CFD Analysis of the Effect on Buoyancy Due to Terrain Temperature Based on an Integrated DEM and Landsat Infrared Imagery

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Manuel Julio García
Pierre Boulanger
Juan Duque
Santiago Giraldo

Keywords

Buoyancy, Landsat, Digital Elevation Models, Computational Fluid Dynamics

Abstract

This paper deals with the influence of concrete structures on atmospheric temperatureand the convection winds generated in the Aburra Valley in Medell´ın,Colombia. This area is characterised by low wind velocities with a high industrydensity. A digital elevation model was used from the Radar ShuttleTopography Mission and post-processed in order to obtain a valid volumetricCFD domain. The construction process includes hole-filling due to imperfectionsin the original radar data, decimation of the original cloud-of-points to reduce the excess of detail in regions with low curvature, and the introductionof a volume of air over the terrain surface (CFD domain). Landsat satellitedata was used to set the terrain temperatures for various material compositions.The converted infrared image was then registered into the CFD domainusing an interpolation technique.

Navier–Stokes Equations were solved for buoyant, turbulent flow of compressiblefluids accounting for convection and heat transfer effects. Simulationincludes buoyancy and turbulence flow through the k–epsilon model using thehigh-performance computing facilities of Westgrid (Western Canada ResearchGrid). Preliminary results show wind distributions that compare to the oneobserved at low–altitude in the region.

PACS: 47, 95.55.Rg, 07.57.Kp

MSC: 76, 97M50 

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References

[1] A. Walton, A.Y.S. Cheng and W.C. Yeung. Large–eddy simulation of pollution dispersion in an urban street canyon–part i: comparison with field data. Atmospheric Environment, ISSN 1352–2310, 36(22), 3601–3613 (2002).

[2] Paolo Monti and Giovanni Leuzzi. A numerical study of mesoscale airflow and dispersion over coastal complex terrain. International Journal of Environment and Pollution, ISSN 0957–4352, 25(1-4), 239–250 (2005).

[3] Paul Dawson, David E. Stock and Brian Lamb. The numerical simulation of airflow and dispersion in three-dimensional atmospheric recirculation zones. Journal of applied meteorology, ISSN 0894–8763, 30(7), 1005–1024 (1991).

[4] Si–Wan Kim, Chin–HohMoeng, Jeffrey C.Weil, andMary C. Barth. Lagrangian particle dispersion modeling of the fumigation process using large–eddy simulation. Journal of athmosferic Sciences, ISSN 0022–4928, 62(6), 1932–1946 (2005).

[5] P. W. Cleary and M. Prakash. Discrete element modelling and smooth particle hydrodynamics: potential in the environmental sciences. Phil. Transaction of Royal Society, 362, 2003–2030 (2004).

[6] Xiuling Wang, Darrell Pepper, Yitung Chen and Sean Hsieh. A three dimensional finite element model for emergency response. 43rd AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 2005.

[7] L. B. Hansen, N. Kamstrup and B. Ulf Hansen. Estimation of net short-wave radiation by the use of remote sensing and a digital elevation model: a case study of a high arctic mountainous area. International Journal of Remote Sensing, ISSN 0143–1161, 23(21), 4699–4718 (2002).

[8] NGA and NASA. Shuttle radar topography mission (srtm). http://www2.jpl. nasa.gov/srtm/, 2000.

[9] Pat L. Scaramuzza, Brian L. Markham, Julia A. Barsi, and Ed Kaita. Landsat–7 etm+ on–orbit reflective–band radiometric characterization. IEEE Transactions on geoscience and remote sensing, ISSN 0196–2892, 42(12), 2796–2809, (December 2004).

[10] Landsat Project Science Office. Landsat 7 Science Data Users Handbook. Goddard Space Flight Center, NASA, Washinghton, 2002.

[11] P. Dash, F. M. Gottsche, F. S. Olesen and H. Fischer. Land surface temperature and emissivity estimation from passive sensor data: theory and practice–current trends. International Journal of Remote Sensing, ISSN 0143–1161, 23(13), 2563–2594 (2002).

[12] A. J. Prata, V. C. Coll, J. A Sobrino, and C. Ottle. Thermal remote sensing of land surface temperature from satellites: Current status and future prospects. Remote Sensing Reviews, ISSN 0275–7257, 12, 175–224 (1995).

[13] G. B. Franca and A. P. Cracknell. Retrieval of land and sea surface temperature using noaa-11 avhrr data in north-eastern brazil. International Journal of Remote Sensing, 15(8), 1695–1712 (1994).

[14] M. A. Friedl. Forward and inverse modeling of land surface energy balanceusing surface temperature measurements. Remote Sensing of Environment, ISSN 0034-4257, 79(2–3), 344–354 (2002).

[15] Manuel J. Garc´ıa. Fixed Grid Finite Element Analysis in Structural Design and Optimization. PhD Thesis, The University of Sydney, Sydney, 1999.

[16] Joachim Sch¨oberl. NETGEN, 4.3 edition, http://www.hpfem.jku.at/netgen. Johannes Kepler University Linz, Austria, 2005.

[17] T. Schmugge, S. J. Hook, and C. Coll. Recovering surface temperature and emissivity from thermal infrared multispectral data. Remote Sensing of Envi- ronment, ISSN 0034–4257, 65(2), 121–131 (1998).

[18] D.A. Artis and W. H. Carnahan. Survey of emissivity variability in thermography of urban areas. Remote Sensing of Environment, ISSN 0034–4257, 12, 313–329 (1982).

[19] A. Van de Griend and M. Owe. On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, ISSN 0143–1161, 14(6), 1119–1131 (1993).

[20] Jinqu Zhang, Yupeng Wang and Yan Li. A C++ program for retrieving land surface temperature from the data of landsat tm/etm+ band6. Computers & Geosciences, ISSN 0098–3004, 32(10), 1796–1805 (2006).

[21] Michael F. Jansinski. Sensitivity of the normalized difference vegetation index to subpixel canopy cover, soil albedo, and pixel scale. Remote Sensing of Environment, ISSN 0034-4257, 32, 169–187 (1990).

[22] National Oceanic and Atmospheric administration and NASA and United States Airforce. U.S. Standard atmoshpere, 1976.

[23] H.G. Weller, G. Tabor, H. Jasak and C. Fureby. A tensorial approach to computational continuum mechanics using object orientated techniques. Computers in Physics, 12(6), 620–631 (1998).

[24] Hrvoje Jasak, Henry G. Weller and Niklas Nordin. In-cylinder CFD simulation using a C++ object-oriented toolkit. SAE technical papers, document number 2004-01-0110, 2004.

[25] Erik N. Rasmussen, Robert Davies-Jones and Ronald L. Holle. Terrestrial photogrammetry of weather images acquired in uncontrolled circumstances. Journal of atmospheric and oceanic technology, ISSN 0739–0572, 20(12), 1790–1803 (2003).

[26] Harold D. Orvill and A. Richard Kassander Jr. Terrestrial Photogrammetry of Clouds. Journal of the Atmospheric Sciences, ISSN 0022–4928, 18(5), 682–687 (1961).

[27] R. M. Hardesty, R. M. Banta, M. J. Post and W. L. Eberhard. A decade of atmospheric studies using a pulsed CO2 Doppler lidar. Geoscience and Remote Sensing Symposium, 1994. IGARSS ’94. Surface and Atmospheric Remote Sensing: Technologies, Data Analysis and Interpretation., International, IEEE, ISBN 0–7803–1497–2, 2, 926–928 (1994).

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