Data Center Transient Thermal Modeling for Power Outage Prediction
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Solution Overview
Problem
Current methods for predicting transient cooling performance in data centers are inadequate, particularly during utility power outages, as they fail to accurately model the transient temperature changes and cooling system restart dynamics, leading to potential overheating of sensitive electronics.
Innovation Solution
A flow network model is developed to estimate air and cooling fluid temperatures in data centers, incorporating energy balance and heat transfer equations for various components, including chillers, water storage tanks, cooling coils, and IT equipment, to predict temperature changes during transient events like power failures and system restarts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current prediction methods are used, then computational simplicity is maintained, but temperature prediction accuracy deteriorates during transient events
Solution Approach 1:
The data center cooling system is divided into discrete thermal zones (IT equipment racks, cooling equipment, water storage tanks, chilled water pipes) with energy balance equations applied to each segment. This segmentation allows accurate tracking of transient temperature changes in each component while maintaining a manageable computational structure.
Solution Approach 2:
The model transitions from static steady-state assumptions to dynamic transient analysis by incorporating time-dependent energy balance equations that capture the temporal evolution of temperatures during cooling failures and restart events. This enables accurate prediction of temperature changes as the system evolves through transient states.
2Reliability
If detailed energy balance equations are incorporated, then temperature prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The model pre-calculates and stores thermal mass parameters, heat transfer coefficients, and energy balance relationships for each component before transient events occur. During actual power outages, the system rapidly evaluates these pre-established relationships to predict temperature evolution, ensuring reliable predictions without requiring complex real-time computations.
Solution Approach 2:
The patent introduces an intermediary computational layer that bridges detailed physical models and practical prediction needs. Energy balance equations serve as intermediaries between the complex thermal dynamics of individual components and the overall system-level temperature predictions, enabling reliable forecasts while managing computational complexity through structured intermediate calculations.
3Ease of operation
If cooling system restart dynamics are modeled, then operational guidance accuracy improves, but model complexity increases
Solution Approach 1:
The model continuously tracks the thermal state of all components throughout the cooling failure and restart sequence, maintaining unbroken energy balance calculations from the moment cooling fails through the restart process. This continuous modeling provides uninterrupted operational guidance, showing how temperatures evolve and how restart timing affects overall system recovery.
Solution Approach 2:
The model incorporates feedback mechanisms where predicted temperature changes from restart actions are fed back into the energy balance equations to refine subsequent predictions. This allows the system to evaluate the impact of different restart strategies and provide optimized operational guidance based on how actual system responses compare to predictions.
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 more accurate predictions of temperature changes and cooling runtime, allowing data center operators to optimize design and operation, mitigate temperature fluctuations, and improve cooling efficiency, thereby ensuring the integrity of IT equipment during transient events.
Implementation Method 1
incorporating energy balance and heat transfer equations for various components, including chillers, water storage tanks, cooling coils, and IT equipment, to predict temperature changes during transient events
Implementation Method 2
A flow network model is developed to estimate air and cooling fluid temperatures in data centers, incorporating energy balance and heat transfer equations for various components
Data Source
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AI summary
A system and method is provided for predicting the effect of a transient event on a data center. According to one aspect, embodiments herein provide a method that comprises receiving input data related to a data center that includes at least one equipment rack and at least one cooling provider, the input data including data center architecture information, building data, and operating data, generating, by a computing device, a model based at least in part on the input data and on a set of energy balance and heat exchange equations for the data center that account for heat added by the at least one equipment rack and removed by the at least one cooling provider, and a thermal mass of the at least one equipment rack and a thermal mass of the at least one cooling provider, the model configured to predict at least one temperature in the data center during a transient event, and controlling a display device to display the at least one predicted temperature.