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.
The proposed development will contribute to NASA multidisciplinary design optimization studies of a variety of aircraft configurations of current and future interest controlled by many control effectors, including variable camber continuous trailing edge wings, distributed propulsion, morphing supersonic configurations, etc. NASA will benefit from the new technology by either using the numerical capabilities developed or by integrating selected modules of the new capabilities into NASA’s multidisciplinary optimization environment.
The new technology will create numerical capabilities not yet available for multidisciplinary design of aircraft controlled by many control effectors. All developers of new aircraft utilizing active control of large numbers of control effectors would benefit. Customers of the new capability will be able to also integrate its key modules into their multidisciplinary design optimization systems.