Stormwater treatment: examples of computational fluid dynamics modeling

  • Gaoxiang Ying
  • John Sansalone
  • Srikanth Pathapati
  • Giuseppina Garofalo
  • Marco Maglionico
  • Andrea Bolognesi
  • Alessandro Artina
Research Article

Abstract

Control of rainfall-runoff particulate matter (PM) and PM-bound chemical loads is challenging; in part due to the wide gradation of PM complex geometries of many unit operations and variable flow rates. Such challenges and the expense associated with resolving such challenges have led to the relatively common examination of a spectrum of unit operations and processes. This study applies the principles of computational fluid dynamics (CFD) to predict the particle and pollutant clarification behavior of these systems subject to dilute multiphase flows, typical of rainfall-runoff, within computationally reasonable limits, to a scientifically acceptable degree of accuracy. The Navier-Stokes (NS) system of nonlinear partial differential equations for multiphase hydrodynamics and separation of entrained particles are solved numerically over the unit operation control volume with the boundary and initial conditions defined and then solved numerically until the desired convergence criteria are met. Flow rates examined are scaled based on sizing of common unit operations such as hydrodynamic separators (HS), wet basins, or filters, and are examined from 1 to 100 percent of the system maximum hydraulic operating flow rate. A standard turbulence model is used to resolve flow, and a discrete phase model (DPM) is utilized to examine the particle clarification response. CFD results closely follow physical model results across the entire range of flow rates. Post-processing the CFD predictions provides an in-depth insight into the mechanistic behavior of unit operations by means of three dimensional (3-D) hydraulic profiles and particle trajectories. Results demonstrate the role of scour in the rapid degradation of unit operations that are not maintained. Comparisons are provided between measured and CFD modeled results and a mass balance error is identified. CFD is arguably the most powerful tool available for our profession since continuous simulation modeling.

Keywords

stormwater unit operations and processes (UOPs) hydrodynamic separation filtration adsorption computational fluid dynamics (CFD) turbulence modeling discrete phase model particle separation detention/retention basins clarification 

