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Passive Strategies for Improving Energy Efficiency of Buildings - Optimisation of a low-energy ventilation façade with novel integration of Helical Co

Passive Strategies for Improving Energy Efficiency of Buildings - Optimisation of a low-energy ventilation façade with novel integration of Helical Co
提高建筑能源效率的被动策略 - 通过螺旋 Co 的新颖集成优化低能耗通风立面
批准号:
2430941
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

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中文摘要
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英文摘要
The built environment represents a significant 30-40% of global energy consumption through its Heating, Ventilation and Air-Conditioning (HVAC) demands which are resultant of people spending around 90% of their daily lives in buildings. This contributes 40-50% of global carbon emissions, and therefore there needs to be a drastic change in the way that building inhabitants consume energy and, in the technology, and building services which provide and utilise it. This project addresses the motive for increasing the research and development of new, innovative low energy HVAC technologies. Specifically, the main aim of this research project is to investigate and optimise a low-energy heating and cooling ventilation façade with novel integration of Helical Coil Heat Transfer Device (HCHTD) using the UK as the focus climate. The research will focus on the optimisation of the HCHTD for maximum efficiency through a detailed parametric study of various geometric features of the helical coils. By completing this optimisation, as well as a re-design of the traditional ventilation façade structure to improve ventilation performance, I would be able to produce a scaled prototype of the final concept for wind tunnel test and field study validation purposes. The main methodologies involved in this research are literature review, Computational Fluid Dynamics (CFD) modelling through ANSYS package, Wind Tunnel tests and Life Cycle Assessment (LCA). The long-term objective of this project is to developing the concept technologically and economically for patent and commercialisation.
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Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis