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  1. .gitattributes +5 -0
  2. CylinderKnMa/Kn0p26/simulation0540_Kn0p26Ma5p9/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p26/simulation0541_Kn0p26Ma6p0/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p26/simulation0542_Kn0p26Ma6p1/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p26/simulation0545_Kn0p26Ma6p4/output/grid.300000.dat +3 -0
  6. CylinderKnMa/Kn0p26/simulation0546_Kn0p26Ma6p5/output/grid.300000.dat +3 -0
  7. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/Lambda/Lambda_0_300000.vtu +0 -0
  8. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/Lambda/Lambda_1_300000.vtu +0 -0
  9. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/Lambda/Lambda_2_300000.vtu +0 -0
  10. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/Lambda/Lambda_300000.pvtu +21 -0
  11. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/Lambda/Lambda_3_300000.vtu +27 -0
  12. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_1_300000.vtu +0 -0
  13. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_2_300000.vtu +0 -0
  14. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_300000.pvtu +29 -0
  15. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_3_300000.vtu +0 -0
  16. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/output/lambdakn.300000.dat +0 -0
  17. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/output/surf.300000.dat +209 -0
  18. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/surf/Surf_300000.vtu +38 -0
  19. CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/surf/geom.surf +610 -0
  20. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Grid.pvd +9 -0
  21. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda.pvd +9 -0
  22. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda/Lambda_0_300000.vtu +0 -0
  23. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda/Lambda_1_300000.vtu +0 -0
  24. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda/Lambda_2_300000.vtu +0 -0
  25. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda/Lambda_300000.pvtu +21 -0
  26. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Lambda/Lambda_3_300000.vtu +27 -0
  27. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Surf.pvd +9 -0
  28. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Surf/Surf_300000.vtu +38 -0
  29. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Surf/geom.surf +610 -0
  30. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/config.txt +78 -0
  31. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/cylinder.surf +410 -0
  32. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/gas.species +14 -0
  33. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/gas.vhs +9 -0
  34. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/Grid_0_300000.vtu +0 -0
  35. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/Grid_1_300000.vtu +0 -0
  36. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/Grid_2_300000.vtu +0 -0
  37. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/Grid_300000.pvtu +29 -0
  38. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/Grid_3_300000.vtu +0 -0
  39. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/grid/final-grid.dat +0 -0
  40. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/log.sparta +319 -0
  41. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/mir.txt +3 -0
  42. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/output/lambdakn.300000.dat +0 -0
  43. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/output/surf.300000.dat +209 -0
  44. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/runmpirun128.sh +18 -0
  45. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/runmpirun64.sh +18 -0
  46. CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/slurm-28429331.out +205 -0
  47. CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Grid.pvd +9 -0
  48. CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda.pvd +9 -0
  49. CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda/Lambda_0_300000.vtu +0 -0
  50. CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda/Lambda_1_300000.vtu +0 -0
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AQAAAACAAAAoUwAAWwIAAA==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AgAAAACAAACMAgAAZRsAABIBAAA=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AQAAAACAAAAoUwAAzQ0AAA==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AQAAAACAAABlCgAAGgAAAA==eJztwSEBAAAAwyC9/oVf4hIoAAA4G4r2XY4=
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+ </AppendedData>
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+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_1_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_2_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_300000.pvtu ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="PUnstructuredGrid" version="0.1"
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+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
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+ <PUnstructuredGrid GhostLevel="0">
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+ <PCellData>
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+ <PDataArray type="Float64" Name="id"/>
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+ <PDataArray type="Float64" Name="xc"/>
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+ <PDataArray type="Float64" Name="yc"/>
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+ <PDataArray type="Float64" Name="f_1[1]"/>
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+ <PDataArray type="Float64" Name="f_1[2]"/>
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+ <PDataArray type="Float64" Name="f_1[3]"/>
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+ <PDataArray type="Float64" Name="f_1[4]"/>
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+ <PDataArray type="Float64" Name="f_1[5]"/>
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+ <PDataArray type="Float64" Name="f_1[6]"/>
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+ <PDataArray type="Float64" Name="f_1[7]"/>
