Dynamic Data Center Partitions for Thermal Hot Spot Isolation
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Solution Overview
Problem
Current data center climate control systems are inefficient, leading to increased operational costs and energy waste due to cooling the entire data center to below the set point to accommodate moving hot spots, which also increases the load on HVAC units.
Innovation Solution
A method and system that perform a thermal analysis of the data center, dynamically controlling moveable ceiling, floor, and wall partitions to concentrate cooling capacity and airflow where needed, reducing energy consumption and strain on HVAC units by isolating hot spots from critical computing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire data center is cooled to below the set point temperature to accommodate moving hot spots, then equipment reliability is improved, but energy consumption increases and operational costs rise
Solution Approach 1:
The data center is divided into multiple thermal zones using movable partitions (walls, ceilings, floors) that can be reconfigured to segment hot spots from cooler regions. This allows independent temperature control in different zones, cooling only the areas that need it rather than the entire facility, thereby maintaining equipment reliability while reducing overall energy consumption.
Solution Approach 2:
Movable partitions with adjustable positions enable dynamic reconfiguration of thermal zones to track moving hot spots. The system continuously monitors temperature distribution and repositions partitions to maintain optimal thermal boundaries, adapting to changing heat generation patterns from IT equipment while minimizing cooling energy requirements.
2Loss of energy
If movable partitions are used to dynamically reconfigure thermal zones, then cooling efficiency is improved and energy consumption is reduced, but system complexity increases
Solution Approach 1:
The movable partitions serve multiple functions: they act as thermal barriers to define zones, provide structural support for HVAC equipment, and can be automatically repositioned via motorized actuators. This multi-functionality reduces the need for separate systems while achieving improved cooling efficiency, thereby limiting the increase in overall system complexity.
Solution Approach 2:
Temperature sensors continuously monitor the data center environment and provide feedback to the control system. The controller processes this thermal data and automatically adjusts partition positions to maintain optimal thermal zones, creating a closed-loop system that improves cooling efficiency while automating the complexity management through intelligent control algorithms.
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 approach improves airflow and temperature management within the data center, reducing energy consumption, lowering operational costs, and extending the mean-time to failure for critical systems by optimizing cooling resources and shielding important equipment from hot spots.
Implementation Method 1
perform a thermal analysis of a data center to identify cooler regions of the data center and hotter regions of the data center
Implementation Method 2
the airflow of a data center is normally regulated by the amount of equipment and heat that is generated by the individual computing resources
Implementation Method 3
dynamically controlling moveable ceiling, floor, and wall partitions to concentrate cooling capacity and airflow where needed, reducing energy consumption and strain on HVAC units by isolating hot spots from critical computing resources
Data Source
AI summary
A method implemented in a computer infrastructure having computer executable code embodied on a computer readable medium being operable to perform a thermal analysis of a data center and overlay the thermal analysis on a map of the data center to provide an overlaid thermal analysis. Additionally, the computer executable code is operable to dynamically control at least one partition in the data center based on the overlaid thermal analysis.