Modeling ventilation in a low-income house in Dhaka, Bangladesh

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 and 95% confidence intervals for the quantities of interest, namely temperatures and ventilation rates in terms of air changes per hour (ACH). The model predictions are compared to on-site field measurements of air and thermal mass temperatures, and of ACH. The results indicate that the use of standard cross- or single-sided ventilation models limits the accuracy of the ACH predictions; in contrast, a model based on a similarity relationship informed by the available ACH measurements can produce more accurate predictions with confidence intervals that encompass the measurements for 12 of the 17 available data points.