A framework for multidisciplinary design and optimisation In aeronautics

This paper examines the initial development and application of a framework for Multidisciplinary Design and Optimisation (MDO) in aeronautics. Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. These methods are efficient to find optimal global solutions if the objective and constraints are differentiable. But if a broader application of the optimiser is desired, or when the complexity of the problem arises because they are multi-modal, involve approximation, are non-differentiable, or involve multiple objectives and physics, more robust and alternative numerical tools are required. Emerging techniques such as Evolutionary Algorithms (EAs) have shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions amongst many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and implementation of a framework for analysis and optimisation of multidisciplinary and multi-objective optimisation problems in aeronautics is described. The framework includes a Graphics User Interface (GUI) a robust EA optimiser, several design modules, and post-processing capabilities. The application of the method is then illustrated with application to a multi-objective wing design problem. Results indicate the practicality and robustness of the method in finding optimal solutions and trade-offs between the disciplinary analyses, and in producing a set of individuals represented in an optimal Pareto front