Numerical or False Diffusion (CFD Tutorial)

#modeling
#cfd
#simulation
False diffusion, or numerical diffusion, is a common issue in Computational Fluid Dynamics (CFD) simulations where artificial smearing of sharp gradients in scalar fields like temperature or concentration occurs due to discretization errors. This problem is particularly evident in advection-dominated flows where convection effects overshadow diffusion. False diffusion arises mainly from the numerical schemes used to discretize the convective terms in transport equations, especially when upwind-biased schemes are applied. Additionally, grid alignment plays a crucial role, as structured grids can exacerbate numerical errors when the flow direction is oblique to grid lines.
Improving grid resolution is one of the most effective ways to mitigate false diffusion in CFD simulations. Grid refinement, by increasing the number of grid points in areas with sharp gradients, can significantly reduce the artificial smearing effect. Adaptive Mesh Refinement (AMR) dynamically enhances grid resolution in regions with high errors or steep gradients, thereby capturing transient phenomena and localized features more accurately while managing computational costs. Non-uniform grids with smaller cells in critical areas can also help, concentrating resolution where it is needed most without excessively increasing computational expense.
Aligning the grid with the flow direction is another crucial strategy, as it minimizes numerical errors in convective term discretization. Grid generation techniques that create structured or unstructured grids aligned with flow features can reduce false diffusion. Additionally, employing higher-order numerical schemes, such as second-order upwind or flux-limited schemes, can complement grid refinement by reducing numerical diffusion and providing more accurate solutions. These strategies, combined, ensure sharp gradients and significant flow features are accurately captured, leading to more reliable and realistic CFD simulations.
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Good

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Thanks

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Nice tutorial

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