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+ <PDataArray type="Float64" Name="f_1[8]"/>
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+ <PDataArray type="Float64" Name="f_1[9]"/>
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+ <PDataArray type="Float64" Name="f_1[10]"/>
20
+ </PCellData>
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+ <PPoints>
22
+ <PDataArray type="Float32" Name="Points" NumberOfComponents="3"/>
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+ </PPoints>
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+ <Piece Source="Grid_0_300000.vtu"/>
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+ <Piece Source="Grid_1_300000.vtu"/>
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+ <Piece Source="Grid_2_300000.vtu"/>
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+ <Piece Source="Grid_3_300000.vtu"/>
28
+ </PUnstructuredGrid>
29
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/grid/Grid_3_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0599_Kn0p28Ma6p8/output/surf.300000.dat ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ITEM: TIMESTEP
2
+ 300000
3
+ ITEM: NUMBER OF SURFS
4
+ 200
5
+ ITEM: BOX BOUNDS oo oo pp
6
+ -5 6
7
+ -5 5
8
+ -0.5 0.5
9
+ ITEM: SURFS id v1x v1y v2x v2y f_2[1] f_2[2] f_2[3] f_2[4] f_2[5] f_2[6] f_2[7] f_2[8] f_2[9] f_2[10] f_2[11]
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AQAAAACAAABABgAAtAUAAA==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AQAAAACAAABABgAA2wUAAA==eJw9VHk41GsUrjREZClRmoqZLGkwXUWS30lKlBa0XLIk96JkXypdKUvJVkgL0ybdW1laMINpDmYYkxkk3VJET1pUdmmRuvOj555/vuc7z/t+5z3vd77Pnv6wXj9LiIX9RhoDLAHK/2CnU7VqMHOUenLKX7X4zNyLYUURYKRPT+l0SRXeZfX9XbuBj2HvH/gcnC/AmjRmJEMbMcA0hg3HqrF7hpePAguxhS3uvnuuEjfcNs/5+piLlIB2iRO1HOd/jT369kUFLpnhE71MjoMhs0eK+R4VaBjQ5hUdwMGRs+0Zx+PL0Xhsg+N5r1KkGU1Kac5j4zJ6753hKxWoVp2bQvu3HPvbojhdnIr/z73yhfmmcBEXaV867CP8K7FJQfH39vdV6BfYHy9yrkILt9kzs9P46JSlnRoezMeck86artNq0VZ0K1SxrhZ3nPlrx9SfQqSXfT8+/FyIBu9G0mt9RZiwjXv6YFc9Ngd6yDXMkWCGxWSHOKVGbFPdNN19WILmvBFx+iQJXrGXzPelNOAicfGYoaEEzcyu6y0ZEGNuOkkQYbvcwpLEDCFeuyl/tVdGgH7UbQ4q4YipodlBReJ7SD/cF9jkzsIKzyAJdewSofvdkQhcXE5QKg/IKo0JieWW9x2ztJuJ5XV0ms/uViItBbiFHi+JSHXHaMeeLkKfs3NrrN97ItDWnHHgYR9xjrTRZYhQzLhxbKvdCPGxcgXNPuobEU0197Hx+0FIxNLYOBmS/N+O3hiQgbM/T0WPWshCe0nc4abf5OEiec1sRcjedulWwVNlENwxCsg9qgrPv0bv5ZrOhKhY443X7dVhT6+LtZW+BnAparMI2Tkw5cI0rYvuWjB1+wzGXTcqpFV/0t+VtgDG6ylqg8NQqlNYlzbEKW0uyqTQYEvK2QC1NXT4czwWwcqOzdKMLlxdWzo0lqkHxu102aPqBnC65zCx7IUBCDeYJa7uXAyDzQ5eKppL4JF02bmCAdq3Q3xP7zcCBjmgCUZAdtWcYjyx1zWB1obPTq/iTeDmgJR4ygTK7RSnGMox4Ulu0CoFMybsJwlLmdAoheW5Mid8CWYC79Jc4/Y45kRdTyaQcs7QmOBNCpXynF/lZQsmMUGaXZOUbwLT8ue5eEeZwEYSTjEBPtn+MWMg5fVwjYCE+b9lgLXn6yYdLQb4ko19MoRZhbYvD7UsBpvxwga/9vogba64rlQPRBFSJYG6sIls13TRLxwdYrfasWJ0aRDyVbmbrawDoeSBHQuhz3ytbciKBWBDPgB5KozPtasWTKfrRyeXacJe8iJMNUCuf/b6LZbqMCad+ieTZ0KB34vgk5tUJ/KHlUHcK+JbMxThzB8n9rjtkocPoSXqo/Wy4O3Wtk7miwwkSB/XP/TJv3T8IHhVGrpOlG9E2LrMEVmPEUKpk3Hq8pdB4plMQeWpx30EOX2B/d3EI+6rWVRRF3FVv0lvkN5JPI9/Y5FS/JQ4spcblqHZTOTSSMFCwuPcRvVk+3JC5jxrbZ/zZcJnvXrNrXwWZqW/pmfyitH0id+1vEEeZjUOWnJW8tHMOXnI7EodFpgaUNrdH2DUPs9bU5PqkRNutjm8ToIzeCbnF2tK0DZSZaXTOzFGdOao5t2XoHdR5BHuDgkeGbUIajjRgCfiXaQSxWh1jw4OjHrsZhfJvE2uQ0MH+9Xss3VotL3Fc26CECOuNx1McatFqug1ca2jGtXIb0FdgK7WVu9a86vxw895b+qnV6KCr47/gUNV2Ce1s8CPh5azKN9sLnJxtJ40+D4ORmX/+Ghbji1rTMtjn5ahdQ874iqdg3OZ9xr3ZLIxsZHl+TO89Fd9DtqpFMZYryrDzTBzN/9RBertI5kVGDluJBeH6hYoWn7moQOv9U3tcCUSk2445LQhWh8oDha4VGKPd1Am14KPrGHeQIlPNS4d/4j5qBOclqfsJsAYz9T5yK7CCw2bZG1FUpxRUqJEWIO7QtoMqPl8PG1jnLszoAb/A640ITo=AQAAAACAAABABgAA8QUAAA==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AQAAAACAAABABgAACgYAAA==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AQAAAACAAABABgAANAYAAA==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AQAAAACAAABABgAALQYAAA==eJwtkns4FIgaxq3LMVpKFlnVal3W0GXbVAbxEdkMLTEGU6zdLoaJqEEdjVskYWuYRmRqatxGs2aGuW1tPuvOjsamOoddl+1YinRqhXbUduxz9v/3fZ7f83vfc+wu5SEnPfwwZNUDPk8H01hb3RO4DkAe9pX+WGsL+dSQySiiJ4ymx+1NzvQCd3ZsQ4XcDwwdYQf/USC0DXJ11n/tCyfLvmYnM4JgaHP0uheUQJiYohy7NRIMzpNV4o7eL6Au7Zxwc2oIWF7b9ZM1MRwiV7hquhkU+OCr9sGNR6KgItU0f1yXBpdaAgb59BhQ1D7IriqiAa19KVZT8iUoHv90Ji/kMJA7TcnCy4fAhnhwu9HZOOh3kHBafY5CtsObxfw3dBiwm7G4Y5sIkiNPvpGtSIEncwvFTxtT4M2JH5jyZCY4tesKY3qYMD/bSy90PwVDhI4ZrUEGfOeiCmX2suDkXzFZJlyOtczKpuXAxH2eesI7D5zdHBcGvM6BT6WU7u5RCDlpBeZ69hdAwV/sMtUpBpeaeaX++EX4vPFqaMxGNvRt65BuSSqDN9YBuckGHGg7z9Il+nNBq9pJKZ4rh+QzsxUO71UC99m/lwkqwfOKuc31SzwI5uT5DXddB/s9hyM7196AvLjdZR2lNyHowsXgnnsC8PH3zrfbWwN1/SST/fl18FHb6/hfrtYBb/FdSlueEChhpamk2w1wyscvYbhTBJIN/ZHbed/C3foRse4ZMViHSbqGmBL44qWq3nK1FFpaRSHVS1Ko+mHs06OBTUC67k518mwG8nNJofqgDLpNddZlLMjA8e7xZWI5xO+wfy59JYd0emTx7UcKcLWalrRtVsKrUSvd7EPKvz0oYZ9FMXlaXwVaX5fgRicVNDx4lkONVYH3mOWmhwYqsIjUCfvHShWEh+yf38hTwkmeP835uBKmNZnV542VIJAU3TT0UoB8LUttqK+AAPI/rWg8Gag1JvuGnjbD/PAxUceuZlgZl7nDkNAELYyB8tE/JeCx38wsfq0Y1nO2Di6IRcA0cdl+jdAAczSjEYeUWpCpbc0P+AiATujelGHGBxZn9Zps/wqIINyS5snZ8LqOUtikzIPYrb1mU78nQxv/5VMqMxktLi1AKy0f17P/Gzpdysb/96+g58b4TlfzazjTw77X2iNAitusk5tjLUZMR5n2LdXj75URqdT3RZi2JNoWRhMjXWreG6ErxTlqdEECuQnbWROPqn9uwhXZL42ttc1o3J9qHS+U4W4ZVeZ2T45Mv5ZAYaAC28kGFuGowHIbVd8HBCVqzN3F0kAVnrab67ExVmFAXOzEkX0qlEw4W6KTCt/Ze6XnvlXisqRlQiX6Jh/s2meiwvrHXZsnpxR49SLDt5OkxIfec27pdxRYw/850Fogxydar3X2fnK01SEtPPtOhl764YIIPRn6G3sUB2Q1I8m01CBovAl3SlsjmDNS1GwZ00pGJeix/JrBQAnOLq/cM92ItNu5iR1l32LihuhjNCcRvrNxDLLqb0A1kXuAZiJE1yW93xSsOlyd2P0geqgGaxZLxJ7N1bj3jwB5cLcAbWv9zO4W38QNxrsWP9pyA7n2mnkWiY8DMaxdr0Z4WAdLbNttVahL9i3ZXVqByk9GuD0G5fgH2jlTXMvxuWaPb5eAg+2VfTclRaVY/WFRbXMZGz/WXXmD3P8NqrxzH3dfKcLGVTMltK8u4F/tuBcFWGVN95pKKsCcZs4mxxt5uCZ0e+f9tFzsMSt2axFlYcOfi1ziZ5moKqx2TMvMwLefxIi1iafx15ez0WdbmPjZjjDGLQET+YuMiJK+E3inXn22oSAJBRxTUUP5cQyd3CqcqUzAcotP76jD6Zj//WmDt+NxKBwbPxq07jBmThoaidIP4fTZtWP9RrFIc39o+j4jCj9/kUq4nxGFQ8PSbu33kXj1vJ3sgiACF1nPbydepmJS1qm6oAwKHvBYiLy+JgyJA2pSU3koMv7jRRxVhuCVS5Na/RQynj6lyfmNEIA+VFvLUG0AmkwmMqee+WCWnuGJSJc9mODJJI47+2AwZf0qo49JmDTQa+XNJOHrDM6/GAuOOLGTW6Dk2OORjodHPaIJ8D83mB7lAQAAAACAAABABgAAuwMAAA==eJwtVGlMk0EQNcTghXgE7zNqEIPEGKLGAx2g7ddypBwNIF6IByCHQFHLoRYRKyIiaMWCaBUQUKylJYrGmIgRNIBHPGpVYoiKMYqKgsYQjd/27f7oZGbevDe7X2dcvSyV3+YH0KPf4e9qBuV0YvLQCFcvBRmnLuqa16Mg51y9f2GSQCW9OWuWrPYjlrV0yujaz7/PNAlycjMJ3Vn9EvoS2jwzf51AroyvTUK6ifaHv7dIKebD4zmv7b5kTo8v6X0joeKWAY/1xX60nZ1DUoreuvKtMligtsBlBb6pUsr85L54+C0B/HYZ9I8KNMMBVJK8O0uUDAZOpaJpYpvO9hCqaxUJ7KHQHRWJvt5HkdjV51BNNIm/zTO3baBfonpL/iZaIJqB+M1Uneoz0qbeCt8jju6y9rLiUGdMIG92KlKg+3EnbKKaspnAzXQSq6tSP2SAd7MG7+CtIQddRg7q2/eCd2Eu9J7nAp+cB7zhIA1h51U++ryuQ93lAmL0PuZC4K8UUblBPAPF1NkhHpdS1HmUIu5zAnWZJ5H/qoc/owx5vQHv3mSAv6ACuO+V8OeeQz7QCN90HvyrLgBnvIB4QjXibdXg31iD/Oxa2El1yK+tB97G7dNLHH8Z9kgDrOkKdMeY4NtMqD9w1eFbbWZH/ZQBM/iDGx026Foj6nobgV9uRXyOBfF+C/huW5CPtiLua3X42lru7+Z1XTx+B/Ge6U3Q3wXrHd6EezjBTolBXUeaFXFfbnOswHfzPtqhrw2wAJ9qAe4G+tbW4R49KxrB22KGrzPz+quwfSbYddzuMYGvpgF8d/i7qvg7D9YBp+VWxb+L7SLyP/l3m16DeEMVcE/4947ldsJ52Agj8h3ngA+phI2qgP7oclj1aeD2lUHngR71L07C/1aK/Cr+/00/Dt/5OM1iA+BxDPMXcRR1zUcwZ+rD8HPyMV/lB4EPOYD5Mmsxl61azHPzfj7P2cTWgdupTMybbjfPq8HrlYG5U6Sh/wdp2FPDkqATlAj8yzhi4zhSGQfdf1vAlxeLe+XFoN6wHvtn6Aa6lSFu1LMqCmMXGhdJG28qXJwSw6HbpaSxUcub7ruHYQ92KrH3CgLJxclTN3GEQDsYgURC4latbS0UaM0SkeGMP8WzhXyP7912GclY4WkpFZWljM/TSihIXKcxehkxOc/5cipM+jhYXyKhe7PFTdznR/+eaT65l8iIrevaHjkFMMGlAiWLsL4fUgphROH+xORcYgWa+/rPvh1JCt6fFPf/KqP/0EfHZw==AQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==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AQAAAACAAABABgAAXwEAAA==eJwtxVFEHAAAANBKkpzkJEmSJMlJkiRJkiRJkiQnSXKSJEmSJEmSJEmSZGYmmUmSJEmSJEkyMzOTzCRJkmT20Xs/LybqXazjHO8EB5zoJAed7BSnOs3pznCms5ztHOc6zyHnu8CFLnKxS1zqMpe7wpWucrVrXOs617vBjW5ys1vc6rDb3O4Od7rLEXe7x73uc78HPOghD3vEox7zuCc86SlPe8aznvO8F7zoJS97xate8wd/9Cd/9ro3/MVfvektb3vHu97zvg986CMf+8SnPvO5L3zpK1/7m7/7h3/6l3/7xrf+47++870f/OgnP/vFr37zP0dFvxfjWMc53gkOONFJDjrZKU51mtOd4UxnOds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AQAAAACAAAAoUwAAWwIAAA==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AQAAAACAAAAoUwAAzQ0AAA==eJw13KG7OWCYJuCzu3bHzJpdM2tmzazZNTNm1l6XIAiCIAiCIAiCIAiCIAiCIAiCIAgnCIIgCIIgCIIgCIJwgiAIgiAIgrDh3L+v3H/D977P8wa+fl+QIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeE3p5xxzgWXXHHNDbfccc8DjzzxzB9eeOWNdz745Itvfvj1H34JMMgQw4wwyhjjTDDJFNPMMMsc8yywyBLLrLDKGutssMkW2+ywyx77HHDIEcec8JtTzjjngku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CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Surf/Surf_300000.vtu ADDED
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eqrrRUxZFIMg96URgNB0fK8V7mmoioUMsOOGQHoWJjR5FmutjMcCJaRzXuQG+/jX/xWdvwQF/GARZxOHzPGNS76sEbKq2sOteehdhzm+mmWNJ0DFpirT+mgziHVqttHkqTnJ/5hE6UqEt5dPek8FErIRAwTTYzC8Y6WemQ4tQubcqOhPrd6ziBoVmQaPrmc64WA7WTHbv83LORf6+zF/ipvKQNtZ88LRMIQZ8+4iNmk9g0UUjHx8tgrzWFatzH55CaJtSGcI7HERcr5TjYADhUEY0C5+VN2mPRFcga6pwh3l4Jd73MPpMBqpgLxnP5a2rgXeK5m8rhmrQR+4/6uH3DJKbjnBslF7AN6c+YkCibu7/LyFsd2E9rj+eMNS3asDOSsLkP1psfLX7/OrPu2x80tPQsqU0QVpMP9tPohlJhvkf/uU0w1P4YAvO+h/Y4i7PwTxhYxykGQdmBaq0otZveGKE3oqDkymXuszbEP5h512ZeVxUdB4rfqfPRUpix36lZC5+FBrCQ0a0HfXMCR4MiyXVweShwNvEu3uSBw1W7+wP+Bgu116zz4cPiVk3ZYl8rNq/qiixlA/5e4phqTl8GAsF46Pdwnjq9zw+HGIXPGDa8bHkrdwFhicfg6pKnb8O83C5y/xeBY8H9qV7VOmHPBwnhPho+vJAoc3jD3VyUSIy/ZYTw0UL6wuCp9owpdnoX5zTBqFs+9sQ0tz1t1RUKy7LScqSxVsxGvjcnPWAg5C8ySUifS0YCgvIj9vfgo0Spofr2pqxjjL/lvjyZgy5JAxeu90E4TaBDcc+6uwNNni2Ed5Uo0YE3sy/bJdSP6fDS5TsEij4ArwJKa/K/OdQrbfcFh//DB7xRsc3uNZi/tMfFi9BDXZkWlyt7qmC1NqfLoU/qURAQn1aY2sF9u4xSN5NZWEDU45twCvHgN2Suo2PS6GguzrVYHEJ3OU/5VaeKoYgFUH0QmjtpXd3fCyA5tM6w4tq+dhWdkt0gUfuXF5yoORS/iZYNhvEMXWRmq2ZsPE6pCzv9BiDuiLMg3GPMCMhCFgaYjQEBe7P1UlF42YVn5LpZIyaCpxIAv2w59rrM4k4QNud4j9xBzNWz2kRenHQt3nNXt1xEwMLT89G+Dos5Aw6xVRiYDZLsWvjUZgNpf753Eh0CTlIR3+dVCbdJAJVp6K2D6qGwk+DnyVLDcawY3DZemIAssqGE3Q0AyCxPOGkep0fFGd0+yqu+EFV2PBFCLG2zHtujr2wsFTr5gYFChqoAoM9vq12AnvOkWHLiMzJvE/CUVUBAUkwEhpLxJExo/7IWXoIx+iJI7LPrJHJGLLHmdMf3EgPvnPx/3w4fuOlSrPrKbEGR6T57lLeavqdp4PZN3gXrYmYPVQ0aydiOl38awGD9I2rbgLMy7jA+tED1jZpV4jsenl9mY0rYtcLJsoJ9RQlR+ZT0lyeXdHmUsy2vu+G/wCi/AnoAQAAAACAAABABgAAvAUAAA==eJwtVHs8lGkU1iQhJRF+5Jpbjct8KnI9h0q5lEsqSu1kKSmpKCrtuk8tSVe7lgqVkmypVathZpgh5JJcKkQhypgwoovY+Ybz1/e+33vO8zzn95zj06eaWvKWB9bP01ToWjzwCYump2iWQ7ZgduobLg+e7m7Lfi7FA1+3A/YF83lAt41LnJSpgPCTzr8+SikDLVet4uulHIgU2PkdaedAWPbOhEvX2EAJ3FwwZFoOzZEfDQhpNhB1nfGOkmy4Qyta2HOdCRnZS/+bYDCBnxiSZK7OBM5WGWXGChaM3PfYnx7BBgbLYH/TGiZcvBPr6ZzJhi8mYcXGBmWQnRG0Tae3HI4dVp/fZcKFvHTtT/YSPKg+kOrsVFcBoz+bRIhVEMKbLDw58gwEjmGZTtsbwC3F5mbcl3pgnj/eIy/VCAp5TfyYrS3AW/27oqxCC9QIuwT5Ha/gnEWKT5J7G0xSqRtdHDphRW6Nv7+wC8TyF3cDg2t3xT68FwwmvCB0eT8EM8Mvqkp+gvX5vFwJVQEctpNtzTk0BOM9u7NaVo6ApRp7h7aDEIrioxoe+n4BwQDbaqnLOOxduy90KOE7uPomrm7h/QC+2VND+XlTELXbfAttuQSeHm1rjRHOwnXrj3yT/0hBje0BNp3uc/BQ3enGt/S5KNmXbDR4VhpFYCJEWczt32A6lSWH4XppU6mTC3Bnu9PsyJqFeM3WuCn9gwL2xF2tzZNXRJ37R4LOhyjhwZXRj5GijMltQRKhDiro3X3zb26XKqb/JQpfNbSwLfG6kqWOYUHnB6NYGnjC4q79th1auDjZ5ZOktzbeknXSP+qog5R0afWru3TRRY5CZYzp4nNBVbnj4FK8+jm3wvWxHu4Rhz526EnFXC43wN4G3bZvvxli/fhmEaIRSucv2R5wchmStAajlqOP1aNnx+yp02dbY1y1q9hZjmKCgigQfZrgubIvRn7uphjS9+POsJcZUhnKr+tUaJjB1Ral0JB0W34UDTvdz6YdHKGhUsH6dyeUCKSTwL4EfiVh4wn0IwsxiWmerQSeIst/nDmrmGPCgOcTzTnm6PVEM2HgBYEryBAS0zq6CRTTqiFm8Ajc5L/QxyqcwLzhxo3+HsR037QIXBTHrs7i0aZ1xNLQqUj4s2k1Dcmynl/NUNSMYOZKM7Q840Dv9TBF8nfkPhPkVGetKxIaIymzsZaKJ0kh1lT8nTScz3KUICN6Gd6ucLU8wzFCsT3OGmLpNTWzjhsGKGK5Jomvj2TZhvd6OCSSZSmjhyQtWLx0+l5XF02ndH6ObdHBBSaFmZ8Z2qhR1Qs3ftFClQ0eE6oPNWZ8qI6p178SHwrUsD+YOpbWoYqwbccDVr4KittbsBhzLljP2hivhDQ3kTMCFTGP7u1lSFdA5eCNwpSXC6fzTeUxzkz0oEJuhp8sphwVDTJdBo3k5/1767gUNjRvFY2CJBaaHsyZZSuJ7a0xYU6XKEhVvdwkEySB4meVkyDG0Z+Aurx6UcY3yH5k+bUsYBzE/XAYg24LcvCEsFO8CIZn9sIQIOPbCIXPh8CJH8nz5g6AamFxc1hjH8S+r/zz6ZVeiNkRIfRmdEP/6/zvfiHvgK9Cbqi3MJme7yn/4g3ETt0LqHR6Ay1WUgH/RDRD2T5OUmlsE/jcXlVrs+kFPAjx3BPn1gAT3SXBgWU14CVbXHKDqIb15zrbRvdWgre1z1QfrRIoC7JcajW5oKM1emDrqTIQz2c9Bx6w+FdtjdkgZzuuaWpVCuKxe8CCIPBam7iFCX52hiWhUSVQlOWfwZLhgOEmQYSRLBukFilBRXsJGFRmNnK+s+Dsupevq7rYsEe8iNgwkBRflOHKgeS9DQ3A4cAHvz/uVpeWg6dSv+LxC1y4l5Mnky0ohyHWZf4raS5E+rckZWVwoXI0Xl/TowzW5ticzlzGhSjZ6GE5tQpYs2TgvYXaM/gfqi3sqw==AQAAAACAAABABgAA3gUAAA==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AQAAAACAAABABgAA5wUAAA==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AQAAAACAAABABgAA8AUAAA==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AQAAAACAAABABgAAGgYAAA==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AQAAAACAAABABgAANQYAAA==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AQAAAACAAABABgAANwYAAA==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AQAAAACAAABABgAAzwMAAA==