Stochastic aerodynamics and aeroelasticity of a flat plate via generalised Polynomial Chaos

Bruno, Luca; Canuto, Claudio; Fransos, Davide

doi:10.1016/j.jfluidstructs.2009.06.001

Cited by 31 publications

(20 citation statements)

References 33 publications

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“…The indicial approach has also been applied in the frame of a probabilistic 90 study of the aerodynamic and aeroelastic responses of a flat plate (Bruno et al, 2009 …”

mentioning

confidence: 99%

Bridge deck flutter derivatives: Efficient numerical evaluation exploiting their interdependence

Nieto

Owen

Hargreaves

et al. 2015

Journal of Wind Engineering and Industrial Aerodynamics

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Highlights: An efficient CFD approach for the computation of flutter derivatives is presented.  Relationships between flutter derivatives allow halving the number of simulations.  Successfully applied at a ratio 4.9:1 rectangular cylinder and the G1 box section.  Great potential in industrial applications and shape optimal design problems. Highlights (for review) 16It has been found that the proposed methodology offers results which agree well with the 17 experimental data and the accuracy of the estimated flutter derivatives is similar to the results 18reported in the literature where the complete set of numerical simulations has been performed for 19both heave and pitch degrees of freedom.20 21

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“…The indicial approach has also been applied in the frame of a probabilistic 90 study of the aerodynamic and aeroelastic responses of a flat plate (Bruno et al, 2009 …”

mentioning

confidence: 99%

Bridge deck flutter derivatives: Efficient numerical evaluation exploiting their interdependence

Nieto

Owen

Hargreaves

et al. 2015

Journal of Wind Engineering and Industrial Aerodynamics

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“…(13)(14)(15) are combined in a Gaussian process prior distribution. Conditioning upon training data y at n data points yields the predictive distribution to be the Gaussian process 18 [ ( )| , 2 , , ]~( * ( ), * ( , ′)) (16) where…”

Section: B Gaussian Processesmentioning

confidence: 99%

“…The perturbation method models uncertainties as Taylor series expansions of small perturbations from the mean, though cannot be used to obtain the full output distribution. Polynomial Chaos Expansion (PCE) has been used to model uncertainty in a number of models, such as a pitch and plunge aerofoil model with uncertain spring stiffness coefficients 9 a composite plate with uncertain ply orientions 12 , and a flat plate subject to uncertain aerodynamic load 13 . Stochastic Collocation has been used for numerous applications, including the bifurcation analysis of a pitching aerofoil with uncertain nonlinear stiffness, natural frequency and equilibrium pitch angle 14 .…”

Section: Introductionmentioning

confidence: 99%

Robust Aeroelastic Design of a Composite Wing-Box

Scarth

Sartor

Cooper

et al. 2015

17th AIAA Non-Deterministic Approaches Conference

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An approach is presented for the robust stacking sequence design of composite plate wings with uncertain ply orientations. An aeroelastic model is constructed using the Rayleigh-Ritz technique coupled with modified strip theory aerodynamics. Gaussian processes are used as emulators for the aeroelastic instability speed in order to efficiently quantify the effects of uncertainty. The critical instability speed is discontinuous as a result of the different potential instability mechanisms, therefore multiple Gaussian processes are fitted to ensure computational efficiency. An order of two magnitude reduction in model runs is achieved for the majority of examples, and an order of magnitude reduction is achieved when a switch between flutter modes occurs. The emulators are used to estimate the probability that instability occurs at a given design speed, which is minimized using a genetic algorithm. Results are compared to deterministic optima for maximal instability speed. Two lay-up strategies are undertaken, a first in which ply orientations are limited to 0°, ±45° and 90°, and a second in which values of ±30° and ±60° may also be taken. Improvements in reliability of at least 85% are achieved. The inclusion of ±30° and ±60° plies enables a 1.7% increase in the nominal instability speed, and an increase in reliability of at least 59%.

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“…Under these circumstances, undesired oscillations appear in the global expansion, especially if high-order polynomials are employed . In order to adapt the gPC approach to BBA applications, a stochastic domain decomposition can be adopted, by defining gPC local (element-wise) expansions on subsets of I (Multi-Element generalised Polynomial Chaos,ME-gPC, Wan and Karniadakis, 2006;Bruno et al, 2009). Furthermore, an adaptive version of the MEgPC is proposed herein, bearing in mind that the fluid flow phenomena of interest are hard to be predicted and that the values of the system parameters corresponding to the critical regime are a priori unknown.…”

Section: Generalised Polynomial Chaos In Bluff Body Aerodynamics (Blomentioning

confidence: 99%

“…Its main advantage is represented by the possibility to obtain an explicit form of the solution, from which the moments and the probability distribution of the solution itself can be obtained. In this work, an adaptive version of the Multi-Element generalized Polynomial Chaos (aME-gPC) method is introduced on the basis of the ME-gPC methods used by Wan and Karniadakis (2006) and Bruno et al (2009). The method exploits the classical technology of weighted orthogonal polynomials, where two/three-point Gauss-Lobatto quadrature formulas are adopted in each element.…”

Section: Introductionmentioning

confidence: 99%