Application of Data Analytics to Heat Transfer Phenomena for Optimal Design and Operation of Complex Systems

Abstract

Heat transfer phenomena in complex physical systems like multiphase environments, multidimensional geometries can be difficult to capture in terms of correlations, analytical functions or numerical models using conventional techniques. Such systems are designed based on approximations, thumb-rules or semi-empirical correlations between parameters based on averaged values and are operated likewise using another set of rules derived from bulk thermodynamic performance parameters. With the development of nano-scale sensors and advanced data aggregation techniques, there is a need for analytical techniques that can discover the complex interrelationships between the thermodynamic parameters of the process, geometry constraints and the governing outcomes of the process. Such techniques can leverage the possibility of deployment of thousands of sensors to extract the key relationships that drive the transport phenomena for advanced development of process control tools and methodologies. Heat and mass transfer equipment design and operation can benefit from knowledge discovered through analytics applied on thermo-physical data obtained from real time processes. We present illustrative use cases of application of data analytics and knowledge discovery techniques to a richly instrumented data center where computer room air conditioning (CRAC) units provide cooling for IT equipment arranged in rows of racks. Sensors located at each rack provide temperature measurements which are analyzed in real-time and also archived. Rack temperatures, together with operating parameters of CRAC units such as supply air temperature (SAT), and variable speed drive (VFD) settings, are analyzed together to derive design insights and detect anomalies.

Sustainable data centers

“…Since the sheer volume of data precludes manual inspection, automated data mining and knowledge discovery techniques are applied to identify trends, patterns, and models for more efficient operation of a data center. In particular, we focus on three key areas: (1) detecting uncorrelated data center events using Principal Component Analysis [8]; (2) visual analytics for thermal state management [9], and (3) temporal data mining of common motifs or patterns for enhancing operational efficiency [10]. Among other benefits, the tools will help optimize resource utilization, predict events, manage growth and improve reliability.…”