This paper presents an automated multiobjective design methodology for the aerodynamic optimisation of turbomachinery blades. In this approach several operating-points of the compressor are considered and the flow-characteristics of the different flow-solutions are combined to one or more objective functions. The optimisation strategy is based on multiobjective asynchronous evolutionary algorithms (MOEA’S) which are accelerated using additive local neural networks and kriging procedures. Common operators: Mutation, Crossover and Differential-Evolution are used to create a new population. In addition to these common operators the optimisation runs temporarily on the response-surface created by the neural networks and/or kriging-processes respectively. Only the Pareto-optimal solutions obtained from this metamodel are evaluated using the numerical expensive flow-solver. Therefore, the response-surface is just a new operator that creates auspicious members. One of the main differences between the presented code to usual and traditional MOEA’S is the selection of parents. While traditional codes choose potential parents of a new population from the previous population, the current method selects parents from the database of all evaluated members. This approach allows the user to run the optimisation asynchronously and with a smaller size of population, treducing numerical costs, without influencing the diversity of the optimal solutions over the whole Pareto-front. This aspect is very important when evaluating very complex and/or discontinuous fronts.

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