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A semi-intrusive stochastic perturbation method for lift prediction and global sensitivity analysis

Published online by Cambridge University Press:  03 August 2017

Anup Suryawanshi  and
Debraj Ghosh
Anup Suryawanshi
Affiliation:
Subduction Zone Consultants, Pune, Maharashtra, India
Debraj Ghosh*
Affiliation:
Department of Civil Engineering, Indian Institute of Science, Bangalore, India
*
Reprint requests to: Debraj Ghosh, Department of Civil Engineering, Indian Institute of Science, Bangalore, India 560012. E-mail: dghosh@civil.iisc.ernet.in
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Abstract

Sensitivity analysis plays an important role in finding an optimal design of a structure under uncertainty. Quantifying relative importance of random parameters, which leads to a rank ordering, helps in developing a systematic and efficient way to reach the optimal design. In this work, lift prediction and sensitivity analysis of a potential flow around a submerged body is considered. Such flow is often used in the initial design stage of structures. The flow computation is carried out using a vortex-panel method. A few parameters of the submerged body and flow are considered as random variables. To improve the accuracy in lift prediction in a computationally efficient way, a new semi-intrusive stochastic perturbation method is proposed. Accordingly, a perturbation is applied at the linear system solving level involving the inuence coefficient matrix, as opposed to using perturbation in the lift quantity itself. This proposed method, which is partially analogous to the intrusive or Galerkin projection methods in spectral stochastic finite element methods, is found to be more accurate than using perturbation directly on the lift and faster than a direct simulation. The proposed semi-intrusive stochastic perturbation method is found to yield faster estimates of the Sobol’ indices, which are used for global sensitivity analysis. From global sensitivity analysis, the flow parameters are found to be more important than the parameters of the submerged body.

Keywords

Monte Carlo SimulationsPerturbationPolynomial Chaos ExpansionSensitivityUncertainty AnalysisVortex-Panel Method
Type
Special Issue Articles
Information
AI EDAM , Volume 31 , Special Issue 3: Uncertainty Quantification for Engineering Design , August 2017 , pp. 277 - 285
Copyright
Copyright © Cambridge University Press 2017 

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