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.

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.

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 Multi-objective aerodynamic optimization of flying-wing configuration based on adjoint method

2021 ◽  
Vol 39 (4) ◽  
pp. 753-760
Author(s):  
Xiaodong Liu ◽  
Peiliang Zhang ◽  
Guanghong He ◽  
Yongen Wang ◽  
Xudong Yang
Keyword(s):  
Optimization Design ◽  
Adjoint Method ◽  
Stokes Equations ◽  
Design Method ◽  
Adjoint Equation ◽  
Geometric Constraints ◽  
Navier Stokes ◽  
Aerodynamic Design ◽  
Aerodynamic Optimization ◽  
Multi Objective

In order to solve the multi-objective multi-constraint design in aerodynamic design of flying wing, the aerodynamic optimization design based on the adjoint method is studied. In terms of the principle of the adjoint equation, the boundary conditions and the gradient equations are derived. The Navier-Stokes equations and adjoint aerodynamic optimization design method are adopted, the optimization design of the transonic drag reduction for the two different aspect ratio of the flying wing configurations is carried out. The results of the optimization design are as follows: Under the condition of satisfying the aerodynamic and geometric constraints, the transonic shock resistance of the flying wing is weakened to a great extent, which proves that the developed method has high optimization efficiency and good optimization effect in the multi-objective multi-constraint aerodynamic design of the flying wing.

scholarly journals Wing Section Optimization for Supersonic Viscous Flow

1998 ◽  
Vol 120 (1) ◽  
pp. 102-108 ◽  
Author(s):  
Cem C. Item ◽  
Oktay Baysal
Keyword(s):  
Optimization Problem ◽  
Stokes Equations ◽  
Design Method ◽  
Leading Edge ◽  
Adverse Pressure Gradient ◽  
Navier Stokes ◽  
Test Cases ◽  
Aerodynamic Optimization ◽  
Wing Section ◽  
Problem Formulations

To improve the performance of a highly swept supersonic wing, it is desirable to have an automated design method that also includes a higher fidelity to the flow physics. With this impetus, an aerodynamic optimization methodology incorporating the thin-layer Navier-Stokes equations and sensitivity analysis had previously been developed. Prior to embarking upon the full wing design task, the present investigation concentrated on the identification of effective optimization problem formulations and testing the feasibility of the employed methodology, by defining two-dimensional test cases. Starting with two distinctly different initial airfoils, two independent optimizations resulted in shapes with similar features: cambered, parabolic profiles with sharp leading- and trailing-edges. Secondly, an outboard wing section normal to the subsonic portion of the leading edge, which had a high normal angle-of attack, was considered. The optimization resulted in a shape with twist and camber that eliminated the adverse pressure gradient, hence, exploiting the leading-edge thrust. The wing section shapes obtained in all the test cases included the features predicted by previous studies. This was considered as a strong indication that the flow field analyses and sensitivity coefficients were computed and provided to the present gradient-based optimizer correctly. Also, from the results of the present study, effective optimization problem formulations could be deduced to start a full wing shape optimization.

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.

Optimization of Flatback Airfoils for Wind Turbine Blades Using a Multi-Objective Genetic Algorithm

2012 ◽  
Author(s):  
Xiaomin Chen ◽  
Ramesh Agarwal
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Turbine Blades ◽  
Navier Stokes ◽  
Wind Turbine Blades ◽  
Multi Objective ◽  
Artificial Neural Network Ann ◽  
Drag Ratio ◽  
Lift To Drag Ratio ◽  
Single Objective

In recent years, the airfoil sections with blunt trailing edge (called flatback airfoils) have been proposed for the inboard regions of large wind-turbine blades because they provide several structural and aerodynamic performance advantages. In a previous paper, ASME ES2010-90373, we employed a single objective genetic algorithm (GA) for shape optimization of flatback airfoils for generating maximum lift to drag ratio. The computational efficiency of GA was significantly enhanced with an artificial neural network (ANN). The commercially available software FLUENT was employed for calculation of the flow field using the Reynolds-Averaged Navier-Stokes (RANS) equations in conjunction with a turbulence model. In this paper, we employ a multi-objective GA to optimize the flatback airfoils to achieve two objectives, namely the generation of maximum lift as well as the maximum lift to drag ratio. It is shown that the multi-objective GA optimization can generate superior flatback airfoils compared to those obtained by using single objective GA algorithm.

Optimization of Wind Turbine Airfoil With Good Stall Characteristics by Genetic Algorithm Using CFD and Neural Network

2013 ◽  
Author(s):  
Mohammad H. Djavareshkian ◽  
Amir Latifi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wind Turbine ◽  
Stokes Equations ◽  
Computational Cost ◽  
Numerical Procedure ◽  
Optimization Method ◽  
Navier Stokes ◽  
Design Variables ◽  
Wind Turbine Airfoil

In this research, optimization of a wind turbine airfoil is done by Genetic Algorithm (GA) as optimization method, coupled with CFD (Computational Fluid Dynamics) and Artificial Neural Network (ANN). A pressure-based implicit procedure is used to solve the Navier-Stokes equations on a nonorthogonal mesh with collocated finite volume formulation to calculate the aerodynamic coefficients. The boundedness criteria for the numerical procedure are determined from Normalized Variable Diagram (NVD) scheme and the k–ε eddy-viscosity turbulence model is utilized. ANN has been used as surrogate model to reduce computational cost and time. Single objective and multi objective optimization of wind turbine airfoil have been performed and the results of optimization are presented. To reduce the number of design variables and producing a smooth shaped airfoil, modified Hicks-Henne functions are used. In this process, the Eppler E387 airfoil has been applied as the base airfoil. Angle of attack varies from 0 to 20 degrees and Reynolds number of the flow is 460000.

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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