Modeling ventilation in a low-income house in Dhaka, Bangladesh
Yunjae Hwang, Laura (Layla) Kwong, Mohammad Saeed Munim, Fosiul Alam, Nizame, Stephen Luby, Catherine Gorl\'e

TL;DR
This study develops and validates a computational framework combining a building thermal model and uncertainty quantification to accurately predict ventilation rates in slum houses in Dhaka, Bangladesh, aiding pneumonia prevention efforts.
Contribution
It introduces a validated modeling approach that incorporates uncertainty analysis to improve ventilation rate predictions in low-income housing.
Findings
Standard models are less accurate for ACH prediction.
A similarity-based model improves ACH prediction accuracy.
Model predictions align with 12 of 17 field measurements.
Abstract
According to UNICEF, pneumonia is the leading cause of death in children under 5. 70% of worldwide pneumonia deaths occur in only 15 countries, including Bangladesh. Previous research has indicated a potential association between the incidence of pneumonia and the presence of cross-ventilation in slum housing in Dhaka, Bangladesh. The objective of this research is to establish a validated computational framework that can predict ventilation rates in slum homes to support further studies investigating this correlation. To achieve this objective we employ a building thermal model (BTM) in combination with uncertainty quantification (UQ). The BTM solves for the time-evolution of volume-averaged temperatures in a typical home, considering different ventilation configurations. The UQ method propagates uncertainty in model parameters, weather inputs, and physics models to predict mean values…
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Taxonomy
TopicsBuilding Energy and Comfort Optimization
