Improvement of Mathematical Models for Plume Rise and Drift Deposition from Cooling Towers

CFD prediction of cooling tower drift

“…The authors observed that even for this ''best available'' data the statistical significance of the data may be questionable considering only 10-50 droplets were typically obtained on the sensitive measurement papers over a 4-h period. Policastro et al [27,28] developed the SACTI model specifically to improve drift prediction, yet they concluded for a model to predict within a factor of 3 of measured data can be considered successful prediction. (Success within a factor of 3 means the prediction is within the range encompassed by one-third and three times the measured value, but samples where either the measured value is zero or the model prediction is zero are not counted.…”

“…Staff at the Argonne National Laboratory working for the Electric Power Research Institute developed this model to predict plume and drift behavior from multiple tower and cell configurations of natural and mechanical draft cooling towers [27,28]. The SACTI Model incorporates a number of significant improvements over previous analytic models.…”

On the influence of psychrometric ambient conditions on cooling tower drift deposition

“…They constructed a very simple binormal plume dispersion model to update the probability of a cooling tower infection given a case of Legionella. Brown et al, 1999, presented Policastro et al (1981)). Unfortunately, neither of these approaches allows including the influence of nearby large buildings on the flow fields, which affect the local building downwash and cooling tower drift.…”

Protocol for CFD prediction of cooling-tower drift in an urban environment

“…Unfortunately, most CFD methods which include building configurations and adjust for enhanced turbulence and downwash in the building wakes are not conveniently applied to the superposition of such a wide range of situations due to computational time and economic constraints. On the other hand, computationally efficient analytic codes such as SACTI [2] which do permit seasonal and annual weighting of air pollution drift and deposition predictions do not incorporate the full effects of building wakes.…”

“…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) [1] or SACTI (Seasonal-Annual Cooling Tower Impact) [2] codes; however, these codes are less suitable when cooling towers are located midst taller structures and buildings. A computational fluid dynamics (CFD) code including Lagrangian prediction of the gravity driven but stochastic trajectory descent of droplets was previously considered and compared to data from the 1977 Chalk Point Dye Tracer Experiment [3].…”