An imperialist competitive algorithm for optimal design of plate-fin heat exchangers

This paper presents the application of imperialist competitive algorithm (ICA) for optimization of a cross-flow plate fin heat exchanger. Minimization of total weight and total annual cost are considered as objectives. Seven design parameters, namely, heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin-strip length and number of hot side layers are selected as optimization variables. A case study from literature is presented to show the effectiveness of the proposed algorithm. The numerical results reveal that ICA can find optimum configuration with higher accuracy in less computational time when compared to conventional genetic algorithm (GA).     

A design optimization tool of earth-to-air heat exchanger using a genetic algorithm

Advancement in genetic algorithm (GA) optimization tools for design applications, coupled with techniques of soft computing, have led to new possibilities in the way computers interact with the optimization process. In this paper, the concept of goal-oriented GA has been used to design a tool for evaluating and optimizing various aspects of earth-to-air heat exchanger behavior. A new optimization method based on GA is applied as a generative and search procedure to optimize the design of earth-to-air heat exchanger. The GA is used to generate possible design solutions, which are evaluated in terms of passive heating and cooling of building, using a detailed thermal analysis of non air-condition building environment The results from the simulations are subsequently used to further guide the GA search to find the high-energy solutions for optimized design parameters. The specific problem addressed in this study is the sizing of earth-to-air heat exchanger in a non air-conditioned residential building. The developed algorithm is suitable for the calculation of the outlet air temperature and therefore of the heating and cooling potential of the earth-to-air heat exchanger system. This methodology is applicable to a wide range of design optimization problems like choice of building such as green house, solar house, or heating and cooling of buildings by mechanical system.     

Applying multiple decomposition methods and optimization techniques for achieving optimal cost in mixed materials heat exchanger networks

The optimization of the total annual cost in heat exchanger networks has been one of the overarching goals when synthesizing these networks. Several methodologies and techniques have been developed to achieve optimal costs in mixed material heat exchanger networks. This paper demonstrates the application of two decomposition methodologies (total decomposition and partial decomposition) for typical cost rules. The objective function was defined as the optimization and minimization of the total annual cost in mixed materials heat exchanger network. Three optimization algorithms, hybrid genetic‐particle swarm optimization (GA‐PSO), shuffled frog leaping algorithm (SFLA) techniques, and ant colony optimization (ACO), were used to further optimize the total cost in mixed materials heat exchanger network. The results indicate that the total annual cost in partial decomposition method was smaller than that in full integration method and total decomposition method. The reduction of the total annual cost was about 27% for GA‐PSO algorithm, 24% for SFLA and 10% for ACO relative to the results reported in this work. In partial decomposition method, at least one mixed material of heat exchanger was used to reduce the hot and cold utility for decreasing the total annual cost. Partial decomposition method resulted in the highest reduction of the total annual cost compared with other methods. Percentage of difference of the total annual cost were 0.36%, 1.92%, and 5.05% for full integration, total decomposition, and partial decomposition methods, respectively, in comparison with the previous studies. Results have been compared with the results of other studies to demonstrate the accuracy of the applied algorithms.     

Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique

Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).     

A robust learning based evolutionary approach for thermal-economic optimization of compact heat exchangers

This paper presents a robust, efficient and parameter-setting-free evolutionary approach for the optimal design of compact heat exchangers. A learning automata based particle swarm optimization (LAPSO) is developed for optimization task. Seven design parameters, including discreet and continuous ones, are considered as optimization variables. To make the constraint handling straightforward, a self-adaptive penalty function method is employed. The efficiency and the accuracy of the proposed method are demonstrated through two illustrative examples that include three objectives, namely minimum total annual cost, minimum weight and minimum number of entropy generation units. Numerical results indicate that the presented approach generates the optimum configuration with higher accuracy and a higher success rate when compared with genetic algorithms (GAs) and particle swarm optimization (PSO).     

Design optimization of shell and tube heat exchangers using global sensitivity analysis and harmony search algorithm

This study explores the use of global sensitivity analysis (GSA) and harmony search algorithm (HSA) for design optimization of shell and tube heat exchangers (STHXs) from the economic viewpoint. To reduce the size of the optimization problem, non-influential geometrical parameters which have the least effect on total cost of STHXs are identified using GSA. The HSA which is a meta-heuristic based algorithm is then applied to optimize the influential geometrical parameters. To demonstrate the effectiveness and accuracy of the proposed algorithm, an illustrative example is studied. Comparing the HSA results with those obtained using genetic algorithm (GA) reveals that the HSA can converge to optimum solution with higher accuracy.     

