Optimization of fuel consumption is pivotal in truck platooning, but analysis of computational fluid dynamics (CFD) is expensive. This research proposes an AI-based surrogate model to enable near real-time optimization of platoon configurations.
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Dr. Meidani is an Assistant Professor of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign. He earned his Ph.D. in Civil Engineering and M.S. in Electrical Engineering from the University of Southern California in 2012. Prior to joining UIUC, he was a postdoctoral research associate in the Department of Aerospace and Mechanical Engineering at USC in 2012 and in the Scientific Computing and Imaging Institute at the University of Utah in 2013. He is the recipient of the NSF CAREER Award for studying fast computational models for energy-transportation systems. He is also the recipient of the student paper award in probabilistic methods at the ASCE Engineering Mechanics Institute Conference in 2012. His research interests are uncertainty quantification, scientific machine learning, and decision-making under uncertainty.
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