Present a new multi objective optimization statistical Pareto frontier method composed of artificial neural network and multi objective genetic algorithm to improve the pipe flow hydrodynamic and thermal properties such as pressure drop and heat transfer coefficient for non-Newtonian binary fluids

2019 ◽  
Vol 535 ◽  
pp. 122409 ◽  
Author(s):  
Huawei Wu ◽  
Seyed Amin Bagherzadeh ◽  
Annunziata D’Orazio ◽  
Navid Habibollahi ◽  
Arash Karimipour ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Pipe Flow ◽  
Pareto Frontier ◽  
Multi Objective Optimization ◽  
Binary Fluids ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

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 Twin Shaft-Geared Crankweb Crankshaft System with Optimization of Crankshaft Dimensions Using Integrated Artificial Neural Network-Multi Objective Genetic Algorithm

2017 ◽  
Vol 47 (1) ◽  
pp. 68-81
Author(s):  
Anierudh Vishwanathan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Journal Bearing ◽  
Combustion Engine ◽  
Design Parameters ◽  
Data Set ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

This paper suggests a novel design of a multi cylinder internal combustion engine crankshaft which will convert the unnecessary/extra torque provided by the engine into speed of the vehicle. Transmission gear design has been incorporated with crankshaft design to enable the vehicle attain same speed and torque at lower R.P.M resulting in improved fuel economy provided the operating power remains same. This paper also depicts the reduction in the fuel consumption of the engine due to the proposed design of the crankshaft system. In order to accommodate the wear and tear of the crankshaft due to the gearing action, design parameters like crankpin diameter, journal bearing diameter, crankpin fillet radii and journal bearing fillet radii have been optimized for output parameters like stress which has been calculated using finite element analysis with ANSYS Mechanical APDL and minimum volume using integrated Artificial Neural Network-Multi objective genetic algorithm. The data set for the optimization process has been generated using Latin Hypercube Sampling technique.

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