CFD modeling of airflows and contaminant transport in an aircraft cabin

Abstract

There are an increasing number of passengers undertaking air travels on commercial airliners throughout the world annually. During the flights, passengers are possibly exposed to different contaminants such as bacterial and CO 2 from other passengers. As airliner cabins have high occupant density and flights can last from 1 to 20 hours, transport of contaminant could have serious impact on both passengers and aircraft crew. It is important to understand airflows and contaminant transport inside the aircraft cabin in order to reduce the negative impacts. Current aircraft cabin airflows can be analyzed by two different methods, experimental measurements and computer modeling. With the rapid increase in computer power, computer modeling is becoming more popular in study of aircraft cabin flows. Computational fluid dynamics (CFD) is the most used modeling approach since it is relatively inexpensive, fixable and able to obtain high level resolution results.The main scope of this research is to develop a frame work to simulate airflows and trace contaminant transport in an aircraft cabin using CFD. The predicted airflows and contaminant concentration are then used to train an Artificial Intelligent (AI) system. This trained AI system will be able to trace back the possible source of the contaminant once the transmission of contaminant happens in the aircraft cabin, e.g. the severe acute respiratory syndrome (SARS) transmission in a flight in Hong Kong in 2003.This paper reports the development of the CFD model of aircraft cabin flows and the transport of SARS in the cabin. In the project, the first milestone is to produce a section of an aircraft cabin of Airbus 320 using ANSYS/Design-Modeller. The cabin model includes half of the cabin with 7 rows of seats. The second milestone is to mesh the geometry using ANSYS/Meshing. The third milestone is to set up boundary conditions for both airflows and contaminant in ANSYS/CFX. The final objective is to solve the solutions in CFD and transfer the CFD results to an AI system developed by the authors. Some CFD predictions of the airflow patterns and contaminant transport in the cabin are reported in the paper. It is found that the flow in the cabin is quit complex. There is a weak longitudinal flow that plays a significant role in the spread of contaminant in the cabin. Some preliminary results of the AI system are also presented in the paper.