Dynamic Fan Speed Control for Server Chip Heat Dissipation
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Solution Overview
Problem
Existing PID control systems for fan rotation in servers fail to optimally adjust to sudden fluctuations in chip power consumption, leading to temperature overshoot or oscillation, affecting performance, power consumption, and noise.
Innovation Solution
A dynamic control system using a neural network combined with open and closed loop circuits to adjust fan speed based on power consumption and temperature variations, employing an open loop for rapid changes and a closed loop for moderate changes to maintain optimal chip temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If PID control is used to control fan rotation speed, then power saving and noise reduction can be achieved, but temperature overshoot or oscillation occurs when chip power consumption fluctuates suddenly
Solution Approach 1:
The patent applies dynamics by making the control system adaptive through neural network learning. The system transitions from static PID parameters to dynamic parameters that automatically adjust based on real-time chip temperature and power consumption patterns, enabling optimal fan speed control under varying load conditions without overshoot or oscillation
Solution Approach 2:
The patent implements feedback through a closed-loop neural network control system that continuously monitors chip temperature and power consumption. The neural network processes this feedback information to adjust fan rotation speed dynamically, ensuring stable temperature control while minimizing power loss and noise
2Reliability
If fan rotation speed varies rapidly to respond to power consumption spikes, then temperature control improves, but oscillation and noise increase
Solution Approach 1:
The system dynamically adjusts fan speed based on learned patterns from neural network training. Instead of rigid rapid responses, the system adapts its response characteristics based on historical data, achieving timely temperature control while avoiding excessive oscillations and associated noise
Solution Approach 2:
The neural network learns from historical data to anticipate temperature changes before they occur. By preparing appropriate control actions in advance based on learned patterns, the system prevents temperature overshoot and reduces the need for dramatic fan speed changes that cause oscillation and noise
Data Source
AI summary
Provided is an electronic apparatus, including a heat generating element, a heat dissipation module, and a control unit. The heat dissipation module is adapted for performing heat dissipation on the heat generating element. The control unit is coupled to the heat dissipation module and is adapted for measuring temperature variation of at least one temperature module and state variation of at least one system component and for adjusting the heat dissipation module via a control signal based on the state variation and the temperature variation. In addition, a dynamic heat dissipation control method and a dynamic heat dissipation control system are also provided.