A Numerical Methodology for Aerodynamic Shape Optimization in Turbulent Flow Enabling Large Geometric Variation

The increase in the availability and power of computational resources over the last fifteen years has contributed to the development of many different types of numerical optimization methods and created a large area of research focussed on numerical aerodynamic shape optimization and, more recently, high-fidelity multidisciplinary optimization. Numerical optimization provides dramatic savings when designing new aerodynamic configurations, as it allows the designer to focus more on the development of a well-posed design problem rather than on performing an exhaustive search of the design space via the traditional cut-and-try approach, which is expensive and time-consuming. It also reduces the dependence on the designer’s experience and intuition, which can potentially lead to more optimal designs. Numerical optimization methods are particularly attractive when designing novel, unconventional aircraft for which the designer has no pre-existing studies or experiences from which to draw; these methods have the potential to discover new designs that might never have been arrived at without optimization. This work presents an extension of an efficient gradient-based numerical aerodynamic shape optimization algorithm to enable optimization in turbulent flow. The algorithm includes an integrated geometry parameterization and mesh movement scheme, an efficient parallel Newton-Krylov-Schur algorithm for solving the Reynolds-Averaged Navier-Stokes (RANS) equations, which are fully coupled with the one-equation Spalart-Allmaras turbulence model, and a discrete-adjoint gradient evaluation. In order to develop an efficient methodology for optimization in turbulent flows, the viscous and turbulent terms in the ii governing equations were linearized by hand. Additionally, a set of mesh refinement tools was introduced in order to obtain both an acceptable control volume mesh and a sufficiently refined computational mesh from an initial coarse mesh. A series of drag minimization studies was carried out which show that the algorithm is able to maintain robustness in the mesh movement and flow analysis in the presence of large shape changes, an important requirement for performing exploratory optimizations aiming to discover novel configurations and for multidisciplinary optimization. Additionally, the algorithm is able to find incremental improvements when given well-designed initial planar and nonplanar geometries. A comparison of Euler-based and RANS-based optimizations highlights the importance of considering viscous and turbulent effects. A multi-point optimization demonstrates that the algorithm is able to address practical aerodynamic design problems.Ph