Axial Turbine Blade Aerodynamic Optimization Using a Novel Multi-Level Genetic Algorithm

2008 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Optimization Problems ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for single objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid genetic algorithm successfully accelerates the optimization at the initial generations for both optimization problems, while preventing converging to local optimums.

Multi-Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multilevel Genetic Algorithm

2010 ◽  
Vol 132 (4) ◽  
Author(s):  
Özhan Öksüz ◽  
İbrahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for a multi-objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size, and cost of the gas turbine engine. A 3D steady Reynolds averaged Navier–Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well-known test cases. The blade geometry is modeled by 36 design variables plus the number of blade variables in a row. Fine and coarse grid solutions are respected as high- and low-fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization and prevents the convergence with local optimums.

Multi Objective Aerodynamic Optimization of Axial Turbine Blades Using a Novel Multi-Level Genetic Algorithm

2008 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Design Variables

In this paper, a new multiploid genetic optimization method handling surrogate models of the CFD solutions is presented and applied for multi objective turbine blade aerodynamic optimization problem. A fast, efficient, robust, and automated design method is developed to aerodynamically optimize 3D gas turbine blades. The design objectives are selected as maximizing the adiabatic efficiency and torque so as to reduce the weight, size and cost of the gas turbine engine. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. Fine and coarse grid solutions are respected as high and low fidelity models, respectively. One of the test cases is selected as the baseline and is modified by the design process. It was found that the multiploid multi-objective genetic algorithm successfully accelerates the optimization, and prevents converging to local optimums.

Turbine Blade Shape Aerodynamic Design Using Artificial Intelligence

2005 ◽  
Author(s):  
O¨zhan O¨ksu¨z ◽  
I˙brahim Sinan Akmandor
Keyword(s):  
Artificial Intelligence ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Design Method ◽  
Low Cost ◽  
Flow Characteristics ◽  
Navier Stokes ◽  
Test Cases ◽  
Turbine Engine ◽  
Design Variables

This paper describes a fast, efficient, robust, and automated design method used to aerodynamically optimize 3D gas turbine blade shapes implementing artificial intelligence. The design objectives are maximizing the aerodynamic efficiency and torque so as to reduce the weight and size and cost of the gas turbine engine. The procedure described here will allow a rapid, practical and low cost design that will answer the need of gas turbine industry. A 3-Dimensional steady Reynolds Averaged Navier Stokes solver is coupled with an automated unstructured grid generation tool. The solver is verified using two well known test cases. Blade geometry is modeled by 36 design variables plus the number of blades variable in a row. A genetic algorithm is used for global optimization purposes. One of the test cases is selected as the baseline and is modified by the design process. It was found that the efficiency can be improved from 83.9% to 85.9%, and the torque can be improved as much as 7.6%. The flow field investigations indicate enhanced secondary flow characteristics of the blade passage.

Optimization of Wind Turbine Blades Using Lifting Surface Method and Genetic Algorithm

2011 ◽  
Author(s):  
Xin Shen ◽  
Xiao-cheng Zhu ◽  
Zhao-hui Du
Keyword(s):  
Genetic Algorithm ◽  
Design Method ◽  
Turbine Blades ◽  
Optimization Method ◽  
Efficient Design ◽  
Wind Turbine Blades ◽  
Surface Method ◽  
Lifting Surface ◽  
Horizontal Axis Wind Turbines ◽  
Wake Model

This paper describes an optimization method for the design of horizontal axis wind turbines using the lifting surface method as the performance prediction model and a genetic algorithm for optimization. The aerodynamic code for the design method is based on the lifting surface method with a prescribed wake model for the description of the wake. A micro genetic algorithm handles the decision variables of the optimization problem such as the chord and twist distribution of the blade. The scope of the optimization method is to achieve the best trade off of the following objectives: maximum of annual energy production and minimum of blade loads including thrust and blade rood flap-wise moment. To illustrate how the optimization of the blade is carried out the procedure is applied to NREL Phase VI rotor. The result shows the optimization model can provide a more efficient design.

scholarly journals A comparative evaluation of genetic and gradient-based algorithms applied to aerodynamic optimization

2008 ◽  
pp. 103-126
Author(s):  
David W. Zingg ◽  
Marian Nemec ◽  
Thomas H. Pulliam
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Comparative Evaluation ◽  
Pareto Front ◽  
Optimization Problems ◽  
Aerodynamic Optimization ◽  
Design Variables ◽  
Gradient Based ◽  
Degree Of Convergence ◽  
Multipoint Optimization

A genetic algorithm is compared with a gradient-based (adjoint) algorithm in the context of several aerodynamic shape optimization problems. The examples include singlepoint and multipoint optimization problems, as well as the computation of a Pareto front. The results demonstrate that both algorithms converge reliably to the same optimum. Depending on the nature of the problem, the number of design variables, and the degree of convergence, the genetic algorithm requires from 5 to 200 times as many function evaluations as the gradientbased algorithm.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

scholarly journals Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade

2021 ◽  
Vol 11 (13) ◽  
pp. 5838
Author(s):  
Liang Xu ◽  
Qingyun Shen ◽  
Qicheng Ruan ◽  
Lei Xi ◽  
Jianmin Gao ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Gas Turbine ◽  
Optimization Design ◽  
Mechanical Performance ◽  
Optimization Problems ◽  
Lattice Structure ◽  
Turbine Blades ◽  
Optimization Method ◽  
Internal Cooling ◽  
Geometric Variables

