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290 results on '"Hangan, Horia"'

1. Effect of Wind on the Thermal Performance of a Building Envelope in Cold Climate

Author

Jafri, Ahmed, Villeneuve, Eric, Agelin-Chaab, Martin, Hangan, Horia, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Lu, Xinzheng, Series Editor, and Berardi, Umberto, editor

Published
2025
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11. A Comparison of ASCE/SEI 7–22 Tornado-Induced Load Provisions for Residential Low-Rise Buildings to Those Evaluated Using Physical Simulation.

Author

Narancio, Gabriel, Romanic, Djordje, Chowdhury, Jubayer, Hong, Han-Ping, and Hangan, Horia

Subjects
WIND pressure, CORRECTION factors, TORNADOES, DWELLINGS, VELOCITY
Abstract

In this study, the loads induced by tornado-like vortices on scaled models of eight low-rise residential buildings with real-world shapes in a typical North American community are quantified and compared to the provisions provided by ASCE/SEI 7–22. Physical simulations of the interaction between translating tornado-like vortices representative of EF1-, EF2- and EF3-rated tornadoes and the scaled models were performed in the WindEEE Dome at the University of Western Ontario. Three internal pressure scenarios were numerically simulated. The tornado velocity gust factor was identified as a critical parameter when translating loads from the model to full-scale. The uplift forces on the whole roof in the internal pressure scenarios with one dominant opening are between 44% and 63% higher than the distributed leakage scenario, highlighting the importance of keeping the integrity of the envelope. Revised values of the internal pressure coefficients and external pressure coefficients or correction factors may be used to improve the ability of the standard to provide safer design loads. [ABSTRACT FROM AUTHOR]

Published
2024
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19. Introduction

Author

Hangan, Horia, Kareem, Ahsan, Hangan, Horia, book editor, and Kareem, Ahsan, book editor

Published
2021
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23. Towards a Model of Snow Accretion for Autonomous Vehicles

Author

Carvalho, Mateus, primary, Moradi, Sadegh, additional, Hosseinnouri, Farimah, additional, Keshavan, Kiran, additional, Villeneuve, Eric, additional, Gultepe, Ismail, additional, Komar, John, additional, Agelin-Chaab, Martin, additional, and Hangan, Horia, additional

Published
2024
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46. Unsupervised machine learning method for analysis of snowfall data recorded from test vehicle

Author

Carvalho, Mateus, Hangan, Horia, Carvalho, Mateus, and Hangan, Horia

Abstract

Systems that rely on the recognition of surroundings, such as autonomous vehicles (AVs) and unmanned aerial vehicles (UAVs), can be severely affected by weather stressors like rain and snow. Their proper functioning depends on advanced driverassistance systems (ADAS) which consist of sensing devices such as LiDAR, RADAR, and cameras. Low visibility range caused by intense precipitation as well as covering of sensor surfaces due to snow accumulation impede the operation of AVs at their full potential. Developing strategies to mitigate these factors is necessary to improve vehicle performance and ensure driver safety. For this, climatic wind tunnels (CWT), such as the one of the Automotive Centre of Excellence (ACE) team at Ontario Tech University, offer the possibility of simulating adverse conditions in controlled environments. Using this type of facility allows the generation of robust databases that cover different precipitation cases. However, it is necessary to use realistic precipitation parameters for this type of experiment, which remains a gap in literature. This paper proposes a Machine Learning-based method to identify recurring types of precipitation in southern Ontario. First, field measurements are performed on an outdoor test track using an SUV equipped with weather sensors. Then, a custom theoretical model is used to fit probability density functions (PDF) of particle size distributions (DSD) recorded during the experimental campaign. The parameters of the theoretical model form a data matrix which has its dimensionality reduced through Principal Component Analysis (PCA). From this new matrix, a K-means clustering model is used to identify patterns among the sampled cases. Finally, statistical analysis is performed within each cluster to identify its main features, such as average particle size, temperature, and relative humidity at the time of measurement. The results obtained represent a step forward for realistic climate simulations and m, Résumé de la communication présentée lors du congrès international tenu conjointement par Canadian Society for Mechanical Engineering (CSME) et Computational Fluid Dynamics Society of Canada (CFD Canada), à l’Université de Sherbrooke (Québec), du 28 au 31 mai 2023.

Published
2023

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