Parameter optimisation of the ventilation system for an underground power space using a hybrid model

Abstract

The ventilation system is one of the essential safety systems in underground power spaces. Over the years, active ventilation has been widely employed for heat dissipation in underground power spaces. In operation, high-power equipment generates significant heat, necessitating sufficient heat dissipation for smooth and efficient functioning. The effectiveness of the ventilation system is influenced by airflow, making aerodynamics a crucial aspect of studying underground power spaces. This study establishes a comprehensive hybrid model (a combination of physical and data-driven models) representing underground power spaces. Ansys Fluent and MATLAB are used to simulate and calculate temperature fields for various structures. The physical model employs model order reduction to achieve efficient computation without compromising accuracy. For the data-driven model, a genetic neural network is developed for multifactor nonlinear optimisation to evaluate and analyse thermal behaviour within the space. The integrated hybrid model enables efficient and high-precision calculations for the underground power space’s ventilation system. The research outcomes provide a theoretical foundation for practical construction and design schemes of underground power spaces, contributing significantly to ensuring their safety and optimal functionality in real-world applications.