

Nonlinear Dyn

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To develop the high performance and high reliability of turbomachinery just like an aeroengine, distributed collaborative time-varying least squares support vector machine (LSSVM) (called as DC-T-LSSVM) method was proposed for the dynamic probabilistic analysis of high-pressure turbine bladetip radial running clearance (BTRRC). For structural transient probabilistic analysis, time-varying LSSVM (called as T-LSSVM) method was developed by improving LSSVM, and the mathematical model of the T-LSSVM was established. The mathematical model of DC-T-LSSVM was built based on T-LSSVM and distributed collaborative strategy. Through the dynamic probabilistic analysis of BTRRC with respect to the nonlinearity of material property and the dynamics of thermal load and centrifugal force load, the probabilistic distributions and features of different influential parameters on BTRRC, such as rotational speed, the temperature of gas, expansion coefficients, the surface coefficients of heat transfer and the deformations of disk, blade and casing, are obtained. The deformations of turbine disk, blade and casing, the rotational speed and the temperature of gas significantly influence BTRRC. Turbine disk and blade perform the positive effects on the BTRRC, while turbine casing has the negative impact. The comparison of four methods (Monte Carlo method, T-LSSVM, DCERSM and DC-T-LSSVM) reveals that the DC-T-LSSVM reshapes the possibility of the probabilistic analysis of complex turbomachinery and improves the computational efficiency while preserving the accuracy. The efforts offer a useful insight for rapidly designing and optimizing the BTRRC dynamically from a probabilistic perspective.

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“…Eqs. (21)- (23) were treated as the collaborative TLSSVMs to accomplish the MOMD dynamic probabilistic analysis of BTRRC.…”

Section: Dynamic Probabilistic Analysis Of Blade-tip Radial Clearancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Fei

Choy

et al. 2016

Nonlinear Dyn

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“…SS roots on the concept of the Markov Chain Monte Carlo (MCMC) simulation and conditional probability theory (Au and Beck 2001). SS has popular applications in numerous fields, such as aerospace engineering (Pellissetti et al 2006), geotechnical engineering (Santoso et al 2011;Li et al 2016) and nuclear engineering (Zio and Pedroni 2012). Technically, SS is used as a numerical simulation engine to improve the computational efficiency of stochastic algorithms (Chiachio et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems

et al. 2020

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In practical engineering and industry fields, complicated and correlated problems are often descripted by implicit expression. The Kriging model is one of the popular spatial interpolation models to surrogate the numerical relationship between input and output variables. But the efficiency of the Kriging surrogate model is limited when confronting with large databases. The subset simulation is a promising selection method to provide more important and typical samples. By the subset simulation, the Kriging surrogate model can significantly reduce the computational cost in regression, since much fewer samples are required. Besides, more reliable prediction results can be obtained because of the emphasis on the samples that are more representative in the Kriging fitting process. Examples are performed to confirm the properties of the Kriging surrogate model based on the subset simulation.

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“…It is now widely used for reliability analyses and estimation of failure probabilities, e.g. in earthquake engineering [2,3], geotechnical engineering [3,53,65], mechanical engineering and fatigue [9], spacecraft engineering [54], and nuclear engineering [66]. An enhanced version of the method was proposed in [67].…”

Section: Introductionmentioning

confidence: 99%

Multilevel Estimation of Rare Events

Ullmann¹,

Papaioannou²

2015

SIAM/ASA J. Uncertainty Quantification

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This work is motivated by the need to estimate the probability of rare events in engineering systems with random inputs. We introduce a multilevel estimator which is based on and generalises the idea of subset simulation. The novel estimator employs a hierarchy of approximations to the system response computed with different resolutions. This leads to reduced computational costs compared to subset simulation. We study the statistical properties and implementation details of the proposed estimator. Markov chain Monte Carlo runs are required within the estimator and we demonstrate that the nestedness of the associated multilevel failure domains enables a perfect MCMC simulation without burn-in. We show that nestedness follows from certain simple one-dimensional failure domains. In high dimensions we propose a modification of the multilevel estimator which uses level-dependent stochastic input dimensions. We report on numerical experiments in 1D and 2D physical space; in particular, we estimate rare events arising from a Darcy flow problem with random permeability.

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Reliability analysis of spacecraft structures under static and dynamic loading

Cited by 42 publications

References 11 publications

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Dynamic probabilistic design approach of high-pressure turbine blade-tip radial running clearance

Efficiency improvement of Kriging surrogate model by subset simulation in implicit expression problems

Multilevel Estimation of Rare Events

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