Predictive Containment Cooling Control for Data Center Airflow
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Solution Overview
Problem
Data center containment systems face airflow mismatches due to varying IT equipment utilization, increased cooling set points, virtualization, and maintenance, leading to inefficiencies and potential overheating, as classical monitoring methods fail to accurately represent IT equipment reliability and heating rates.
Innovation Solution
The implementation of predictive control systems using active and passive flow curve methods to analyze airflow behavior, predict cooling performance, and modulate cooling units and containment structures to maintain optimal airflow and temperature conditions, integrating sensors and automated processors to manage airflow and pressure across containment structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If containment structures are used to segregate cold and hot air streams, then energy efficiency is improved, but airflow mismatch between cooling units and IT equipment occurs
Solution Approach 1:
The system dynamically adjusts cooling unit operation based on real-time airflow monitoring and predictive modeling. Flow curves are generated and updated continuously to adapt to changing IT equipment heat rates and airflow demands, allowing the containment system to maintain optimal performance while preventing airflow mismatches.
Solution Approach 2:
The system implements closed-loop feedback by monitoring actual airflow conditions, comparing them against predictive models, and adjusting cooling unit operations accordingly. This feedback mechanism detects airflow deficiencies early and triggers corrective actions to maintain proper airflow matching between cooling units and IT equipment.
2Loss of energy
If cooling units are tuned down to increase PUE, then energy efficiency is improved, but IT equipment reliability monitoring becomes inaccurate
Solution Approach 1:
The system performs preliminary airflow assessment using flow curves before airflow deficiencies develop. By predicting potential airflow mismatches and heating rates in advance, the system can take corrective actions proactively, ensuring accurate monitoring of IT equipment thermal conditions even when cooling units are operated at reduced capacity for energy efficiency.
Solution Approach 2:
The system replaces reliance on classical inlet temperature sensors with a predictive modeling approach that uses flow curves and airflow analysis. This substitution provides more accurate representation of IT equipment thermal conditions and heating rates, enabling reliable monitoring even when cooling units are tuned down for energy efficiency.
3Device complexity
If external temperature sensors are used for monitoring, then system complexity is reduced, but detection of internal heating rates is insufficient
Solution Approach 1:
The system introduces flow curves as an intermediary tool that bridges external temperature sensor data and internal IT equipment heating rates. The flow curves incorporate airflow conditions, pressure differentials, and equipment characteristics to predict internal thermal conditions, providing accurate heating rate detection without requiring direct internal sensors while maintaining manageable system complexity.
Data Source
AI summary
A method of controlling a data center having a cold air cooling system, and at least one containment structure, comprising: determining a minimum performance constraint; determining optimum states of the cold air cooling system, a controlled leakage of air across the containment structure between a hot region and a cold air region, and information technology equipment for performing tasks to meet the minimum performance constraint, to minimize operating cost; and generating control signals to the cold air cooling system, a controlled leakage device, and the information technology equipment in accordance with the determined optimum states.