MACHINE LEARNING-BASED MODEL FOR OPTIMAL OPERATING CONDITIONS OF THERMOSYPHONS FOR ELECTRONIC COOLING APPLICATIONS

Two-phase cooling systems based on the thermosyphon operating principle exhibit excellent heat transfer performance, reliability, and flexibility, therefore can be applied to overcome thermal challenges in a wide range of electronic cooling applications and deployment scenarios. However, extremely complex nature of two-phase flow physics involving flow patterns and phase transitions has been the major challenge for technology adoption in industry. This paper demonstrates a machine learning (ML) based model for evaluating the thermal performance and refrigerant mass flow rate, of a thermosyphon cooling system for telecom equipment. Unlike conventional laboratory approach that requires numerous sensors attached to a cooling system to capture their thermal performance, the new model requires a minimum number of sensors to monitor the health of a thermal management solution. Using the proposed model, a system control module can be further developed which could identify optimal operating parameters in real-time under dynamically changing heat load conditions and actively maintain safety and thermal requirements.