Modelling of the Plume Rise Phenomenon Due to Warehouse Or Pool Fires Considering Penetration of the Mixing Layer
Boot, Hans
Ruiz Perez, Sonia

How to Cite

Boot H., Ruiz Perez S., 2022, Modelling of the Plume Rise Phenomenon Due to Warehouse Or Pool Fires Considering Penetration of the Mixing Layer, Chemical Engineering Transactions, 91, 127-132.


Smoke plumes containing toxic combustion products will initially rise due to the density difference between the hot combustion products and the ambient air. This density difference is caused by the fact that the plume temperature is significantly higher than the air’s ambient temperature. The theory behind this plume rise phenomenon foresees that there will be a height at which the finally cooled down plume will be in equilibrium with the density of the air at that height, leading to a maximum plume height. The trajectory of the plume and the hazard distances to specific concentration threshold levels will be mainly influenced by the windspeed, atmospheric stability class and the fire’s convective heat production, where the combination of these parameters leads to potential penetration of, or even reflection against the mixing layer.
Typical models that describe the mathematics behind rising of hot plumes include the effects of atmospheric turbulence. However, the plume’s potential penetration of the mixing layer and reflection of the plume is often neglected.
The present study has led to the development of a dedicated model, implemented in Gexcon’s software package EFFECTS, to simulate the plume rise phenomenon due to warehouse and pool fires. The model is based on Briggs’ study of the plume rise phenomenon (Briggs, 1969), the theory of the Yellow Book (Yellow Book, 1992) and uses Mills’ correction for burning fires (Mills, 1987). This model not only calculates the maximum height, plume path, and concentration threshold contours of toxic combustion products at any height, but also includes the effects of the penetration and reflection phenomena. The model has been validated against the experimental data presented by (Hall et al., 1995) and Briggs (1969), as well as other validated mathematical models, to ensure its good performance.