Natural ventilation is often considered to be the most energy efficient and sustainable solution for ventilating a building, with the potential to reduce the energy cost required for air conditioning. Adequate ventilation within a building is necessary to maintain occupants’ health and comfort. The capacity of a natural ventilation is affected by many factors such as local wind resources, urban forms and building design. However, the potential capacity of natural ventilation has been challenged by increased ambient pollution in recent years. This project aims to quantify natural ventilation potential in a dense urban setting considering the effect of air pollution. A roust and fast model to predict the pressure coefficients on building facades in different urban morphology settings will be developed through a combined computational fluid dynamics (CFD) and data-driven analysis. Data of air pollutants of a city such as London will be sourced from existing public domains such as https://www.londonair.org.uk/LondonAir/Default.aspx. A real-time control algorithm for window/vents will be developed based on the actual pollutant data. The outcome of the project will help designers and building scientists understand the constraints to the application of natural ventilation techniques in dense and polluted environments, and not to overestimate the benefits achievable in terms of fresh air provision and thermal comfort conditions. It will also support the window /vent control productions.
Key skills required: Applicants must have a first or upper second, MEng, MSc degree or equivalent with a background in Mechanical/Building Services Engineering, Physics, Applied Mathematics or a related discipline.
For more details, please contact: Professor Runming Yao School of the Built Environment University of Reading [email protected]
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