Optimization Of Plate-fin Heat Exchanger Research Articles

Multi-Objective Optimization of Plate-Fin Heat Exchanger Using Taguchi-based Grey Relational Analysis

This work involves the investigation of values of design constraints which are optimum and which can be used or helpful in design optimization of Plate-Fin Heat Exchanger (PFHE). Core Area, Core Length Reynolds’s Number and Fin Height. Pressure drop (Minimum–Friction factor) and heat transfer (maximum–Nusselt number) are fixed as the responses. The Taguchi method for Design of Experiments (DOE) has been used to frame the number of experiments for the taken values of the factors and Response surface methodology (RSM) has been used as the optimization tool from MINITAB. At the end optimization curves are obtained to find the optimal values of the factors resulting in the desired values of responses MINITAB. A conclusion is made to find out the best statistical model for optimization of performance parameters in a PFHE. For the purpose, Plate fin heat exchanger is considered.

Multi-Objective Optimization of Plate-Fin Heat Exchangers via Non-Dominated Sequencing Genetic Algorithm (NSGA-II)

The rules of heat transfer and fluid flow in plate-fin heat exchanger are intricate and complex, and the selection of boundary conditions is the key to giving full play to the performance of heat exchanger. In this paper, a multi-objective optimization based on computational fluid dynamics (CFD) and non-dominated sequencing genetic algorithm (NSGA-II) was carried out to obtain the optimal performance of a plate-fin heat exchanger for an extended-range hybrid vehicle engine. The angle of serrated staggered fin, oil flow rate, and water flow rate were taken as input parameters, and the heat transfer quantity, oil pressure drop, and oil outlet temperature were taken as objective functions to perform the optimization analysis of the heat exchanger. Support vector machine regression (SVR) was used to establish the objective function, and the NSGA-II algorithm was adopted to obtain the Pareto optimal solution set. The optimal solution was determined in the Pareto optimal solution set by comprehensive evaluation based on technique for order preference by similarity to an ideal solution (TOPSIS). The results showed that the best comprehensive performance of the heat exchanger was achieved at a fin angle of 63.01°, an oil flow rate of 9.7 L/min, and a water flow rate of 6.45 L/min. At this time, the heat transfer quantity was 9.79 kW, the oil pressure drop was 13.63 kPa, and the oil outlet temperature was 65.11 °C.

Passage Arrangement Optimization of Plate-Fin Heat Exchanger under Uncertain Operating Conditions

As the total flow rate and passage number increase, the quality of the passage arrangement has an important influence on the thermal efficiency of a multi-stream plate-fin heat exchanger (PFHE). The operating conditions are uncertain due to changes in environmental and product requirements, which affect the design of passage arrangements. If this influence is not considered enough in design, it will lead to the loss of heat transfer efficiency of the PFHE. Therefore, an optimization design method for passage arrangement is developed considering the influences of uncertain operating conditions. First, the uncertain operating conditions of the PFHE are represented using probability density functions of mass flow rates and temperature. Next, the optimization goals are chosen through the correlation calculation between the passage arrangement and the heat transfer indexes by the integral-mean temperature difference method. The particle swarm optimization algorithm is adaptively improved for the passage arrangement calculation. Then, the result under uncertain operating conditions is compared with the result under a single condition. The total heat transfer rate is increased, and the dimensionless mean square error of the cumulative heat load is decreased, so the method of designing the passage arrangement under uncertain operating conditions is effective.

Design Optimization of Plate-Fin Heat Exchanger in a Gas Turbine and Supercritical Carbon Dioxide Combined Cycle with Thermal Oil Loop

This paper presents an investigation on the optimum design for a plate-fin heat exchanger (PFHE) of a gas and supercritical carbon dioxide combined cycle which uses thermal oil as intermediate heat-transfer fluid. This may promote the heat transfer from low heat-flux exhaust to a high heat-flux supercritical carbon dioxide stream. The number of fin layers, plate width and geometrical parameters of fins on both sides of PFHE are selected as variables to be optimized by a non-dominated sorting genetic algorithm-II (NSGA-II), which is a multi-objective genetic algorithm. For the confliction of heat transfer area and pressure drop on the exhaust side, which are the objective indexes, the result of NSGA-II is a Pareto frontier. The technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to choose the optimum solution from the Pareto frontier. Finally, further simulation is performed to analyze the effect of each parameter to objective indexes and confirm the rationality of optimization results.

