Modular Hot Aisle Cooling Flow Control for Data Center Heat Loads
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Solution Overview
Problem
Colocation data centers require flexible space utilization to accommodate diverse customer requirements, often necessitating closed systems without direct outside air or liquid cooling, and existing cooling systems are inefficient in managing varying heat loads and fluid temperatures.
Innovation Solution
The implementation of modular hot aisle cooling units (MHACUs) connected in series, a pump package, and a fluid supply line, with a computing device controlling fluid distribution based on temperature thresholds to optimize cooling fluid usage and heat rejection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a closed cooling system is used without direct outside air or liquid, then flexibility in space utilization is improved, but cooling efficiency deteriorates when managing varying heat loads
Solution Approach 1:
The system dynamically adjusts cooling fluid flow distribution among multiple MHACUs based on real-time temperature monitoring. The computing device receives temperature data from sensors, determines which MHACUs require cooling fluid based on their current temperatures relative to thresholds, and controls flow distribution accordingly. This dynamic adaptation allows the closed system to maintain cooling efficiency despite varying heat loads while preserving space utilization flexibility.
Solution Approach 2:
The system changes the flow rate parameter of cooling fluid based on temperature conditions. When an MHACU's temperature rises above a threshold, the system increases flow to that unit; when temperature is below the threshold, flow is reduced or redirected. This parameter adjustment enables efficient heat management in a closed system without requiring direct outside air or liquid supply.
2Stability of the object's composition
If cooling fluid is provided to all MHACUs simultaneously, then uniform cooling is maintained, but energy consumption increases
Solution Approach 1:
The system provides cooling fluid selectively to specific MHACUs based on their individual temperature conditions rather than uniformly to all units. The computing device determines which MHACUs require cooling based on local temperature measurements, and directs flow only to those units. This local quality approach maintains uniform cooling where needed while reducing energy consumption in units that do not require cooling.
Solution Approach 2:
The system applies partial cooling action by providing cooling fluid only to MHACUs that exceed temperature thresholds, rather than continuously cooling all units. This partial action reduces overall energy consumption while maintaining adequate cooling in the units that require it, avoiding excessive cooling of units that are already within acceptable temperature ranges.
3Loss of energy
If temperature monitoring and dynamic flow control are implemented, then cooling efficiency is improved, but device complexity increases
Solution Approach 1:
The system implements feedback control by continuously monitoring temperatures in MHACUs with sensors and using this information to adjust cooling fluid flow. The computing device receives temperature data, compares it against thresholds, and modifies flow distribution accordingly. This feedback mechanism improves cooling efficiency while keeping the control logic simple and rule-based, avoiding excessive complexity.
Solution Approach 2:
The system enables self-service cooling by allowing each MHACU to be automatically evaluated based on its own temperature conditions, with the computing device autonomously determining flow distribution without manual intervention. The temperature sensors and control logic work together to automatically adjust cooling, reducing the need for complex manual control systems while maintaining high efficiency.
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
Enhances cooling efficiency by dynamically adjusting to heat loads, reducing energy consumption, and minimizing infrastructure costs through modular design and real-time control, while maintaining uniform cooling temperatures across server racks.
Implementation Method 1
cooling fluid to cool servers in a data hall
Implementation Method 2
cooling fluid...temperature of the cooling fluid in a first MHACU among the multiple MHACUs has risen
Data Source
AI summary
A system includes multiple MHACUs for cooling one or more servers in a data hall. The system also includes a pump package for providing cooling fluid to the MHACUs, and a fluid supply line conveying the cooling fluid to the MHACUs. The system also includes at least one computing device configured to: determine that a cooling fluid temperature in a first MHACU has risen to a first temperature that is less than a predetermined maximum temperature; in response to the determination, control the system to provide at least some of the cooling fluid to a second MHACU; determine that the cooling fluid temperature in the second MHACU has risen to a second temperature that is at least the predetermined maximum temperature; and in response to the determination, control the system to provide the cooling fluid to a fluid return line for return to the pump package.