Analysis of uncertainty in natural ventilation predictions of high-rise apartment buildings

Abstract

Quantification of natural ventilation rates is an important issue in HVAC system design. Natural ventilation in buildings depends on many parameters whose uncertainty varies significantly, and hence the results from a standard deterministic simulation approach could be unreliable and provide a false sense of security that natural ventilation will supply adequate amounts of fresh air. This study performed an uncertainty analysis to predict natural airflow rates. The paper presents relevant uncertainty in model and input parameters such as meteorological data, building properties (leakage areas of windows, doors, etc.), etc. Uncertainties of the aforementioned parameters were quantified based on data available from literature and on-site visits. The Monte-Carlo method with Latin Hypercube Sampling (LHS) was used for uncertainty propagation. The CONTAMW was chosen to simulate natural ventilation phenomena in a high-rise apartment building that is typical of residential buildings in Korea. It is shown that the uncertainty propagated through this process is not negligible and may significantly influence the prediction of the airflow rates. In the paper, the result of a sensitivity analysis is also addressed. Practical application: The paper describes a probablistic approach to predict the natural airflow rates of high-rise apartment buildings under uncertainty and could eventually replace the current conventional deterministic approach.