A novel uncertainty quantification routine in the genre of adaptive sparse grid stochastic collocation (SC) has been proposed in this study to investigate the propagation of parametric uncertainties in a stall flutter aeroelastic system. In a hypercube stochastic domain, presence of strong nonlinearities can give way to steep solution gradients that can adversely affect the convergence of nonadaptive sparse grid collocation schemes. A new adaptive scheme is proposed here that allows for accelerated convergence by clustering more discretization points in regimes characterized by steep fronts, using hat-like basis functions with nonequidistant nodes. The proposed technique has been applied on a nonlinear stall flutter aeroelastic system to quantify the propagation of multiparametric uncertainty from both structural and aerodynamic parameters. Their relative importance on the stochastic response is presented through a sensitivity analysis. Copyright © 2018 by ASME.

Sunetra Sarkar

Department of Aerospace Engineering

Aerodynamic stalling

Flutter (aerodynamics)

Sensitivity analysis

STALL INDICATORS

Stochastic systems

Accelerated convergence

Aerodynamic parameters

NONLINEAR AEROELASTIC SYSTEM

Parametric uncertainties

STOCHASTIC COLLOCATION

Stochastic response

STRONG NONLINEARITY

Uncertainty quantifications

Aeroelasticity

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Propagation of Parametric Uncertainties in a Nonlinear Aeroelastic System Using an Improved Adaptive Sparse Grid Quadrature Routine

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

Simulating the pore-scale flow-field past porous media samples is a computationally expensive exercise. Uncertainty quantification of such systems can turn out to be a real challenge, especially by employing the available spectral approaches such as the polynomial chaos expansion (PCE) technique and its variants. The present study addresses this problem by combining the strengths of a sampling based method and a spectral characterization technique. This novel approach integrates Monte Carlo Simulations (MCS) and Karhunen–Lòeve (K–L) expansion techniques in order to create a framework for the modelling and reconstruction of the output fields of large order complex systems such as the porous media flow. A pore-scale modelling of flow through porous media samples, by simulating the incompressible Navier–Stokes equation in the pore spaces by employing the Lattice Boltzmann method has been taken up in the present study. The modelling of the input randomness also has an element of novelty in the present work as it directly captures the random solid-pore arrangement of the media geometry instead of any macro-properties. The input random geometries are created digitally using limited statistics of an appropriate discrete valued random process. The resulting process is weakly correlated and needs a very large number of input random variables to capture its higher frequency content. Such large random dimensional problems cannot be practically solved using popular spectral tools like the PCE. The proposed integrated MCS–KL approach has been utilized successfully in the present work to propagate their effect on the pore-scale velocity field. Further, the K–L step provides an efficient means for reconstructing physically realistic samples of the chosen output whose statistics match the target statistics well. This is a significant time saver as it effectively bypasses the need of fresh flow-field simulations through a complex large order system such as the present one. © 2016 Elsevier B.V.

Computer Methods in Applied Mechanics and Engineering

An efficient stochastic framework to propagate the effect of the random solid-pore geometry of porous media on the pore-scale flow

Stall induced oscillations are likely instabilities in modern wind engineering systems. For their robust design, it is crucial to take parametric and load uncertainties into consideration. Presently, study of a stall flutter system under the influence of random gust is undertaken using the probability density evolution method, and qualitative changes in the response output are presented. This probability based approach when compared with a spectral tool called polynomial chaos expansion, shows better accuracy for the same level of discretisation. © 2015 Elsevier Ltd.

Computers and Structures

Study of a stall induced dynamical system under gust using the probability density evolution technique

Journal of Computational Physics

Subcell resolution in simplex stochastic collocation for spatial discontinuities

Simplex stochastic collocation with ENO-type stencil selection for robust uncertainty quantification

Journal of Fluids and Structures

Study of the conditions that cause chaotic motion in a two-dimensional airfoil with structural nonlinearities in subsonic flow

Journal	ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Publisher	American Society of Mechanical Engineers (ASME)
ISSN	23329017
Open Access	No