Datacenter Liquid Cooling Control Using Thermal Load Estimation
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Solution Overview
Problem
Existing datacenter liquid cooling techniques struggle to efficiently estimate power consumption and optimize cooling efficiency, particularly in controlling drycooler fan and pump operations.
Innovation Solution
A liquid cooling method and arrangement that includes a dry cooling unit, liquid distribution circuits with pumps, and smart control valves, which estimate power consumption by calculating thermal loads and adjust fan and pump speeds based on measured temperatures and flow rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If liquid cooling blocks are positioned in direct thermal contact with heat-generating components, then heat dissipation efficiency is improved, but system complexity increases
Solution Approach 1:
The system divides the cooling function into separate modular components: liquid cooling blocks for direct component cooling, dry coolers for liquid cooling, and immersion cooling systems for alternative cooling. Each module can be independently configured and optimized, reducing overall system complexity while maintaining effective heat dissipation.
Solution Approach 2:
The system implements dynamic control of cooling operations through real-time power estimation and adaptive adjustment of cooling loop components. This allows the system to optimize cooling efficiency based on actual thermal loads, reducing complexity by only activating necessary cooling capacity rather than maintaining fixed high-capacity cooling infrastructure.
2Reliability
If cooling loop components operate at full capacity, then cooling reliability is improved, but energy consumption increases
Solution Approach 1:
The system employs real-time power estimation mechanisms that monitor thermal loads and provide feedback to dynamically adjust cooling loop component operations. This feedback control ensures cooling reliability by maintaining adequate cooling capacity while reducing energy consumption by scaling back cooling operations when full capacity is not required.
Solution Approach 2:
The system changes operational parameters of cooling loop components based on real-time conditions. By adjusting flow rates, temperatures, and component activation states according to actual thermal demands, the system maintains reliable cooling while optimizing energy consumption across different operating scenarios.
3Temperature
If multiple cooling techniques are combined, then cooling effectiveness is improved, but device complexity increases
Solution Approach 1:
The system integrates multiple cooling techniques (liquid block cooling, dry cooler cooling, and immersion cooling) into a universal cooling platform that can serve different thermal requirements. This multi-functional approach improves cooling effectiveness across diverse scenarios while managing complexity through unified control architecture and standardized interfaces.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This solution enables optimized cooling efficiency by accurately estimating power consumption and dynamically controlling drycooler fan and pump operations, thereby improving the overall efficiency of datacenter liquid cooling systems.
Implementation Method 1
the received cooling liquid absorbs the generated heat and the heated liquid is circulated, via the cooling loop arrangement, back to cooling liquid source for re-cooling
Implementation Method 2
a dry cooling unit to supply a cooling liquid to the rack-mounted processing assemblies and receive a heated liquid from the rack-mounted processing assemblies, the dry cooling unit comprising a fan assembly and a heat exchanger unit
Implementation Method 3
the dry cooling unit comprising a fan assembly and a heat exchanger unit
Data Source
AI summary
A liquid cooling method and system for estimating power consumption of cooling rack-mounted processing assemblies and controlling corresponding fan and pump speeds, is presented. The presented method and system provide for the estimation of the power consumption of the rack-mounted data processing assemblies by calculating a thermal load based on measured cooling liquid temperatures, heated liquid temperatures, ambient dry cooler temperatures, and cooling liquid volume and controlling the fan speed based on the estimated power consumption. The presented method and system also provide for controlling the pump speed based on measured flow rates and corresponding empirically derived pump head pressure values H.