A Framework for Multi-Objective Optimization of Plate-Fin Heat Exchangers Using a Detailed Three-Dimensional Simulation Model

The design of a multi-stream plate-fin heat exchanger is a highly integrated task with multiple opposing objectives and many degrees of freedom. This work shows how it can be fully or partially automated by the combination of a detailed three-dimensional simulation model and an optimization routine. The desired task is formulated as the target of a multi-objective optimization and solved using a genetic algorithm. The workflow is presented using a cryogenic plate-fin heat exchanger with four process streams. The design is optimized towards high efficiency, low pressure drop, and low unit weight by adjusting the outer geometry, the inlet and outlet distributor configuration, and the detailed stream geometry. A detailed analysis of the Pareto-set gives a good overview of possible solutions, and the optimization routine can automatically find a feasible design with a reasonable tradeoff between the objectives. All elements of the framework are implemented in open source software. A highlight of this research is that a very comprehensive and detailed simulation model is employed in the optimization framework. Thus, the presented method can be easily adjusted to fit the needs of other engineering tasks.

Thermal design and optimization of plate-fin heat exchangers based global sensitivity analysis and NSGA-II

Morris and Sobol global sensitivity analyses are firstly studied to evaluate the effects of the design parameters on the performance of plate-fin heat exchanger with serrated fins. Five design parameters such as the fin height, fin space, fin thickness and interrupted length and channel Reynolds number are selected as optimization parameters. Heat flow rate, total annual cost and entropy generation number are considered as objective functions. Furthermore, the five design parameters are optimized by NSGA-II, in which the effectiveness of the proposed algorithm is validated by comparing a previous approach. The results show that Reynolds number and fin space s play a dominant role on the performance of plate-fin heat exchanger with serrated fin. Meanwhile, the interaction effects among these parameters are significant on total annual cost and entropy generation number. Compared with one common design, the thermal performance factor of another common design and two optimal parameters increases 8.4%, 41.2% and 58.4%, respectively. Combining global sensitivity analysis and NSGA-II has the characteristics of easy to implement and high accuracy. Therefore, it is universal and can be used for various complex engineering applications to provide guidance for better selecting the suitable parameters.

Single- and Multi-Objective Design Optimization of Plate-Fin Heat Exchangers Using Jaya Algorithm

ABSTRACTThis paper explores the use of Jaya algorithm for the single- and multi-objective design optimization of plate-fin heat exchangers (PFHEs). Design of PFHEs involves a number of geometric and physical parameters with high complexity. The general design approaches are based on trial and error and become tedious and time consuming and do not guarantee the achievement of an optimal design. Therefore, advanced optimization algorithms are preferred. The Jaya algorithm is a newly developed simple algorithm and it does not have any algorithmic-specific parameters to be tuned and this aspect reduces the designer's effort in tuning the parameters to arrive at the optimum value of the objective function. The Jaya algorithm is proposed for the design optimization of PFHEs by minimizing the total surface area of heat transfer, total annual cost, and total pressure drop of the system and maximizing the effectiveness. Seven design parameters are considered which are imposed by constraints on the design. Single- as well as multi-objective design optimization is carried out using the proposed algorithm. The results obtained by Jaya algorithm are compared with the results of latest reported algorithms. These comparisons revealed that the Jaya algorithm can be successfully applied for the design optimization of PFHEs.

Thermodynamic optimization design for plate-fin heat exchangers by Tsallis JADE

This paper presents an optimization of plate-fin heat exchangers (PFHEs) considering as objective function the minimization of the entropy generation units by Adaptive Differential Evolution with Optional External Archive (JADE) and a novel JADE variant, denominated Tsallis JADE (TJADE). Plate-fin heat exchangers present some important characteristics such as high thermal conductivity and effectiveness and large heat transfer surface area per unit of volume that determinate a reduction of space and energy requirement, weight, and cost, and its design may take into account several geometrical and operational parameters. The variables used for the optimization were the heat exchanger lengths for hot and cold fluids (Lh, Lc), the height of the fin (H), fin frequency (n), the fin thickness (t), the lance length of the fin (l) and the number of fin layers for the hot fluid (Nh). Reductions of entropy generation units up to 24.30%, 9.60% and 9.30% compared to GA (Genetic Algorithm), PSO (Particle Swarm Optimization) and BA (Bees Algorithm) methods, respectively, were obtained for the first case study and reductions about 15.96%, 13.39% and 11.26% in comparison to the results obtained with GA, ICA (Imperialist Competitive Algorithm) and BA methods were obtained for the second case study.

