Geometric Size Optimization of Annular Step Fin Using Multi-Objective Genetic Algorithm

2017 ◽  
Vol 9 (2) ◽  
Author(s):  
Abhijit Deka ◽  
Dilip Datta
Keyword(s):  
Heat Transfer ◽  
Genetic Algorithm ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Pareto Optimal ◽  
Objective Functions ◽  
Cross Sectional ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables

Although an annular stepped fin can produce better cooling effect in comparison to an annular disk fin, it is yet to be studied in detail. In the present work, one-dimensional heat transfer in a two-stepped rectangular cross-sectional annular fin with constant base temperature and variable thermal conductivity is modeled as a multi-objective optimization problem. Taking cross-sectional half-thicknesses and outer radii of the two fin steps as design variables, an attempt is made to obtain the efficient fin geometry primarily by simultaneously maximizing the heat transfer rate and minimizing the fin volume. For further assessment of the fin performance, three more objective functions are studied, which are minimization of the fin surface area and maximization of the fin efficiency and effectiveness. Evaluating the heat transfer rate through the hybrid spline difference method, the well-known multi-objective genetic algorithm, namely, nondominated sorting genetic algorithm II (NSGA-II), is employed for approximating the Pareto-optimal front containing a set of tradeoff solutions in terms of different combinations of the considered five objective functions. The Pareto-optimal sensitivity is also analyzed for studying the influences of the design variables on the objective functions. As an outcome, it can be concluded that the proposed procedure would give an open choice to designers to lead to a practical stepped fin configuration.

Design of Cost-Effective Water-Cooled Pinfin Heatsinks

2013 ◽  
Author(s):  
Keisuke Horiuchi ◽  
Atsuo Nishihara ◽  
Kazuyuki Sugimura
Keyword(s):  
Heat Transfer ◽  
Pressure Drop ◽  
Heat Transfer Rate ◽  
Transfer Rate ◽  
Cost Effective ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Design Variables ◽  
The Relationship ◽  
Effective Water

This paper presents the multi-objective optimization of cost-effective pinfin heatsinks. Heat transfer rate and pressure drop were the objective functions, and four parameters (height, diameter, longitudinal pitch, and transverse pitch) were the design variables. The relationship between the objective functions and the design variables for pinfin heatsink was calculated by using our own empirical equations using the aspect ratio and the minimum gap considering cost-effectiveness and manufacturability. Non-dominated solutions to the objective functions were illustrated, and we derived the design rules for design variables. We found the similarity of the optimum trade-off curves between the heat transfer rate and the pressure drop if the multiplication of the height and the minimum gap were the same for the different constraints. We validated our calculated solutions by comparing them with experimental results and attained reasonable agreement between the calculation and experiment.

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.

Efficiencies for Partially Wetted Spine Fins: Uniform Cross Section, Conical, Concave Parabolic, and Convex Parabolic Spines

2013 ◽  
Vol 135 (8) ◽  
Author(s):  
Worachest Pirompugd ◽  
Somchai Wongwises
Keyword(s):  
Heat Transfer ◽  
Heat Transfer Rate ◽  
Cross Section ◽  
Transfer Rate ◽  
Approximate Equation ◽  
Sectional Area ◽  
Cross Sectional ◽  
Fin Efficiency ◽  
Conduction Heat Transfer ◽  
Uniform Cross Section

In this study, efficiencies for partially wetted fins for the uniform cross section spine, conical spine, concave parabolic spine, and convex parabolic spine are presented using an analytical method. Depending on the set of boundary conditions, there are two methods for deriving the efficiencies of partially wet fins for each spine. The eight equations for fin efficiencies were investigated. Fin efficiency is a function of the length of the dry portion. Thus, the equations for calculating the length of the dry portion are also presented. The findings indicate that a larger cross-sectional fin results in a higher conduction heat transfer rate. Contrarily, the fin efficiency is lower. This is different from the longitudinal fin, for which the trend lines of heat transfer rate and fin efficiency are the same. This converse relationship is due to the effect of the ratio of the cross-sectional area to the surface area. Moreover, partially wet fin efficiencies decrease with increased relative humidity. For convenience, the approximate equation for efficiencies for partially wet fins, which is derived from the equations for fully wet and fully dry fin efficiencies, is also presented.

Design optimization for cogging torque mitigation in brushless DC motor using multi-objective particle swarm optimization algorithm

2015 ◽  
Vol 34 (4) ◽  
pp. 1302-1318 ◽  
Author(s):  
Umadevi Nagalingam ◽  
Balaji Mahadevan ◽  
Kamaraj Vijayarajan ◽  
Ananda Padmanaban Loganathan
Keyword(s):  
Genetic Algorithm ◽  
Design Optimization ◽  
Pareto Optimal ◽  
Bldc Motor ◽  
Swarm Optimization ◽  
Cogging Torque ◽  
Content Type ◽  
Multi Objective ◽  
Brushless Dc ◽  
Design Variables

