Abstract

The effect of cascade aerodynamic optimization on turbomachinery design is very significant. However, for most traditional cascade optimization methods, aerodynamic parameters are considered as boundary conditions and rarely directly used as the optimization variables to realize optimization. Given this problem, this paper proposes an improved cascade aerodynamic optimization method in which an incidence angle and nine geometric parameters are used to parameterize the cascade and one modified optimization algorithm is adopted to find the cascade with the optimal aerodynamic performance. The improved parameterization approach is based on the Non-Uniform Rational B-Splines (NURBS) method, the camber line superposing thickness distribution molding (CLSTDM) method, and the plane cascade design method. To rapidly and effectively find the cascade with the largest average lift-drag ratio within a certain range of incidence angles, modified particle swarm optimization combined with the modified very fast simulated annealing algorithm (PSO-MVFSA) is adopted. To verify the feasibility of the method, a cascade with NACA4412 and a practical cascade are optimized. It is found that the average lift-drag ratios of two optimal performance cascades are respectively increased by 13.38% and 15.21% in comparison to those of two original cascades. Meanwhile, through optimizing the practical cascade of the Blade D500, under different volume flow rates, the pressure coefficient of the optimized cascade is increased by an average of more than 6.12% compared to that of the prototype, and the average efficiency is increased by 11.15%. Therefore, this improved aerodynamic optimization method is reliable and feasible for the performance improvement of cascades with a low Reynolds number.

Highlights

  • Blade design is of great importance to the efficiency and properties of turbomachinery

  • Unlike conventional parameterization approaches in which the airfoil is only parameterized with the geometric feature parameters [30,31,32], an improved aerodynamic parametrization approach combining the plane cascade design method and the camber line superposing thickness distribution molding (CLSTDM)-Non-Uniform Rational B-Splines (NURBS) is proposed, in which the incidence angle, i, and nine geometric parameters are used as control variables

  • The aerodynamic performance of each performance under one constant incidence angle, but to reach the best whole aerodynamic performance cascade parameterized by one incidence and eight variables calculated by the CFD

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Summary

Introduction

Blade design is of great importance to the efficiency and properties of turbomachinery. In this function due to its ability of local control and its conics description over the curve These method, several airfoil geometric parameters are used to parameterize the half-thickness distribution deformative functions are used tothrough generate new airfoil oncurves the point coordinates of an curve and the mean camber curve twoone polynomials. It is convenient for designers to several airfoil geometric parameters are used to parameterize the half-thickness distribution curve use this method to parameterize one blade based on their experience Nowadays, it is widely used in and the mean camber curve through two polynomials. It is convenient the genetic algorithm (GA) [18,19,20,21] and the simulated annealing (SA) algorithm [10,11,22], as two for designers to use this method to parameterize one blade based on their experience Nowadays, it traditional intelligent optimization algorithms, have been widely used in airfoil optimization. Aerodynamic Parameterization Method the blade of FAN D500, are selected to verify the feasibility of the improved parameterization and optimization method

Method
Improved Aerodynamic Parameterization
Modified PSO-MVFSA
Verification of Modified PSO-MVFSA
Rastrigin function:
Fitness Function
Aerodynamic Optimization Process
Optimization of a Cascade with
Geometry comparison: airfoil optimal
Blade D500 Optimization
Validation of the CFD Simulation Based on Experiments
11. The experiment of of
13. Based on Figure
Optimization of Blade
Conclusions
Full Text
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