Adaptive variable channel heat dissipation control of ground control station under various work modes

Area of Science:

• Thermal Management Systems
• Artificial Intelligence in Engineering
• Electronic Cooling Technologies

Background:

• Ground Control Stations (GCS) face thermal challenges due to varying heat generation from multiple modules during different operational modes.
• Existing cooling solutions may not be efficient or adaptive enough to handle dynamic thermal loads, potentially impacting GCS performance and longevity.

Purpose of the Study:

• To develop and evaluate an Adaptive Variable Channel Control (AVCC) cooling approach for GCS, optimizing thermal management.
• To enhance the cooling efficiency and operational endurance of GCS through intelligent thermal control.
• To provide a scalable solution for cooling electronic equipment cabinets with multiple heat sources.

Main Methods:

• Designed a distributed cooling module with multiple fans for a variable channel cooling structure.
• Developed a multi-module temperature control platform simulating diverse heat generation scenarios for algorithm training.
• Implemented a model-free control strategy using the Soft Actor-Critic (SAC) deep reinforcement learning algorithm for AVCC.

Main Results:

• The AVCC method successfully cooled multiple GCS modules (e.g., voltage stabilizer, battery, charger, computer) to 50°C within 280 seconds under maximum load.
• Achieved a 40.4% improvement in cooling efficiency compared to a traditional rule-based policy.
• Reduced energy consumption by 42.2% relative to the rule-based approach.

Conclusions:

• The proposed distributed cooling module and AVCC method effectively enhance GCS cooling performance and reduce energy usage.
• This intelligent cooling system offers a significant advancement in thermal management for electronic equipment.
• The study serves as a valuable reference for designing and controlling cooling systems in similar electronic cabinets.