Direct evaporative cooling system for data center with fault detection
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Solution Overview
Problem
Direct evaporative cooling systems for data centers consume significant water and energy, and existing technologies lack effective methods for fault detection and resource optimization, leading to inefficiencies and potential overheating issues.
Innovation Solution
A method for fault detection and resource optimization in direct evaporative cooling systems, which includes generating expected humidity and temperature values using a model, detecting deviations to identify faults, and adjusting control parameters to optimize water and energy consumption, as well as performing peer analysis to identify outliers and initiate corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If direct evaporative cooling is used to cool data centers, then cooling effectiveness is improved, but water consumption increases
Solution Approach 1:
The system continuously monitors temperature, humidity, and equipment status, comparing actual values against expected model predictions. When deviations indicate potential faults or inefficiencies, the system automatically adjusts cooling operations to optimize water consumption while maintaining effective cooling.
Solution Approach 2:
The system uses predictive modeling to establish expected temperature and humidity values before actual cooling operations. By comparing actual measurements against these pre-established expectations, the system can proactively detect faults and adjust operations to prevent water waste before it occurs.
2Temperature
If direct evaporative cooling is used to cool data centers, then cooling effectiveness is improved, but energy consumption increases
Solution Approach 1:
The system monitors energy consumption alongside temperature and humidity metrics, using feedback loops to identify inefficient cooling operations. When the model detects deviations indicating faults or suboptimal performance, it adjusts cooling parameters to reduce energy consumption while maintaining cooling effectiveness.
Solution Approach 2:
The system dynamically adjusts cooling operations based on real-time conditions and model predictions. By continuously optimizing cooling parameters rather than operating at fixed settings, the system adapts to changing conditions to minimize energy consumption while maintaining effective cooling.
3Device complexity
If fault detection is not implemented, then system complexity is reduced, but reliability deteriorates
Solution Approach 1:
The system performs self-diagnosis by comparing actual sensor measurements against expected values generated by predictive models. This self-service fault detection capability allows the system to monitor its own health and detect anomalies without requiring complex external monitoring infrastructure.
Solution Approach 2:
The patent replaces complex mechanical fault detection systems with a computational approach using predictive modeling and data analysis. By substituting physical monitoring complexity with algorithmic analysis, the system achieves reliable fault detection while maintaining relatively simple hardware architecture.
4Loss of substance
If resource optimization is implemented, then water and energy consumption are reduced, but device complexity increases
Solution Approach 1:
The optimization system uses continuous feedback from sensors and model comparisons to automatically adjust cooling operations. This feedback-driven approach enables resource optimization without requiring complex manual control systems, as the optimization emerges from continuous monitoring and adaptive adjustment.
Solution Approach 2:
The system establishes predictive models and expected value benchmarks before optimization operations begin. By having pre-computed expectations and optimization criteria ready, the system can make rapid optimization decisions without requiring complex real-time computation, reducing the apparent complexity of the control system.
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
The method reduces resource consumption, improves cooling efficiency, and extends the lifespan of evaporative cooling units by identifying and addressing faults promptly, thereby minimizing overheating risks and operational costs.
Implementation Method 1
Direct evaporative cooling uses evaporation of water to create a cooling effect which can be used to affect temperature of a data center
Data Source
AI summary
Systems and methods for detecting and correcting faults in direct evaporative cooling units are provided. In an exemplary embodiment, direct evaporative cooling affects a humidity and a temperature of the data center. An exemplary method includes generating expected values for the humidity of the data center and expected values for the temperature of the data center using a model. A fault condition is determined in response to the actual values for the humidity deviating from the expected values for the humidity while actual values for the temperature track the expected values for the temperature, or in response to the actual values for the temperature deviating from the expected values for the temperature while actual values for the humidity track the expected values for the humidity.