0
Research Papers: Flows in Complex Systems

A Computational Study of Turbulent Airflow and Tracer Gas Diffusion in a Generic Aircraft Cabin Model

[+] Author and Article Information
Khosrow Ebrahimi

Mem. ASME
Department of Mechanical Engineering,
Villanova University,
800 Lancaster Avenue,
Villanova, PA 19085
e-mail: khosrow.ebrahimi@villanova.edu

Zhongquan C. Zheng

Fellow ASME
Department of Aerospace Engineering,
University of Kansas,
1530 W 15th Street
Lawrence, KS 66045
e-mail: zzheng@ku.edu

Mohammad H. Hosni

Fellow ASME
Department of Mechanical and
Nuclear Engineering,
Kansas State University,
Manhattan, KS 66506
e-mail: hosni@ksu.edu

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received December 26, 2011; final manuscript received July 13, 2013; published online September 6, 2013. Assoc. Editor: D. Keith Walters.

J. Fluids Eng 135(11), 111105 (Sep 06, 2013) (15 pages) Paper No: FE-11-1500; doi: 10.1115/1.4025096 History: Received December 26, 2011; Revised July 13, 2013

In order to study the capability of computational methods in investigating the mechanisms associated with disease and contaminants transmission in aircraft cabins, the computational fluid dynamics (CFD) models are used for the simulation of turbulent airflow and tracer gas diffusion in a generic aircraft cabin mockup. The CFD models are validated through the comparisons of the CFD predictions with corresponding experimental measurements. It is found that using large eddy simulation (LES) with the Werner-Wengle wall function, one can predict unsteady airflow velocity field with relatively high accuracy. However in the middle region of the cabin mockup, where the recirculation of airflow takes place, the accuracy is not as good as that in other locations. By examining different k-ε models, the current study recommends the use of the RNG k-ε model with the nonequilibrium wall function as an Reynolds averaged Navier-Stokes model for predicting the steady-state airflow velocity. It is also found that changing the nozzle height has a significant effect on the flow behavior in the middle and upper part of the cabin, while the flow pattern in the lower part is not affected as much. Through the use of LES and species transport model in simulating tracer gas diffusion, a very good agreement between predicted and measured tracer gas concentration is achieved for some monitoring locations, but the agreement level is not uniform for all the locations. The reasons for the deviations between prediction and measurement for those locations are discussed.

Copyright © 2013 by ASME
Your Session has timed out. Please sign back in to continue.

