A growing area in aerospace engineering is the use and development of Unmanned Aerial Vehicles (UAVs) for military and civilian applications. There are difficulties in the design of these vehicles because of the varied and non-intuitive nature of the configurations and missions that can be performed. Similar to their manned counterparts, the challenge is to develop trade-off studies of optimal configurations to produce a high performance aircraft that satisfy mission requirements The goal in the present study is to address these issues from a multi-criteria and multidisciplinary design optimisation (MDO) standpoint. \ud Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. These techniques are efficient in finding locally optimum solutions if the objective and constraints are differentiable. If a broader application of the optimiser is desired or when the problem is multi-modal, involve approximation, is non-differentiable or involve multiple criteria and multi-physics, 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. \ud This paper examines the requirements, initial development, and application of a framework for Multidisciplinary Design and Optimisation (MDO) of UAVs. The framework includes a Graphical User Interface (GUI), a robust EA optimiser, several design modules, mesh generators and post-processing capabilities in an integrated platform. The application of the method is illustrated on a multi-criteria and multidisciplinary design problem. Results indicate the robustness of the method in finding optimal solutions and trade-offs between the disciplinary analyses and producing a set of individuals represented in an optimal Pareto front
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