A Comprehensive Review on the Application of Evolutionary Computation in Design Optimization of Plate-Fin Heat Exchangers

This review study presents the utilization of different evolutionary algorithms (EAs) for plate-fin heat exchanger (PFHE) design optimization in the last decade. The traditional trial-and-error design process of CHEs can be replaced by a more flexible and easy-to-implement evolutionary approach. EAs do need any information of derivatives and therefore can be implemented in a wide range of applications without much effort. Many EAs have been successfully presented and employed in various fields of engineering including heat transfer problems and more specifically CHE design optimization. However, there seems to be gap in the literature regarding analyses of pros and cons of these algorithms for use in this field. This study could be useful for future use of EAs in not only CHE design optimization, but also for the whole field of heat exchanger.

Optimization of plate-fin heat exchangers by minimizing specific entropy generation rate

In the present study, an optimization model is developed for plate-fin heat exchangers (PFHEs) based on the entropy generation minimization method. In the modeling, the entropy generation rate per unit partition plate area, i.e., specific entropy generation rate is proposed as an optimization objective function and the total heat transfer area of a PFHE is considered as a constraint. The model takes into account the irreversibility due to both irreversible heat transfer and pressure drop. The analysis of optimal allocation of total heat transfer area between the hot- and cold-side for a PFHE is conducted with a case study. The results show that optimal geometric parameters of a PFHE such as the fin spacing and fin height on both the hot- and cold-sides can be achieved when the finite total heat transfer area is optimally allocated with minimizing the specific entropy generation rate. In addition, the effects of total heat transfer area, heat transfer rate, as well as the ratio of mass flow rate between the cold- and hot-side fluid on the specific entropy generation rate and optimal allocation ratio of total heat transfer area are also analyzed.

Optimization of a plate-fin heat exchanger design through an improved multi-objective teaching-learning based optimization (MO-ITLBO) algorithm

Teaching-learning-based optimization (TLBO) is a recently developed heuristic algorithm based on the natural phenomenon of teaching-learning process. In the present work, multi-objective improved teaching-learning-based optimization (MO-ITLBO) algorithm is introduced and applied for the multi-objective optimization of plate-fin heat exchangers. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions maintained in an external archive. Minimizing total annual cost and the total weight of heat exchanger as well as minimization of total pressure drop and maximization of heat exchanger effectiveness for specific heat duty requirement are considered as objective functions. Two application examples are presented to demonstrate the effectiveness and accuracy of the proposed algorithm.

A modified imperialist competitive algorithm for constrained optimization of plate-fin heat exchangers

In this study, to handle the design optimization of plate-fin heat exchangers, a constrained version of a newly introduced evolutionary algorithm, the imperialist competitive algorithm, is proposed. Imperialist competitive algorithm was initially established for unconstrained optimization problems and different strategies such as penalty functions were employed to handle the constraints in the previous studies. In the proposed constrained imperialist competitive algorithm, a feasibility-based ranking is employed in the imperialist competitive algorithm method. Seven design parameters and various constraints are considered for the design optimization in which minimum weight and minimum total annual cost were considered as the autonomous objective functions. The results are compared with those obtained by a genetic algorithm approach combined with a static penalty function scheme. Also, a comparison between the proposed algorithm and the original imperialist competitive algorithm is performed. The simulation results show that both the proposed algorithm and imperialist competitive algorithm are of higher accuracy than the genetic algorithm, which is commonly used. Also, the numerical experiment indicates that the modification in the imperialist competitive algorithm leads to better performance of the algorithm.

Optimization of plate-fin heat exchangers by an improved harmony search algorithm

This study explores the use of a proposed variant of harmony search algorithm for design optimization of plate-fin heat exchangers. The algorithm deals with a large number of continuous and discrete variables. To handle the constraints in the optimization problem, a self-adaptive penalty function scheme is used. The efficiency and accuracy of the proposed method are demonstrated through an illustrative example taken from previous studies. Numerical results indicate that the presented approach can generate optimum solutions with higher accuracy when compared to Genetic algorithms (GAs), Particle Swarm Optimization (PSO) and GA hybrids with PSO (GAHPSO).