eJwtVHtQjGsYxyQGgzEpJQpNapxDRwgN84zt2/0+i7UitxqdTqNlckvRCalYGymShkqU3S67VHurSXOcoVPG/TYRGtM0hmpyGeMknM6Z8737e78/9vc9z+/3XN53v+fpDfxl5NVBNTV8+bctdZFIN++WCw0Kgfz0fdrGsRKN37DIedtTScHRp5r7eyRiEOS7nO7s+/OSzziBKkta/K1JAnVqcs/tbFaS/DvhyOoI2sqer0raL8vmlCpplsHz5cMRAs2Uy/k2KWlUu3H3Ep8Iin37eHqHt4LG/mwv/RSmohkd39O3R4uo902kr0Hyy3ANeb18OBA5WktTNsWHd/qtISav8tRSZqEiJzFsLU2e8zrAvTeSwlkjx6KQx209DWFPSAx51Kq60lpiaE3jVH3f8VjkLYyn4iL5keKJHXeqoMP5BhPovWzqJyfSEtZoyi74b+yGvjeJ9jAiOZnErjSP2qoU+qufJUyjUPZ4HUS++YfAaw7jPk9nog9nJrHjj/LTg5f06Gu9gViZ6OvZZJLp9kc59OC+/NzKgb4iD/V/ywfW5IP/eAb3veEs7LZC9JFzDvjsPHj3YvD+JcCdpbifpRehm10GnbIc2FgO3QIj+GAT9H0m1M+uAP+qEvbGKtjvq4HhFsSttiCu6wrytl+FPrgW/nl10I23wk6wIr6Ro4fNpfe+YoPO0w5bbYe+w+7K++6aw8U7VBzdna74Ff0O1CtwArO4/7oTujpg6LJ6+BfWI18gt+u5fhB4Pwz8VrMTuhtOVx8ZGTx/Ave/4HW9wQ+RHPAbHMj3ww68YEf9Hzhn0U825FPhvI7TVsQlWsFPqQO21iKuuwZ9xdSgr9/5Pbda4G83o77OjLjh1bBzK8HbKpCnyYS4KiMwhP/vPfx7yC2HvbgMebovIn4p/44eF0NnKYbOeB66X/l3GFoI/uhZ4MIC+Ged4XN1Cnnm5mHeTHnYA4tPYi5Ks2E/N9ABNkg9esxT81HsE98s2CFZxMZJ+3c6z3MA82VOw9y3pmLuvPbzPZSCukF70ce2JPCGHYgPSMT8vtNhzi/rMP8Z8dgT6jjwq2JRZ9oW9BGwGXtg4kYaM0zegE/WUZMkv/mtw94r0lL1LXXY8aEa6v7H/PnpDg1ZPj9dGUcrkd9fItaGao8aaBOx/zoFimOLWVTTH8kFk9yORVCZj7wAGxTkFiVv0g8C9vgXAXW/c1sRQfeY+5CA+xFV5LwtL/ITEvryEum/ttTewKEqejQQ+aaiRCL5p8U/QyS2vgeMAqXI5aLSl9NeXf6Hg5/U9D/yjskiAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==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AQAAAACAAABABgAAXwEAAA==eJwtxVFEHAAAANBKkpzkJEmSJMlJkiRJkiRJkiQnSXKSJEmSJEmSJEmSZGYmmUmSJEmSJEkyMzOTzCRJkmT20Xs/LybqXazjHO8EB5zoJAed7BSnOs3pznCms5ztHOc6zyHnu8CFLnKxS1zqMpe7wpWucrVrXOs617vBjW5ys1vc6rDb3O4Od7rLEXe7x73uc78HPOghD3vEox7zuCc86SlPe8aznvO8F7zoJS97xate8wd/9Cd/9ro3/MVfvektb3vHu97zvg986CMf+8SnPvO5L3zpK1/7m7/7h3/6l3/7xrf+47++870f/OgnP/vFr37zP0dFvxfjWMc53gkOONFJDjrZKU51mtOd4UxnOds5znWeQ853gQtd5GKXuNRlLneFK13late41nWud4Mb3eRmt7jVYbe53R3udJcj7naPe93nfg940EMe9ohHPeZxT3jSU572jGc95/9sSVRSAQAAAACAAADIAAAADAAAAA==eJxjZh4eAADsVAJZ
37
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38
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CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/Surf/geom.surf ADDED
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CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/config.txt ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ variable BZ equal 1.380649E-23
2
+ variable PI equal 3.1415926536
3
+ variable molecular_mass equal 6.63E-26
4
+ variable molecular_diameter equal 4.17E-10
5
+ variable gamma equal 1.666666667
6
+ variable R equal ${BZ}/${molecular_mass}
7
+ variable L equal 1.0
8
+ variable xmin equal -5.0
9
+ variable xmax equal 6.0
10
+ variable ymin equal -5.0
11
+ variable ymax equal 5.0
12
+ variable Kn equal 0.28
13
+ variable Ma equal 6.9
14
+ variable To equal 273.0
15
+ variable Tb equal 273.0
16
+ variable theta equal 0.0
17
+ variable mfp equal ${Kn}*${L}
18
+ variable cpmfp equal 4
19
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
20
+ variable sos equal sqrt(${gamma}*${R}*${To})
21
+ variable vo equal ${Ma}*${sos}
22
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
23
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
24
+ variable ppc equal 30
25
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
26
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
27
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
28
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
29
+ variable nave equal 10000
30
+ variable noutput equal 300000
31
+ clear
32
+ shell mkdir output
33
+ shell mkdir grid
34
+ shell mkdir surf
35
+ seed 7
36
+ dimension 2
37
+ global nrho ${nden}
38
+ global fnum ${Fnum}
39
+ timestep ${tstep}
40
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
41
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
42
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
43
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
44
+ boundary o o p
45
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
46
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
47
+ balance_grid rcb cell
48
+ species gas.species Ar
49
+ mixture air Ar frac 1.0
50
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
51
+ collide vss air gas.vhs
52
+ collide_modify vremax 100 yes remain no nearcp yes 10
53
+ read_surf cylinder.surf
54
+ surf_collide 1 diffuse 273.0 1.0
55
+ surf_modify all collide 1
56
+ fix in emit/face air xlo ylo yhi
57
+ balance_grid rcb cell
58
+ create_particles air n 0
59
+ stats 10000
60
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
61
+ run 200000
62
+ compute 1 grid all air n nrho massrho u v w
63
+ compute 2 grid all air trot
64
+ compute 3 tvib/grid all air
65
+ compute 4 thermal/grid all air press temp
66
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
67
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
68
+ dump_modify 1 format float %12.5g
69
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
70
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
71
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
72
+ dump_modify 2 format float %12.5g
73
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
74
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
75
+ dump_modify 3 format float %12.5g
76
+ run 100000
77
+ write_grid grid/final-grid.dat
78
+ write_surf surf/geom.surf
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/cylinder.surf ADDED
@@ -0,0 +1,410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ surf file from Pointwise
2
+
3
+ 200 points
4
+ 200 lines
5
+
6
+ Points
7
+
8
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+ # Species data
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+
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+ # species name
4
+ # molecular weight (atomic mass units, e.g. 16 for oxygen)
5
+ # molecular mass (kg)
6
+ # rotational degrees of freedom (integer, unitless)
7
+ # inverse rotational relaxtion number (unitless)
8
+ # vibrational degrees of freedom (integer, unitless)
9
+ # inverse vibrational relaxation number (unitless)
10
+ # vibrational temperature (K)
11
+ # species weight (unitless)
12
+ # multiple of electon charge (1 for a proton)