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References

  1. 1.
    Sansalone J J. Physical and chemical nature of stormwater pollutants. In: Field R, Sullivan D, eds. Wet Weather Flow in the Urban Watershed. Boca Raton: CRC Press, 2002, 43–66Google Scholar
  2. 2.
    Andoh RY G, Saul A J. The use of hydrodynamic vortex separators and screening systems to improve water quality. Water Science and Technology, 2003, 47(4): 175–183Google Scholar
  3. 3.
    USEPA. Stormwater technology fact sheet - Hydrodynamic Vortex Separators. EPA 832-99-017. 1999Google Scholar
  4. 4.
    Kim J Y, Sansalone J J. Event-based size distributions of particulate matter transported during urban rainfall-runoff events. Water Science and Technology, 2008, 42(10–11): 2756–2768Google Scholar
  5. 5.
    Sansalone J J. Adsorptive infiltration of metals in urban drainage-media characteristics. Science of the Total Environment, 1999, 235(1–3): 179–188CrossRefGoogle Scholar
  6. 6.
    Geldof G, Jacobsen P, Fujita S. Urban storm water infiltration perspectives. Water Science and Technology, 1994, 29(1–2): 245–254Google Scholar
  7. 7.
    Wilson M A, Gulliver J S, Mohseni O, Hozalski R M. Assessing the effectiveness of proprietary stormwater treatment devices. In: Proceedings of World Environmental & Water Resources Conference, Tampa, FL, USA, 2007Google Scholar
  8. 8.
    Rossman L A. Stormwater Management Model Users Manual, Version 5.0, USEPA/600/R-05/040, Cincinnati, OH, USA, 2007Google Scholar
  9. 9.
    Huber W C, Dickinson R E. Storm Water Management Model User’s Manual, Version 4, EPA/600/3-88/001a (NTIS PB88-236641/AS). U.S. Environmental Protection Agency, Athens, GA, USA, 1988Google Scholar
  10. 10.
    Huber W C. BMP Modeling Concepts and Simulation. EPA/600/R-06/033, U.S. Environmental Protection Agency, Washington D C, USA, 2006Google Scholar
  11. 11.
    Weib G J. Vortex separator: proposal of a dimensioning method. Water Science and Technology, 1997, 36(8–9): 201–206CrossRefGoogle Scholar
  12. 12.
    Paul T C, Sayal S K, Sakhuja V S, Dhillon G S. Vortex-settling basin design considerations. Journal of Hydraulic Engineering, 1991, 117(2): 172–189CrossRefGoogle Scholar
  13. 13.
    Keblin V, Barrett M, Malina J, Charbeneau R. The effectiveness of permanent highway runoff controls: sedimentation/filtration systems. CRWR Online Report 97-4, University of Texas at Austin, 1997Google Scholar
  14. 14.
    Curtis J S, vanWachem B. Modeling particle-laden flows: a research outlook. American Institute of Chemical Engineers, 2004, 50(11): 2638–2645CrossRefGoogle Scholar
  15. 15.
    Dickenson J A, Sansalone J J. Discrete phase model representation of particulate matter (PM) for simulating PM separation by hydrodynamic unit operations. Environmental Science & Technology, 2009, 43(21): 8220–8226CrossRefGoogle Scholar
  16. 16.
    Pathapati S S, Sansalone J J. Can a stepwise steady flow computational fluid dynamics model reproduce unsteady particulate matter separation for common unit operations? Environmental Science & Technology, 2011, 45(13): 5605–5613CrossRefGoogle Scholar
  17. 17.
    Stovin V R, Saul A J. Efficiency prediction for storage chambers using computational fluid dynamics. Water Science and Technology, 1996, 33(9): 163–170CrossRefGoogle Scholar
  18. 18.
    Stovin V R, Saul A J. A computational fluid dynamics (CFD) particle tracking approach to efficiency prediction. Water Science and Technology, 1998, 37(1): 285–293CrossRefGoogle Scholar
  19. 19.
    Naser G, Karney B W, Salehi A A. Two-dimensional simulation model of sediment removal and flow in rectangular sedimentation basin. Journal of Environmental Engineering, 2005, 131(12):1740–1749CrossRefGoogle Scholar
  20. 20.
    Jayanti S, Narayanan S. Computational study of particle-eddy interaction in sedimentation tanks. Journal of Environmental Engineering, 2004, 130(1): 37–49CrossRefGoogle Scholar
  21. 21.
    Faram M G, Harwood R. A method for the numerical assessment of sediment interceptors. In: Proceedings of 3rd international Conference on Sewer Processes and Networks, Paris, France, 2002Google Scholar
  22. 22.
    Tyack J N, Fenner R A. Computational fluid dynamics modeling of velocity profiles within a hydrodynamic separator. Water Science and Technology, 1999, 39(9): 169–176CrossRefGoogle Scholar
  23. 23.
    Guo Q, England G, Johnston C E. Development of certification guidelines for manufactured stormwater BMPs. ASCE/EWRI task committee on guidelines for certification of manufactured storm-water best management practices (BMPs). In: Proceedings of World Environmental and Water Resources Congress EWRI, 2008Google Scholar
  24. 24.
    Gulliver J S, Guo Q, Wu J S. Scaling relations for manufactured stormwater BMPs. ASCE/EWRI Task Committee on Guidelines for Certification of Manufactured Stormwater Best Management Practices (BMPs). In: Proceedings of World Environmental and Water Resources Congress, 2008Google Scholar
