An ODE-based swift and dynamic sewer airflow model.

Area of Science:

• Environmental Engineering
• Fluid Dynamics
• Computational Modeling

Background:

• Sewer network ventilation is crucial for mitigating odour and corrosion.
• Existing dynamic airflow models are accurate but computationally intensive, limiting practical application.
• A need exists for efficient models to predict in-sewer dynamic airflows.

Purpose of the Study:

• To develop a swift dynamic airflow model for sewer networks.
• To simplify complex Navier-Stokes Equations (NSE) for efficient airflow prediction.
• To provide a computationally efficient tool for sewer ventilation design.

Main Methods:

• Derived a dynamic airflow model using ordinary differential equations (ODE) by simplifying the 1D NSE.
• Validated the ODE model against comprehensive datasets from a pilot sewer.
• Applied the calibrated ODE model to simulated sewer networks under various ventilation scenarios.

Main Results:

• The ODE model achieved high-fidelity predictions comparable to NSE.
• Computational time was reduced by two orders of magnitude compared to traditional models.
• Demonstrated accuracy, robustness, and efficiency in natural and forced ventilation scenarios.

Conclusions:

• The swift dynamic airflow model offers a computationally efficient solution for sewer ventilation design.
• The model supports effective odour and corrosion management in sewer systems.
• This approach enhances the practical application of airflow modeling in environmental engineering.