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.
