A gPC-based approach to uncertain transonic aerodynamics

The present paper focus on the stochastic response of a two-dimensional transonic airfoil to parametric uncertainties. Both the freestream Mach number and the angle of attack are considered as random parameters and the generalized Polynomial Chaos (gPC) theory is coupled with standard deterministic numerical simulations through a spectral collocation projection methodology. The results allow for a better understanding of the flow sensitivity to such uncertainties and underline the coupling process between the stochastic parameters. Two kinds of non-linearities are critical with respect to the skin-friction uncertainties: on one hand, the leeward shock movement characteristic of the supercritical profile and on the other hand, the boundary-layer separation on the aft part of the airfoil downstream the shock. The sensitivity analysis, thanks to the Sobol' decomposition, shows that a strong non-linear coupling exists between the uncertain parameters. Comparisons with the one-dimensional cases demonstrate that the multi-dimensional parametric study is required to get the correct shape and magnitude of the standard deviation distributions of the flow quantities such as pressure and skin-friction. © 2009 Elsevier B.V

A global optimization approach applied to structural dynamic updating

In this paper, the application of stochastic global optimization tech- niques, in particular the GlobalSearch and MultiStart solvers from MatLab®, to improve the updating of a structural dynamic model, are presented. For com- parative purposes, the efficiency of these global methods relatively to the local search method previously used in a Finite Element Model Updating program is evaluated. The obtained solutions showed that the GlobalSearch and MultiStart solvers are able to achieve a better solution than the local solver previously used, in the updating of a structural dynamic model. The results show also that the GlobalSearch solver is more efficient than the MultiStart, since requires less computational effort to obtain the global solution.Fundação para a Ciência e a Tecnologia (FCT

Uncertainty quantification applied to the performance analysis of a conical diffuser

This paper offers an uncertainty quantification (UQ) study applied to the performance analysis of the ERCOFTAC conical diffuser. A deterministic CFD solver is coupled with a non-statistical generalised Polynomial Chaos(gPC)representation based on a pseudo-spectral projection method. Such approach has the advantage to not require any modification of the CFD code for the propagation of random disturbances in the aerodynamic field. The stochactic results highlihgt the importance of the inlet velocity uncertainties on the pressure recovery both alone and when coupled with a second uncertain variable. From a theoretical point of view, we investigate the possibility to build our gPC representation on arbitray grid, thus increasing the flexibility of the stochastic framework

Adaptive generalized polynomial chaos for nonlinear random oscillators

Abstract. The solution of nonlinear random oscillators subject to stochastic forcing is investigated numerically. In particular, solutions to the random Duffing oscillator with random Gaussian and non-Gaussian excitations are obtained by means of the generalized polynomial chaos (GPC). Adaptive procedures are proposed to lower the increased computational cost of the GPC approach in large-dimensional spaces. Adaptive schemes combined with the use of an enriched representation of the system improve the accuracy of the GPC approach by reordering the random modes according to their magnification by the system

Macroscopic model of fluid structure interaction in cylinder arrangement using theory of mixture

International audienceIn the framework of the theory of mixture, the dynamic behaviour of solid cylinder bundles submitted to external hydrodynamic load exerted by surrounding viscous fluid flow is described. Mass conservation and momentum balance formulated on an elementary domain made of a given volume of mixture give rise to a system of coupled equations governing solid space-averaged displacement, fluid velocity and pressure provided that near-wall hydrodynamic load on each vibrating cylinder is expressed as a function of both fluid and solid space-averaged velocity fields. Then, the ability of the macroscopic model to reproduce over time an averaged flow surrounding vibrating cylinders in a large array in the context of small magnitude displacements is pointed out. Numerical solutions obtained on a two-dimensional configuration involving an array of several hundreds of cylinders subjected to an impulsional load are compared to those provided by averaged well-resolved microscopic-scale solutions. The relative error is less than 3% in terms of displacement magnitude and 5% for frequency delay. The proposed macroscopic model does not include any assumption on relative effect contributions to mechanical exchanges occurring in the full domain. Therefore it features interesting properties in terms of fluid solid interaction prediction capabilities. Moreover it contributes to a significant gain in terms of computational time and resources. Further developments are now required in order to extent the formulation to large magnitude displacements including three-dimensional effects. This could be recommended for investigations on fuel assembly vibration risk assessment in Pressure Water, Fast Breeder reactors at a whole core scale or any other large-scale mechanical system involving some kind of periodic geometry

Towards predictive data-driven simulations of wildfire spread – Part I: Reduced-cost Ensemble Kalman Filter based on a Polynomial Chaos surrogate model for parameter estimation

International audienceThis paper is the first part in a series of two articles and presents a data-driven wildfire simulator for forecasting wildfire spread scenarios, at a reduced computational cost that is consistent with operational systems. The prototype simulator features the following components: an Eulerian front propagation solver FIREFLY that adopts a regional-scale modeling viewpoint, treats wildfires as surface propagating fronts, and uses a description of the local rate of fire spread (ROS) as a function of environmental conditions based on Rothermel's model; a series of airborne-like observations of the fire front positions; and a data assimilation (DA) algorithm based on an ensemble Kalman filter (EnKF) for parameter estimation. This stochastic algorithm partly accounts for the nonlinearities between the input parameters of the semi-empirical ROS model and the fire front position, and is sequentially applied to provide a spatially uniform correction to wind and biomass fuel parameters as observations become available. A wildfire spread simulator combined with an ensemble-based DA algorithm is therefore a promising approach to reduce uncertainties in the forecast position of the fire front and to introduce a paradigm-shift in the wildfire emergency response. In order to reduce the computational cost of the EnKF algorithm, a surrogate model based on a polynomial chaos (PC) expansion is used in place of the forward model FIREFLY in the resulting hybrid PC-EnKF algorithm. The performance of EnKF and PC-EnKF is assessed on synthetically generated simple configurations of fire spread to provide valuable information and insight on the benefits of the PC-EnKF approach, as well as on a controlled grassland fire experiment. The results indicate that the proposed PC-EnKF algorithm features similar performance to the standard EnKF algorithm, but at a much reduced computational cost. In particular, the re-analysis and forecast skills of DA strongly relate to the spatial and temporal variability of the errors in the ROS model parameters