13
+
14
+ Ar 39.95 6.63E-26 0 0.0 0 0.0 0.0 1.0 0.0
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/gas.vhs ADDED
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+ # VHS collision model parameters for each species
2
+
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+ # species name
4
+ # diameter (m)
5
+ # omega (unitless)
6
+ # tref (referenced temperature)
7
+ # alpha (unitless)
8
+
9
+ Ar 4.17e-10 0.81 273 1.0
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CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/log.sparta ADDED
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+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ variable BZ equal 1.380649E-23
4
+ variable PI equal 3.1415926536
5
+ variable molecular_mass equal 6.63E-26
6
+ variable molecular_diameter equal 4.17E-10
7
+ variable gamma equal 1.666666667
8
+ variable R equal ${BZ}/${molecular_mass}
9
+ variable R equal 1.380649e-23/${molecular_mass}
10
+ variable R equal 1.380649e-23/6.63e-26
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+ variable L equal 1.0
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+ variable xmin equal -5.0
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+ variable xmax equal 6.0
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+ variable ymin equal -5.0
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+ variable ymax equal 5.0
16
+ variable Kn equal 0.28
17
+ variable Ma equal 6.9
18
+ variable To equal 273.0
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+ variable Tb equal 273.0
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+ variable theta equal 0.0
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+ variable mfp equal ${Kn}*${L}
22
+ variable mfp equal 0.28*${L}
23
+ variable mfp equal 0.28*1
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+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
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+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
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+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
28
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(${mfp}/${cpmfp}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*0.28)
32
+ variable sos equal sqrt(${gamma}*${R}*${To})
33
+ variable sos equal sqrt(1.666666667*${R}*${To})
34
+ variable sos equal sqrt(1.666666667*208.242684766214*${To})
35
+ variable sos equal sqrt(1.666666667*208.242684766214*273)
36
+ variable vo equal ${Ma}*${sos}
37
+ variable vo equal 6.9*${sos}
38
+ variable vo equal 6.9*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 2123.9273932469*cos(${theta}*2*PI/360)
41
+ variable vx equal 2123.9273932469*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 2123.9273932469*sin(${theta}*2*PI/360)
44
+ variable vy equal 2123.9273932469*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
47
+ variable xncells equal (6-${xmin})/${mfp}*${cpmfp}
48
+ variable xncells equal (6--5)/${mfp}*${cpmfp}
49
+ variable xncells equal (6--5)/0.28*${cpmfp}
50
+ variable xncells equal (6--5)/0.28*4
51
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
52
+ variable yncells equal (5-${ymin})/${mfp}*${cpmfp}
53
+ variable yncells equal (5--5)/${mfp}*${cpmfp}
54
+ variable yncells equal (5--5)/0.28*${cpmfp}
55
+ variable yncells equal (5--5)/0.28*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 6.60399822965135e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 6.60399822965135e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/${yncells}
64
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/142.857142857143
65
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
66
+ variable tstep equal (--5+${xmax})/${vx}/${xncells}/10/4
67
+ variable tstep equal (--5+6)/${vx}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/2123.9273932469/${xncells}/10/4
69
+ variable tstep equal (--5+6)/2123.9273932469/157.142857142857/10/4
70
+ variable nave equal 10000
71
+ variable noutput equal 300000
72
+ clear
73
+ Running on 128 MPI task(s)
74
+ shell mkdir output
75
+ shell mkdir grid
76
+ shell mkdir surf
77
+ seed 7
78
+ dimension 2
79
+ global nrho ${nden}
80
+ global nrho 6.60399822965135e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 8.23945303198305e-07
85
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
86
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
87
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
88
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
89
+ boundary o o p
90
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
91
+ create_box -5 ${xmax} ${ymin} ${ymax} -0.5 0.5
92
+ create_box -5 6 ${ymin} ${ymax} -0.5 0.5
93
+ create_box -5 6 -5 ${ymax} -0.5 0.5
94
+ create_box -5 6 -5 5 -0.5 0.5
95
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
96
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
97
+ create_grid 157.142857142857 ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
98
+ create_grid 157.142857142857 142.857142857143 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
99
+ Created 97600 child grid cells
100
+ CPU time = 0.139441 secs
101
+ create/ghost percent = 0.843937 99.1561
102
+ balance_grid rcb cell
103
+ Balance grid migrated 97380 cells
104
+ CPU time = 0.105471 secs
105
+ reassign/sort/migrate/ghost percent = 50.2565 0.0311649 7.88159 41.8308
106
+ species gas.species Ar
107
+ mixture air Ar frac 1.0
108
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
109
+ mixture air Ar vstream 2123.9273932469 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 2123.9273932469 0 0.0 temp ${To}
111
+ mixture air Ar vstream 2123.9273932469 0 0.0 temp 273
112
+ collide vss air gas.vhs
113
+ collide_modify vremax 100 yes remain no nearcp yes 10
114
+ read_surf cylinder.surf
115
+ 200 lines
116
+ -0.5 0.5 xlo xhi
117
+ -0.5 0.5 ylo yhi
118
+ 0 0 zlo zhi
119
+ 0.0157073 min line length
120
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 87188 9956 456 = cells outside/inside/overlapping surfs
122
+ 456 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.126479 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 26.5297 18.7388 2.08077 18.6443 34.0064 5.25236 0.164992
