Mega-Vent the model

Mega-Vent was developed by The WATS Guys, INC. to augment their ability to do sewer odor and corrosion control analysis by providing the following:

• Accurate headspace air flow values needed as inputs to Mega-WATS,
• Prediction of air in/out-gassing under natural ventilation conditions,
• Predication of air eduction at vents due to wind,
• Prediction of the vacuum ‘zone of influence’ resulting from forced ventilation,
• Estimation of fan sizing needed to achieve a given zone of influence, and
• In conjunction with Mega-WATS, estimation of odor loading with to scrubber equipment.

Mega-Vent builds on the work of Ward et al. (2011), in which ventilation in a partially full flowing pipe was posited as a force balance on the air within each pipe, and Bentzen et al. (2016), in which the coefficients needed to describe drag at the air-water interface were determined experimentally.  Forces acting on each air headspace in a pipe network include friction between the moving air and the un-submerged pipe surface, drag between the flowing water surface and air pressure at each boundary, and gravity acting on the mass of air.  Following conservation of momentum, the sum of forces equals the time-rate of change in momentum in the air mass in each pipe. Figure 1 illustrates this concept.

Figure 1. Force Balance on the Air in a Partially Full Pipe

Mega-Vent can be implemented over a branched sewer network containing pipes with inter-connected headspaces.  It represents air static pressure (relative to ambient pressure) at each node between pipes, air flow in each pipe, and air in/out gassing at each node.   Boundary conditions are an important part of the sewer ventilation model and must be specified in terms of the size/shape of openings between the sewer headspace and ambient air and at connections to sewer structures outside the model domain.  Air out/in gassing at openings is calculated using discharge equations appropriate to the size/shape of the opening, e.g. sharp-edged orifice.  Figure 2 shows a diagram of a simple branched network of three pipes with boundaries to the ambient atmosphere.  In Mega-Vent this concept is generalized to any configuration of dendritically connected pipes. 

Figure 2. Branched Network Diagram

Inputs to Mega-Vent include pipe physical geometry, water depth and velocity, configuration of vents, and conditions at connections to sewer headspaces outside the model domain.  In practice conditions at connections to sewer components outside the model domain are not easy to know without actually including those outside components in the model domain – often this is not feasible to do.  Instead, field pressure monitoring should be done to obtain information on outside forces bearing on the model domain.  The siting, number, and duration of pressure monitoring locations depends on the specifics of the model domain and goals of the project.  Pressure monitoring is relatively simple to accomplish using instruments which are robust and inexpensively available for rental.  Also, pressure monitoring for Mega-Vent is easily added as part of a field sampling campaign to gather inputs for Mega-WATS. 

Mega-Vent is a standalone software which is set up and called from Mega-WATS. Upon completion of a (or many) Mega-Vent runs, the output of the runs are loaded back into Mega-WATS. Mega-Vent is downloadable and part of the Mega-WATS package.

References

Bentzen, T. R.; Ostertoft, K.; Vollertsen, J.; Fuglsang, E. D.; Nielsen, A. (2016) “Air Flows in Gravity Sewers – Determination of Wastewater Drag Coefficient” Water Environment Research, Issue 3, Volume 88, pp. 239-256.

Ward M., Hamer G., McDonald A., Witherspoon J., Loh E., and Parker W. (2011) “A Sewer Ventilation Model Applying Conservation of Momentum” Water Science and Technology, Issue 6, Volume 64, pp. 1374 – 1382.

Ward M., Corsi R, Morton R, Knapp T., Apgar D., Quigley C., Easter C., Witherspoon J., Pramanik A., and Parker W. (2011) “Characterization of Natural Ventilation in Municipal Wastewater Collection Systems” Water Environment Research, Volume 83, Number 3, March 2011, pp. 265-273(9).