  25. 25.
    USEPA. Urban stormwater BMP performance monitoring. A Guidance Manual for Meeting the National Stormwater BMP Database Requirements, EPA-821-B-02-001, 2002Google Scholar
  26. 26.
    Annandale G W. Scour Technology. New York: McGraw-Hill Professional, 2005, 24–25Google Scholar
  27. 27.
    Pathapati S, Sansalone J. Application of CFD to Stormwater Clarification Systems. In: Proceedings of World Environmental and Water Resources Congress EWRI, 2007Google Scholar
  28. 28.
    Rushton B. Broadway outfall stormwater retrofit project (Phase II - Monitoring CDS unit and constructed pond). Draft Report to EPA and Southwest Florida Water Management District (SFWMD), 2006, 124Google Scholar
  29. 29.
    American Society for Testing and Materials (ASTM). Standard test method for determining sediment concentration in water samples. Annual Book of Standards, Designation: D 3977-97, Vol. 04.08, Philadelphia, 2000, 395–400Google Scholar
  30. 30.
    American Society for Testing and Materials (ASTM). Standard practice for dry preparation of soil samples for particle size analysis and determination of soil constants. Annual Book of Standards, Designation: D 421-85. Vol. 04.08, Philadelphia, 1993, 8–9Google Scholar
  31. 31.
    Finlayson-Pitts B J, Pitts J N. Chemistry of the Upper and Lower Atmosphere -Theory, Experiments and Applications. CA: Academic Press, 2000, 365–368Google Scholar
  32. 32.
    American Society for Testing and Materials (ASTM). Automated Pore Volume and Pore Size Distribution of Porous Substances by Mercury Porosimetry (ASTM Designation: BMP578-02). West Conshohocken, PA, USA, 2003Google Scholar
  33. 33.
    Kim J Y, Ma J, Pathapati S, Sansalone J. Continuous deflective separation of non-colloidal particulate matter in rainfall-runoff. In: Proceedings of the 3rd Annual North American Surface Water Quality Conference and Exposition, Forrester Communications. CA: Palm Desert, 2004Google Scholar
  34. 34.
    Sheng Y, Ying G, Sansalone J J. Differentiation of transport for particulate and dissolved water chemistry load indices in rainfallrunoff from urban source area watersheds. Journal of Hydrology (Amsterdam), 2008, 361(1–2): 144–158CrossRefGoogle Scholar
  35. 35.
    Urbonas B R. Recommended parameters to report with BMP monitoring data. Water Research, 1995, 121(1): 23–34Google Scholar
  36. 36.
    Versteeg H, Malalasekera W. An Introduction to Computational Fluid Dynamics: The Finite Volume Method Approach. London: Prentice Hall, 1995Google Scholar
  37. 37.
    Nowakowski A F, Cullivan J C, Williams R A, Dyakowski T. Application of CFD to modeling of the flow in hydrocyclones. Is this a realizable options or still a research challenge? Minerals Engineering, 2004, 17(5): 661–669CrossRefGoogle Scholar
  38. 38.
    Statie E C, Salcudean M E, Gartshore I S. The influence of hydrocyclone geometry on separation and fibre classification. Filtration and Separation, 2001, 38(6): 36–41CrossRefGoogle Scholar
  39. 39.
    Petty C A, Parks S M. Flow predictions within hydrocyclones. Filtration and Separation, 2001, 38(6): 28–34CrossRefGoogle Scholar
  40. 40.
    Launder B E, Spalding D B. The numerical computation of turbulent flows. Computer Method in Applied Mechanics, 1974, 3(2): 269–289CrossRefGoogle Scholar
  41. 41.
    Elghobashi S E. Particle laden turbulent flows: direct simulation and closure models. Applied Scientific Research, 1991, 48(3–4): 301–314CrossRefGoogle Scholar
  42. 42.
    Morsi S A, Alexander A J. An investigation of particle trajectories in two-phase flow systems. Journal of Fluid Mechanics, 1972, 55(02): 193–208CrossRefGoogle Scholar
  43. 43.
    Qi D, Lin W. TGrid: a new grid environment. In: Proceedings of First International Multi-Symposiums on Computer and Computational Sciences Conference (IMSCCS’06), 2006Google Scholar
  44. 44.
    Barth T J, Jespersen D. The design and application of upwind schemes on unstructured meshes. In: Proceedings of AIAA 27th Aerospace Sciences Meeting, 1989Google Scholar
  45. 45.
    Patankar S. Numerical Heat Transfer and Fluid Flow. Atlanta, USA: Hemisphere Publishing Corporation, 1980Google Scholar
  46. 46.
    Ranade V V. Computational Flow Modeling for Chemical Reactor Engineering. London: Academic Press, 2002Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gaoxiang Ying
    • 1
  • John Sansalone
    • 1
  • Srikanth Pathapati
    • 1
  • Giuseppina Garofalo
    • 1
  • Marco Maglionico
    • 2
  • Andrea Bolognesi
    • 2
  • Alessandro Artina
    • 2
  1. 1.Engineering School of Sustainable Infrastructure and EnvironmentUniversity of FloridaGainesvilleUSA
  2. 2.DISTARTUniversita di BolognaBolognaItalia

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