Multi-objective optimization of the design of two-stage flash evaporators: Part 2. Multi-objective optimization

Flash evaporation process is currently developing in the wine industry where it is used for flash-cooling or concentration. The design of flash evaporators is faced with specific constraints and must take into account multiple design objectives. In this paper, the development of a multi-objective optimization method is investigated for the joint optimization of design objectives such as process transportability, environmental efficiency, operative cost or cooling power. The optimization method is based on the aggregation of design objectives through desirability functions and indexes. Desirability functions are suitable for formulating design constraints more precisely than inequality relations and, moreover, the global design model results in an unconstrained optimization problem. However, aggregation methods do make it difficult to compute the global optimum of the design problem. This difficulty has been addressed by developing a distributed genetic algorithm which is not so sensitive to this type of numerical solving difficulty. Another difficulty arises from the weighting method for the aggregation of desirability functions since weight parameters have no physical meaning. This weighting problem is approached through a sensitivity analysis of the weight parameters and by observing their relative influence.     

Minimizing hot spot temperature of porous stackings in natural convection

Due to their large surface of heat transfer per volume, porous structures such as metallic foams are considered as an interesting alternative to fins. In this paper, we investigate the optimal configuration of a porous medium structure with the objective to minimize the hot spot temperature in natural convection. The heat sink is adjacent to a heat-generating plate, and consists of a stacking of porous layers, in which a cooling fluid circulates strictly driven by natural convection. The objective of this work is to minimize the hot spot temperature of the system. The design variables are the porosity and the material of each layer. The thermal performance is evaluated with a CFD code based on a finite volume approach. The hot spot temperature minimization is pursued with a genetic algorithm (GA) under global mass and cost constraints. The GA determines the optimal porosity and selects the most appropriate material of each layer. Furthermore, the optimal total length of the stacking is indirectly determined by the GA as layers can be added or removed in order to improve the global performance and/or satisfy the constraints. A mapping of the designs generated by the GA as a function of the mass and cost constraint combination reveals that an appropriate distribution of porosity and material benefits the overall thermal performance of the layered porous medium.     

Placement optimization of Multi-Type fault current limiters based on genetic algorithm

The fault current limiter (FCL) is an effective measure for improving system stability and suppressing short-circuit fault current. Because of space and economic costs, the optimum placement of FCLs is vital in industrial applications. In this study, two objectives with the same dimensional measurement unit, namely, the total capital investment cost of FCLs and circuit breaker loss related to short-circuit currents, are considered. The circuit breaker loss model is developed based on the attenuation rule of the circuit breaker service life. The circuit breaker loss is used to quantify the current-limiting effect to avoid the problem of weight selection in a multi-objective problem. The IEEE 10-generator 39-bus system in New England is used to evaluate the performance of the proposed genetic algorithm (GA) method. Comparative and sensitivity analyses are performed. The results of the optimized plan are validated through simulations, indicating the significant potential of the GA for such optimization.     

Optimization of heat pipe with axial “Ω”-shaped micro grooves based on a niched Pareto genetic algorithm (NPGA)

A niched Pareto genetic algorithm based approach is utilized to optimize a heat pipe with axial “Ω”-shaped micro grooves. The effects of the structural parameters are evaluated and optimized with respect to the heat transfer performance in order to model the heat transfer capability and total thermal resistance of this novel heat pipe. Using the heat transfer capability and total thermal resistance as the objective function and the structure parameters as the decision variable, the optimization design for the heat pipe is performed using the niche genetic algorithm. The results indicate that the heat transfer capability and the total heat resistance are inversely coupled and as a result, the optimization must be constructed on the application objective. Using the niched Pareto genetic algorithm and the pre-specified application constraints, a Pareto-optimal solution set can be determined and the optimal design for a given application is selected.     

