The thermal management system of an aircraft’s environmental control system (ECS) plays a critical role in ensuring a comfortable and suitable environment for both the cabin and avionic. As the civil aviation industry shifts toward more electric aircraft (MEA), the ECS is undergoing a change in its power source, moving from engine bleed air to electric power. As the industry moves toward electric power, it is crucial to address the challenges associated with power optimization for electric-driven ECS (EECS). The purpose of this paper is to present a model-based design methodology for improving the coefficient of performance (COP) of an EECS through prediction and genetic algorithm (GA) optimization. The evaluation process includes a concurrent analysis of entropy generation in both the current and optimized systems to determine the irreversibility of individual components and entire system.
In this study, the endo-reversible and irreversible thermodynamic model (ETM)-based COP correlation was selected as the main objective function that incorporates the breakdown model algorithm and a prediction of GA with 8 degrees of freedom to optimize the thermal performance of a three-wheel air cycle refrigeration system (ACS) in a state-of-the-art civil aircraft EECS. The implementation of the GA optimization approach was carried out using Python.
The results of the study show that after implementing GA optimization, the COP improved by 50% with the optimal values of the ten variables, and the entropy generation number of the system decreased by 8%.
Aircraft manufacturers can use a GA optimization approach to evaluate system performance and convey specifications to their parts suppliers. For comparable applications, GA optimization estimation can minimize the required number of tests and experiments.
