Revolutionizing Flow, Heat and Dispersion Predictions over Complex Urban Environments


PI: Dan Li, Assistant Professor of Earth and Environment, CAS
Co-PI: Kia Teymourian, Assistant Professor of Computer Science, MET

As more and more of the population lives in the city, it is more and more challenging to understand and make predictions of heat transfer and dispersion of air pollutants over urban environments. The team will use and develop the machine learning models to execute the project. It is less computationally expensive and much faster, and may better capture the small-scale variabilities. The new model could help to identify key locations for sensor deployment. The investigators aim to submit full-blown proposals to the Army Research Office at the Department of Defense and/or the Environmental Sustainability Program at the National Science Foundation.