Optimized design of indoor temperature and humidity control equipment based on genetic algorithm

Abstract

This study optimized the shape design of indoor temperature and humidity control equipment using genetic algorithms and fluid dynamics simulation, aiming to enhance indoor environmental stability, human comfort, and energy-saving emission reduction. The research methodology included mathematical modeling, fluid dynamics simulation based on the Navier-Stokes equations, genetic algorithm optimization, and performance verification of the equipment. The optimization results indicated that the improved designs for air conditioning and humidifiers can maintain indoor temperature uniformity across different seasons and provide appropriate humidity, significantly improving energy efficiency and comfort. The innovation of this study lies in the combination of genetic algorithms with fluid dynamics simulation, effectively addressing the resource consumption and overfitting issues of traditional optimization methods when dealing with complex datasets. This approach enhances the predictive accuracy and robustness of the model, providing new theoretical support for the field of indoor environmental control and demonstrating practical application value in promoting green buildings and sustainable development.