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首页> 外文期刊>Journal of Heat Transfer >Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm
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Cost and Entropy Generation Minimization of a Cross-Flow Plate Fin Heat Exchanger Using Multi-Objective Genetic Algorithm

机译：多目标遗传算法的横流板翅式换热器成本和熵产生最小化

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In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an e -NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm Ⅱ) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pare to curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irrevers-ibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.
机译：在当前的工作中，进行了热模型设计，以优化紧凑型热交换器，以最大程度地降低成本和产生熵。在这方面，将e -NTU方法应用于热交换器压力降以及有效性的估计。翅片节距，翅片高度，翅片偏移长度，冷流长度，无流长度和热流长度被认为是六个决策变量。采用快速精英的非支配排序遗传算法（即非支配排序遗传算法Ⅱ），以使最小的熵产生单位和总年度成本（初始投资与运维成本之和）最小化。帕累托最优阵线的结果清楚地揭示了两个目标函数，熵生成单位的数量和年度总成本之间的冲突。它表明，任何几何变化都会减少熵产生单位的数量，从而导致年度总成本增加，反之亦然。此外，为了预测板翅式换热器的最佳设计，推导了Pare曲线的熵产生单元数与年度总成本的关系式。此外，在考虑了火用破坏的基础上对热交换器进行优化，发现不可逆性（如压降和冷热流之间的高温差）是火用破坏的关键问题。因此，更有效的热交换器导致具有更高总成本率的热交换器。最后，对板翅式换热器的最佳决策数量和最优总熵值的变化进行敏感性分析，并报告了结果。

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