I am doing a simple ANSYS CFX simulation of airflow through the increasing cross section with no obstacles and the geometry is symmetry But the problem is that the flow is not symmetric!!
I have tried different meshing but I still have the same problem!
How can I get a symmetrical flow in the denter image description hereuct?
With high Reynolds numbers, the Navier Stokes equation solutions are not symmetrical anymore for flows between parallel walls, maybe that's the case in your CFD. That you gain the same results even after remeshing indicates something in this direction. Decreasing the flow velocity of your fluid could give you another hint.
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I am using OpenCascade to import STEP/IGES as meshes in my software. Works nicely.
But I need small triangles, and the one I get are sometimes very large (in flat area), or very elongated (eg. when meshing a cylinder). The best would be to split triangle's edge bigger than some absolute value. Avoiding T vertices, too.
I was'nt able to google anything about that... So, currently, I pass the mesh to OpenMesh, apply the OpenMesh::Subdivider::Uniform::LongestEdgeT operator, then pass it back to my software. Tedious and costly when I manage several M triangles...
Questions:
Is there an equivalent in OpenCascade ?
Or a simple code snipet to implement my own loop to do so ?
Thanks !
The meshing algorithm BRepMesh_IncrementalMesh coming with Open CASCADE Technology is mainly focused on two usage scenarios:
Visualization in 3D Viewer. Large and prolonged triangles make no harm to presentation, as vertex normals ensure proper smooth shading. Deflection parameters allows managing presentation quality.
Computing Algorithms using triangulation as approximation (to speed up calculations compared to the same algorithm working on exact geometry). In this case, deflection parameters determine the target precision of the algorithm. Large and prolonged triangles should not cause problems here, as deviation from exact geometry is controlled by meshing parameters.
There are, however, some categories of algorithms, where shape of mesh element is very important. Solvers (numerical simulation) make one of such categories, where unfortunate mesh elements may cause numerical instability or other issues. What exactly matters / cause issues depend on specific algorithm - this may include element skewness, element aspect ratio, element size and elements grid. Some solvers work much better on quads rather than on triangles.
If you take a look onto meshing result of BRepMesh_IncrementalMesh algorithm, you may notice that not only large prolonged triangles, but entire mesh structure is somewhat suboptimal for solver algorithms:
There are several options you may consider:
Triangulation refinement algorithm. Such algorithm processes existing triangulation and tries healing some properties like skewness. This what does OpenMesh from your question, I suppose. Such postprocessing algorithm might give satisfactory results at good performance, but final result will dramatically depend on properties of original meshing algorithm. For the moment, OCCT doesn't have any refinement tool, although it is possible writing such algorithm on your own (I cannot give you a small code snippet, because such algorithm is not that small an trivial as it may look from a first glance).
Consider alternative meshing algorithm. Probably incomplete list:
Express Mesh by Open Cascade (hence, working directly on OCCT shapes). This tool generates triangulation having nice grid-alike structure (for smooth surfaces), configurable element size and quad-dominant generation option. This is a commercial product though.
Netgen mesher. This open source tool provides bindings to OCCT, and although it is focused on 3D tetrahedral mesh generation, it may be also used for generating a common triangular mesh. I cannot say something good about this tool - it was rather slow and unstable when I've seen its work many years ago.
MeshGems surface meshing. Another commercial tool providing an interface to OCCT. Never worked with this product, so cannot share any opinion on it.
Consider improving BRepMesh_IncrementalMesh. As OCCT is an open source framework, you may consider extending its meshing algorithm with more parameters and contribute to the project.
I am attempting to mesh a complicated design (~80,000 faces) for a microchannel heat sink, as pictured, and I would appreciate some advice. I have tried a range of different mesh controls (especially face sizing and body sizing), mesh settings and element sizes, and all have failed to produce a working mesh. The most common errors are shown in the linked picture, in particular the one regarding "The following surfaces cannot be meshed with acceptable quality. Try using a different element size or virtual topology." However, I have already reduced the element size to 2x10^-6 m, which takes two days to resolve before failure.
Unfortunately I cannot alter the geometry significantly, as it is imported from generation in SolidWORKS as either a STEP or an x.t file. As such, any advice for how I can successfully mesh the geometry for CFD analysis in FLUENT would be greatly appreciated.
