scholarly journals Optimization Design by Coupling Computational Fluid Dynamics and Genetic Algorithm

2018 ◽  
Author(s):  
Jong-Taek Oh ◽  
Nguyen Ba Chien
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Optimization Design

scholarly journals Thermal prediction of turbulent forced convection of nanofluid using computational fluid dynamics coupled genetic algorithm with fuzzy interface system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Mashallah Rezakazemi ◽  
Azam Marjani ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Kinetic Energy ◽  
Population Size ◽  
Turbulent Kinetic Energy ◽  
Forced Convection ◽  
Inlet Temperature ◽  
Fluid Temperature ◽  
Interface System

AbstractComputational fluid dynamics (CFD) simulating is a useful methodology for reduction of experiments and their associated costs. Although the CFD could predict all hydro-thermal parameters of fluid flows, the connections between such parameters with each other are impossible using this approach. Machine learning by the artificial intelligence (AI) algorithm has already shown the ability to intelligently record engineering data. However, there are no studies available to deeply investigate the implicit connections between the variables resulted from the CFD. The present investigation tries to conduct cooperation between the mechanistic CFD and the artificial algorithm. The genetic algorithm is combined with the fuzzy interface system (GAFIS). Turbulent forced convection of Al2O3/water nanofluid in a heated tube is simulated for inlet temperatures (i.e., 305, 310, 315, and 320 K). GAFIS learns nodes coordinates of the fluid, the inlet temperatures, and turbulent kinetic energy (TKE) as inputs. The fluid temperature is learned as output. The number of inputs, population size, and the component are checked for the best intelligence. Finally, at the best intelligence, a formula is developed to make a relationship between the output (i.e. nanofluid temperatures) and inputs (the coordinates of the nodes of the nanofluid, inlet temperature, and TKE). The results revealed that the GAFIS intelligence reaches the highest level when the input number, the population size, and the exponent are 5, 30, and 3, respectively. Adding the turbulent kinetic energy as the fifth input, the regression value increases from 0.95 to 0.98. This means that by considering the turbulent kinetic energy the GAFIS reaches a higher level of intelligence by distinguishing the more difference between the learned data. The CFD and GAFIS predicted the same values of the nanofluid temperature.

Optimal Arrangement Design of a Tube Bundle in Cross-Flow Using Computational Fluid Dynamics and Multi-Objective Genetic Algorithm

2019 ◽  
Vol 141 (7) ◽  
Author(s):  
Ya Ge ◽  
Feng Xin ◽  
Yao Pan ◽  
Zhichun Liu ◽  
Wei Liu
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Pressure Drop ◽  
Tube Bundle ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Optimal Arrangement ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

scholarly journals Framework for the Shape Optimization of Aerodynamic Profiles Using Genetic Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
D. López ◽  
C. Angulo ◽  
I. Fernández de Bustos ◽  
V. García
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Genetic Algorithms ◽  
Shape Optimization ◽  
Computational Fluid Dynamics Cfd

This study developed a framework for the shape optimization of aerodynamics profiles using computational fluid dynamics (CFD) and genetic algorithms. A genetic algorithm code and a commercial CFD code were integrated to develop a CFD shape optimization tool. The results obtained demonstrated the effectiveness of the developed tool. The shape optimization of airfoils was studied using different strategies to demonstrate the capacity of this tool with different GA parameter combinations.

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.

Effects of Volute Cross-Sectional Area Distribution on Performance of Double-Suction Volute Pump

2019 ◽  
Author(s):  
Szu Yung Chen ◽  
Lu Zhang ◽  
Yumiko Sekino ◽  
Hiroyoshi Watanabe
Keyword(s):  
Fluid Dynamics ◽  
Genetic Algorithm ◽  
Computational Fluid Dynamics ◽  
Secondary Flow ◽  
Cross Sections ◽  
Cross Sectional Area ◽  
Sectional Area ◽  
Cross Sectional ◽  
Suction Pump ◽  
Computational Fluid Dynamics Cfd

Abstract The following study describes the optimization design procedure of a double-suction pump. BASELINE pump is designed as inlet nozzle diameter 800 mm and impeller outlet diameter 740 mm. Each component of a BASELINE pump, impeller configurations, discharge volute, and the suction casing were determined by DOE (Design of Experiments) and sensitivity analysis. However, finite selected design parameters for each component are mostly restricted to the free surface design of the pump casing. In this study, the optimization method approach along with steady Computational Fluid Dynamics (CFD) is introduced to achieve the high efficiency request of a double-suction pump. To investigate the matching optimization of the impeller and discharge volute at design point, the full parametric geometry of discharge volute was developed referred to the BASELINE shape and Multi-Objective Genetic Algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II) was used. Optimization result shows that by increasing the volute cross-sectional area from the volute tongue till the circumferential angle 180 deg. provides lower loss. This is due to the improvement achieved for the better distribution of the velocity gradient within the volute. A validated unsteady computational fluid dynamics (CFD) was also employed to investigate the performance difference between optimized volute design and the BASELINE which correlated to the pressure fluctuation and secondary flow behavior inside the cross-sections from 80% to 120% of nominal flow rate. The result shows that the flow distortion in the streamwise direction is stronger with the BASELINE and sensitively affects the operation stability. This is due to the different secondary flow pattern in the cross-sections, hence demonstrating a design direction of desired volute cross-sectional shape for high-performance can be used in a double-suction volute pump.

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