Smart IoT-Based Leak Forecasting and Detection for Energy-Efficient Liquid Cooling in AI Data Centers
This study presents a proof-of-concept coolant-leak monitoring system for direct-to-chip liquid cooling in GPU-focused data centers. Its architecture combines an LSTM model for probabilistic forecasting of time to leak with a Random Forest model for rapid detection of leaks that have already occurred, using MQTT, InfluxDB, and Streamlit for data flow and visualization. The evaluation relies solely on synthetic data aligned with ASHRAE 2021, meaning that the reported performance and energy benefits require validation at live sites.
Data centersAI infrastructureLiquid coolingDirect-to-chip cooling
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