Aerodynamic optimization of turbomachinery blades using evolutionary methods and ANN-based surrogate models

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.

Optimizing Rotor Blades with Approximate British Experimental Rotor Programme Tips

“…The optimization method used is based on a GA coupled with a neural network metamodel (ANN). This evolutionary type optimization technique ensures a higher probability of obtaining the global optimum compared to its gradient-based counterpart [24]. The efficiency lost in using this method is regained by use of the metamodel.…”

Development of a method for geometrical modeling of the airfoil profile of an axial turbomachine blade

“…Studies in this direction are carried out by introducing the mentioned changes of geometric shape of the blade airfoil into the methods used in designing these important components of axial turbomachine flow parts. For example, study [5] proposes a method for improving axial turbomachine blade airfoil profile by solving an optimization problem and constructing cascade of profiles with optimal profiling of blade crown.…”

Multi-objective Optimization of Draft Tube in Francis Turbine Using DOE, RBF and NSGA-II

“…Therefore, the automatic optimization methodologies using the geometrical parameters of the cross-sections of the draft tube were previously examined, but these methods had not considered the geometrical parameters such as the median section affecting significantly on the performance of the draft tube [7][8][9]. While, the multi-objective optimization methods have been employed in the optimization design of turbomachinery [10][11][12], and the optimization methods in the engineering design have been also suggested using the CFD, DOE technique and multi-objective genetic algorithm [13][14][15].…”