524
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Surge predictions in a large stormwater tunnel system using SWMM

ORCID Icon, &
Pages 577-584 | Received 02 Jul 2020, Accepted 01 Apr 2021, Published online: 03 May 2021
 

ABSTRACT

Stormwater tunnels often have massive geometries, with conduit lengths of several kilometers and a wide range of diameter sizes. Modeling rapid filling of these systems is a complex task and needs adequate methodology. One model used in hydraulic analysis of stormwater tunnels is the EPA’s Storm Water Management Model (SWMM). However, model setup conditions related to a pressurization algorithm can significantly affect SWMM’s accuracy in surge prediction. This work evaluates SWMM 5.1 accuracy in simulating rapid filling of tunnels, particularly surging conditions. This evaluation is done using a real-world tunnel geometry, the Upper Des Plaines Tunnel, which is part of Chicago’s TARP tunnel system. Variables considered in the SWMM model setup include discretization strategy and pressurization algorithm, and its results are compared with HAST predictions, a model specifically designed to represent surges in tunnels. This work shows that, with adequate setup, SWMM can represent surging in stormwater tunnels much more precisely.

Acknowledgements

The authors would like to acknowledge the MWRDGC’s support in this work by providing the geometry and inflow data for the TARP UDP system.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 239.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.