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Colloquium on Digital Transformation Science

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  October 28December 2, 3 pm CT Deep Learning to Replace, Improve, or Aid CFD analysis in Built Environment Applications Quantifying Carbon Credit over U.S. Midwestern Cropland Using AI-Based Data-Model Fusion Kaiyu Guan, Blue Waters Associate Professor of Natural Resources and Environmental Sciences, University of Illinois at Urbana-ChampaignWei Liu, Assistant Professor of Civil and Architectural Engineering, KTH Royal Institute of Technology REGISTER FOR ZOOM WEBINAR Fast and accurate airflow simulations in the built environment are critical to provide acceptable thermal comfort and air quality to occupants. Computational Fluid Dynamics (CFD) offers detailed analysis on airflow motion, heat transfer, and contaminant transport in indoor environments, as well as wind flow and pollution dispersion around buildings in urban environments. However, CFD still faces many challenges, mainly in terms of computational expense and accuracy. With the increasing availability of large amounts of data, data-driven models are starting to be investigated to either replace, improve, or aid CFD simulations. More specifically, the abilities of deep learning and Artificial Neural Networks (ANN) as universal non-linear approximators, handling of high dimensionality fields, and lower computational expense are very appealing. In built environment research, deep learning applications to airflow simulations show the ANN as a surrogate, replacement for expensive CFD analysis. Surrogate modeling enables fast or even real-time predictions, but usually at the cost of degraded accuracy. This talk presents the deep learning interactions with fluid mechanics simulations in general and proposes different techniques other than surrogate modeling for built environment applications. There are promising methods largely yet to be explored in the built environment scene. In this talk, we will first provide an overview of the background on agriculture carbon credit, and then focus on the quantification of field-level carbon credit, including the issues in the conventional methods, and our proposed “System-of-Systems” solution that leverages various sources of sensing data, process-based modeling, and AI-based Model-Data Fusion methods. to achieve field-level, accurate, scalable and cost-effective quantification. Kaiyu Guan is the Blue Waters Professor at the University of Illinois at Urbana-Champaign. He got his PhD at Princeton University and was a postdoctoral scholar at Stanford University. His research group uses satellite data, computational models, field work, and AI to address how climate and human practices affect crop productivity, water resource availability, and ecosystem functioning. He has published over 100 papers in leading scientific journals and leads over 15 federal grants from NASA, NSF, DOE, and USDA. He is the recipient of an NSF CAREER Award, the NASA New Investigator Award, the AGU Early Career Award in Global Environmental Change, and was a Blavatnik National Award for Young Scientists Finalist. Wei Liu is an assistant professor at the Division of Sustainable Buildings, Department of Civil and Architectural Engineering, KTH Royal Institute of Technology in Stockholm, Sweden. Liu's current research topics include Indoor Air Quality and Air Distribution, Inverse Design and Control of indoor environments, and Data-Driven/AI-based Smart Buildings. He has published 47 journal papers and 30 conference papers. Liu is an Outstanding Winner and recipient of INFORMS Award from the Mathematical Contest in Modeling 2019, Best Paper Award from ROOMVENT 2018, Bilsland Dissertation Fellowship from Purdue University in 2016, and First Prize of the RP-1493-Shootout Contest from ASHRAE in 2012.

  
  

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