126
+ surf2grid time = 0.0235811 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 15.1755 23.7927 5.98439 1.80131 11.0988 6.81324
128
+ surf_collide 1 diffuse 273.0 1.0
129
+ surf_modify all collide 1
130
+ fix in emit/face air xlo ylo yhi
131
+ balance_grid rcb cell
132
+ Balance grid migrated 1938 cells
133
+ CPU time = 0.0528506 secs
134
+ reassign/sort/migrate/ghost percent = 5.3838 0.0526579 17.2839 77.2796
135
+ create_particles air n 0
136
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
137
+ Created 668660 particles
138
+ CPU time = 0.0168705 secs
139
+ stats 10000
140
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
141
+ run 200000
142
+ Memory usage per proc in Mbytes:
143
+ particles (ave,min,max) = 1.79297 0 3.375
144
+ grid (ave,min,max) = 4.297 1.51379 7.13879
145
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
146
+ total (ave,min,max) = 6.11057 3.22189 8.84689
147
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
148
+ 0 0 668660 0 0 0 0 0
149
+ 10000 15.147114 737886 10062 8889 26 696177.38 23.1949
150
+ 20000 37.435165 745373 10233 9060 22 720026.77 23.5352
151
+ 30000 58.790498 745449 10278 9117 21 728750.64 23.6588
152
+ 40000 80.201683 746431 10350 9159 23 733196.07 23.695275
153
+ 50000 102.20439 745804 10210 9061 32 735680.03 23.73492
154
+ 60000 123.22278 745982 10296 9104 18 737461.79 23.7511
155
+ 70000 144.67696 746140 10277 9079 21 738695.6 23.771871
156
+ 80000 167.11125 746702 10358 9245 22 739622.94 23.794138
157
+ 90000 188.53975 745590 10205 9020 28 740412.38 23.802956
158
+ 100000 209.78712 746390 10243 9050 24 740997.34 23.82226
159
+ 110000 231.93738 745708 10208 9098 18 741463.83 23.823845
160
+ 120000 254.07736 746527 10395 9190 29 741872.44 23.826483
161
+ 130000 275.66033 745697 10325 9102 26 742193.56 23.8286
162
+ 140000 297.07866 746005 10195 9005 19 742466.88 23.830607
163
+ 150000 318.25642 745797 10270 9118 25 742718.27 23.832333
164
+ 160000 340.77496 745059 10289 9117 19 742908.22 23.836656
165
+ 170000 361.53459 745785 10468 9266 21 743094.65 23.839412
166
+ 180000 383.12843 745037 10298 9078 32 743232.14 23.840033
167
+ 190000 405.53706 747522 10405 9188 26 743386.98 23.845568
168
+ 200000 426.9034 745651 10334 9101 27 743545.42 23.85467
169
+ Loop time of 426.904 on 128 procs for 200000 steps with 745651 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.024013 | 18.416 | 123.65 | 564.6 | 4.31
175
+ Coll | 0.49404 | 16.401 | 160.63 | 599.3 | 3.84
176
+ Sort | 0.16594 | 3.5577 | 34.805 | 277.5 | 0.83
177
+ Comm | 67.101 | 100.79 | 112.39 | 89.3 | 23.61
178
+ Modify | 0.020444 | 0.14494 | 2.7506 | 107.2 | 0.03
179
+ Output | 0.0045126 | 0.018257 | 0.02013 | 1.6 | 0.00
180
+ Other | | 287.6 | | | 67.36
181
+
182
+ Particle moves = 148709084980 (149B)
183
+ Cells touched = 155260469334 (155B)
184
+ Particle comms = 245812725 (246M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 23460202 (23.5M)
187
+ SurfColl checks = 302751826 (303M)
188
+ SurfColl occurs = 4770934 (4.77M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 2509212615 (2.51B)
191
+ Collide occurs = 1956841585 (1.96B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.72143e+06
197
+ Particle-moves/step: 743545
198
+ Cell-touches/particle/step: 1.04406
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00165298
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000157759
203
+ Surface-checks/particle/step: 0.00203587
204
+ Surface-collisions/particle/step: 3.20823e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0168733
207
+ Collisions/particle/step: 0.0131589
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 5825.4 ave 40690 max 0 min
211
+ Histogram: 84 16 6 10 2 6 0 2 0 2
212
+ Cells: 762.5 ave 763 max 762 min
213
+ Histogram: 64 0 0 0 0 0 0 0 0 64
214
+ GhostCell: 19721.7 ave 36096 max 1099 min
215
+ Histogram: 20 10 12 10 18 2 6 4 16 30
216
+ EmptyCell: 0 ave 0 max 0 min
217
+ Histogram: 128 0 0 0 0 0 0 0 0 0
218
+ Surfs: 200 ave 200 max 200 min
219
+ Histogram: 128 0 0 0 0 0 0 0 0 0
220
+ GhostSurf: 0 ave 0 max 0 min
221
+ Histogram: 128 0 0 0 0 0 0 0 0 0
222
+ compute 1 grid all air n nrho massrho u v w
223
+ compute 2 grid all air trot
224
+ compute 3 tvib/grid all air
225
+ compute 4 thermal/grid all air press temp
226
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
229
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
230
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
231
+ dump_modify 1 format float %12.5g
232
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
233
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
235
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
236
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
238
+ dump_modify 2 format float %12.5g
239
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
240
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
242
+ dump_modify 3 format float %12.5g
243
+ run 100000
244
+ Memory usage per proc in Mbytes:
245
+ particles (ave,min,max) = 1.8457 0 5.0625
246
+ grid (ave,min,max) = 4.297 1.51379 7.13879
247
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
248
+ total (ave,min,max) = 6.41953 3.47786 9.10336
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 745651 10334 9101 0 0 0
251
+ 210000 39.394978 745977 10373 9210 14 746345.75 23.9061
252
+ 220000 79.526765 746059 10307 9194 22 746247.74 23.90805
253
+ 230000 119.29974 746279 10272 9111 21 746318.7 23.918733
254
+ 240000 160.42715 746222 10281 9144 24 746414.85 23.906075
255
+ 250000 199.116 745996 10265 9100 19 746399.12 23.91456
256
+ 260000 238.34923 745122 10339 9181 32 746286.38 23.898817
257
+ 270000 277.96519 746509 10309 9210 9 746342.87 23.919229
258
+ 280000 316.41724 746026 10426 9280 22 746391.56 23.918275
259
+ 290000 355.89088 744794 10331 9114 24 746339.24 23.905711
260
+ 300000 394.96543 745339 10184 9073 25 746209.05 23.8906
261
+ Loop time of 394.966 on 128 procs for 100000 steps with 745339 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.013848 | 9.3153 | 61.838 | 399.6 | 2.36
267
