An optimized design of finned-tube evaporators using the learnable evolution model

Optimizing the refrigerant circuitry for a finned-tube evaporator is a daunting task for traditional exhaustive search techniques due to the extremely large number of circuitry possibilities. For this reason, more intelligent search techniques are needed. This paper presents and evaluates a novel optimization system called ISHED1 (intelligent system for heat exchanger design). This system uses a recently developed non-Darwinian evolutionary computation method to seek evaporator circuit designs that maximize the capacity of the evaporator under given technical and environmental constraints. Circuitries were developed for an evaporator with three depth rows of 12 tubes each, based on optimizing the performance with uniform and nonuniform airflow profiles. ISHED1 demonstrated the capability to generate designs with capacity equal or superior to that of best human designs, particularly in cases with non-uniform airflow.