Artificial neural network based inverse design method for circular sliding slopes

2004 ◽  
Vol 11 (1) ◽  
pp. 89-92 ◽  
Author(s):  
De-xin Ding ◽  
Zhi-jun Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Method ◽  
Inverse Design ◽  
Artificial Neural ◽  
Inverse Design Method

An inverse design method of diffuser blades by genetic algorithms

1998 ◽  
Vol 212 (4) ◽  
pp. 261-267 ◽  
Author(s):  
H-Y Fan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Velocity Distribution ◽  
Design Method ◽  
Back Propagation ◽  
Inverse Design ◽  
Surface Velocity ◽  
Target Velocity ◽  
Constant Height ◽  
Inverse Design Method

A genetic algorithm incorporating a neural network technique is proposed to search for a turbo-machinery diffuser blade profile that produces a given velocity distribution on its surface. Such a new inverse design method works through minimizing the error between the surface velocity distribution of candidate blades and the target velocity distribution. For ease of employing the genetic algorithm, the blade profiles to be searched are parameterized by Bezier curves. To fix the surface velocity distribution of a candidate blade, a special type of back propagation (BP) neural network is implemented. The proposed approach is illustrated by a diffuser having two-dimensional blades with constant height and thickness. The simulations show that the new method is not only feasible but also reliable and efficient.

The use of genetic algorithm and self-updating artificial neural network for the inverse design of cabin environment

2016 ◽  
Vol 26 (3) ◽  
pp. 347-354 ◽  
Author(s):  
Tian-hu Zhang ◽  
Xue-yi You
Keyword(s):  
Neural Network ◽  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Artificial Neural Network ◽  
Computational Cost ◽  
The Self ◽  
Inverse Design ◽  
Artificial Neural ◽  
The Impact

The inverse process of computational fluid dynamics was used to explore the expected indoor environment with the preset objectives. An inverse design method integrating genetic algorithm and self-updating artificial neural network is presented. To reduce the computational cost and eliminate the impact of prediction error of artificial neural network, a self-updating artificial neural network is proposed to realize the self-adaption of computational fluid dynamics database, where all the design objectives of solutions are obtained by computational fluid dynamics instead of artificial neural network. The proposed method was applied to the inverse design of an MD-82 aircraft cabin. The result shows that the performance of artificial neural network is improved with the increase of computational fluid dynamics database. When the number of computational fluid dynamics cases is more than 80, the success rate of artificial neural network increases to more than 40%. Comparing to genetic algorithm and computational fluid dynamics, the proposed hybrid method reduces about 53% of the computational cost. The pseudo solutions are avoided when the self-updating artificial neural network is adopted. In addition, the number of computational fluid dynamics cases is determined automatically, and the requirement of human adjustment is avoided.

Optimization of Maximum Lift to Drag Ratio on Airfoil Design Based on Artificial Neural Network Utilizing Genetic Algorithm

2014 ◽  
Vol 493 ◽  
pp. 123-128 ◽  
Author(s):  
Ismoyo Haryanto ◽  
Tony Suryo Utomo ◽  
Nazaruddin Sinaga ◽  
Citra Asti Rosalia ◽  
Aditya Pratama Putra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Design Method ◽  
Aerodynamic Characteristics ◽  
Drag Coefficients ◽  
Artificial Neural ◽  
Lift And Drag ◽  
Drag Ratio ◽  
Lift To Drag Ratio

.This paper deals with an alternative design method of airfoil for wind turbine blade for low wind speed based on combination of smart computing and numerical optimization. In this work, a simulation of Artificial Neural Network (ANN) for determining the relation between airfoil geometry and its aerodynamic characteristics was conducted. First, several airfoil geometries were generated through transformation of complex variables (Joukowski transformation), and then lift and drag coefficients of each airfoil were determined using CFD (Computational Fluid Dynamics). In present study, the ANN training was conducted using airfoil geometry and its aerodynamic characteristics as input and output, respectively. Therefore, lift and drag coefficients can be directly determined only by giving the airfoil geometry without having to perform wind tunnel experiment or numerical computation. Moreover, the optimization was conducted to obtain an airfoil geometry which gives maximum lift to drag ratio (CL/CD) for specific Reynolds number. For this purpose Genetic Algorithm (GA) was applied as optimizer. The results were validated using commercial CFD and it can be shown that the result are satisfactory with error approximately of 6%.

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