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
Toronto Downtown
CityFFD Modeling Process
Xi'an China, Downtown (5000 buildings, 30 million grids, 3 hours)
Tianjin, China (Tianjin University and Nankai University)
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.
Research Highlights:
CityBEM: A physics-based simulation model for calculation of buildings energy usage and saving from energy retrofits.
Import 3D buildings from OpenStreetMap (OSM)
Modeling Snow-Storm of the Centrury 1971 March 4 - 7 of Montreal, Nun's Island (1500 buildings)
Nun's Island model
Buildings temperature map during power outage caused by Snow-storm
Total heat loss of the buildings during the storm
Buildings resilience study after the power outage
Small-scale Built Environment Testing and Similarities Applied to 3D-Printing
Chang, S., L. Wang. 2019. Revisiting Reynolds independent phenomenon by dimensionless CFD analysis of urban and built environment airflows. Submitted to ISHVAC 2019, Harbin, China, 8 pages.
Highlights:
Natural Ventilation Potential Map
Students: Ali Katal, PhD Candidate and Jun Cheng M.A.Sc.
Sponsor:Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants
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.
Research Highlights:
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.
Research Highlights:
Use CFD software, FLUENT, to generate detailed data as numerical experimental data
Use CONTAM in the data assimilation toolbox: OpenDA
Forecasts are made in the time frame of days in terms of airflow infiltrations and indoor contaminant levels
Different data assimilation methods will be tested within OpenDA
L. Wang and Z. Zhong. 2014.An approach to determine infiltration characterisitcs of building entrance equipped with air curtains. Energy and Buildings. Volume 75, June 2014, Pages 312–320. (IF: 2.679)
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.
Cheng-Chun Lin and L. Wang. 2015.Real-time forecasting of building fire growth and smoke transport. Submitted to Fire Safety Journal.
Cheng-Chun Lin and L. Wang. 2014. Forecasting smoke transport in a compartment fire using Ensemble Kalman Filter. Journal of Fire Sciences. DOI: 10.1177/0734904114548837 (IF: 1.26)
Cheng-Chun Lin and L. Wang. 2013. Forecasting simulations of indoor environment using data assimilation via an Ensemble Kalman filter. Building and Environment, Vol. 64: 169–176. (IF: 2.43)
Motivation:
Data assimilation is a technique to combine numerical simulations with measured data from sensors to forecast future events, or reconstruct past events.
In building simulations, measured data are often used to validate/calibrate a building model. If the comparison is good, the model is believed to be ok; If it is not good, reasons have to be provided.
Data assimilation can make more use of measured data by combining them with numerical model, allowing one to "make sense" of the observations.
Research Highlights:
1. Indoor Contaminant Transport Prediction
2. Forecasting Building Smoke Transport During Fires
Real-time Forecasting of Building Fire and Smoke Spread for a 3-zone Case
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
Dahai, Q, L. Wang and R. Zmeureanu. 2013. An analytical model of heat and mass transfer through high-rise shafts during fires. Submitted to International Journal of Heat and Mass Transfer.
Dahai, Q, L. Wang and R. Zmeureanu. 2013. Verification of a multizone network model for modeling coupled thermal airflows in buildings. Submitted to Energy and Buildings.
Research Highlights:
1. Analytical models of smoke spread in high-rise buildings
An analytical model was developed for the coupled fire smoke heat and mass transfer through shafts of high-rise buildings.
A hand-calculation procedure was proposed to obtain the solution to the analytical model.
Smoke temperatures and pressures, and shaft wall temperatures all depend on a non-dimensional parameter.
The analytical solutions were plotted in non-dimensional forms, which can be used for designs of smoke control systems in high-rise buildings
2. An updated numerical model of whole-building airflow and thermal model - CONTAM97R
CONTAM97R belongs to the family of CONTAM, a computer program developed for about 30 years at the US NIST.
Energy balance model was added to CONTAM97R and is being tested for different applications of whole-building airflow and termal simulations.
CONTAM97R here is applied to the modeling of fire smoke spread in high-rise buildings.
3. Integration of FDS and CONTAM97R
Fire dynamics simulation (FDS) is a well-known LES computer program for building fire dynamics simulations
We are integrating FDS with CONTAM97R for whole-building fire and smoke simulations by jusing Java scripts.
Modeling and Sub-scale Experiments of Building Fire Smoke Management
Student: Guanchao (Jeremy) Zhao, PhD Candidate
Sponsor: Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants; Concordia OVPPGS SEED Individual Funding Program
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.
Motivation:
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
Research Highlights:
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
Research Associate
Dr. Wenhui Peng (AI-Driven CFD Applications to Urban Microclimate Modeling)
Dr. Ali Katal
Dr. Mohammad Mortezazadeh
Research Assistant
Shaoxiang Qin (AI CFD Applications)
Maher Albettar (Digital Twin)
Zhaoyu Zheng (Digital Twin)
Postdoc Fellow
Dr. Michael Kim (co-supervised with Dr. Andreas Athienitis)
PhD Students
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)
Master Students
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)
