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 April 24, 2012, 10:24 node based gradient #1 Member   lalupp Join Date: Jul 2010 Location: India Posts: 38 Rep Power: 8 Hi All, In unstructured mesh strategy it is generally agreed that node based gradient gives better results than the cell centered approach(green gauss). I assume that gradients are due to spatial change in th solution or attributed to high mesh skewness or mesh grading. But is there any gradient smoothing method available which considers all these factors. In my case it produces abnormal gradient for highly skewed cells Any suggestions are most welcome

 April 24, 2012, 12:09 #2 Senior Member   Chris DeGroot Join Date: Nov 2011 Location: Canada Posts: 388 Rep Power: 8 I think you need to clarify your question more. Yes, it is true that abnormal gradients may be computed for poor quality cells. All you can do is try to maintain a good quality mesh and ensure you are using an accurate gradient reconstruction method. What method are you using? Is your code cell centred or nodal?

 April 25, 2012, 09:42 node based gradient #3 Member   lalupp Join Date: Jul 2010 Location: India Posts: 38 Rep Power: 8 Thanks Chris for your kind reply my code is cell based but for gradient estimation I used node based approach. I am using green gauss method . My case is 2d. For flux reconstruction at the face(edge) I first estimated the number of nodes surrounding a particular node in that edge and found distance weighted average. For a simple case of assumed solution distribution like phi = 6x+3 (rectangular domain) i.e d(phi)/dx =6 . But it gives abnormal gradients in highly skewed cells (value ranges from 4 to 7) . Is there any averaging method available which considers the skewness or geometric properties like area or something else other than simple distance weighing in node based formulation . Second thing is that it is always not possible to produce high quality meshes. Assuming the mesh is bad ,question is what can be done to improve the solution. (like the reconstruction at faces ( or edges in 2d) for gradient estimation ) regards