Optimization of Natural Ventilation via Computational Fluid Dynamics Simulation and Hybrid Beetle Antennae Search and Particle Swarm Optimization Algorithm for Yungang Grottoes, China

The Yungang Grottoes are undergoing degradation by weather and environmental erosion. Here, we propose a natural ventilation strategy to optimize the environments in Cave 9 and Cave 10 of the Yungang Grottoes. The novelty of this work is to use an effective computational fluid dynamics (CFD) simulation and a hybrid of the beetle antennae search and particle swarm optimization algorithms (BAS–PSO) to determine which natural ventilation scenario yields the maximum total heat transfer rate (Qmax). A CFD hygrothermal model is first developed and shows high precision in predicting temperature and humidity conditions based on real-time measured data. The natural ventilation efficiency is enhanced by different configurations of doors and windows with four ventilation rates. Combined with eXtreme Gradient Boosting (XGBoost) fitting, the hybrid BAS–PSO algorithm yields the largest Qmax (5746.74 W), which is further confirmed by CFD simulations with the outcome of a comparable Qmax (5730.67 W). It indicates that the hybrid algorithm exhibits a good performance in the identification of optimal configurations. The effectiveness of the proposed natural ventilation strategy is verified by on-site measured data. Our findings provide an effective natural ventilation strategy that is beneficial to the energy-efficient preservation of the Yungang Grottoes.