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TUFLOW 2D Hydraulic Structures: Difference between revisions

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== Can I model bridge piers explicitly in 2D using very small cells? ==
It isn't recommended to explicitly model bridge piers by blocking out the pier faces in TUFLOW, or any otherhydraulic 2Dmodelling orsoftware 3Dbased softwareon solving Shallow Water Equations(SWE), due to the possiblemesh exceptionresolution beingrequired CFDand software),the 3-dimentiality butof the reasonsflow whyand areturbulence aaround littlethe complicatedpiers.
 
 
Small scale obstructions to the flow, such as trees, poles, piers, etc. cause additional head losses along a flow path due to their drag characteristics. Historically, form loss (or drag) coefficients for various profile shapes have been determined as a function of Reynold’s number through experimental testing. More recently, computational fluid dynamics (CFD) has been used to attempt to reproduce the velocity field in the wake of such objects. Although providing better results than 2D modelling, the results have not always agreed well with physical tests. In particular, the drag of a given profile depends on the exact location of flow separation points, which in turn depends on the ability of the CFD code to predict the laminar to turbulent transition in the boundary layer, which is many times smaller than the profile shape itself. In general, the form loss results from CFD models show significant sensitivity to mesh size, mesh design, and choice of turbulence model. Considerable caution needs to be exercised even for CFD modelling.<br>▼
 
Small scale obstructions to the flow, such as trees, poles, piers, etc. cause additional head losses along a flow path due to their drag characteristics. Historically, form loss (or drag) coefficients for various profile shapes have been determined as a function of Reynold’s number through experimental testing. <br>
 
[[File:Flow round a cylinder.png]]
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The safest and strongly recommended approach with regard to establishing head losses and consequently flood levels, is to not resolve the obstructions in the mesh but instead model the effects of such obstructions with form loss (drag) coefficients (applied to selected mesh cells) that have been derived from physical testing. This approach has been shown to provide the most consistent results across various mesh resolutions. It also has the added benefit that, by avoiding small cells in the mesh, it will provide much more efficient run times for flow solvers.
 
(the possible exception being CFD software)
 
▲Small scale obstructions to the flow, such as trees, poles, piers, etc. cause additional head losses along a flow path due to their drag characteristics. Historically, form loss (or drag) coefficients for various profile shapes have been determined as a function of Reynold’s number through experimental testing. More recently, computational fluid dynamics (CFD) has been used to attempt to reproduce the velocity field in the wake of such objects. Although providing better results than 2D modelling, the results have not always agreed well with physical tests. In particular, the drag of a given profile depends on the exact location of flow separation points, which in turn depends on the ability of the CFD code to predict the laminar to turbulent transition in the boundary layer, which is many times smaller than the profile shape itself. In general, the form loss results from CFD models show significant sensitivity to mesh size, mesh design, and choice of turbulence model. Considerable caution needs to be exercised even for CFD modelling.<br>
 
== What are the limitations of explicitly modelling bridge piers in TUFLOW? ==
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