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Why Deep Mines Heat Up: Groundbreaking Study Explains Airflow Temperature Variations

A recent article in Scientific Reports examined airflow temperature patterns during ultra-deep shaft construction using Newton’s law of cooling, the first law of thermodynamics, and fluid dynamics. The study introduced the equilibrium enthalpy interface theory, derived critical conditions for temperature changes, and validated the findings through numerical simulations. The results showed a non-linear cubic function-like variation in airflow temperature, which first decreased, then increased, and decreased again. This research provided theoretical insights and supported future ventilation and cooling efforts in ultra-deep shaft construction.

mining tunnel, abandoned gold mine

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Related Work

Past work on mine air temperature research dates back to 1740, with studies on geothermal measurements and thermodynamic laws in deep mines across France, the United Kingdom (UK), Germany, South Africa, and the Soviet Union. Over time, advancements in thermal hazard management introduced methods for predicting mine wind temperature, improving accuracy with techniques like neural networks and convective heat transfer models. Research has shown that increasing mining depth significantly raises airflow and rock temperatures, worsening underground conditions and posing safety risks.

Airflow Modeling in Shafts

The analysis of the airflow model in ultra-deep shaft construction highlights the necessity of artificial forced mechanical ventilation to maintain proper air circulation. Fresh air is introduced via ventilation ducts and expelled after heat exchange with the shaft walls. Due to the extreme underground conditions, including high humidity and elevated temperatures, airflow undergoes compression effects, increasing its internal energy and temperature. The geothermal effect further contributes to temperature variations as heat from the surrounding rock transfers to the airflow. These complex interactions make mathematical modeling challenging, resulting in primarily qualitative studies rather than precise quantitative research.

The study employs an idealized approach to address these challenges by assuming incompressible airflow and incorporating factors such as pressure and density changes. The proposed Equilibrium Enthalpy Interface Theory, based on Newton’s law of cooling and the first law of thermodynamics, examines the transformation of enthalpy energy within the shaft.

Energy transformation equations are derived using integral and differential mathematical methods to analyze temperature variations at different shaft depths. The study also formulates a one-dimensional energy equation for the airflow temperature field, incorporating heat transfer, gravitational potential energy changes, and the effects of humidity.

The energy equation considers primary influencing factors such as heat transfer from the shaft walls, gravitational potential energy variations, and enthalpy changes in high-humidity environments. These aspects contribute to the equilibrium enthalpy interface theory, which identifies critical conditions for airflow temperature changes at various depths. The study introduces theoretical representations of equilibrium enthalpy interfaces at different control volumes, providing insights into airflow behavior during deep shaft construction. These findings offer a structured framework for understanding and optimizing ventilation strategies in extreme underground environments.

Shaft Airflow Simulation

A numerical simulation was conducted using geological parameters from a gold mine in Shandong Province, China, to analyze the equilibrium enthalpy interface theory. The shaft airflow was treated as a quasi-ideal gas, assuming it to be compressible, non-viscous, and with density variations based on pressure. The study utilized the turbulent k-ε model in Comsol Multiphysics to simulate non-isothermal flow fields under steady-state conditions. Engineering parameters were set based on actual mine conditions, with airflow inlets and outlets defined by theoretical cross-sectional areas.

The numerical results show airflow behavior under three different inlet temperatures. Wind speed and pressure decreased as the airflow moved upward from the shaft bottom, leading to heat transfer between the shaft surface and airflow. Sensible heat transfer raised the airflow temperature, whereas latent heat and gravitational potential energy changes caused cooling effects. Increasing airflow-shaft contact decreased humidity, reducing water vapor saturation and altering heat absorption properties. When the shaft surface temperature equaled the airflow temperature, the heat transfer direction reversed, marking a peak in airflow temperature.

The simulation confirmed that the highest temperature zone was not at the shaft bottom but in the middle. Temperature variations are highlighted, with different colors indicating low and high-temperature zones. The blue area represents the maximum exothermic region, where external heat transfer significantly alters airflow characteristics. These findings align with the enthalpy interface theory, reinforcing the numerical model’s validity in predicting shaft airflow temperature dynamics.

Conclusion

Shaft airflow temperature followed a curvilinear pattern, initially decreasing, then increasing, and finally decreasing again. The high-temperature zone shifted downward with rising intake air temperature, occurring between 1375 m and 1622 m depths. As inlet air temperature increased, the temperature rise at the shaft bottom decreased due to geothermal heat effects. Local temperature minima and maxima appeared during ventilation, with the maximum point crucial for thermal hazard management. These findings are vital for controlling high-temperature risks in ultra-deep shaft construction.

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Source:

Siyu, Z., et al. (2025). Study on the change rule of airflow temperature field in ultra-deep mining shaft. Scientific Reports, 15:1, 1-12.  DOI: 10.1038/s41598-025-88247-2, https://www.nature.com/articles/s41598-025-88247-2

Silpaja Chandrasekar

Written by

Silpaja Chandrasekar

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.

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