Two Schemes of Multi-Objective Aerodynamic Optimization for Centrifugal Impeller Using Response Surface Model and Genetic Algorithm

2010 ◽  
Author(s):  
Xiaomin Liu ◽  
Wenbin Zhang
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Optimization Design ◽  
Aerodynamic Performance ◽  
Centrifugal Impeller ◽  
Surface Model ◽  
Aerodynamic Optimization ◽  
Optimization Scheme ◽  
Impeller Blade ◽  
Multi Objective

This paper presents two schemes of multi-objective aerodynamic optimization design for centrifugal impeller blade. One is genetic algorithm(GA) combined with a commercial computational fluid dynamics(CFD) software, and the other is GA combined with the surrogate model. The two schemes are respectively applied to multi-objective optimization for the same centrifugal impeller blade. For multi-objective genetic algorithm(MOGA), non-uniform mutation and Pareto ranking and fitness-sharing technique are used to obtain fast convergence speed and good capability to search the Pareto front of GA. For the surrogate model based on radial basis function(RBF), design of experiments(DOE) technology is adopted to select samples. The parameters and weight coefficients in the surrogate model are solved by GA instead of traditional least square method. According to the geometrical feature of centrifugal impeller, a three-dimensional reconstruction method for the blade shape based on non-uniform rational B-spline(NURBS) is introduced. The numerical simulation is used to evaluate the aerodynamic performance of the optimal and initial impeller. The computational results show that the aerodynamic performance of impellers designed by both optimization schemes is improved to some extent. At the same time, the main reasons for the improvement in aerodynamic performance of the optimal impeller are revealed. For the optimal impellers, the isentropic efficiency and total pressure ratio are increased by about 1.0% and 3.0% respectively. Through comparison of two schemes applied to the centrifugal impeller optimization design, it is found that the computational performance of the second optimization scheme is superior to that of the first optimization scheme.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

Aerodynamic Optimization Design of Vaned Diffusers for the 100 kW Micro Gas Turbine’s Centrifugal Compressor

2008 ◽  
Author(s):  
Guang Xi ◽  
Zhiheng Wang ◽  
Chunmei Zhang ◽  
Minjian Yuan
Keyword(s):  
Centrifugal Compressor ◽  
Surrogate Model ◽  
High Speed ◽  
Optimization Design ◽  
Performance Test ◽  
Three Dimensional ◽  
Centrifugal Impeller ◽  
Aerodynamic Optimization ◽  
Vaned Diffuser ◽  
Practical Engineering

In this paper the design optimization of vaned diffuser for the 100kW microturbine’s centrifugal compressor is carried out. The forward-loaded and the conventional airfoil diffusers are respectively redesigned based on the surrogate model and the three dimensional viscous flow analyses. The objective of the optimization is to redesign the diffuser that assures, for a given operating condition of the centrifugal impeller, the stage isentropic efficiency to be highest. Using the surrogate model the optimization process is accelerated and the 3D flow analysis’s application to the practical engineering design is efficiently realized. To validate the optimization result, the compressor stage performance test is performed on a high speed centrifugal compressor test rig with one original diffuser and its redesigned, respectively.

Aerodynamic optimization to tandem stators by using sweep and dihedral

2020 ◽  
Vol 234 (6) ◽  
pp. 1225-1236
Author(s):  
Jinouwen Zhang ◽  
Haowan Zhuang ◽  
Jinfang Teng ◽  
Mingmin Zhu ◽  
Xiaoqing Qiang
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Aerodynamic Performance ◽  
High Dimensional ◽  
Two Dimensional ◽  
Multi Objective Optimization ◽  
Optimization Scheme ◽  
Multi Objective ◽  
Design Variables ◽  
Dimensional Optimization

