Sponsors

Rodman Linn, Los Alamos National Laboratory, United States

 

Short biography: Rodman (Rod) Linn is a Senior Scientist, Laboratory Fellow and FIRE Team Leader in the Earth and Environmental Sciences Division at Los Alamos National Laboratory (LANL). Rod is also a professor in the Halıcıoğlu Data Science Institute at the University of California San Diego and serves as the Associate Director for Fire Science for the WIFIRE lab at UC San Diego.  Dr. Linn leads LANL efforts to use next-generation process-based wildfire models for the study of fundamental wildfire behavior, evaluation of prescribed fire tactics, understanding influences of complex environmental conditions on fire behavior, and wildfire’s interaction with other landscape disturbances such as insects or drought.  Rod was the principal investigator for a process-based coupled fire/atmosphere model, FIRETEC, and lead developer of the fast-running coupled fire-atmosphere model QUIC-Fire.  Rod has a PhD in Mechanical Engineering from New Mexico State University and has also led effort tied to wind energy harvesting, dispersion, and vegetation-influenced atmospheric turbulence.

 

Short Summary: Wildfires pose a threat to life, property and critical infrastructure, but wildland fire is an unavoidable part of the natural environment.  To improve our ability to cope with wildfires, anticipate their impacts, and even safely use prescribed fires is important to better understand how fires interact with their surrounding environment. Numerical modeling is one vehicle to investigate and explore aspects of fire phenomenology and in scenarios where we have confidence in modeling skill, numerical models can even assist with decision support. Recent fire behavior research has illustrated the dynamic multi-scale coupling between fire and surrounding atmosphere, but what is often overlooked is the critical role the fire-atmosphere coupling has in connecting fire behavior to its surroundings.  When we look beyond free-burning head fires on homogenous landscapes, fire/atmosphere feedbacks play critical roles in a fires response to many elements of the fire environment through a phenomena referred to as wildland fire entrainment (WFE).  To enable fire modeling to help increase our understanding of fire behavior and even provide decision support for an increasingly complex number of fire scenarios, next-generation wildland fire modeling tools must be able to account for WFE.  WFE is influenced by the vegetation structure, nearby topography, ambient wind conditions, and especially the configuration of the fire itself.  The effectiveness and consequences of fuels management activities, safe use of prescribed fires, interaction of multiple fires, influence of fire shape, and even many of the influences of topography are tied to WFE.  Capturing the influences of WFE in fire activity requires being able to represent the fire-influenced three-dimensional wind field surrounding the fire and the influences of the fire-environment on this field, which feedback on the fire.  Computational fluid mechanics (CFD) is a natural approach to representing WFE as it can capture the influences of heterogeneous buoyancy patterns, vegetation drag, and topographic features.  However, CFD calculations with adequate resolution to capture vegetation structure and domains large enough to simulate fire behavior over landscape scales can be computationally expensive. Therefore, investigators are looking to reduced-order methodologies to capture fire/atmosphere feedbacks and WFE.