| Biography | |
|---|---|
 Dr. Amirul Khan School of Civil Engineering,University of Leeds, UK |
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| Title: Sensor supported GPU-based indoor and urban air-quality prediction: towards a symbiotic decision support and control toolkit | |
| Abstract: Air quality (AQ), whether indoors or outdoors, together with thermal comfort, is fundamental for the wellbeing of humans, especially inside buildings, as we spend more than 90% of our time indoors. Therefore, it is crucial to control indoor air quality (IAQ) to provide a comfortable microclimate for occupants and protect humans from health hazards such as airborne infections in hospitals and mitigate exposure during the accidental or deliberate release of contaminants. A complex relationship exists between building energy use, personal comfort, indoor air quality and health. Moreover, the urban landscape and indoor air quality are intrinsically linked, but wind conditions are challenging to model computationally, particularly in urban street canyons between buildings. Traditionally, the indoors and outdoors have been modelled separately due to computational meshing size constraints, unknown or uncertain boundary conditions, and wide separation of spatial and temporal scales. Furthermore, transient events such as occupant movement (indoors) or environmental change (outdoor traffic and meteorological changes) can significantly affect the airflow and contaminant dispersion, such as airborne transmission in hospital rooms or air pollution in the urban street canyons. Computational fluid dynamics (CFD) can predict the dynamic nature of airflow and contaminant transport in great detail and accuracy; however, its usage as a forecasting or control tool is prohibitive due to its long computing times and uncertainty in boundary conditions. Hence our work aims to present an investigation into the development and implementation of a novel sensor supported method for real-time GPU-based CFD approach. This methodology will enable the prediction/forecast of contaminant dispersion due to movement or microclimatic changes, including thermal buoyancy effects, thereby transforming the traditional CFD-based AQ analysis into a viable tool for indoor/outdoor environment forecasting and decision support. | |
| Biography: Dr Amirul Khan is an Assistant Professor in Environmental Fluid Mechanics with extensive experience in innovative computational approaches for wall-bounded turbulent flow simulation applied to the indoor built environment and outdoor environments. He has expertise in mathematical modelling, numerical optimisation methods and high-performance computing. His expertise in turbulent flow modelling allowed him to lead and co-lead several inter-disciplinary research projects, including a CONFAP-Newton Fund award, EU-GeoTech, EPSRC HECOIRA UKRI GCRF COVID-19 grant. He is currently the director of the Centre for Computational Engineering, Leeds and co-leading the computational group of Leeds Institute for Fluid Dynamics (LIFD), which is a university-wide hub to facilitate world-leading research and education in fluid dynamics. He has developed computational fluid dynamics (CFD)-based optimisation approaches to design healthcare environments and developed the novel massively parallel lattice Boltzmann (LBM) based method for real-time building environment simulation, which was recognised with a best paper award. He has over 50+ peer-reviewed journal and conference papers across top journals in fluid mechanics and computational methods, including leading journals for the built environment. Amirul obtained his PhD from the Department of Applied mathematics & Theoretical Physics (DAMTP) at Cambridge University. Before joining Leeds, he carried out research work at University of Manchester’s Centre for Nonlinear Dynamics (MCND), the Department of Mathematics and the Department of Physics at the University of Glasgow, UK. | |
