Tube-fin heat exchanger circuitry optimization using integer permutation based Genetic Algorithm

The performance of tube-fin heat exchangers is strongly influenced by the refrigerant circuitry, i.e., the refrigerant flow path along different tubes. Since for a given number of tubes, the number of possible circuitries is exponentially large, neither exhaustive search nor traditional optimization algorithms can be used to optimize the circuitry. Researchers previously used Evolutionary Algorithms (EAs) coupled with a learning module or other heuristic algorithm 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 novel integer permutation-based Genetic Algorithm (IPGA) for optimizing circuitry with manufacturability and operating constraints. The novel genetic operators are designed such that all chromosomes generated by IPGA can be mapped to a valid circuitry. As a result, the proposed approach can explore the solution space more efficiently than conventional GA. A constraint-dominated sorting technique is used in the fitness assignment stage to handle manufacturability constraints. An exhaustive search on a small heat exchanger proves IPGA can find optimal or near-optimal circuitries using relatively small population size and low number of iterations. The analyses of four case studies show that IPGA can find circuitry designs with capacities superior to those designed based on engineers’ experience and meanwhile guarantee good manufacturability. Overall, a 2.4–14.6% increase in heat exchange capacity is observed by applying IPGA to an A-shaped indoor unit. Comparison with other optimization methods in literature shows that IPGA finds better designs exhibiting higher capacity, lower pressure drop and better manufacturability.