CDU Digital Twin for Data Center Cooling Failure Prediction

The patent combines physics-based models (thermal, fluid dynamics) with AI/ML models (neural networks, random forests) into a unified digital twin framework. This merging allows the system to leverage the interpretability and physical consistency of physics-based models while incorporating the predictive power and adaptability of AI models, achieving superior thermal behavior simulation accuracy compared to using either approach alone.

The digital twin acts as an intermediary between the physical CDU system and the control system. It receives real-time operational data from sensors, processes this information through multiple models (physics-based and AI), and generates optimized control parameters. This intermediary layer enables predictive maintenance and optimization without directly modifying the physical CDU hardware.

The system performs preliminary actions by continuously training and updating AI models with historical operational data before real-time prediction is needed. This pre-training enables the models to make accurate predictions with minimal real-time computational resources. The digital twin also pre-identifies potential failure modes and optimization opportunities, allowing for proactive maintenance scheduling rather than reactive responses.

The system implements periodic data sampling and model updating rather than continuous high-frequency processing. Sensors collect operational data at optimized intervals, and AI models are retrained periodically with accumulated historical data. This periodic approach maintains predictive accuracy while significantly reducing computational energy consumption compared to continuous real-time processing.

The digital twin divides the comprehensive system analysis into separate, modular physics-based models: thermal models for temperature distribution, stress models for mechanical loads, vibration models for structural integrity, and reliability models for failure prediction. Each model can be independently trained, validated, and updated based on specific operational data relevant to that aspect, reducing the overall training time compared to a single monolithic model while maintaining comprehensive analysis capability.

The digital twin framework is designed as a universal platform that can accommodate multiple physics-based models and AI algorithms simultaneously. This multi-functional architecture allows the same infrastructure to perform thermal analysis, stress analysis, vibration analysis, and reliability assessment using different models as needed, maximizing resource utilization and reducing redundant computational overhead.