An ODE-based swift and dynamic sewer airflow model

Abstract

Airflow models are powerful tools for ventilation design to achieve odour and corrosion mitigation in sewer networks. Currently, there lacks a model able to efficiently predict in-sewer dynamic airflows, as all available dynamic models with an acceptable accuracy are computationally demanding. In this study, a swift dynamic airflow model based on an ordinary differential equation (ODE) is derived by simplifying the one-dimensional Navier Stokes Equations (NSE), supported by the observation that the NSE solutions always display negligible spatial variations in air velocity when applied to a sewer conduit. The ODE model reproduces the NSE airflow predictions with a high-level fidelity, with time consumption reduced by two orders of magnitude. The ODE model was calibrated and validated using comprehensive datasets collected from a pilot sewer. The calibrated ODE model was applied to simulated sewer networks in both natural and forced ventilation scenarios, which demonstrates the accuracy, robustness, and efficiency of the model. The swift dynamic airflow model will provide strong support to effective sewer ventilation design for odour and corrosion management in sewers.