References

Occupational Outlook Handbook, 2011, Bureau of Labor Statistics, 2010-11 Edition, June 10, 2011, http:// www.bls.gov/oco/ocos107.htm.
National Transportation Statistics, 2010, Bureau of Transportation, June 10, 2011, http://www.bts.gov
Garner, R. P., Wong, K. L., Ericson, S. C., Baker, A. J., and Orzechowaki, J. A., 2004, “CFD Validation for Contaminant Transport in Aircraft Cabin Ventilation Fields,” U.S. Department of Transportation and Federal Aviation Administration, Office of Aerospace Medicine, Washington DC, Report No. DOT/FAA/AM-04/7.
Lin, C. H., Horstman, R. H., Ahlers, M. F., Sedgwick, L. M., Dunn, K. H., Topmiller, J. L, Bennett, J. S., and Wirogo, S., 2005, “Numerical Simulation of Airflow and Airborne Pathogen Transport in Aircraft Cabins–Part I: Numerical Simulation of the Flow Field,” ASHRAE Trans., 111, pp. 755–763.
Lin, C. H., Horstman, R. H., Ahlers, M. F., Sedgwick, L. M., Dunn, K. H., Topmiller, J. L, Bennett, J. S., Wirogo, 2005, “Numerical Simulation of Airflow and Airborne Pathogen Transport in Aircraft Cabins–Part II: Numerical Simulation of Airborne Pathogen Transport,” ASHRAE Trans., 111, pp. 764–768.
Lin, C. H., Horstman, R. H., Lebbin, P. A., Hosni, M. H., Jones, B. W, and Beck, B. T., 2006, “Comparison of Large Eddy Simulation Predictions With Particle Image Velocimetry Data for the Airflow in a Generic Cabin,” HVAC&R Special Issue, 12(3c), pp. 935–951. [CrossRef]
Tang, Y., Ratko, D., and Wang, X., 2009, “3D Numerical Simulation of Contaminant Distribution in an Aircraft,” Proceedings of the ASME 2009 International Mechanical Engineering Congress & Exposition, Lake Buena Vista, FL, Paper No. IMECE2009-13214.
Yan, W., Zhang, Y., Sun, Y., and Li, D., 2009, “Experimental and CFD Study of Unsteady Airborne Pollutant Transport Within an Aircraft Cabin Mock-up,” Build. Environ., 44(1), pp. 34–43. [CrossRef]
Su, M., Chen, Q., and Chiang, C. M., 2001, “Comparison of Different Sub-Grid-Scale Models of Large Eddy Simulation for Indoor Airflow Modeling,” ASME J. Fluids Eng., 123, pp. 628–639. [CrossRef]
Zhao, B., Li, X., and Yan, Q., 2003, “A Simplified System for Indoor Airflow Simulation,” Building Environ., 38(4), pp. 543–552. [CrossRef]
Zhang, T., and Chen, Q., 2007, “Novel Air Distribution Systems for Commercial Aircraft Cabins,” Building Environ., 42(4), pp. 1675–1684. [CrossRef]
Liu, W., Zhang, Z., Poussou, B. S., Liu, J., Lin, C. H., and Chen, Q., 2012, “State-of-the-Art Methods for Studying Air Distribution in Commercial Airliner Cabins,” Building Environ., 47, pp. 5–12. [CrossRef]
Abdilghanie, A. M., Collins, L. R., and Caughey, D. A., 2009, “Comparison of Turbulence Modeling Strategies for Indoor Flows,” ASME J. Fluids Eng., 131, p. 051402. [CrossRef]
Lebbin, P. A., 2006, “Experimental and Numerical Analysis of Air, Tracer Gas and Particulate Movement in a Large Eddy Simulation Chamber,” Ph.D. thesis, Kansas State University, Manhattan, KS.
Smagorinsky, J., 1963, “General Circulation Experiments With the Primitive Equations: I. The Basic Experiment,” Mon. Weather Rev., 91, pp. 99–164. [CrossRef]
Lilly, D. K., 1966, “On the Application of the Eddy Viscosity Concept in the Internal Sub-Range of Turbulences,” National Center for Atmospheric Research, Boulder, CO, Manuscript No. 123.
Fluent 6.3 User's Manual Guide, 2008, Nov. 12, 2011, http:/my.fit.edu/itresources/manuals/fluent6.3
Werner.H., and Wengle, H., 1993, “Large Eddy Simulation of Turbulent Flow Over and Around a Cube in a Plate Channel,” Selected Papers from the 8th Symposium on Turbulent Shear Flows, F.Durst, R.Friedrich, B. E.Launder, U.Schumann, and J. H.Whitelaw, eds., Springer, New York, pp. 155–168.
Bird, B. R., Steward, W. E., and Lightfoot, E. N., 2001, Transport Phenomena, 2nd ed., Wiley, New York.
Landahl, M. T., and Mollo-Christensen, M., 1992, Turbulence and Random Processes in Fluid Mechanics, 2nd ed., Cambridge, UK.
Smirnov, R., Shi, S., and Celik, I., 2001 “Random Flow Generation Technique for Large Eddy Simulations and Particle-Dynamics Modeling,” ASME J. Fluids Eng., 123, pp. 359–371. [CrossRef]
Launder, B. E., and Spalding, D. B., 1972, Lectures in Mathematical Models of Turbulence, Academic, New York.
Yakhot, V., and Orszag., S. A., 1986, “Renormalization Group Analysis of Turbulence: I. Basic Theory,” J. Sci. Comput., 1(3), pp. 1–51. [CrossRef]
Shih, T. H., Lieu, W. W., Shabbir, A., Yang, Z., and Zhu, J., 1995, “A New k-ε Eddy-Viscosity Model for High Reynolds Number Turbulent Flows–Model Development and Validation,” Comput. Fluids, 24(3), pp. 227–238.
Padilla, A. M., 2008, “Experimental Analysis of Particulate Movement in a Large Eddy Simulation Chamber,” M.S. thesis, Kansas State University, Manhattan, KS.