Minimizing the entropy generation rate of the plate-finned heat sinks using computational fluid dynamics and combined optimization

The multi-parameter constrained optimization procedure integrating the design of experiments (DOE), response surface models (RSM), genetic algorithm (GA), mixed integer optimization (MOST), and computational fluid dynamics (CFD) is proposed to design the plate finned heat sinks by minimizing their rates of entropy generation. The results of three cases demonstrate that the combination optimization algorithm is feasible. In these cases, the overall rate of entropy generation decreases as the result of introducing the additional constrained variables into the optimization procedure. Consequently, the general thermal and fluid performance of the heat sink is dramatically improved.Based on the results derived by the optimization, the overall thermal and fluid performance of the plate finned heat sinks with both side and top bypass flow is investigated. In addition, two correlations describing Nusselt number and friction factor, as the functions of geometrical and operational parameters, are established by means of the multivariate non-linear regression analysis. The specific expressions to compute the thermal resistance and the rate of entropy generation are deduced.     

Thermodynamic optimization of cross flow plate-fin heat exchanger using a particle swarm optimization algorithm

This study explores the use of particle swarm optimization (PSO) algorithm for thermodynamic optimization of a cross flow plate-fin heat exchanger. Minimization of total number of entropy generation units for specific heat duty requirement under given space restrictions, minimization of total volume, and minimization of total annual cost are considered as objective functions and are treated individually. Based on the applications, heat exchanger length, fin frequency, numbers of fin layers, lance length of fin, fin height and fin thickness or different flow length of the heat exchanger are considered for optimization. Heat duty requirement constraint is included in the procedure. Two application examples are also presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO are validated by comparing with those obtained by using genetic algorithm (GA). Parametric analysis is also carried out to demonstrate the effect of heat exchanger dimensions on the optimum solution. The effect of variation of PSO parameters on convergence and optimum value of the objective has also been presented.     

Thermodynamic and Economic Optimization of Plate Fin Heat Exchangers Using the Bees Algorithm

This study presents the successful application of the bees algorithm (BA) for optimal design of a cross‐flow plate fin heat exchanger by offset strip fins. The ε – NTU method is used to approximate the heat exchanger effectiveness and pressure drop. Two different objective functions including the minimization of total annual cost (sum of investment and operational costs) and total number of entropy generation units for certain heat duty required under given space constraints are considered as targets of optimization separately. Based on the applications, seven design parameters (heat exchanger length at hot and cold sides, fin height, fin frequency, fin thickness, fin‐strip length, and number of hot side layers) are selected as optimization variables. Two examples from the literature are presented to illustrate the efficiency and accuracy of the proposed algorithm. Results showed that the BA can detect an optimum configuration with higher speed (short computational time) and accuracy compared to the imperialist competitive algorithm (ICA) and the genetic algorithm (GA). © 2013 Wiley Periodicals, Inc. Heat Trans Asian Res, 43(5): 427–446, 2014; Published online 3 October 2013 in Wiley Online Library ( wileyonlinelibrary.com/journal/htj ). DOI 10.1002/htj.21087     

Optimization of Microchannel Heat Sinks Using Genetic Algorithm

In this study, a genetic algorithm is employed to minimize the entropy generation rate in microchannel heat sinks. The entropy generation rate allows the combined effects of thermal performance and pressure drop to be assessed simultaneously as the heat sink interacts with the surrounding flow field. Previously developed models for the heat transfer, pressure drop and entropy generation rate are used in the optimization procedure. The results of optimization are compared with existing results obtained by the Newton–Raphson method. It is observed that the GA gives better overall performance of the microchannel heat sinks.     

Optimizing heat exchanger networks with genetic algorithms for designing each heat exchanger including condensers

This paper presents a procedure for the design of the components of a heat exchanger network (HEN). The procedure first uses pinch analysis to maximize heat recovery for a given minimum temperature difference. Using a genetic algorithm (GA), each exchanger of the network is designed in order to minimize its total annual cost. Eleven design variables related to the exchanger geometry are considered. For exchanger involving hot or cold utilities, mass flow rate of the utility fluid is also considered as a design variable. Partial or complete condensation of hot utility fluid (i.e., water vapor) is allowed. Purchase cost and operational cost are considered in the optimization of each exchanger. Combining every exchanger minimized cost with the cost of hot utility and cold utility gives the total cost of the HEN for a particular ΔT_min. The minimum temperature difference yielding the more economical heat exchanger network is chosen as the optimal solution. Two test cases are studied, for which we show the minimized total cost as a function of the minimum temperature difference. A comparison is also made between the optimal solution with the cost of utilities and without it.     

