AI-based prediction of urban climate and its impact on built environments
About the project
This project plans to develop AI-based CFD simulation to realise accurate and fast prediction of urban climate and the built environment. This study will focus on two aspects in developing the AI-based model: AI-based turbulence model by learning the “behaviour” of turbulence and AI-based surrogate model. In order to train and test the artificial neural network (ANN) model, this project will collect experimental data of both indoor and outdoor airflow from on-site and lab measurements. In terms of accuracy, the predictions by AI-based models are expected to be within 10% difference from that by the conventional CFD simulations. In terms of efficiency, the AI-based models are expected to be at least 10 times faster than the conventional CFD simulations.
The urban climate determines the environmental quality in urban areas by removing or dispersing the airborne pollutants generated by human activities or providing cleaner external (rural) air. The study of urban climate and its impact on built environments would help provide guidelines and tools for urban planners and building engineers to evaluate the environmental quality in our living space. Given the practical difficulties of performing city-scale or multi-scale experiments, accurate simulation and fast decision-supporting tools are urgently in need to provide pollutant mitigation strategies for researchers, urban planners, environmental engineers and decision-makers. The existing development of such tools has been plagued mainly by three scientific challenges: computational speed, accuracy, and robustness.
The researchers in the team represent the KTH ABE School and the Department of Computer Science and Engineering, Blekinge Institute of Technology