Physics-informed machine learning-driven active utilization of ventilation and heat exchange in large underground tunnels

Mengru Ma, Zhengfei Zhang, Jun Chen, Qian Xiong, Eric Hu, Tao Wang and Yimin Xiao

Renewable Energy, 2025, vol. 250, issue C

Abstract: Underground ventilation tunnels have significant potential for shallow geothermal utilization. Traditional methods struggle to provide accurate real-time conditions for calculating tunnel ventilation heat exchange. This study presents a physics-informed machine learning (PIML) framework to predict tunnel air parameters, using an LSTM-based Seq2Seq model with an attention mechanism for real-time multi-step predictions of temperature and humidity at the tunnel outlet. Based on year-round simulation data from 16 intake tunnels and field data from the target tunnel, the framework achieves accurate predictions of air temperature and humidity. Results show that the average real-time prediction errors for temperature and relative humidity are below 0.4 °C and 2 %, demonstrating the method's reliability. Attention heatmaps and feature ablation tests reveal the model's interpretability: it adapts to focus on key time steps and daily cycles, with physical factors like time cycles, enthalpy, and wind speed crucial for accuracy. During the measurement period, the 1950m-long tunnel's average heat exchange rate was 199 kW, indicating its substantial energy regulation potential. This research provides a feasible approach for real-time heat exchange prediction in large tunnels and lays the foundation for efficient ventilation heat use in underground power stations, energy storage, mines, and subways.

Keywords: Large underground tunnels; Deep learning; Geothermal utilization; Earth-air heat exchanger (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:250:y:2025:i:c:s0960148125009450

DOI: 10.1016/j.renene.2025.123283

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