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FESWMS点滴

(2012-09-12 20:48:48)
标签:

杂谈

分类: SMS

1. FESWMS is an implicit model so the time step size is not dependent upon the courant number.

2. Roughness Parameters

The roughness helps determine the energy losses as water flows over elements. Each material includes roughness information.

  • Manning n values (n1, n2, depth1, depth2)

The primary roughness property is the manning n value associated with the element. The manning n value can vary with depth by specifying n values at two depths. The n1 value is used below depth1. The n2 value is used above depth2. Between depth1 and depth2 the n value is linearly interpolated.

  • Wall roughness

The wall roughness is used on the edge of the model domain. Wall roughness is ignored unless the model is using semi-slip boundaries.(壁粗糙系数用于模型区域边界。仅在设置半滑移边界时壁粗糙系数的设置才有效)

  • Soil Liners

FESWMS can be used with materials representing a soil liner. To use a liner, turn on the linear critical shear stress and set the value. When using a liner n1, n2, depth1, and depth2 are ignored.

  • Pressure flow

It is required to toggle on "potential pressure flow" with all materials that are assigned to elements with a ceiling elevation. If pressure flow is enabled, the deck roughness is the manning value for the bridge deck. Otherwise deck roughness is ignored.

  • Chezy

Chezy values are an alternative to using Manning n values for roughness. You must turn on chezy in the model parameters to use this value.

  • Bed critical shear stress

The bed critical shear stress is used to compute clear water scour.

 

关于糙率设置值n1、n2的考虑;以及松弛系数的设置。

(1) The roughness by depth option is useful for cases where the effective roughness changes with the depth of water. For example, elements in a floodplain with trees could have two roughness values: lower roughness for lower water depths and higher roughness once the depth reaches the branches and leaves of the trees. 
 
(2) The relaxation factor controls the solution step size between iterations. A lower relaxation factor can help with getting your model to converge, but it can increase the total number of iterations which means longer model runs. The recommended value for the relaxation factor is 0.67. If you are seeing changes in the final solution after changing the relaxation factor, I would check your convergence parameters to see if the model really converged.

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