High-Fidelity Multidisciplinary Design Optimization of Aircraft Configurations


Title of the Project:

High-Fidelity Multidisciplinary Design Optimization of Aircraft Configurations

Project Leader:

David W. Zingg, Institute for Aerospace Studies , University of Toronto

Project Objectives:

This project involves four researchers from three Canadian universities who are collaborating on the development of a high-fidelity multidisciplinary design tool for aircraft configurations that integrates structures, aerodynamics, and controls. Significant contributions in applied mathematics will be made by developing, implementing, and evaluating new techniques needed for tackling the challenges inherent in high-fidelity multidisciplinary design optimization.

Scientific Rationale:

Computational fluid dynamics (CFD) and structural finite-element methods (FEM) have been important tools for aircraft designers over the past thirty years. The use of FEM in conjunction with numerical optimization created the field of structural optimization, which has proved to be extremely useful. Aerodynamic shape optimization emerged later, and was pioneered in the late eighties. The remarkable breakthrough in both of these fields was the development of adjoint methods, which efficiently compute gradients of selected functions of interest with respect to large numbers of design variables. When used with gradient-based optimization algorithms, adjoint methods make high-fidelity aerodynamic shape optimization feasible.

While there have been significant advances in aerodynamic shape optimization, its use in industry has had limited success, and adjoint methods have fallen short of achieving their full potential. The main reason for this is that the aerodynamic optimization of three-dimensional shapes is particularly challenging. One of the major challenges is the parameterization of aircraft geometries, especially when considering unconventional designs. Another closely related challenge is the mesh perturbation associated with changes in shape: The changes in the mesh must be such that the accuracy of the CFD solution is not compromised. Finally, aerodynamic shape optimization with no consideration for other disciplines such as structures and aeroelasticity is bound to result in infeasible designs, unless constraints are imposed on the shape parameters. Even then, because the optimization problem does not take advantage of multidisciplinary trade-offs, only suboptimal designs are found.

The Mprime Seed Project enabled the development of efficient aerodynamic shape optimization algorithms. In this project we improve upon the aerodynamic optimization capability and incorporate it into an MDO framework that includes structures and control. These algorithms are currently limited by the challenges mentioned above. By adding the capability for designing the structure and aerodynamic shape simultaneously, the resulting design will be both more realistic and more optimal. This is the main motivation behind the use of multidisciplinary design optimization (MDO). The capability of simultaneously optimizing a control system for flutter suppression will result in even lighter wings and thus more efficient aircraft configurations.

Considerable research has been conducted on the MDO of aerospace systems. These efforts have ranged from the development of techniques for discipline coupling to actual optimization of real-world design projects. The vast majority of these research efforts have shown the importance of inter-disciplinary coupling, as well as the inability of sequential disciplinary optimization to achieve the true global optimum of a multidisciplinary system. In the case of aircraft design, for example, it was shown that in order to obtain realistic designs, it is necessary to include multiple disciplines and a complete set of real-world constraints while optimizing the true figure of merit. Unfortunately, the modeling of the various disciplines in most of the work on MDO has remained at a relatively low level. While useful at the conceptual design stage, lower-order models cannot accurately represent a variety of nonlinear phenomena; these phenomena can play an important role in the search for the optimum design.

MDO is now poised to make a significant impact on the aircraft industry, but a few barriers preventing its full deployment in an industrial setting remain. Only by considering all aspects of aircraft design simultaneously will it be possible to achieve truly optimal (and therefore competitive) designs.