Multi-point and multi-objective optimization design method for industrial axial compressor cascades

Abstract

Modern aerodynamic optimization design methods for the industrial axial compressor cascade mainly aim at improving both design point and off-design point performance. In this study, a multi-point and multi-objective optimization design method is established for the cascade, particularly aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. The design objectives are to maximize the static pressure ratio and minimize the total pressure loss coefficient at the design point, and to maximize the operating range for the positive and negative incidences. To alleviate the computational load, a design of experiment (DOE)-based GA-BP-ANN model is constructed to rapidly approximate the cascade aerodynamic performance in the optimization process. The artificial neural network (ANN) is trained by the genetic algorithm (GA) technique and back propagation (BP) algorithm, where the training cascades are sampled by the DOE method and analysed by the computational fluid dynamics method. The multi-objective genetic algorithm is used to search for a series of Pareto-optimum solutions, from which an optimal cascade is found out whose objectives are all better than (ABT) those of the original design. The ABT cascade is characterized by the lower camber and higher turning angle, leading to better aerodynamic performance in a widened operating range. Compared with the original design, the ABT cascade decreases the total pressure loss coefficient by 1.54 per cent, 23.4 per cent, and 7.87 per cent at the incidences of 5 • , −9 • , and 13 • , respectively. The established optimization design method can be extended to the three-dimensional aerodynamic design of axial compressor blade.

Artificial intelligence metamodel comparison and application to wind turbine airfoil uncertainty analysis

“…In this study, both the weights and bias are calculated via the back propagation (BP) algorithm, 7 and the sigmoid function is selected for the input-hidden transfer function while the linear function for the hidden-output. As demonstrated in our previous work, 19 the initial weights used in the BP algorithm are often difficult to determine but significantly affect the ANN performance. These parameters are thus taken as the predetermined ones and optimized by the genetic algorithm (GA), as will be discussed in section ''Use of GA.''…”

“…Detailed implementations of GA in metamodel optimization can be found in Ju and Zhang. 19 Numerical assessment of metamodeling performance…”

Multi-point robust design optimization of wind turbine airfoil under geometric uncertainty

“…To tackle this problem, an optimum reservation strategy has been proposed, whereby the current Pareto optimum solutions are copied to an additional space, the so-called gene pool, without imposing any interference on the evolution; the true Pareto optimum solutions emerge from the final competition among the reserved optimums [24].…”

Multi-Level Multi-Objective Multi-Point Optimization System for Axial Flow Compressor 2D Blade Design

“…The blending functions are defined based on the distance to the nearest wall and on the flow variables by Eqs. (13,14).…”

“…By the optimization, maximum efficiency and total pressure are increased by 1.76 and 0.41 %, respectively. Ju and Zhang [13] established a multi-point and multi-objective optimization design method, particularly, aiming at widening the operating range while maintaining good performance at the acceptable expense of computational load. They used the artificial neural network and the genetic algorithm technique and back-propagation algorithm along with the computational fluid dynamics.…”