This product is a zip file that contains files that consist of PowerPoint slides synchronized with the audio-recording of the speaker, PDF files of the slides, and audio only (mp3 format) as noted.

Humans are on a never-ending quest for speed; this desire even applies to our analysis tools. This seminar session will look at various approaches being taken for faster solutions in Computational Fluid Dynamics (CFD) analysis. The increased speed of solution will allow for wider usage, along with the ability to look at larger and more complex models. The urban environment provides an extremely complex and unpredictable flow pattern that is tamed through machine learning. Similarly, artificial intelligence can be used to speed up air distribution models for indoor spaces.

1. Artificial Intelligence for Indoor Airflow Simulation< /br>Wangda Zuo, Ph.D., Member, University of Colorado, Boulder, CO
2. Using Wind Tunnel Measurements to Validate External Flow Simulations< /br>Goncalo Pedro, Ph.D. and Duncan Phillips, Ph.D., P.E., Member, Rowan Williams Davies & Irwin, Guelph, ON, Canada
3. Accelerating RANS Simulations Using a Data-Driven Framework for Eddy-Viscosity Emulation< /br>Himanshu Sharma, Ph.D., Pacific Northwest National Laboratory, Richland, WA
4. Estimating Urban Wind Power Potential Based on Machine Learning with City Fast Fluid Dynamics Training Data< /br>Leon Wang, Concordia University, Montreal, QC, Canada