学术文献库

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.