Data Center Thermal Influence Indices for Cooling Optimization
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Solution Overview
Problem
Current methods for determining cooling characteristics in data centers are inadequate, as they fail to account for the complex interactions between various components, leading to inefficiencies and hot spots due to limited scope and lack of comprehensive thermal management solutions.
Innovation Solution
The development of thermal influence indices that quantify the airflow and temperature interactions between components in a data center, allowing for the calculation of specific influence indices to optimize cooling performance and identify areas for improved heat management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If general measures such as decreasing supply temperature or increasing cooling capacity are taken to handle hot spots and low tile flow rates, then the temperature safety of electronic equipments is improved, but the cooling efficiency decreases
Solution Approach 1:
The data center is divided into multiple zones with different thermal characteristics. Thermal influence indices are calculated for different regions (e.g., near CRAC units, far from CRAC units, different rows) to enable zone-specific cooling control rather than uniform cooling across the entire facility, thereby improving cooling efficiency while maintaining temperature safety.
Solution Approach 2:
Different cooling strategies are applied to different locations based on their specific thermal needs. Areas with high thermal influence from CRAC units receive different treatment compared to areas with low thermal influence. This localized approach ensures temperature safety is maintained where needed while avoiding unnecessary cooling in other areas, thus improving overall cooling efficiency.
2Temperature
If cooling capacity is designed and run for maximum heat load conditions, then the temperature safety is ensured under peak conditions, but the cooling efficiency decreases during typical operation when data centers rarely operate at maximum conditions
Solution Approach 1:
The cooling system transitions from static design-based control to dynamic operation-based control. Thermal influence indices are calculated in real-time based on actual operating conditions, CRAC unit positions, and heat load distributions. This enables the cooling system to adapt dynamically to changing conditions, maintaining temperature safety while optimizing cooling efficiency for the current operating level rather than always running at maximum capacity.
Solution Approach 2:
The system changes operational parameters (supply temperature, tile flow rates, CRAC unit activation) based on calculated thermal influence indices and current operating conditions. Instead of maintaining fixed parameters designed for maximum load, the system dynamically adjusts parameters to match actual heat load conditions, improving cooling efficiency during typical operation while ensuring temperature safety is maintained.
3Productivity
If workload consolidation and virtualization are carried out to increase space utilization, then the space efficiency is improved, but the cooling infrastructure may not be sufficient to handle concentrated heat loads
Solution Approach 1:
Before implementing workload consolidation, the system calculates thermal influence indices to predict the thermal impact of concentrating heat loads in specific locations. This preliminary thermal analysis allows planners to identify potential hot spots and adjust cooling infrastructure or workload distribution in advance, ensuring that space utilization improvements do not compromise temperature safety.
Solution Approach 2:
The system continuously monitors thermal conditions and calculates thermal influence indices based on actual workload distributions and cooling performance. This feedback mechanism allows real-time detection of thermal issues resulting from consolidation and enables dynamic adjustment of cooling strategies or workload placement to maintain temperature safety while preserving space utilization benefits.
4Ease of operation
If CRAC units are controlled according to heat loads in a very elementary manner, then the ease of operation is maintained, but the cooling efficiency decreases
Solution Approach 1:
The system replaces elementary manual or simple mechanical control of CRAC units with automated computational control based on thermal influence indices. The automated system calculates optimal control parameters (supply temperature, flow rates) based on thermal models and operating conditions, eliminating the need for complex manual adjustments while significantly improving cooling efficiency. The interface remains simple for operators while the backend performs complex optimization.
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 enables effective thermal management by pinpointing inefficiencies and optimizing data center configurations, leading to improved cooling efficiency and reduced energy consumption.
Implementation Method 1
thermal influence indices calculations are based on information related to configuration of data center, air flow, temperature and heat pertaining to the source and target components in a data center
Implementation Method 2
air flow, temperature and heat pertaining to the source and target components
Data Source
AI summary
The invention provides a method and system for quantitative determination of cooling characteristics of a data center by calculating thermal influence indices. The invention further provides a method and system for providing effective thermal management in a data center using quantitative determination of cooling characteristics of a data center.