Aerodynamic design, like many other engineering applications, is increasingly relying on computational power. The growing need for multi-disciplinarity and high fidelity in design optimization for industrial applications requires a huge number of repeated simulations in order to find an optimal design candidate. The main drawback is that each simulation can be computationally expensive this becomes an even bigger issue when used within parametric studies, automated search or optimization loops, which typically may require thousands of analysis evaluations.

The core issue of a design-optimization problem is the search process involved. However, when facing complex problems, the high-dimensionality of the design space and the high-multi-modality of the target functions cannot be tackled with standard techniques.

In recent years, global optimization using meta-models has been widely applied to design exploration in order to rapidly investigate the design space and find sub-optimal solutions. Indeed, surrogate and reduced-order models can provide a valuable alternative at a much lower computational cost.

In this context, this volume offers advanced surrogate modeling applications and optimization techniques featuring reasonable computational resources. It also discusses basic theory concepts and their application to aerodynamic design cases. It is aimed at researchers and engineers who deal with complex aerodynamic design problems on a daily basis and employ expensive simulations to solve them.

Innovative methods are presented opening new frontiers towards a more efficient aerodynamic design The usage of surrogate models in the preliminary or detailed aerodynamic design phase is envisaged to provide significant benefits in terms of computational cost and time savings The proposed methods and applications are in line with industrial interest

Inhalt
Fast aerodynamic coefficients prediction using support vector machines for global shape optimization.- Adaptive sampling strategies for surrogate-based aerodynamic optimization.- PCA-enhanced metamodel-assisted evolutionary algorithms for aerodynamic optimization.- Multi-objective surrogate based optimization of gas cyclones using support vector machines and CFD simulations.