Simulation-driven design using surrogate-based optimization and variable-resolution computational fluid dynamic models

Reliable and robust computational fluid dynamics (CFD) solvers are nowadays commonly utilized in the design and analyses of various engineering systems, such as aircraft, turbomachinery, ships, and automotives. Although this has resulted in a drastic decrease of the number of prototype and experimental testing, the use of CFD in the design automation process is still limited. In practice, the geometry parameters that ensure satisfaction of assumed performance requirements are often obtained by repetitive CFD simulations guided by engineering experience. This is a tedious process which does not guarantee optimal results. On the other hand, straight forward automation attempts by employing the CFD solvers directly in the optimization loop are typically impractical, even when using adjoint sensitivity information, because high-fidelity CFD simulations tend to be computationally very expensive. In this paper, we describe a surrogate-based design optimization methodology that shifts the computational burden from the accurate and expensive high-fidelity CFD model to its fast and yet reasonably accurate surrogate. As the surrogate models are computationally much cheaper than the high-fidelity ones, the cost of the design process is greatly reduced. Here, the surrogates are constructed using low-fidelity CFD models and response correction techniques. Application examples, involving the design of axisymmetric hulls in subsonic flow, and airfoils in both subsonic and transonic flows, are presented.