Aerodynamic Optimization of a Compressor Rotor Using Genetic Algorithm

2020 ◽  
pp. 187-202
Author(s):  
Orçun Kor ◽  
Sercan Acarer
Keyword(s):  
Genetic Algorithm ◽  
Aerodynamic Optimization ◽  
Compressor Rotor

Aeroelastic Optimization of an Industrial Compressor Rotor Blade Geometry

2018 ◽  
Author(s):  
Federico Vanti ◽  
Lorenzo Pinelli ◽  
Andrea Arnone ◽  
Andrea Schneider ◽  
Pio Astrua ◽  
...  
Keyword(s):  
Numerical Procedure ◽  
Optimization Procedure ◽  
Multidisciplinary Optimization ◽  
Optimization Process ◽  
Geometrical Parameters ◽  
Aerodynamic Optimization ◽  
Compressor Rotor ◽  
Wide Range ◽  
Automatic Optimization ◽  
Blade Geometry

This paper describes a multidisciplinary optimization procedure applied to a compressor blade-row. The numerical procedure takes into account both aerodynamic (efficiency) and aeromechanic (flutter-free design) goals nowadays required by turbo-machinery industries and is applied to a low pressure compressor rotor geometry provided by Ansaldo Energia S.p.A.. Some typical geometrical parameters have been selected and modified during the automatic optimization process in order to generate an optimum geometry with an improved efficiency and, at the same time, a safety flutter margin. This new automatic optimization procedure, which now includes a flutter stability assessment, is an extension of an existing aerodynamic optimization process, which randomly perturbs a starting 3D blade geometry inside a constrained range of values, build the fluid mesh and run the CFD steady analysis. The new implementation provides the self-building of the solid mesh, the FEM analysis and finally the unsteady uncoupled aeroelastic analysis to assess the flutter occurrence. After simulating a wide range of geometries, a database with all the constraint parameters and objective functions is obtained and then used to train a neural network algorithm. Once the ANN validation error is converged, an optimization strategy is used to build the Pareto front and to provide a set of optimum geometries redesigning the original compressor rotor. The aim of this paper is to show the opportunity to also take into account the aeroelastic issues in optimization processes.

A Novel Combination of Adaptive Tools for Turbomachinery Airfoil Shape Optimization Using a Real-Coded Genetic Algorithm

2013 ◽  
Author(s):  
A. Safari ◽  
H. G. Lemu ◽  
M. Assadi
Keyword(s):  
Genetic Algorithm ◽  
Shape Optimization ◽  
Performance Enhancement ◽  
Flow Analysis ◽  
Three Dimensional ◽  
Pressure Rise ◽  
Computation Time ◽  
Point Of View ◽  
Compressor Rotor ◽  
Real Coded Genetic Algorithm

An automated shape optimization methodology for a typical heavy-duty gas turbine (GT) compressor rotor blade section is presented in this paper. The approach combines a Non-Uniform Rational B-Spline (NURBS) driven parametric geometry description, a two-dimensional flow analysis, and a Genetic Algorithm (GA)-based optimization route. The objective is minimizing the total pressure losses for design condition as well as maximizing the airfoils operating range which is an assessment of the off-design behavior. To achieve the goal, design optimization process is carried out by coupling an established MATLAB code for the Differential Evolution (DE)-based optimum parameterized curve fitting of the measured point cloud of the airfoils’ shape, a blade-to-blade flow analysis in COMSOL Multiphysics, and a developed real-coded GA in MATLAB script. Using the combination of these adaptive tools and methods, the first results are considerably promising in terms of computation time, ability to extend the methodology for three-dimensional and multidisciplinary approach, and last but not least airfoil shape performance enhancement from efficiency and pressure rise point of view.

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.

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.

Aerodynamic Optimization of Industrial Fan Blade Cascades

2005 ◽  
Author(s):  
O. Lotfi ◽  
J. A. Teixeira ◽  
P. C. Ivey ◽  
G. Sheard ◽  
I. R. Kinghorn
Keyword(s):  
Genetic Algorithm ◽  
Flow Rate ◽  
Thickness Distribution ◽  
Automated Design ◽  
Axial Fan ◽  
Aerodynamic Optimization ◽  
Flow Solver ◽  
Blade Thickness ◽  
Fitness Value ◽  
Fan Blade

The paper describes the application of a Genetic Algorithm (GA) to the aerodynamic shape optimization of a low speed fan cascade. This task is accomplished by modifying the blade camber line while keeping the same blade thickness distribution and mass flow rate. A number of design examples have been studied and the behaviour of the genetic algorithm has been tested. The fitness value of the objective function is evaluated using the commercial turbomachinery CFD code CFX-TASCflow. In this work Bezier curves were used for the description of the blade camber line. Specific interfaces were developed in order to link the optimization code, the grid generator TASCgrid utilized to define the computational meshes and the commercial flow solver TASCflow employed within an automated design loop. The obtained results show that the genetic algorithm is capable of automatically improving the design of axial fan blade profiles.

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