I can provide more details or the geometry file itself if required.
Thanks in advance.
Meshing Attempt
Probably your cad design is not clean at all. But it is impossible to notice from this image. If you don't have control over the geometry source it is trouble. Because you might ask somebody else about check and fix something. First check you can do with your model it's trying to reduce the number of elements until the minimum possible value. Then if the mesh runs properly you can relay in the surfaces of your cad model. After that, you can refine the mesh, but the refining process is something that you have to do following some error criteria. If you are also the designer why not try to simplify a bit the geometry if you consider it is really hard to mesh? Meshing properly is a hard task, you should go step-by-step until you reach some solution. Also, you must not allow the preprocessor mesh automatically, without giving some criteria. Probably the first thing you have to answer even before apply any mesh is, what is your Reynolds number? And what is the most valuable result in which you can base the goodness of your discretization?
Thank you for your suggestions. In the end I solved the issue by importing the original mesh generated by COMSOL into SpaceClaim, then employing both the "Smooth" and "Reduce Faces" tools in tandem to simplify the geometry, before finally using SolidWORKS to turn the smoothed mesh into a solid body. This body retained many of the same features as the original, but was much less complex, having two orders of magnitude fewer faces. In turn, this permitted both meshing and heat transfer analysis in FLUENT.
I am currently developing hair strand system for my project. Currently I am using verlet integration to simulate gravity and wind.
Wind vector is currently just a vector. But I want to make a more realistic wind.
Is there any papers or articles that I should read about? Thanks.
It depends on how deep you want to go with the simulation. I suppose that you want something more interesting than uniform wind with varying direction and intensity.
I would suggest adding turbulent velocity to each strand with 3D Curl/Simplex noise. Even animated Perlin noise might be cheap and fast enough for your needs, but you might be able to get more dramatic effects with curl noise.
The original paper for curl noise is here: http://www.cs.ubc.ca/~rbridson/docs/bridson-siggraph2007-curlnoise.pdf
You can also find several implementations of it, but the basic idea is still the same - perturbing particles according to an underlying flow-field.
How can I find maximum flow in this undirected graph? Can anyone show the step?
There is algorithm called Ford-Fulkerson algorithm which gives the maximum flow of a flow network in polynomial time, you can look it up in the book Algorithm Design by Kleinberg and Tardos, or even in CLRS.
The only thing you need to do to solve your problem is that you should replace every edge in your undirected grah by two edges backwards and forwards with the same capacity and then solve your problem using the Ford-Fulkerson algorithm. It can be easily proven that in such conversion, flow only propagates through one of the two edges and always one of them is not used.
There is a cubic block of fractured rock; the question is:
how to simulate fluid flow from top-side to down-side or left-side to right-side?
Is FEA (FEM,...) the only practical solution?
If so for the question above in its simplest conditions, that is, flow can happen only through fractures; no interaction between matrix and the fluid; etc etc how to have a quick simulation with FEA?
Is this practical someone with professionality in FEA could do this in a few minutes? Suppose there is already a suitable mesh generated.
If not what would you recommend to get started rapidly to be able to solve such simple cases?
Is there anybody having experience with similar problem (flow modeling); if so what did you use and how did you fulfilled the job?
Note that we are aware of availability of many FEM packages e.g., FEniCS, OpenFoam, ....
Your question refers to simulation of the fluid in the porous medium, e.g. the rock.
I highly recommend using LBM instead of FEM-based methods. LBM simulates the flow in porous media by nature. Phys Review E contains publications about that approach. What is even more attractive, LBM can be also easily parallelized on GPU.
There are a number of numerical techniques that could be used to solve this problem, finite elements being probably the most common. If you have a mesh of the fluid flow domain already (presumably the voids/cracks in the rock) it would be very straightforward to set up and run the flow model with pretty much any CFD package (finite element based or not) and most people with any exposure to FEA should be able to do it. I am assuming that you want to understand the fluid flow within the rock in some detail, rather than just evaluate the effects of the rock on the flow in some larger flow domain. In the latter case, there are other approaches which might be more computationally efficient.
You could use the one-dimensional form of Darcy's Law.