Purpose – The purpose of this paper is to propose a multi-objective particle swarm optimization (MOPSO) algorithm based design optimization of Brushless DC (BLDC) motor with a view to mitigate cogging torque and enhance the efficiency. Design/methodology/approach – The suitability of MOPSO algorithm is tested on a 120 W BLDC motor considering magnet axial length, stator slot opening and air gap length as the design variables. It avails the use of MagNet 7.5.1, a Finite Element Analysis tool, to account for the geometry and the non-linearity of material for assuaging an improved design framework and operates through the boundaries of generalized regression neural network (GRNN) to advocate the optimum design. The results of MOPSO are compared with Multi-Objective Genetic Algorithm and Non-dominated Sorting Genetic Algorithm-II based formulations for claiming its place in real world applications. Findings – A MOPSO design optimization procedure has been enlivened to escalate the performance of the BLDC motor. The optimality in design has been out reached through minimizing the cogging torque, maximizing the average torque and reducing the total losses to claim an increase in the efficiency. The results have been fortified in well-distributed Pareto-optimal planes to arrive at trade-off solutions between different objectives. Research limitations/implications – The rhetoric theory of multi objective formulations has been reinforced to provide a decisive solution with regard to the choice of the design obtained from Pareto-optimal planes. Practical implications – The incorporation of a larger number of design variables together with an orientation to thermal and vibration analysis will still go a long way in bringing on board new dimensions to the fold of optimality in the design of BLDC motors. Originality/value – The proposal offers a new perspective to the design of BLDC motor in the sense it be-hives the facility of a swarm based approach to optimize the parameters in order that it serves to improve its performance. The results of a 120 W motor in terms of lowering the losses, minimizing the cogging torque and maximizing the average torque emphasize the benefits of the GRNN based multi-objective formulation and establish its viability for use in practical applications.

scholarly journals MULTI-OBJECTIVE OPTIMIZATION OF REINFORCED CONCRETE FRAMES USING NSGA-II ALGORITHM

2016 ◽  
Vol 8 (4) ◽  
pp. 157-164 ◽  
Author(s):  
Mehdi Babaei ◽  
Masoud Mollayi
Keyword(s):  
Genetic Algorithm ◽  
Reinforced Concrete ◽  
Steel Structures ◽  
American Concrete Institute ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Design Variables ◽  
Pareto Fronts

In recent decades, the use of genetic algorithm (GA) for optimization of structures has been highly attractive in the study of concrete and steel structures aiming at weight optimization. However, it has been challenging for multi-objective optimization to determine the trade-off between objective functions and to obtain the Pareto-front for reinforced concrete (RC) and steel structures. Among different methods introduced for multi-objective optimization based on genetic algorithms, Non-Dominated Sorting Genetic Algorithm II (NSGA II) is one of the most popular algorithms. In this paper, multi-objective optimization of RC moment resisting frame structures considering two objective functions of cost and displacement are introduced and examined. Three design models are optimized using the NSGA-II algorithm. Evaluation of optimal solutions and the algorithm process are discussed in details. Sections of beams and columns are considered as design variables and the specifications of the American Concrete Institute (ACI) are employed as the design constraints. Pareto-fronts for the objective space have been obtained for RC frame models of four, eight and twelve floors. The results indicate smooth Pareto-fronts and prove the speed and accuracy of the method.

Optimization of Force and Moment Balance of a Four-Bar Linkage Using Multi-Objective Genetic Algorithm

2009 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
A. Jaamiolahmadi
Keyword(s):  
Genetic Algorithm ◽  
Objective Functions ◽  
Fuzzy Decision ◽  
Nsga Ii ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Feasible Solutions ◽  
Four Bar Linkage ◽  
External Loads ◽  
Vibration Noise

In this study, force and moment balance of a four-bar linkage is implemented by using a Multi-Objective Genetic Algorithm (MOGA). During the time that an unbalanced linkage moves, it transmits shaking forces and moments to its surroundings. These transmitted forces and moments may cause some serious and undesirable problems such as vibration, noise, wear, and fatigue. In the current problem, the concepts of inertia counterweights and physical pendulum are utilized to complete balance of all mass effects (both linear and rotary, but excluding external loads), independent of input angular velocity. In this paper, Non-Dominated Genetic Algorithm (NSGA-II) is applied to minimize two objective functions subject to some different design constraints. The applied algorithm produced a set of feasible solutions called Pareto optimal solutions for the design problem. Finally, a fuzzy decision maker is applied to select the best solution among the obtained Pareto solutions based on design criteria. The results show that obtained solutions minimize the weights of applied counterweights and eliminate both shaking forces and moments transmitted to the ground, simultaneously.

Decomposition Templates and Joint Morphing Operators for Genetic Algorithm Optimization of Multicomponent Structural Topology

2013 ◽  
Vol 136 (2) ◽  
Author(s):  
Zebin Zhou ◽  
Karim Hamza ◽  
Kazuhiro Saitou
Keyword(s):  
Genetic Algorithm ◽  
Search Space ◽  
Structural Topology ◽  
Algorithm Optimization ◽  
Two Stage ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Loaded Structure ◽  
Monolithic Structure

This paper presents a continuum-based approach for multi-objective topology optimization of multicomponent structures. Objectives include minimization of compliance, weight, and cost of assembly and manufacturing. Design variables are partitioned into two main groups: those pertaining to material allocation within a design domain (base topology problem), and those pertaining to decomposition of a monolithic structure into multiple components (joint allocation problem). Generally speaking, the two problems are coupled in the sense that the decomposition of an optimal monolithic structure is not always guaranteed to produce an optimal multicomponent structure. However, for spot-welded sheet-metal structures (such as those often found in automotive applications), certain assumptions can be made about the performance of a monolithic structure that favor the adoption of a two-stage approach that decouples the base topology and joint allocation problems. A multi-objective genetic algorithm (GA) is used throughout the studies in this paper. While the problem decoupling in two-stage approaches significantly reduces the size of the search space and allows better performance of the GA, the size of the search space can still be quite enormous in the second stage. To further improve the performance, a new mutation operator based on decomposition templates and localized joints morphing is proposed. A cantilever-loaded structure is used to study and compare various setups of single and two-stage GA approaches and establish the merit of the proposed GA operators. The approach is then applied to a simplified model of an automotive vehicle floor subject to global bending loading condition.

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