Study on a Dynamic Numerical Model of an Underground Air Tunnel System for Cooling Applications—Experimental Validation and Multidimensional Parametrical Analysis

The underground air tunnel system shows promising potentials for reducing energy consumption of buildings and for improving indoor thermal comfort, whereas the existing dynamic models using the computational fluid dynamic (CFD) method show computational complexity and are user-unfriendly for parametrical analysis. In this study, a dynamic numerical model was developed with the on-site experimental calibration. Compared to the traditional CFD method with high computational complexity, the mathematical model on the MATLAB/SIMULINK platform is time-saving in terms of the real-time thermal performance prediction. The experimental validation results indicated that the maximum absolute relative deviation was 3.18% between the model-driven results and the data from the on-site experiments. Parametrical analysis results indicated that, with the increase of the tube length, the outlet temperature decreases with an increase of the cooling capacity whereas the increasing/decreasing magnitude slows down. In addition, the system performance is independent on the tube materials. Furthermore, the outlet air temperature and cooling capacity are dependent on the tube diameter and air velocity, i.e., a larger tube diameter and a higher air velocity are more suitable to improve the system’s cooling capacity, and a smaller tube diameter and a lower air velocity will produce a more stable and lower outlet temperature. Further studies need to be conducted for the trade-off solutions between air velocity and tube diameter for the bi-criteria performance enhancement between outlet temperature and cooling capacity. This study proposed an experimentally validated mathematical model to accurately predict the thermal performance of the underground air tunnel system with high computational efficiency, which can provide technical guidance to multi-combined solutions through geometrical designs and operating parameters for the optimal design and robust operation.