Figures

Grahic Jump Location
Fig. 1

Full scale generic aircraft half-cabin mockup model [14]

Grahic Jump Location
Fig. 2

Dimensions of the generic cabin model and the locations of PIV monitoring windows on the cabin central plane (units in mm) [25]

Grahic Jump Location
Fig. 3

A schematic view of the experimental setup for tracer gas measurements. The xy view of the setup shows the tracer gas sampling points above and below the injection tube (units in mm).

Grahic Jump Location
Fig. 4

The results of uncertainty study for the cabin with full-height nozzle when x component of velocity data are predicted for the location 5 of the PIV measuring windows. The PIV data were produced by Lebbin [14].

Grahic Jump Location
Fig. 5

The converging behavior of x-component of velocity deviations with respect to the corresponding prediction from the finest grid for the location 5 of PIV measuring windows

Grahic Jump Location
Fig. 6

The converging behavior of x-component of velocity deviations with respect to the corresponding prediction from the finest grid from steady RANS solution for all the PIV measuring windows

Grahic Jump Location
Fig. 7

Contours of airflow velocity magnitude on the central plane of the cabin with full-height inlet nozzle at six different flow-times: 3.6, 9.8, 16.6, 38.1, 43.7, and 123.7 s. These contours show how the two main flow circulations in the upper and lower regions of the cabin are gradually developed.

Grahic Jump Location
Fig. 8

Comparison of the predicted values (this study), PIV measurements [14] and predictions by Lin et al. [6] for the airflow velocity data corresponding to the location 1 of the cabin with full height nozzle

Grahic Jump Location
Fig. 9

Study the effect of type of monitoring surface on the LES velocity predictions through the comparison of LES predicted and PIV measured velocity data at location 2 of the cabin with full height nozzle [14]

Grahic Jump Location
Fig. 10

Comparison of the steady RANS predictions for the x-component of velocity data with the corresponding time dependent PIV data [14] at location 3 of the PIV measuring window

Grahic Jump Location
Fig. 11

Comparison of the steady RANS predictions for the x-component of velocity data with the corresponding time dependent PIV data [14] at location 5 of the PIV measuring window

Grahic Jump Location
Fig. 12

Velocity vectors at each of the five PIV measurement locations at different flow times. As the time progresses the center of flow circulation moves around and approaches the center of PIV location 3.

Grahic Jump Location
Fig. 13

Comparison of the LES predictions and PIV measurements [14] for the x component of velocity data corresponding to the location 1 of the cabin with half-height nozzle

Grahic Jump Location
Fig. 14

Comparison of the LES prediction and PIV measurements [14] for velocity data corresponding to the location 3 of the cabin with half- height nozzle

Grahic Jump Location
Fig. 15

Comparison of the sub-grid and resolved turbulent kinetic energy on the central plane of the cabin (In order to show the discrepancy between the magnitude of resolved and sub-grid turbulent kinetic energy, the same scale range is used for the contours)

Grahic Jump Location
Fig. 16

Comparison of the LES predictions for airflow velocity at the PIV location 3 corresponding to three different levels of turbulence intensity at the inlet of the cabin with half-height nozzle

Grahic Jump Location
Fig. 17

Comparison of the LES predictions for airflow velocity at the PIV location 3 in the cabin with half-height nozzle resulted from two different time step sizes: 0.01 and 0.05 s

Grahic Jump Location
Fig. 18

The effect of decreasing the cabin nozzle height through a comparison between the PIV measured velocity data [14] corresponding to the location 5 of the PIV measuring windows for two cases of full and half-height nozzle

Grahic Jump Location
Fig. 19

The effect of decreasing the cabin nozzle height through a comparison between the PIV measured velocity data [14] corresponding to the location 3 of the PIV measuring windows for two cases of full and half-height nozzle

Grahic Jump Location
Fig. 20

3D schematic of the unstructured grid (for the generic cabin with the injection tube) used in CFD simulation of the carbon dioxide diffusion in the generic cabin model

Grahic Jump Location
Fig. 21

Comparison between the times averaged predictions and measurements of dimensionless CO2 concentration [14] for the sampling points located along the x-axis above the injection tube

Grahic Jump Location
Fig. 22

Comparison between the times-averaged predictions and measurements of dimensionless CO2 concentration [14] for the sampling points located along the x-axis below the injection tube

Grahic Jump Location
Fig. 23

Tracer gas (CO2) propagation inside the cabin at different flow times

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In