In the modern aerodynamic design of turbomachinery blades, the geometries of blades often need to be reshaped to achieve better aerodynamic performance by introducing extra parametric design variables. A higher variable dimension will lead to a larger sampling range as well as a sparser sample distribution, which challenges the effectiveness and stability of optimization schemes based on surrogate model by making the model prediction quality even poorer. In this paper, a multi-objective optimization based on Gaussian process model was carried out for a high dimensional design space. Based on the previous two-dimensional optimization, tandem stators of a modern compressor were optimized by the design of sweep and dihedral. The purpose of the study is to improve the aerodynamic performance of the compressor tandem stators as well as to provide an effective optimization scheme for high dimensional multi-objective optimization problems. The design of sweep and dihedral for reshaping the tandem stators consists of a total of 18 design variables. An improvement in total pressure recovery coefficient of at least 0.7% at positive incidence and at least 0.3% at negative incidence was obtained, much larger than that in the previous two-dimensional optimization. The optimization process shows that, by using Gaussian process as the surrogate model and a special sampling strategy, this optimization scheme is effective and efficient to handle this high dimensional space. The aerodynamic influences of design parameters of tandem blades were analyzed in detail and the superiority of sweep and dihedral in reducing aerodynamic loss was confirmed.

Aerodynamic optimization design of single-layer spherical domes using kriging surrogate model

2021 ◽  
pp. 136943322199248
Author(s):  
Ye Qiu ◽  
Haiyun He ◽  
Chen Xu ◽  
Bingbing San
Keyword(s):  
Surrogate Model ◽  
Optimization Design ◽  
Stokes Equations ◽  
Single Layer ◽  
Vertical Displacement ◽  
Aerodynamic Performance ◽  
Geometric Parameters ◽  
Kriging Surrogate Model ◽  
Aerodynamic Optimization ◽  
Element Analysis

This paper aims to provide an aerodynamic optimization procedure to improve the aerodynamic performance of single-layer spherical domes, by coupling the kriging surrogate model with computational fluid dynamics (CFD) and finite element analysis (FEA). Firstly, a series of wind tunnel tests on the mean pressures and wind-induced behavior of a single-layer spherical latticed shell, were carried out to investigate the effect of dome geometric parameters. Then, the Reynolds-averaged Navier-Stokes equations and RSM turbulence model were utilized for simulating the wind loads on spherical domes, and the numerical results are validated with experimental data. On this basis, the single-objective aerodynamic optimization of spherical domes based on ordinary kriging surrogates has been carried out to find out the optimal geometric parameters (rise/span and wall-height/span ratios). The objectives were minimizing the highest mean suction and the maximum vertical displacement, respectively. The optimization results showed that the optimal design of spherical domes exhibits a reasonable aerodynamic performance improvement compared with the near optimal solutions. In addition, the highest mean suction and the maximum vertical displacement can be reduced by decreasing the wall-height of the dome, and a good trade-off between the two objectives can be achieved by selecting suitable dome geometric parameters.

Multi-Objective Aerodynamic Optimization Design and Data Mining of a High Pressure Ratio Centrifugal Impeller

2015 ◽  
Vol 137 (9) ◽  
Author(s):  
Zhendong Guo ◽  
Liming Song ◽  
Zhiming Zhou ◽  
Jun Li ◽  
Zhenping Feng
Keyword(s):  
Data Mining ◽  
High Pressure ◽  
Optimization Design ◽  
Pressure Ratio ◽  
Differential Evolution Algorithm ◽  
Centrifugal Impeller ◽  
Pareto Solutions ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
High Pressure Ratio

An automated three-dimensional multi-objective optimization and data mining method is presented by integrating a self-adaptive multi-objective differential evolution algorithm (SMODE), 3D parameterization method for blade profile and meridional channel, Reynolds-averaged Navier–Stokes (RANS) solver technique and data mining technique of self-organizing map (SOM). Using this method, redesign of a high pressure ratio centrifugal impeller is conducted. After optimization, 16 optimal Pareto solutions are obtained. Detailed aerodynamic analysis indicates that the aerodynamic performance of the optimal Pareto solutions is greatly improved. By SOM-based data mining on optimized solutions, the interactions among objective functions and significant design variables are analyzed. The mechanism behind parameter interactions is also analyzed by comparing the data mining results with the performance of typical designs.

Multi-objective aerodynamic optimization design of high-speed train head shape

2017 ◽  
Vol 18 (11) ◽  
pp. 841-854 ◽  
Author(s):  
Liang Zhang ◽  
Ji-ye Zhang ◽  
Tian Li ◽  
Ya-dong Zhang
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
High Speed Train ◽  
Head Shape ◽  
Aerodynamic Optimization ◽  
Multi Objective
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