Convergence Based Prediction Surrogates for High-lift CFD Optimization

Using a surrogate model to evaluate the expensive fitness of candidate solutions in an evolutionary algorithm can significantly reduce the overall computational cost of optimization tasks. In this paper we analyze the convergence profiles of a multi-element high-lift system, revealing insights into the flow physics of the system and how CL varies for different numbers of flow iterations. A hybrid multi-objective evolutionary algorithm that trains and optimizes the structure of a recurrent neural network ensemble is then introduced as a surrogate for the long-term prediction of the high-lift systems computational fluid dynamic convergence data. The intermediate data is used for training the networks and results presented show that the trends of the design space can be better predicted than the absolute magnitudes of the CL convergence histories.