Recently, the inlet temperatures in gas turbine units have been drastically increased, which extremely affects the lifespan of gas turbine blades. Traditional cooling structures greatly improve the high temperature resistance of the blade; however, these structures scarcely concern both heat transfer and mechanical performances. Lattice structure (LS) can realize these requirements because of its characteristics of light weight, high strength, and porosity. Although the topology of LS is complex, it can be manufactured with the 3D metal printing technology. In this study, an integral optimization method of lattice cooling structure, used at the trailing edge of turbine blades, concerned with heat transfer and mechanical performance, was presented. Firstly, functions between the first-order natural frequency (freq1), elasticity modulus (E), relative density (ρ¯), and Nusselt number (Nu), and the geometric variables of pyramid type LS (PLS) and X-type LS (XLS) were established, and the reliability of these functions was verified. Then, a mathematical optimization model was developed based on these functions which contained two selected optimization problems. Finally, relations among objectives were analyzed; influence law of geometric variables to objectives were discussed, and the accuracy of the optimal LS was proved by experiment and numerical simulation. The optimization results suggest that, compared to the initial LS, Nu increases by 24.1% and ρ¯ decreases by 31% in the optimal LS of the first selected problem, and the Nu increases by 28.8% while freq1 and ρ¯ are almost unchanged in the optimal LS of the second selected problem compared to the initial LS. This study may provide a guidance for functions integration design of lattice cooling structures used at turbine blades based on 3D printing.

A novel surrogate-based aerodynamic optimization method using field approximate model

2017 ◽  
Vol 233 (3) ◽  
pp. 883-895 ◽  
Author(s):  
Wenjie Wang ◽  
Zeping Wu ◽  
Donghui Wang ◽  
Weihua Zhang
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Optimization Method ◽  
Approximate Model ◽  
Dynamics Simulation ◽  
Aerodynamic Optimization ◽  
Optimal Point ◽  
Design Variables ◽  
Sampling Set ◽  
Optimization Efficiency

An efficient surrogate-based aerodynamic shape optimization method is developed to improve the optimization efficiency. In this method, the field approximate model is presented firstly to predict the flow field parameters of interest for specific aerodynamic optimization problems with respect to the design variables and sequentially updated. The differential evolution is used to locate the optimum of field approximate model coupled with the analytical post-processing to calculate the objective and constraints for aerodynamic optimization. This optimal point is calculated by time-consuming computational fluid dynamics simulation and the result is added to the sampling set to update the sampling points and field approximate model. The proposed method is compared with conventional sequential approximate optimization and shows great advantages in accuracy and efficiency. Two shape optimization test cases are provided to verify the efficacy and efficiency of the proposed method.

A Dynamic Optimization Design Method for the Turbine Engine Frame Foundation

2012 ◽  
Vol 226-228 ◽  
pp. 784-787
Author(s):  
Zhao Jun Li ◽  
Xi Cheng Wang
Keyword(s):  
Dynamic Optimization ◽  
Optimization Design ◽  
Optimization Problems ◽  
Design Method ◽  
Kriging Model ◽  
Optimization Method ◽  
Turbine Engine ◽  
Design Variables ◽  
Side Constraints ◽  
Dynamic Reanalysis

An effective optimization method using Kriging model and parametric sampling evaluation strategy is proposed to solve dynamic optimization design. The optimization problem is to find the design variables such that the structural weight is minimum and dynamic displacement of the points concerned plus certain side constraints are satisfied. The types of design variables are considered as the sizing variables of the beams and columns. Kriging model is used to build the approximate mapping relationship between the forced vibration amplitude and design variables, reducing expensive dynamic reanalysis. A dynamic analysis program is used as black-box to obtain dynamic response. Numerical examples show that the method has good accuracy and efficiency. Versatility of this method can be expected to play an important role in future engineering optimization problems.

scholarly journals A Comprehensive Optimization Design Method of Aerodynamic, Acoustic, and Stealth of Helicopter Rotor Blades Based on Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingxu Yi ◽  
Yalin Pan ◽  
Jun Huang ◽  
Lifeng Wang ◽  
Dawei Liu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Integrated Design ◽  
Optimization Method ◽  
Rotor Blades ◽  
Helicopter Rotor ◽  
Optimization Approach ◽  
Design Variables ◽  
Helicopter Rotor Blades

In this paper, a comprehensive optimization approach is presented to analyze the aerodynamic, acoustic, and stealth characteristics of helicopter rotor blades in hover flight based on the genetic algorithm (GA). The aerodynamic characteristics are simulated by the blade element momentum theory. And the acoustics are computed by the Farassat theory. The stealth performances are calculated through the combination of physical optics (PO) and equivalent currents (MEC). Furthermore, an advanced geometry representation algorithm which applies the class function/shape function transformation (CST) is introduced to generate the airfoil coordinates. This method is utilized to discuss the airfoil shape in terms of server design variables. The aerodynamic, acoustic, and stealth integrated design aims to achieve the minimum radar cross section (RCS) under the constraint of aerodynamic and acoustic requirement through the adjustment of airfoil shape design variables. Two types of rotor are used to illustrate the optimization method. The results obtained in this work show that the proposed technique is effective and acceptable.

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