Heated metal particles and sparks produced mechanically or through power grid failure represent significant wildland fuel ignition risk. Characterizing conditions under which a heated particle may lead to a successful ignition in the wildland is thus a primary concern for fire prevention. With the purpose of gaining greater understanding of metal particle and fuel properties that lead to successful wildland fire ignition, we conduct bench scale experiments that characterize the influence of key fire drivers on ignition behavior.
Computer vision approaches to fire behavior characterization
Imagery from flames provide data critical to the understanding of fire behavior. Our group develops innovative approaches to characterize flame geometry imagery through methods like computer vision. Our most recent work focuses on developing algorithms to obtain rate of spread from videos of spreading fires.
Fires in the wildland urban interface increasingly place people, property and the environment at risk. With global increases in wildfire activity, understanding how fires spread from the wildland to the built environment and between adjacent structures is critical to protecting life and property. In collaboration with materials scientists and engineers, we examine the flammability of novel materials designed to provide sustainable and affordable building solutions.
Fire Wind Tunnel
Wind tunnels have been an essential tool for fire behavior modeling since the 1930's. They provide controlled conditions to test the behavior of wind driven fires for countless applications. Our group will welcome a fire behavior wind tunnel to our laboratory starting Spring 2021. The wind tunnel has been designed by UC Merced Senior Design students and members of the Cobian-Iñiguez laboratory.