Lift maximization with uncertainties for the optimization of high‐lift devices

In this paper, the aerodynamic shape optimization problems with uncertain operating conditions have been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the modified non‐dominated sorting genetic algorithms are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier–Stokes computation using an unstructured mesh. In order to reduce the number of expensive evaluations of the fitness function a response surface modeling is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem, a semi‐torsional spring analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high‐lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free‐stream angle of attack at landing flight condition

Multiobjective Design Optimization using Nash Games

International audienceIn the area of pure numerical simulation of multidisciplinary coupled systems, the computational cost to evaluate a configuration may be very high. A fortiori, in multi- disciplinary optimization, one is led to evaluate a number of different configurations to iterate on the design parameters. This observation motivates the search for the most in- novative and computationally efficient approaches in all the sectors of the computational chain : at the level of the solvers (using a hierarchy of physical models), the meshes and geometrical parameterizations for shape, or shape deformation, the implementation (on a sequential or parallel architecture; grid computing), and the optimizers (deterministic or semi-stochastic, or hybrid; synchronous, or asynchronous). In the present approach, we concentrate on situations typically involving a small number of disciplines assumed to be strongly antagonistic, and a relatively moderate number of related objective functions. However, our objective functions are functionals, that is, PDE-constrained, and thus costly to evaluate. The aerodynamic and structural optimization of an aircraft configuration is a prototype of such a context, when these disciplines have been reduced to a few major objectives. This is the case when, implicitly, many subsystems are taken into account by local optimizations. Our developments are focused on the question of approximating the Pareto set in cases of strongly-conflicting disciplines. For this purpose, a general computational technique is proposed, guided by a form of sensitivity analysis, with the additional objective to be more economical than standard evolutionary approaches

CFD Design in Aeronautics Using a Robust Multilevel Parallel Evolutionary Optimiser

This chapter explores the potential merit of an innovative parallel evolutionary algorithm (EA) coupled with current computational fluid dynamics (CFD) solvers. The chapter outlines the hierarchical asynchronous parallel evolution algorithm (HAPEA), and its important differences to a more conventional evolutionary method. A multi-objective test case involving the reconstruction of a set of two dimensional aerofoil geometries from scratch that are found by considering multiple prescribed pressure distributions at two different flow states is described. The chapter presents conclusions on the increased speed and robustness of the HAPEA algorithm operating in various parallel states, as compared to a more traditional method. Numerical experiments presented in this chapter provide the designer a gateway for practical applicability of parallel EAs in 3D industrial environments and MDO approaches using Navier-Stokes flow analysis solvers coupled with complex turbulence models. The increased speed and robustness obtained from the results underscore that the proper application of sound engineering judgment in conjunction with evolutionary techniques and parallel computing architectures can lead to optimal design solutions and significant computational savings when applied to real world problems.</p