Digital twin model and optimization: CFD and genetic algorithm for indoor ventilation systems

Abstract

To better understand the application of computational fluid dynamics (CFD) and genetic algorithms (GAs) in indoor ventilation systems, the author proposes a study based on digital twin simulation and optimization: CFD and GAs for indoor ventilation systems. The author first analyzes the basic concepts, application prospects, technological connotations, and development trends of digital twin technology in the fields of complex industrial systems and complex equipment. Second, a classroom model is established through CFD, and relevant data are obtained. The BP neural network possesses strong nonlinear mapping capabilities and robustness, thereby meeting the requirements for fitting complex fluid simulation data. A substitute model for the CFD model is established using the BP neural network, and a GA objective function is formulated based on predicted mean vote indicators and air-age data. Different weights are set to optimize the model and then compared with the original CFD model. The results show that the CFD-coupled GA model takes only 1–3 h. The combination of CFD and GA can effectively replace the model optimized by directly calling the CFD program within the GA, reducing computation time and improving indoor air quality.