scholarly journals A Three-Dimensional Numerical and Multi-Objective Optimal Design of Wavy Plate-Fins Heat Exchangers

Processes ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
Chao Yu ◽  
Xiangyao Xue ◽  
Kui Shi ◽  
Mingzhen Shao
Keyword(s):  
Heat Exchangers ◽  
Cfd Simulation ◽  
Three Dimensional ◽  
Basis Functions ◽  
Approximation Model ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Colburn Factor ◽  
Heat Exchange Efficiency

This paper presents a method for optimizing wavy plate-fin heat exchangers accurately and efficiently. It combines CFD simulation, Radical Basis Functions (RBF) with multi-objective optimization to improve the performance. The optimization of the Colburn factor j and the friction coefficient f is regarded as a multi-objective optimization problem, due to the existence of two contradictory goals. The approximation model was obtained by Radical Basis Functions, and the shape of the heat exchanger was optimized by multi-objective genetic algorithm (MOGA). The optimization results showed that j increased by 17.62% and f decreased by 20.76%, indicating that the heat exchange efficiency was significantly enhanced and the fluid structure resistance reduced. Then, from the aspects of field synergy and tubulence energy, the performance advantage of the optimized structure was further confirmed.

Significance of Delta Winglets on the Air Side Performance of Flat Finned Versus Wavy Finned-Tube Heat Exchangers

2020 ◽  
Author(s):  
Tariq Amin Khan ◽  
Nasir Mehdi Gardezi ◽  
Wei Li ◽  
Yang Zhou ◽  
Zahid Ayub
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Exchangers ◽  
Three Dimensional ◽  
Finned Tube ◽  
Maximum Heat ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Maximum Heat Transfer ◽  
Side Flow

Abstract The performance on the air side flow is often limited due to its lower heat transfer coefficient. This work is related to numerical simulation to study the significance of employing delta winglets in flat finned and wavy finned-tube heat exchangers. For this purpose, three-dimensional simulation data and a multi-objective genetic algorithm are employed. The angle of attack (α) of delta winglets and Reynolds number varied from 15° to 75° and 500 to 1300, respectively. Employing delta winglets has increased the heat transfer per unit temperature and per unit volume (Z) and the fan power per unit core volume (E) for both flat finned and wavy finned-tube heat exchangers. To achieve a maximum heat transfer enhancement and a minimum friction factor, the optimal values of these parameters (Re and α) are calculated using the Pareto optimal strategy. For this purpose, CFD data, a surrogate model (neural network) and a multi-objective optimization genetic algorithm are combined. Results show that the performance of wavy finned-tube heat exchangers is higher than flat-finned tube heat exchangers which signify the importance of delta winglets in the wavy finned-tube heat exchangers.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

Applications of Multiple Objective Genetic Algorithms in the Optimization Design of Tracked Self-Moving Power’s Layout

2013 ◽  
Vol 307 ◽  
pp. 161-165
Author(s):  
Hai Jin ◽  
Jin Fa Xie
Keyword(s):  
Optimization Design ◽  
Pareto Front ◽  
Layout Optimization ◽  
Multiple Objective ◽  
Multi Objective Optimization ◽  
Layout Problem ◽  
Basic Principles ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Set Up

A multi-objective genetic algorithm is applied into the layout optimization of tracked self-moving power. The layout optimization mathematical model was set up. Then introduced the basic principles of NSGA-Ⅱ, which is a Pareto multi-objective optimization algorithm. Finally, NSGA-Ⅱwas presented to solve the layout problem. The algorithm was proved to be effective by some practical examples. The results showed that the algorithm can spread toward the whole Pareto front, and provide many reasonable solutions once for all.

scholarly journals A Smart-Distributed Pareto Front Using the ev-MOGA Evolutionary Algorithm

2014 ◽  
Vol 23 (02) ◽  
pp. 1450002 ◽  
Author(s):  
J. M. Herrero ◽  
G. Reynoso-Meza ◽  
M. Martínez ◽  
X. Blasco ◽  
J. Sanchis
Keyword(s):  
Pareto Front ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initial Condition ◽  
Multi Objective Optimization ◽  
Computational Burden ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Normalized Normal Constraint ◽  
Normal Constraint

Obtaining multi-objective optimization solutions with a small number of points smartly distributed along the Pareto front is a challenge. Optimization methods, such as the normalized normal constraint (NNC), propose the use of a filter to achieve a smart Pareto front distribution. The NCC optimization method presents several disadvantages related with the procedure itself, initial condition dependency, and computational burden. In this article, the epsilon-variable multi-objective genetic algorithm (ev-MOGA) is presented. This algorithm characterizes the Pareto front in a smart way and removes the disadvantages of the NNC method. Finally, examples of a three-bar truss design and controller tuning optimizations are presented for comparison purposes.

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