Bing Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Bing Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
Ali Malkawi
1 shared publication- Ali Malkawi
Graduate School of Design, Harvard University, Cambridge, MA 02138, USA
1 shared publication - Bin Yan
Harvard Center for Green Buildings and Cities, Harvard University, Cambridge, 02138 MA, USA
1 shared publication - Nari Yoon
Department of Architecture, Ajou University, Suwon 16499, Republic of Korea
1 shared publication - Wentao Wu
Department of Civil & Architectural Engineering, College of Engineering, Tennessee State University, Nashville, TN 37209, USA
1 shared publication - Zlatan Sehovic
Harvard Center for Green Buildings and Cities, Harvard University, Cambridge, 02138 MA, USA
1 shared publication
Latest Publications
- 1 February 2019
- Published by Elsevier in Sustainable Cities and Society
- Vol. 45, 25-37
Design-based natural ventilation evaluation in early stage for high performance buildings
- 1 February 2019
- Published by Elsevier in Sustainable Cities and Society
- Vol. 45, 25-37
Design-based natural ventilation evaluation in early stage for high performance buildings
- Open Access
A Method toward Real-Time CFD Modeling for Natural Ventilation
Natural ventilation is often used as a passive technology to reduce building energy consumption. To leverage the rule-based natural ventilation control to more advanced control at multiple spatial scales, mathematical modeling is needed to calculate the real-time ventilation rate, indoor air temperatures, and velocities at high spatial resolution. This study aims to develop a real-time mathematical modeling framework based on computational fluid dynamics (CFD). The real-time concept is implemented by using real-time sensor data, e.g., wall surface temperatures as boundary conditions, while data assimilation is employed to implement real-time self-calibration. The proof of concept is demonstrated by a case study using synthetic data. The results show that the modeling framework can adequately predict real-time ventilation rates and indoor air temperatures. The data assimilation method can nudge the simulated air velocities toward the observed values to continuously calibrate the model. The real-time CFD modeling framework will be further tested by the real-time sensor data once building construction is fully completed.
Open AccessA Method toward Real-Time CFD Modeling for Natural Ventilation
Natural ventilation is often used as a passive technology to reduce building energy consumption. To leverage the rule-based natural ventilation control to more advanced control at multiple spatial scales, mathematical modeling is needed to calculate the real-time ventilation rate, indoor air temperatures, and velocities at high spatial resolution. This study aims to develop a real-time mathematical modeling framework based on computational fluid dynamics (CFD). The real-time concept is implemented by using real-time sensor data, e.g., wall surface temperatures as boundary conditions, while data assimilation is employed to implement real-time self-calibration. The proof of concept is demonstrated by a case study using synthetic data. The results show that the modeling framework can adequately predict real-time ventilation rates and indoor air temperatures. The data assimilation method can nudge the simulated air velocities toward the observed values to continuously calibrate the model. The real-time CFD modeling framework will be further tested by the real-time sensor data once building construction is fully completed.