This research area focuses on the development and application of modern numerical and evolutionary optimisation techniques to a number of problems in the field of Multidisciplinary Design Optimisation of Unmanned Airborne Systems (UAS). The research is led by Dr. Luis Felipe Gonzalez T. Background UASs are a growing area in aerospace engineering for military and civilian applications. There are difficulties in the design of these systems 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/UAS that satisfy mission requirements. Optimisation is a sensitive integrated part of global aeronautical design as small changes in geometry may result on reduced structural weight, and aerodynamic drag and increased payload capacity. In aerospace engineering design and optimisation the engineer is usually presented with a problem which involves not only one single objective but also numerous objectives and multi-physics environments. Hence a systematic approach, which accounts for the interaction and trade-offs between multiple objectives, variables, constraints and disciplines, is required. This approach is called Multi-objective (MO) and Multidisciplinary Design Optimisation (MDO).
Research Objectives The objectives of the research are to address these issues from a multi-criteria and multidisciplinary design optimisation (MDO) standpoint. 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. Current Work 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. One example application of current research is in the optimal design of lifting surfaces for UASs.
Some input design factors
Computed Pareto optimal aerofoils Software for MDO: HAPMOEA HAPMOEA is a single and multi-objective, multidisciplinary and design optimisation software based on EA. HAPMOEA can be coupled to most commercial or in-house CAE tools (e.g. CFD, structural analysis, flight analysis software). HAPMOEA is based on evolution algorithms uses a hierarchical / multi fidelity approach and can be used for parallel computations. Contact Dr Luís Felipe González Toro if you want a license for academic or research purposes. Publications Related Research Research Personnel |
Australian Research Centre for Aerospace Automation