Role of Melt Convection on Optimization of PCM-Based Heat Sink Under Cyclic Heat Load

In this article, we study the thermal performance of phase-change material (PCM)-based heat sinks under cyclic heat load and subjected to melt convection. Plate fin type heat sinks made of aluminum and filled with PCM are considered in this study. The heat sink is heated from the bottom. For a prescribed value of heat flux, design of such a heat sink can be optimized with respect to its geometry, with the objective of minimizing the temperature rise during heating and ensuring complete solidification of PCM at the end of the cooling period for a given cycle. For given length and base plate thickness of a heat sink, a genetic algorithm (GA)-based optimization is carried out with respect to geometrical variables such as fin thickness, fin height, and the number of fins. The thermal performance of the heat sink for a given set of parameters is evaluated using an enthalpy-based heat transfer model, which provides the necessary data for the optimization algorithm. The effect of melt convection is studied by taking two cases, one without melt convection (conduction regime) and the other with convection. The results show that melt convection alters the results of geometrical optimization.     

Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study

Nowadays, it becomes the head of concern for many modern power girds and energy management systems to derive an optimal operational planning with regard to energy costs minimization, pollutant emissions reduction and better utilization of renewable resources of energy such as wind and solar. Considering all the above objectives in a unified problem provides the desired optimal solution. In this paper, a Fuzzy Self Adaptive Particle Swarm Optimization (FSAPSO) algorithm is proposed and implemented to dispatch the generations in a typical micro-grid considering economy and emission as competitive objectives. The problem is formulated as a nonlinear constraint multi-objective optimization problem with different equality and inequality constraints to minimize the total operating cost of the micro-grid considering environmental issues at the same time. The superior performance of the proposed algorithm is shown in comparison with those of other evolutionary optimization methods such as conventional PSO and genetic algorithm (GA) and its efficiency is verified over the test cases consequently.     

Optimum design of a channel roughened by dimples to improve cooling performance

Staggered arrays of dimples imprinted on opposite surfaces of an internal flow channel have been formulated numerically to enhance turbulent heat transfer compromising with pressure drop. The channel is simulated with the help of three-dimensional Reynoldsaveraged Navier-Stokes (RANS) analysis. Three nondimensional design variables based on dimple size and channel dimensions and two objectives related to heat transfer and pressure drag have been considered for shape optimization. A weighted-sum method for multi-objective optimization is applied to integrate multiple objectives into a single objective and polynomial response surface approximation (RSA) coupling with a gradient based search algorithm has been implemented as optimization technique. By the present effort, heat transfer rate is increased much higher than pressure drop and the thermal performance also has shown improvement for the optimum design as compared to the reference one. The optimum design produces lower channel height, wider dimple spacing, and deeper dimple as compared to the reference one.     

非均匀热负荷条件下的平板冲击冷却优化

冲击冷却是涡轮冷却中常见的方式,其优化设计涉及多种几何参数,是典型的高维问题。在冲击冷却结构的设计过程中,需要根据涡轮的热负荷情况适应性地设计冷却结构,以提高综合冷却效率和表面温度的均匀性。实验或数值模拟耗时长且成本高,而代理模型可以快速预测结果,配合计算机自动寻优算法可显著提高设计效率和效果。为了降低优化设计的成本、提高优化过程的效率,以平板冲击冷却为研究对象,同时考虑非均匀热负荷的影响,通过数值模拟构建数据集,建立了基于迭代算子神经网络的代理模型,并使用遗传算法对斑状非均匀热载荷条件下孔位置排布进行了优化。优化结果显示：对于优化潜力较低的结构,优化策略保持了靶板平均温度水平不变;对于优化潜力较高的结构,可以降低靶板平均温度约2.6 K;所研究各结构的表面温度标准差普遍降低70%以上。     

Optimum design of a channel roughened by dimples to improve cooling performance

Staggered arrays of dimples imprinted on opposite surfaces of an internal flow channel have been formulated numerically to enhance turbulent heat transfer compromising with pressure drop. The channel is simulated with the help of three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis. Three non-dimensional design variables based on dimple size and channel dimensions and two objectives related to heat transfer and pressure drag have been considered for shape optimization. A weighted-sum method for multi-objective optimization is applied to integrate multiple objectives into a single objective and polynomial response surface approximation (RSA) coupling with a gradient based search algorithm has been implemented as optimization technique. By the present effort, heat transfer rate is increased much higher than pressure drop and the thermal performance also has shown improvement for the optimum design as compared to the reference one. The optimum design produces lower channel height, wider dimple spacing, and deeper dimple as compared to the reference one.