Method and system for predicting effect of a transient event on a data center
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Solution Overview
Problem
Current data center designs face challenges in predicting and managing temperature fluctuations during transient events like power outages, leading to potential overheating of sensitive electronics due to cooling system delays and inefficiencies.
Innovation Solution
A method and system that utilize energy balance and heat exchange equations to model data center temperature dynamics, accounting for thermal masses of equipment racks and cooling providers, allowing for the prediction of temperatures and optimization of cooling runtime, which can be displayed and used to adjust operating parameters and improve data center design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cooling system delays are reduced to prevent overheating during transient events, then temperature control reliability improves, but cooling system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting future temperature conditions during transient events before they occur. The predictive model calculates expected temperature excursions and triggers cooling adjustments in advance, allowing the cooling system to respond proactively rather than reactively, thereby improving temperature control reliability without requiring overly complex real-time control mechanisms
Solution Approach 2:
The system applies beforehand cushioning by using the predictive model to anticipate temperature rises during cooling failures or transient events. By knowing the predicted temperature trajectory in advance, the system can prepare and activate cooling measures at optimal times, cushioning against potential overheating before it occurs, thus improving reliability without proportionally increasing system complexity
2Measurement precision
If thermal mass of equipment racks and cooling providers is accounted for in the model, then temperature prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies parameter changes by incorporating thermal mass parameters of equipment racks and cooling providers into the predictive model. By including these additional parameters that represent the heat storage capacity of different components, the model achieves more accurate temperature predictions while managing computational complexity through efficient mathematical formulations
3Measurement precision
If energy balance and heat exchange equations are used to model temperature dynamics, then temperature prediction accuracy improves, but computational resources required increase
Solution Approach 1:
The system applies mechanics substitution by replacing complex computational mechanics with optimized mathematical models. The energy balance and heat exchange equations are formulated and solved using efficient computational methods that reduce resource requirements while maintaining high temperature prediction accuracy, substituting brute-force computation with mathematically elegant solutions
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 prediction and management of data center temperatures during transient events, optimizing cooling performance and reducing the risk of overheating, thereby improving operational efficiency and energy conservation.
Implementation Method 1
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
Implementation Method 2
a thermal mass of the at least one equipment rack and a thermal mass of the at least one cooling provider
Implementation Method 3
the set of energy balance and heat exchange equations for the model account for heat exchange between at least one of the ceiling, the walls, and the floor of the data center and an external environment to the data center
Data Source
AI summary
A system and method for predicting the effect of a transient event on a data center. A method 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, 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 removed and added heat 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.