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Tạp chí quốc tế 2018
Số tạp chí 9(2018) Trang: 1-19
Tạp chí: Atmosphere
Natural ventilation and the use of fans are recognized as sustainable design strategies to reduce energy use while reaching thermal comfort. A big challenge for designers is to predict ventilation rates of buildings in dense urban areas. One significant factor for calculating the ventilation rate is the wind pressure coefficient (Cp). Cp values can be obtained at a high cost, via real measurements, wind tunnel experiments, or high computational effort via computational fluid dynamic (CFD) simulation. A fast surrogate model to predict Cp for a schematic urban environment is required for the integration in building performance simulations. There are well-known surrogate models for Cp. The average surface pressure coefficient model integrated in EnergyPlus considers only a box-shaped building, without surrounding buildings. CpCalc, a surrogate model for Cp, considers only one height of neighbouring buildings. The Toegepast Natuurwetenschappelijk Onderzoek (TNO) Cp Generator model was available via web interface, and could include several box-shaped buildings in the surrounding area. These models are complex for fast integration in a natural ventilation simulation. For optimization processes, with thousands of simulation runs, speed is even more essential. Our study proposes a new surrogate model for Cp estimation based on data obtained from the TNO CP Generator model. The new model considers the effect of different neighbouring buildings in a simplified urban configuration, with an orthogonal street pattern, box-shaped buildings, and repetitive dimensions. The developed surrogate model is fast, and can easily be integrated in a dynamic energy simulation tool like EnergyPlus for optimization of natural ventilation in the urban areas.
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Số tạp chí 25, Issue 17(2018) Trang: 17207–17215
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Tác giả: Võ Văn Tài
Tạp chí: Fuzzy Optimization and Decision Making
Số tạp chí 3(4)(2018) Trang: 15-28
Tạp chí: European Journal of English Language Teaching
Số tạp chí 7(2018) Trang: 1-6
Tạp chí: International Journal of Engineering Science and Innovative Technology (IJESIT)
Số tạp chí 19(2018) Trang: 429-433
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Số tạp chí 161(2018) Trang: 539-546
Tạp chí: Construction and Building Materials
Số tạp chí Vol. 9, No. 4(2018) Trang: 176-181
Tạp chí: International Journal of Advanced Computer Science and Applications
Số tạp chí Volume 8, Number 3(2018) Trang: 223-228
Tạp chí: International Journal of Machine Learning and Computing
Số tạp chí 51(2018) Trang: 324002
Tạp chí: Journal of Physics D: Applied Physics


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