Data Center Cooling Performance Prediction for Improper Clusters
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Solution Overview
Problem
Existing data center cooling performance analysis tools are limited in handling improper clusters with gaps or unequal row lengths, as they either require complex algorithms or are restricted to simple clusters without gaps, making real-time prediction of cooling performance challenging.
Innovation Solution
A computer-implemented method and system that determine the capture index for equipment racks in improper clusters by modeling gaps as blank panels, applying a corrector value based on gap characteristics, and displaying the cooling performance, allowing for real-time prediction and optimization of data center layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cooling performance analysis tools are used for improper clusters with gaps, then the analysis is restricted to simple clusters without gaps, but the ability to handle real-world improper clusters is lost
Solution Approach 1:
The improper cluster is segmented into multiple proper clusters by identifying and treating gaps as boundaries. Each proper cluster between gaps is analyzed independently using existing algorithms, then results are combined. This allows the system to handle complex improper clusters by breaking them into manageable proper cluster segments.
Solution Approach 2:
A corrector value acts as an intermediary to adjust the cooling performance prediction. The system first calculates capture index assuming blank panels at gap locations, then applies a corrector value that accounts for the actual gap characteristics (size, position, airflow effects). This intermediary correction step enables accurate analysis of improper clusters without requiring completely new algorithms.
2Measurement precision
If complex algorithms are used to analyze improper clusters with gaps, then accurate cooling performance prediction is achieved, but real-time prediction capability is lost
Solution Approach 1:
The system performs preliminary analysis by first calculating capture index for proper clusters assuming blank panels at gap locations. This preliminary calculation uses existing efficient algorithms and provides a baseline prediction. Then, a corrector value is applied to adjust for actual gap conditions. This two-stage approach maintains real-time capability while improving accuracy.
Solution Approach 2:
The system changes the parameter representation by introducing a corrector value that adjusts the capture index based on gap characteristics. Instead of using complex algorithms that directly model gap airflow, the system uses parameter adjustment (corrector values) to account for gap effects. This parameter-based approach maintains computational efficiency while improving prediction accuracy for improper clusters.
Data Source
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AI summary
A system and method for evaluating equipment in an improper cluster in a data center, the equipment including a plurality of equipment racks, and at least one cooling provider. In one aspect, the method includes receiving data regarding each of the plurality of equipment racks and the -at least one cooling provider, the data including a layout of the improper cluster of equipment racks and the at least one cooling provider, storing the received data, identifying at least one gap 304, 306 in the layout, determining cooling performance of at least one of the plurality of equipment racks based, at least in part, on characteristics of the at least one gap, and displaying the layout of the data center, wherein the layout includes an indication of the cooling performance of the at least one of the plurality of equipment racks.