Reliability analysis of a CRAC system under uncertainties

Abstract

The rapid increase in data center size and number, driven by escalating internet and cloud computing demands, has led to high energy consumption and public concern. Densely packed high-powered systems within data centers generate significant radiant heat, necessitating effective and Reliable cooling solutions for maintaining uptime. This research presents a novel method to evaluate Computer Room Air Conditioning (CRAC) system performance and efficiency. Firstly, a rack-level heat transfer probabilistic constraint is introduced, integrating environmental conditions such as ambient temperature, humidity, and airflow patterns, which significantly impact heat transfer processes and are accurately incorporated to reflect real-world scenarios. Additionally, the model accounts for specific configurations and thermal properties of data server racks, enabling precise simulation of heat generation and dissipation patterns. The probabilistic variables undergone training including the layout of servers, types of cooling mechanisms employed, and the material properties of the racks. Secondly, modelling the CRAC system’s heat transfer rate as random distribution facilitates effective thermal load management and balances computational demands with accuracy. Based on the output from two probabilistic performance functions, a multi-response Gaussian process (AMRGP) model is developed using an adaptive sampling technique, enhancing predictive accuracy and efficiency by training the predicted responses with a learning U-function to calculate the probability of failure and reliability of the model. The proposed method also improves risk assessment by predicting the likelihood of failure events, aiding in the development of a powerful tool for designing and evaluating CRAC system reliability in complex and uncertain environments. This research thus represents a significant advancement in the field of data center engineering, providing a robust framework for future development in thermal management and reliability assessment.