Math Modeling Seminar: Fire/Atmosphere Modeling: Opportunities and Challenges

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math modeling seminar rodman linn

Math Modeling Seminar
Fire/Atmosphere Modeling: Opportunities and Challenges

Dr. Rodman Linn
Senior Scientist
Computational Earth Science Group
Earth and Environmental Sciences Division
Los Alamos National Laboratory

You may attend this lecture in person at 2305 Gosnell Hall or virtually via Zoom.
If you’d like to attend virtually, you may register here for Zoom link.


Wildland fires continue to pose risk to lives and property and thus practitioners and scientists continue to work to gain better understanding and ability to predict their behavior. Simultaneously, wildland fire decision makers are working towards more proactive approaches to managing the risk of wildfire, such as fuels treatments and prescribed fire. Executing such measures requires the ability to explore the ramification of such treatments as well as ensure that prescribed fires will meet their objectives. Experiments and observations have demonstrated that the two-way feedbacks between fires and atmosphere play critical roles in determining how fires spread or if they spread. Advancements in computing and numerical modeling have generated new opportunities for the use of models that couple process-based wildfire models to atmospheric computational fluid dynamic (CFD) models. These process-based coupled fire/atmosphere models, which simulate critical processes such as heat transfer, buoyancy-induced flows and vegetation aerodynamic drag, are not practical for operational faster-than-real-time fire prediction due to their computational and data requirements. However, these process-based coupled fire-atmosphere models make it possible to represent many of the fire-atmosphere feedbacks and thus have the potential to complement experiments, add perspective to observations, bridge between idealized-fire scenarios and more complex and realistic landscape fire scenarios, allow for sensitivity analysis that is impractical through observations and pose new hypothesis that can be tested experimentally. Additionally, coupled wildfire/atmosphere modeling opens new possibilities for understanding the sometime counterintuitive impacts of fuel management and exploring the implications of various prescribed fire tactics. Certainly, there need to be continued efforts to validate the results from these numerical investigations, but, even so, they suggest relationships, interactions and phenomenology that should be considered in the context of the interpretation of observations, design of fire behavior experiments, development of new operational models and even risk management. One additional goal for the use of CFD based coupled fire/atmosphere models is to highlight the necessary phenomenology that is necessary in fast running models.

Speaker Bio:
Dr. Rodman Linn is a senior scientist in the Earth and Environmental Sciences Division at Los Alamos National Laboratory and a Professor in the Halıcıoğlu Data Science Institute at the University of California San Diego (Joint UCSD/LANL appointment). For over two decades, he has served as principal investigator for a process-based coupled fire/atmosphere model, FIRETEC. 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. Dr. Linn is the co-lead developer of the new fast-running coupled fire-atmosphere model QUIC-Fire. Read more here.

Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.

The Math Modeling Seminar will recur each week throughout the semester on the same day and time. Find out more about upcoming speakers on the Mathematical Modeling Seminar Series webpage.
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Nathan Cahill
Event Snapshot
When and Where
October 26, 2021
2:00 pm - 2:50 pm
Room/Location: See Zoom Registration Link

Open to the Public

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