- 1. Building Airflow and Thermal Management
- CityFFD - City Fast Fluid Dynamics
- CityBEM - City Building Energy Model and Whole-building energy analysis
- Built Environment Scaling and Similarity with 3D Printing Applications
- Natural Ventilation Potential Map
- Forecasting Built Environment by Numerical Weather Prediction Models
- Smart Air Curtains
- Sub-Scale Model Experiment and Dimensionless Design
- Modular Growing Bed Ventilation System Design With SLA(Stereolithography Apparatus) 3D Printing Technology
- 2. Building Fire Protection and Smoke Management
- Forecasting Fire Smoke Safety
- High-rise Fire Smoke Management
- Modeling and Sub-scale Experiments
- People
Forecasting Fire Smoke Safety in Buildings and Hybrid Building Simulations Using Data Assimilation
Student: Cheng-Chun Lin, Ph.D.
Sponsor: Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grants
Publications:
- 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
(the case is 1:5 scale of the NIST fire test)
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).