Integrating Distributed Control into High-Fidelity Aircraft Multidisciplinary Design Optimization

Because of the formidable problem formulation and computational challenges associated with bringing high-fidelity modeling in an integrated way to the actively-controlled airplane design process, this SBIR project proposes major innovations in computational aeroservoelastic optimization technology, including the construction and utilization of Projection-Based Reduced-Order Models (PROMs) using linear as well as nonlinear, State-of-the-Art (SoA) Galerkin and Petrov-Galerkin Projection-based Model Order Reduction (PMOR) methods. These include recently developed approaches for mitigating the curse of dimensionality when training PROMs, SoA hyperreduction methods for achieving practical wall-clock solution times in the presence of structural nonlinearities and/or turbulence modeling, and various approaches for incorporating PMOR and PROMs in MultiDisciplinary Analysis and Optimization (MDAO) processes.The main goal of all innovations outlined above is to make high-fidelity aeroservoelastic MDAO problems feasible and practical, including for a relatively large number of design variables and constraints when needed for capturing the design problem in a realistic way -- that is, of relevance and interest to industrial problems.