Data Center Airflow Redundancy Control for Efficient Cooling
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Solution Overview
Problem
Conventional data center cooling redundancy techniques lead to inefficient cooling efficiency and high operational costs due to over-provisioning and lack of consideration for the positioning and capacity of cooling units, resulting in varying redundancy levels across different locations.
Innovation Solution
The implementation of a cooling influence redundancy level system, where fluid moving devices actively monitor and regulate conditions at specific locations, allowing for dynamic redundancy management by assigning multiple devices to subsets of locations based on their influence levels, using a model predictive controller to minimize energy consumption and respond to failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all cooling units are constantly run at full capacity to accommodate worst-case failure scenarios, then cooling redundancy is achieved, but cooling efficiency greatly decreases and operational costs increase
Solution Approach 1:
The system dynamically adjusts cooling unit operation based on real-time conditions and failure scenarios. Instead of static full-capacity operation, the controller continuously optimizes which cooling units operate and at what capacity, transitioning between different operational states to maintain redundancy while minimizing energy consumption.
Solution Approach 2:
The system changes operational parameters (which cooling units are active, their capacity levels) based on the specific failure scenario and current conditions. By adjusting these parameters dynamically, the system achieves the required redundancy level without constantly operating all units at maximum capacity, thereby improving cooling efficiency.
2Reliability
If all cooling units are constantly run at full capacity to accommodate worst-case failure scenarios, then cooling redundancy is achieved, but operational costs increase
Solution Approach 1:
The system dynamically determines which cooling units to operate based on the current failure scenario and environmental conditions. This dynamic approach allows the system to maintain required redundancy levels while minimizing the number of active cooling units and their operating capacity, thereby reducing energy loss and operational costs.
Solution Approach 2:
The controller adjusts operational parameters such as the selection of active cooling units and their capacity levels based on real-time conditions. By optimizing these parameters, the system achieves the necessary cooling redundancy without the excessive energy consumption and operational costs associated with running all units at full capacity continuously.
3Reliability
If conventional redundancy practices are used without considering positioning and capacity of cooling units, then simple redundancy is achieved, but cooling efficiency decreases and costs increase
Solution Approach 1:
The system assigns different roles and capacities to different cooling units based on their specific positioning and capabilities. Instead of treating all cooling units uniformly, the controller optimizes the contribution of each unit according to its local characteristics, thereby improving overall cooling efficiency while maintaining the required redundancy level.
Solution Approach 2:
The system pre-determines the influence levels of cooling units on different locations and uses this information to optimize the assignment of cooling units to specific zones. By performing this optimization in advance based on positioning and capacity data, the system achieves efficient cooling distribution without requiring all units to operate at full capacity, thereby improving productivity.
Data Source
AI summary
In an implementation, airflow provisioning in an area by a plurality of fluid moving devices is managed through assignment of the fluid moving devices to monitor and regulate conditions at respective subsets of a plurality of locations based upon determined influence levels of the fluid moving devices on the respective locations to meet a predefined cooling influence redundancy level. The predefined cooling influence redundancy level for a particular location identifies a number of the fluid moving devices that are to monitor and regulate a condition at the particular location.