+ Coll | 0.24839 | 8.4198 | 87.856 | 439.8 | 2.13
268
+ Sort | 0.099143 | 1.8462 | 14.603 | 191.9 | 0.47
269
+ Comm | 44.821 | 61.567 | 67.787 | 60.4 | 15.59
270
+ Modify | 3.4806 | 25.74 | 158.89 | 614.2 | 6.52
271
+ Output | 0.055199 | 0.06009 | 0.065157 | 0.7 | 0.02
272
+ Other | | 288 | | | 72.92
273
+
274
+ Particle moves = 74620904934 (74.6B)
275
+ Cells touched = 77905892271 (77.9B)
276
+ Particle comms = 123239482 (123M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 11769677 (11.8M)
279
+ SurfColl checks = 151989868 (152M)
280
+ SurfColl occurs = 2389060 (2.39M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 1267279351 (1.27B)
283
+ Collide occurs = 988154137 (988M)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.47602e+06
289
+ Particle-moves/step: 746209
290
+ Cell-touches/particle/step: 1.04402
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00165154
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000157726
295
+ Surface-checks/particle/step: 0.00203683
296
+ Surface-collisions/particle/step: 3.2016e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0169829
299
+ Collisions/particle/step: 0.0132423
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 5822.96 ave 40953 max 0 min
303
+ Histogram: 84 16 6 10 2 5 1 2 0 2
304
+ Cells: 762.5 ave 763 max 762 min
305
+ Histogram: 64 0 0 0 0 0 0 0 0 64
306
+ GhostCell: 19721.7 ave 36096 max 1099 min
307
+ Histogram: 20 10 12 10 18 2 6 4 16 30
308
+ EmptyCell: 0 ave 0 max 0 min
309
+ Histogram: 128 0 0 0 0 0 0 0 0 0
310
+ Surfs: 200 ave 200 max 200 min
311
+ Histogram: 128 0 0 0 0 0 0 0 0 0
312
+ GhostSurf: 0 ave 0 max 0 min
313
+ Histogram: 128 0 0 0 0 0 0 0 0 0
314
+ write_grid grid/final-grid.dat
315
+ grid cells = 97600
316
+ CPU time = 0.0146445 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00162296 secs
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/mir.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ dimension 2
2
+ create_box -5.0 6.0 -5.0 5.0 -0.5 0.5
3
+ read_grid grid/final-grid.dat
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/output/surf.300000.dat ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ITEM: TIMESTEP
2
+ 300000
3
+ ITEM: NUMBER OF SURFS
4
+ 200
5
+ ITEM: BOX BOUNDS oo oo pp
6
+ -5 6
7
+ -5 5
8
+ -0.5 0.5
9
+ ITEM: SURFS id v1x v1y v2x v2y f_2[1] f_2[2] f_2[3] f_2[4] f_2[5] f_2[6] f_2[7] f_2[8] f_2[9] f_2[10] f_2[11]
10
+ 1 0.5 0 0.49975 -0.015705 0.0039 0.17086 -0.17084 0.0026837 -9.5373e-05 -0.0060711 -0.0026849 -5.3208e-05 55.974 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.19231 8.6679 5.4196 -6.7647 1.2728 1.0197 0.10512 -0.090239 2776.9 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.0038 0.16502 -0.16484 0.0077735 6.8736e-05 0.0014575 -0.0025881 0.00014499 48.449 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.18308 8.2655 4.9628 -6.6098 1.2779 0.95946 0.098024 -0.088752 2653.1 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00353 0.14937 -0.14891 0.01172 0.00031131 0.0039555 -0.0023341 0.00024621 42.529 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.1738 7.8434 4.51 -6.417 1.2673 0.89071 0.090746 -0.086804 2515.7 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00393 0.17007 -0.16904 0.018662 0.0014899 0.013495 -0.0026318 0.00050511 50.115 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.16314 7.4249 4.0764 -6.2058 1.3356 0.87731 0.085008 -0.083696 2456.2 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00404 0.16427 -0.16264 0.023146 -0.0002787 -0.0019582 -0.0025589 0.00033281 44.178 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.1541 6.9878 3.6511 -5.9581 1.2733 0.78027 0.077349 -0.08133 2292.1 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00375 0.16448 -0.16203 0.028279 0.0016508 0.0094586 -0.0025191 0.00059275 51.82 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.14715 6.6708 3.3051 -5.7945 1.382 0.78829 0.073622 -0.078634 2246.7 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00407 0.17218 -0.16861 0.034917 0.0018759 0.0090583 -0.0026189 0.00069073 52.22 0 0
23
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150
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154
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155
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169
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173
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174
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185
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186
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195
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197
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198
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200
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201
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202
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203
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204
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205
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208
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209
+ 128 -0.33066 0.37506 -0.31871 0.38526 0.20089 9.068 5.8892 -6.8954 1.2096 1.0331 0.1115 -0.092081 2886.5 0 0
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/runmpirun128.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 2
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 128 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/runmpirun64.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 1
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 64 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0600_Kn0p28Ma6p9/slurm-28429331.out ADDED
@@ -0,0 +1,205 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ Running on 128 MPI task(s)
4
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
5
+ Created 97600 child grid cells
6
+ CPU time = 0.139441 secs
7
+ create/ghost percent = 0.843937 99.1561
8
+ Balance grid migrated 97380 cells
9
+ CPU time = 0.105471 secs
10
+ reassign/sort/migrate/ghost percent = 50.2565 0.0311649 7.88159 41.8308
11
+ Reading surface file ...
