METHODOLOGY TO DEVELOP ARTIFICIAL NEURAL NETWORK BASED CONTROL STRATEGIES FOR MULTIPLE AIR-COOLING UNITS IN A RAISED FLOOR DATA CENTER

Author

Vibin Shalom Simon

ORCID Identifier(s)

0000-0002-6544-5575

Graduation Semester and Year

2019

Language

English

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Dereje Agonafer

Abstract

A data center consists of a hierarchy of systems with dedicated control algorithms dictating their operational states. We could say, there exists an ensemble of dynamical systems, each executing a control task, while the global objective is to drive the overall system to an optimum i.e. minimum total operational power at desired rack inlet temperatures. The dynamics of the Information Technology Equipment (ITE) workload and the cooling provisioning is non-linear in spatial and temporal parameter space. Data-driven modelling is one method to realistically model such non-linear dynamics and make predictions that are necessary for improved control design of the cooling system. In this study, the data center non-linear dynamics are approximated by well-defined operational scenarios. Multiple ACU’s are to be optimally controlled in provisioning a varying rack-level workload within the data center. CFD tool 6SigmaRoom is used to model and simulate a raised-floor data center with multiple Air-Cooling Unit (ACU)s. The input parameter space and boundary conditions to be applied to the simulations are sampled on a random basis using the Latin Hypercube Sampling technique. Artificial Neural Network (ANN) is a suitable data driven technique that has the ability to capture the non-linear relationships between the dynamic operation of ACU’s setpoints and the ITE’s workload. Training data is obtained from the results and observations of a large number of parametric CFD simulations. An objective function is defined. ANN model is developed to predict the parameters that are necessary in designing a data-driven control framework.

Keywords

Data center, Artificial neural network, Control strategy

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Comments

Degree granted by The University of Texas at Arlington

Recommended Citation

Simon, Vibin Shalom, "METHODOLOGY TO DEVELOP ARTIFICIAL NEURAL NETWORK BASED CONTROL STRATEGIES FOR MULTIPLE AIR-COOLING UNITS IN A RAISED FLOOR DATA CENTER" (2019). Mechanical and Aerospace Engineering Theses. 993.
https://mavmatrix.uta.edu/mechaerospace_theses/993