Jun, Cheng (2017) An Experimental and Computational Study of Natural and Hybrid Ventilation in Buildings. Masters thesis, Concordia University.
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Abstract
This thesis presents a few empirical formulas established by previous studies and considers their viability for more general use to determine natural ventilation airflow rates under both ventilation strategies, i.e. single-sided ventilation and cross-ventilation. By utilizing computational fluid dynamics (CFD), a series of computational simulations are conducted to determine decisive ventilation variables such as the wind incidence angle and the height of the building. Both main turbulence models, the Reynolds-averaged Navier-Stokes (RANS) two-equation standard k-ε model and the Large Eddy Simulation (LES) model are used in CFD simulations for model validation and results are compared with experimental data under steady state. The natural ventilation energy saving potentials for both ventilation strategies are determined and compared based on the empirical equations with newly developed coefficients. Additionally, such a method of evaluating natural ventilation energy saving potential can be applied during the building’s early design stage as shown by a case study. Nevertheless, as a practical application of natural ventilation in a high-rise building, the hybrid ventilation system in Concordia University’s EV building is studied for greater understanding and optimization of its performance. Throughout the full-scale measurements and whole-building simulations (by CONTAM), it is determined that the five-zone simplified model is accurate and helpful for further developing predictive control strategies in real buildings. A demo case study of damper opening optimization is also presented.
Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering Concordia University > Research Units > Centre for Zero Energy Building Studies |
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Item Type: | Thesis (Masters) |
Authors: | Jun, Cheng |
Institution: | Concordia University |
Degree Name: | M.A. Sc. |
Program: | Building Engineering |
Date: | March 2017 |
Thesis Supervisor(s): | Wang, Liangzhu and Stathopoulos, Theodore |
ID Code: | 982538 |
Deposited By: | JUN CHENG |
Deposited On: | 07 Jun 2017 17:45 |
Last Modified: | 18 Jan 2018 17:55 |
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