Improved Dynamic Geometry Control Algorithms for Efficient Aerodynamic Shape Optimization

Abstract

In aerodynamic shape optimization, traditional static geometry control methods can produce suboptimal performance by introducing performance tradeoffs at various stages of optimization, enforcing arbitrary constraints on open-ended optimization, and necessitating foreknowledge of problem behavior to design an effective control scheme. These shortcomings can be mitigated through dynamic geometry control, which partly automates the geometry control design process by refining the geometry control topology throughout optimization. Such refinement can occur in a predetermined fashion (as in progressive control) or more automatically using sensitivity information to guide refinement (as in adaptive control). Both progressive control and adaptive control are implemented in the context of axial and free-form deformation geometry control, and novel contributions are made to the adaptive algorithm, including the treatment of active constraints and several novel “potential indicators” to rank candidate refinements. Application to a wide suite of aerodynamic shape optimization problems demonstrates that dynamic geometry control is effective, producing lower final drag than well-designed static schemes while reducing required iterations to convergence by 50% or more, and simultaneously reducing labor requirements on the user. These benefits are demonstrated across a wide variety of problems, representative of detailed and exploratory problems often encountered in both academia and industry.