Artificial Neural Networks to Assess the Effect of Window Parameters on Indoor Natural Ventilation in “Sultan Al-Ashraf Qaytbay” Mosque | ||||
| The Egyptian International Journal of Engineering Sciences and Technology | ||||
| Article 3, Volume 30, Mechanical Engineering, August 2020, Page 51-65 PDF (1.74 MB) | ||||
| DOI: 10.21608/eijest.2020.104941 | ||||
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| Authors | ||||
Amr Gomaa Mohammed  ; Ahmed Farouk AbdelGawad; Mofreh Melad Nassief
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| Mechanical Power Engineering Dept., Faculty of Engineering, Zagazig University, Egypt | ||||
| Abstract | ||||
| The Mosque of Sultan al-Ashraf Qaytbay is seen as one of the most beautiful features of late Egyptian Mamluk architecture. The architectural design of the mosque is exceptional due to its fine ranges and wonderful decoration. In this paper, Artificial Neural Networks (ANN) model was incorporated with wind tunnel experiments to investigate the properties of airflow within the Sultan al-Ashraf Mosque Qaytbay due to the natural ventilation caused by window openings. Wind tunnel experiments were utilized to supply the ANN model with the required data essential for the model training and establishment. The outcomes from the ANN model were validated using the results from the wind tunnel experiments. The comparison confirms the veracity of the ANN model results. Present results assured that it is very important to consider the aerodynamics and the bases of the natural ventilation when carrying out the restoration process by specialists to maintain the same performance of the original construction. ANN enables to easy predict the flow field when operating conditions are changed much easier and faster than the traditional computational and experimental methods. | ||||
| Keywords | ||||
| Archaeological mosque; Natural ventilation; Artificial neural networks; Wind tunnel experiments | ||||
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