Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models |
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Authors: | Temesgen Mengistu Wahid Ghaly |
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Affiliation: | (1) Concordia University, Montreal, Quebec, Canada;(2) Present address: CENAERO, Gosselies, Belgium |
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Abstract: | A fast, flexible, and robust simulation-based optimization scheme using an ANN-surrogate model was developed, implemented,
and validated. The optimization method uses Genetic Algorithm (GA), which is coupled with an Artificial Neural Network (ANN)
that uses a back propagation algorithm. The developed optimization scheme was successfully applied to single-point aerodynamic
optimization of a transonic turbine stator and multi-point optimization of a NACA65 subsonic compressor rotor in two-dimensional
flow, both were represented by 2D linear cascades. High fidelity CFD flow simulations, which solve the Reynolds-Averaged Navier-Stokes
equations, were used in generating the data base used in building the ANN low fidelity model. The optimization objective is
a weighted sum of the performance objectives and is penalized with the constraints; it was constructed so as to achieve a
better aerodynamic performance at the design point or over the full operating range by reshaping the blade profile. The latter
is represented using NURBS functions, whose coefficients are used as the design variables. Parallelizing the CFD flow simulations
reduced the turn-around computation time at close to 100% efficiency. The ANN model was able to approximate the objective
function rather accurately and to reduce the optimization computing time by ten folds. The chosen objective function and optimization
methodology result in a significant and consistent improvement in blade performance. |
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Keywords: | Artificial neural networks Genetic algorithm Computational fluid dynamics Aerodynamic design Global optimization NURBS Response surface approximation |
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