Tube-fin Heat Exchanger, Circuitry Optimization, Integer Permutation, Genetic Algorithm
Tube-fin heat exchangers (HXs) are widely used in air-conditioning and heat pumping applications. The performance of these heat exchangers is strongly influenced by the refrigerant circuitry, i.e. the refrigerant flow path along the different tubes in the HX core. Since for a given number of tubes, the number of possible circuitries is exponentially large, neither the exhaustive search nor traditional optimization algorithms can be used to optimize the circuitry for a given application. Researchers have previously used Genetic algorithms (GA) coupled with a learning module to solve this problem, but there is no guarantee that the resulting circuitry can be manufactured in a cost-effective manner. In this paper, we present a GA-based integer permutation approach for solving the circuitry optimization problem. A finite volume heat exchanger simulation tool is used to simulate the performance of different circuitries generated by the optimizer. The crossover, mutation and individual generation genetic operators are designed such that all individuals generated by the GA are in the feasible domain. The proposed approach can explore the solution space more efficiently than a conventional GA. Exhaustive search and results from the literature are used to verify the results obtained from the proposed optimization scheme for small heat exchangers. The result shows that integer permutation based GA (IPGA) is capable of finding optimal or near-optimal refrigerant circuitry designs using a relatively low population size and iterations. Furthermore, the limits on in-tube refrigerant mass flux obtained from empirical data, are used to assist the IPGA. The manufacturability aspect is handled using a constraint-dominated sorting in the fitness assignment stage of GA with a goal of obtaining the shortest tube joints. It is shown that the proposed constraint handling technique significantly improves the manufacturability of the optimal circuits. Overall, the analyses of several test heat exchanger cases show that the constrained integer permutation based GA can generate circuitry designs with capacities superior to those obtained manually and are manufacturable. Compared to a conventional GA, it exhibits faster convergence and higher quality optimal solutions. In addition, a 3.1-8.8% increase in heat exchange capacity is obtained by IPGA compared with the conventional counter-flow circuitry.