Edge Cooling Control Circuitry for Dynamic Fluid Redistribution
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Solution Overview
Problem
Existing liquid cooling systems for electronic components in edge environments face inefficiencies in cooling resource distribution and management, particularly in varying thermal demands across different edge locations, leading to potential overheating issues.
Innovation Solution
Implementing a control circuitry system that monitors actual cooling parameters in real-time and brokers the redistribution of cooling resources between edge locations based on service-level agreements, enabling dynamic allocation and deallocation of cooling fluid to maintain optimal temperatures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cooling resources are statically allocated to edge locations, then system simplicity is maintained, but cooling efficiency deteriorates when thermal demands vary across locations
Solution Approach 1:
The patent implements dynamic cooling resource allocation where cooling fluid flow rates are continuously adjusted based on real-time thermal conditions at different edge locations. The system transitions from static to dynamic control by monitoring temperature sensors and automatically redistributing cooling resources through controllable valves and pumps, enabling adaptation to varying thermal demands while maintaining manageable system complexity through automated control logic.
Solution Approach 2:
The system employs feedback mechanisms by continuously monitoring actual cooling parameters (temperatures, flow rates) at edge locations and using this information to adjust cooling resource distribution. Temperature sensors provide real-time data to the control system, which then modifies cooling fluid allocation to maintain optimal temperatures, creating a closed-loop control system that improves adaptability while keeping complexity manageable through automated decision-making algorithms.
2Productivity
If cooling fluid is redistributed in real-time based on thermal demands, then cooling efficiency is improved, but system complexity increases due to monitoring and control requirements
Solution Approach 1:
The cooling system implements self-service capabilities where the system automatically monitors its own thermal state and adjusts cooling resource distribution without external intervention. Temperature sensors and flow meters provide self-diagnosis information, and the control algorithm autonomously decides how to redistribute cooling fluid, enabling the system to self-optimize cooling efficiency while managing complexity through automated self-regulation rather than requiring complex external control infrastructure.
Solution Approach 2:
The control system is designed with multi-functionality to handle multiple tasks within a unified architecture. The same control platform that manages cooling fluid redistribution also handles monitoring, data logging, and adjustment decisions, reducing overall system complexity compared to having separate dedicated systems for each function. This universal control approach improves cooling efficiency while keeping the monitoring and control infrastructure manageable.
3Temperature
If cooling resources are concentrated at high-demand locations, then local cooling performance is improved, but other locations may experience overheating
Solution Approach 1:
The system applies local quality by allowing different edge locations to receive customized cooling resource allocation based on their specific thermal demands. Each location's cooling flow rate is independently controlled according to its local temperature conditions and thermal load characteristics, enabling optimized local temperature control. The system maintains system-wide thermal balance by continuously monitoring and adjusting allocations across all locations, preventing overheating at any single site while adapting to varying local requirements.
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 enhances cooling efficiency across edge environments by ensuring real-time adaptation to changing thermal demands, preventing overheating and optimizing resource utilization.
Implementation Method 1
liquid has inherent advantages of higher specific heat (when no boiling is involved) and higher latent heat of vaporization (when boiling is involved)
Implementation Method 2
liquid has inherent advantages of higher specific heat (when no boiling is involved)
Implementation Method 3
brokers the redistribution of cooling resources between edge locations based on service-level agreements, enabling dynamic allocation and deallocation of cooling fluid
Data Source
AI summary
Methods, systems, apparatus, and articles of manufacture to control cooling in an edge environment are disclosed. An example apparatus disclosed herein includes programmable circuitry to determine whether a first cooling parameter for a first edge node is satisfied based on first cooling availability information for the first edge node, when the first cooling parameter is satisfied, cause a first distribution unit to maintain an amount of cooling fluid to the first edge node, and when the first cooling parameter is not satisfied, cause at least one of the first distribution unit or a second distribution unit to adjust the amount of cooling fluid to at least one of the first edge node or a second edge node based on the first cooling availability information and second cooling availability information, the second cooling availability information for the second edge node.