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In the present study, the laws of smoke diffusion during forest fires are determined using the general principles of fluid mechanics and dedicated data obtained experimentally using an “ ad hoc ” imaging technology. Experimental images mimicking smoke in a real scenario are used to extract some “statistics”. These in turn are used to obtain the “divergence” of the flow (this fluid-dynamic parameter describing the amount of air that converges to a certain place from the surroundings or vice versa). The results show that the divergence of the smoke depends on the outside airflow and finally tends to zero as time passes. Most remarkably, compared with clouds and fog, smoke has a unique dynamic time-evolution curve. The present study demonstrates that as long as image processing technology and intelligent monitoring technology are used to monitor the gas flow in a forest, the occurrence of forest fires can be quickly diagnosed.

Using Image Processing Technology and General Fluid Mechanics Principles to Model Smoke Diffusion in Forest Fires