Detection and mitigation of contaminant transport in commercial aircraft cabins

Tengfei Zhang, Purdue University

Abstract

As more people are traveling by air and some of them have medical conditions, it spawns more concerns about the cabin environment. Since air cabins are packed, it seems that infectious diseases could be easily transmitted. Moreover, the air transportation system may be an attractive target for terrorist attacks by using chemical or biological agents. Hence, it is important to develop technologies that can detect and mitigate air contamination. With the above objectives, this thesis uses Computational Fluid Dynamics (CFD) as the main tool to study contaminant transport in aircraft cabins. Since a CFD model might use approximations, the CFD program was first validated with experimental data obtained in several enclosed environments including an aircraft cabin mockup. Then CFD was used to study airborne contaminant sensor placement in a nine-row section of a twin-aisle aircraft cabin. By assuming different contaminant release rates, time and sensor sensitivities, the optimal sensor location and number were determined. The available information from sensors was used to identify contaminant sources with inverse CFD modeling. To make inverse problems solvable with numerical stability, this study proposed to solve the quasi-reversibility (QR) equation by replacing the second-order diffusion term with a fourth-order stabilization term in the governing equation, or solve the pseudo-reversibility (PR) equation by reversing airflows. Further, an under-floor displacement and a personalized air distribution system were developed to mitigate air contamination. The thesis found that the CFD program with the RNG k-ϵ model can reasonably well predict airflow and contaminant dispersion in an aircraft cabin. The optimal location for a contaminant sensor is in the middle of the ceiling in a cross section. A sensor associated with a multiple-point sampler in each passenger row can significantly improve contaminant detectivity. With limited available sensor information, both quasi-reversibility and pseudo-reversibility methods can be numerically stable to identify contaminant source locations and strengths. However, these sensors should be properly placed in the down stream of the contaminant sources. Lastly, it found that the personalized air distribution system can remarkably reduce contaminant exposure without draft risk and therefore the system is recommended for potential use in commercial airliner cabins.

Degree

Ph.D.

Advisors

Chan, Purdue University.

Subject Area

Mechanical engineering|Environmental engineering

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