HVAC Thermal Management of a Data Center ANSYS Fluent CFD Simulation Training

Description

Project Description

This project presents a CFD simulation of HVAC thermal management in a data center using ANSYS Fluent. The model captures airflow distribution from perforated tiles, heat generation across server racks, and hot-air recirculation between aisles. Thermal performance is evaluated through key indices SHI, RHI, RTI, and RCI  to quantify cooling efficiency and identify problem racks. The results reveal how airflow management and rack positioning affect server inlet temperatures and overall cooling effectiveness.

Objectives

This project focuses on the HVAC thermal management of a raised-floor data center using a three-dimensional CFD simulation in ANSYS Fluent. The HVAC system is modeled by capturing the cold airflow supplied through perforated tiles, the heat generated across the server racks, and the recirculation and bypass that reduce HVAC cooling efficiency. The performance of the data center HVAC design is evaluated through key thermal indices SHI, RHI, RTI, and RCI  to quantify cooling effectiveness and detect rack-level problems. The results show how proper HVAC airflow management and rack positioning improve server inlet temperatures and enhance the overall efficiency of the data center cooling system.

Results and Discussion

From the simulation of the HVAC thermal management, the key thermal performance indices were extracted for each rack and are presented below:

SHI

The Supply Heat Index (SHI) measures how much the cold supply air is heated by recirculated hot air before it reaches the server racks, where a lower value (below 0.2) indicates better airflow separation. In this simulation, the SHI values stay around 0.43–0.60 across the racks, showing significant hot-air recirculation reaching the rack inlets and indicating that the cold and hot airstreams are not well separated.

RCI

The Rack Cooling Index (RCI) evaluates whether the servers receive air within the recommended temperature range, where RCI_HI reflects overheating (too hot) and RCI_LO reflects over-cooling (too cold), with 100% being ideal. The results show reduced RCI HI values at several racks indicating servers exposed to temperatures above the recommended limit  while the low RCI LO values reveal that other servers receive air well below the recommended minimum, confirming uneven cooling across the racks.