A. Katal, M. Mortezazadeh and L. Wang. 2018. Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations. Applied Energy. Under Review. Pages 29.
M. Mortezazadeh, Z. Jandaghian and L. Wang. 2018. Integrating CityFFD and WRF for modeling urban microclimate in street canyon. Journal of Building Performance Simulation. Under Review. Pages 23.
M. Mortezazadeh and L. Wang. 2018. SLAC – A semi-Lagrangian artificial compressibility solver for steady-state incompressible flows. International Journal of Numerical Methods for Heat and Fluid Flow.
M. Mortezazadeh and L. Wang. 2016. A high-order backward forward sweep interpolation algorithm for semi-Lagrangian method. International Journal for Numerical Methods in Fluids. Volume 84, Issue 10, 584–597.
M. Mortezazadeh and L. Wang. 2018. Modelling urban airflows by a new parallel high-order semi-Lagrangian 3D fluid flow solver. The 4th Conference on Building Energy Environment (COBEE 2018), Melbourne, Australia, 2018/2.
Research Highlights (A few hours computing time for 10-50 million grids on a PC with one GPU card):
1. Modeling Urban Microclimates
Montreal 2017 Summer Heatwaves and Urban Heat Island - Montreal Downtown with Mont Royal
CityFFD Modeling Process
Xi'an China, Downtown (5000 buildings, 30 million grids, 3 hours)
Tianjin, China (Tianjin University and Nankai University)
Cheng J, Qi D, Katal A, Wang LL, Stathopoulos T. Evaluating wind-driven natural ventilation potential for early building design. Journal of Wind Engineering and Industrial Aerodynamics. 2018 Nov 1;182:160-9.
Natural ventilation potential map of North America
Maximum annual NV potentials (hours/year) across North America: single-sided (left), cross ventilation (right)
Daytime percentage of NV potential hours across North America: single-sided (left), cross ventilation (right)
Forecasting Simulations in Built Environment by Ensemble Kalman Filter (EnKF) (Data Assimilation)
Student:Danlin Hou, Ph.D. Candidate and Cheng-Chun Lin, Ph.D.
Sponsor:Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants
C. Lin and L. Wang. 2013. Forecasting simulations of indoor environment using data assimilation via an Ensemble Kalman Filter. Building and Environment. Volume 64, pp. 169-176.
C. Lin and L. Wang. 2014. Applications of Data Assimilation to Forecasting Indoor Environment. 2014 IEEE International Conference on Automation Science and Engineering (CASE). Taipei, Taiwan, August 18-22, 2014.
D. Hou, C. Lin, A. Katal and L. Wang. 2019. Forecasting Cooling Load and Energy Saving Potential by Ensemble Kalman Filter for an Institutional High-Rise Building with Hybrid Ventilation. Submitted to ISHVAC 2019. Harbin, China, 8 pages.
Use CFD software, FLUENT, to generate detailed data as numerical experimental data
Investigated the performances of a hybrid ventilation system for the airflow pattern and temperature distribution in a sub-scale 17-story building model by comparing with its natural ventilation performances by PIV. Then a multi-zone building model can be developed and validated by comparing the results to the measurement data.
Modular Growing Bed Ventilation System Design With SLA(Stereolithography Apparatus) 3D Printing Technology
Investigated the flow distribution of localized mechanical ventilation in a warehouse. PIV results were used to correlate the empirical equation so that the jet flow velocities can be predicted under different conditions.
At 180 seconds, heat release rate increased from 1.5 kW to 2.8 kW. The system was able to respond dynamically: the forecasted parameters (lines) updated dynamically according to the measured data (dots).
High-rise Building Fire Smoke Management
Student: Dahai (Darren) Qi, PhD Candidate (co-supervised with Dr. Radu Zmeureanu)
Sponsor: Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants
Wang, L. and Guanchao Zhao. 2013. Numerical study on smoke movement driven by pure helium in atria. Fire Safety Journal 61(0): 45-53. (IF: 1.22)
Wang, L., W. Z. Black and Guanchao Zhao. 2013. Comparison of simulation programs for airflow and smoke movement during high-rise fires. ASHRAE Transactions (Technical Paper), Vol. 119, Part 2, DE-13-014, Pages 12.
A high-rise building is defined as a building with the height more than 23 m (roughly 7 stories)
Fires in high-rise buildings are often disastrous and cause huge amount of losses.
Driven by stack effect, fire smoke may spread to the higher levels easily via the vertical shafts, e.g. stairs, elevators, light wells, ventilation ducts.
It was reported that smoke spread through shafts accounts for about 95% or more of the upward movement of smoke in high-rise buildings
1. A new method of using pure helium as a surrogate of fire smoke
The helium smoke test provides an alternative to the hot smoke test, which is often of safety concerns because it uses actual fires in the commissioning test of a smoke control system.
The new method spawns of a few safe applications of using helium to simulate flammable and/or poisonous gases for lab and field tests.
2. A new numerical model for high-rise smoke controls - COSMO (collaborated with Dr. William Black at Georgia Tech, US)
A computer model, COSMO (COntrol of SMOke in high-rise buildings), is also being collaboratively developed with Dr. William Black at Georgia Tech.
The foundation of COSMO is based on a formulation of the three conservation equations; conservation of mass, energy and momentum.
We compared CONTAM (developed by the US NIST) and COSMO.
Overall, there is a reasonable comparison between the two computer predictions; however, large errors can result if the models employ unrealistic temperature distributions throughout the building structure.
CURRENT RESEARCH STAFF
Dr. Wenhui Peng (AI-Driven CFD Applications to Urban Microclimate Modeling)
Dr. Ali Katal
Dr. Mohammad Mortezazadeh
Shaoxiang Qin (AI CFD Applications)
Maher Albettar (Digital Twin)
Zhaoyu Zheng (Digital Twin)
Dr. Michael Kim (co-supervised with Dr. Andreas Athienitis)
Travis Moore (co-supervised with Dr. Michael Lacasse)
Ibrahim Abdehlhady (co-supervised with Dr. Dahai Qi), PhD Student, University of Sherboorke
Saeed Rayegan (co-supervised with Dr. Radu Zmeureanu)
Shujie Yan (co-supervised with Dr. John Zhai and Dr. Shelly Miller)
Jiwei Zou (co-supervised with Dr.
Dongxue Zhan (co-supervised with Dr. Ibrahim Hassan)
Senwen Yang (co-supervised with Dr. Ted Stathopoulos)
Houzhi Wang (co-supervised with Dr. Ruoyu You, The Hong Kong Polytechnic University)
Ahmed Moustafa Marey (co-supervised with Dr. Sherif Goubran from American University in Cairo)
Cheng Chen (co-supervised with Dr. Weiyi Shang from Computer Science at Concordia University and Dr. David Vidal at Polytechnique Montreal)
Eslam Mohamed Ali (co-supervised with Dr. Andreas Athienitis)
Omar Hassan (co-supervised with Dr. Ibrahim Hassan)
Zahra Salehi (co-supervised with Dr. Hua Ge)
Majd Moujahed (co-supervised with Dr. Ibrahim Hassan)
Kathryn Chung Tze Cheong (co-supervised with Dr. Hua Ge)