CFD Prediction of Cooling Tower Drift

Drift of small water droplets from mechanical and natural draft cooling tower installations can contain water treatment chemicals such that contact with plants, building surfaces and human activity can be hazardous. Prediction of drift deposition is generally provided by analytic models such as the US Environmental Protection Agency approved ISCST3 (Industrial Source Complex Short Term Version 3) or SACTI (Seasonal-Annual Cooling Tower Impact) codes; however, these codes are less suitable when cooling towers are located midst taller structures and buildings. A computational fluid dynamics code including Lagrangian prediction of the gravity driven but stochastic trajectory descent of droplets is considered and compared to data from the 1977 Chalk Point Dye Tracer Experiment. The CFD (computational fluid dynamics) program predicts plume rise, surface concentrations, plume centerline concentrations and surface drift deposition within the bounds of field experimental accuracy.

[1]  K. S. Rao.  Plume concentration algorithms with deposition, sedimentation, and chemical transformation , 1983 .

[2]  J. D. McAlpine,et al.  Using CFD to Study Air Quality in Urban Microenvironments , 2004 .

[3]  Robert N. Meroney,et al.  Surface Pattern Comparability of Wind-Tunnel Simulations of the Thorney Island Dense Gas Dispersion Trials , 1989 .

[4]  I P Castro,et al.  NUMERICAL WIND ENGINEERING: THE WAY AHEAD ? , 1999 .

[5]  Ronald L. Petersen.  ISC3 and PRIME Versus Wind Tunnel Observations for a Power Plant with Hyperbolic Cooling Towers , 2004 .

[6]  K. S. Rao,et al.  Analytical solutions of a gradient-transfer model for plume deposition and sedimentation , 1981 .

[7]  Donald J. Bergstrom,et al.  A study on the effects of wind on the air intake flow rate of a cooling tower: Part 2. Wind wall study , 1996 .

[8]  Michael Schatzmann,et al.  An advanced integral model for cooling tower plume dispersion , 1984 .

[9]  J. Anderson,et al.  Computational fluid dynamics : the basics with applications , 1995 .

[10]  Dafydd Gibbon,et al.  1 User’s guide , 1998 .

[11]  Michael Schatzmann,et al.  Wind-tunnel and numerical modeling of flow and dispersion about several building shapes , 1999 .

[12]  Steven R. Hanna,et al.  Drift modeling and monitoring comparisons , 1978 .

[13]  Dennis Fowler,et al.  Net News , 1999, The Lancet.

[14]  D. Carruthers,et al.  Comparisons between FLUENT and ADMS for atmospheric dispersion modelling , 2004 .

[15]  John F. Kennedy,et al.  Plume recirculation and interference in mechanical draft cooling towers , 1974 .

[16]  Steven R. Hanna.  Fog and drift deposition from evaporative cooling towers , 1974 .

[17]  Donald J. Bergstrom,et al.  Numerical study of wind flow over a cooling tower , 1993 .

[18]  William H. Snyder,et al.  Wind tunnel investigation of the effects of a rectangular-shaped building on dispersion of effluents from short adjacent stacks , 1982 .

[19]  J. Cermák.  Wind climate in cities , 1995 .

[20]  Estimating Odor Impact with Computational Fluid Dynamics , 2004 .

[21]  Cheng-Hsin Chang,et al.  Numerical and physical modeling of bluff body flow and dispersion in urban street canyons , 2001 .

[22]  Michael W. Golay,et al.  Comparison of methods for measurement of cooling tower drift , 1983 .

[23]  J. C. Ovard,et al.  Cooling tower drift: its measurement, control, and environmental effects , 1973 .

[24]  L L Schulman,et al.  Development and Evaluation of the PRIME Plume Rise and Building Downwash Model , 2000, Journal of the Air & Waste Management Association.

[25]  Theodore Stathopoulos,et al.  Computational wind engineering: Past achievements and future challenges , 1997 .

[26]  Chris Letchford,et al.  Comparison of numerical and wind tunnel simulation of wind loads on smooth, rough and dual domes immersed in a boundary layer , 2002 .