12
+ 200 lines
13
+ -0.5 0.5 xlo xhi
14
+ -0.5 0.5 ylo yhi
15
+ 0 0 zlo zhi
16
+ 0.0157073 min line length
17
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 87188 9956 456 = cells outside/inside/overlapping surfs
19
+ 456 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.126479 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 26.5297 18.7388 2.08077 18.6443 34.0064 5.25236 0.164992
23
+ surf2grid time = 0.0235811 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 15.1755 23.7927 5.98439 1.80131 11.0988 6.81324
25
+ Balance grid migrated 1938 cells
26
+ CPU time = 0.0528506 secs
27
+ reassign/sort/migrate/ghost percent = 5.3838 0.0526579 17.2839 77.2796
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
30
+ Created 668660 particles
31
+ CPU time = 0.0168705 secs
32
+ Memory usage per proc in Mbytes:
33
+ particles (ave,min,max) = 1.79297 0 3.375
34
+ grid (ave,min,max) = 4.297 1.51379 7.13879
35
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
36
+ total (ave,min,max) = 6.11057 3.22189 8.84689
37
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
38
+ 0 0 668660 0 0 0 0 0
39
+ 10000 15.147114 737886 10062 8889 26 696177.38 23.1949
40
+ 20000 37.435165 745373 10233 9060 22 720026.77 23.5352
41
+ 30000 58.790498 745449 10278 9117 21 728750.64 23.6588
42
+ 40000 80.201683 746431 10350 9159 23 733196.07 23.695275
43
+ 50000 102.20439 745804 10210 9061 32 735680.03 23.73492
44
+ 60000 123.22278 745982 10296 9104 18 737461.79 23.7511
45
+ 70000 144.67696 746140 10277 9079 21 738695.6 23.771871
46
+ 80000 167.11125 746702 10358 9245 22 739622.94 23.794138
47
+ 90000 188.53975 745590 10205 9020 28 740412.38 23.802956
48
+ 100000 209.78712 746390 10243 9050 24 740997.34 23.82226
49
+ 110000 231.93738 745708 10208 9098 18 741463.83 23.823845
50
+ 120000 254.07736 746527 10395 9190 29 741872.44 23.826483
51
+ 130000 275.66033 745697 10325 9102 26 742193.56 23.8286
52
+ 140000 297.07866 746005 10195 9005 19 742466.88 23.830607
53
+ 150000 318.25642 745797 10270 9118 25 742718.27 23.832333
54
+ 160000 340.77496 745059 10289 9117 19 742908.22 23.836656
55
+ 170000 361.53459 745785 10468 9266 21 743094.65 23.839412
56
+ 180000 383.12843 745037 10298 9078 32 743232.14 23.840033
57
+ 190000 405.53706 747522 10405 9188 26 743386.98 23.845568
58
+ 200000 426.9034 745651 10334 9101 27 743545.42 23.85467
59
+ Loop time of 426.904 on 128 procs for 200000 steps with 745651 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.024013 | 18.416 | 123.65 | 564.6 | 4.31
65
+ Coll | 0.49404 | 16.401 | 160.63 | 599.3 | 3.84
66
+ Sort | 0.16594 | 3.5577 | 34.805 | 277.5 | 0.83
67
+ Comm | 67.101 | 100.79 | 112.39 | 89.3 | 23.61
68
+ Modify | 0.020444 | 0.14494 | 2.7506 | 107.2 | 0.03
69
+ Output | 0.0045126 | 0.018257 | 0.02013 | 1.6 | 0.00
70
+ Other | | 287.6 | | | 67.36
71
+
72
+ Particle moves = 148709084980 (149B)
73
+ Cells touched = 155260469334 (155B)
74
+ Particle comms = 245812725 (246M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 23460202 (23.5M)
77
+ SurfColl checks = 302751826 (303M)
78
+ SurfColl occurs = 4770934 (4.77M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 2509212615 (2.51B)
81
+ Collide occurs = 1956841585 (1.96B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.72143e+06
87
+ Particle-moves/step: 743545
88
+ Cell-touches/particle/step: 1.04406
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00165298
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000157759
93
+ Surface-checks/particle/step: 0.00203587
94
+ Surface-collisions/particle/step: 3.20823e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0168733
97
+ Collisions/particle/step: 0.0131589
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 5825.4 ave 40690 max 0 min
101
+ Histogram: 84 16 6 10 2 6 0 2 0 2
102
+ Cells: 762.5 ave 763 max 762 min
103
+ Histogram: 64 0 0 0 0 0 0 0 0 64
104
+ GhostCell: 19721.7 ave 36096 max 1099 min
105
+ Histogram: 20 10 12 10 18 2 6 4 16 30
106
+ EmptyCell: 0 ave 0 max 0 min
107
+ Histogram: 128 0 0 0 0 0 0 0 0 0
108
+ Surfs: 200 ave 200 max 200 min
109
+ Histogram: 128 0 0 0 0 0 0 0 0 0
110
+ GhostSurf: 0 ave 0 max 0 min
111
+ Histogram: 128 0 0 0 0 0 0 0 0 0
112
+ Memory usage per proc in Mbytes:
113
+ particles (ave,min,max) = 1.8457 0 5.0625
114
+ grid (ave,min,max) = 4.297 1.51379 7.13879
115
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
116
+ total (ave,min,max) = 6.41953 3.47786 9.10336
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 745651 10334 9101 0 0 0
119
+ 210000 39.394978 745977 10373 9210 14 746345.75 23.9061
120
+ 220000 79.526765 746059 10307 9194 22 746247.74 23.90805
121
+ 230000 119.29974 746279 10272 9111 21 746318.7 23.918733
122
+ 240000 160.42715 746222 10281 9144 24 746414.85 23.906075
123
+ 250000 199.116 745996 10265 9100 19 746399.12 23.91456
124
+ 260000 238.34923 745122 10339 9181 32 746286.38 23.898817
125
+ 270000 277.96519 746509 10309 9210 9 746342.87 23.919229
126
+ 280000 316.41724 746026 10426 9280 22 746391.56 23.918275
127
+ 290000 355.89088 744794 10331 9114 24 746339.24 23.905711
128
+ 300000 394.96543 745339 10184 9073 25 746209.05 23.8906
129
+ Loop time of 394.966 on 128 procs for 100000 steps with 745339 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.013848 | 9.3153 | 61.838 | 399.6 | 2.36
135
+ Coll | 0.24839 | 8.4198 | 87.856 | 439.8 | 2.13
136
+ Sort | 0.099143 | 1.8462 | 14.603 | 191.9 | 0.47
137
+ Comm | 44.821 | 61.567 | 67.787 | 60.4 | 15.59
138
+ Modify | 3.4806 | 25.74 | 158.89 | 614.2 | 6.52
139
+ Output | 0.055199 | 0.06009 | 0.065157 | 0.7 | 0.02
140
+ Other | | 288 | | | 72.92
141
+
142
+ Particle moves = 74620904934 (74.6B)
143
+ Cells touched = 77905892271 (77.9B)
144
+ Particle comms = 123239482 (123M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 11769677 (11.8M)
147
+ SurfColl checks = 151989868 (152M)
148
+ SurfColl occurs = 2389060 (2.39M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 1267279351 (1.27B)
151
+ Collide occurs = 988154137 (988M)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.47602e+06
157
+ Particle-moves/step: 746209
158
+ Cell-touches/particle/step: 1.04402
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00165154
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.000157726
163
+ Surface-checks/particle/step: 0.00203683
164
+ Surface-collisions/particle/step: 3.2016e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.0169829
167
+ Collisions/particle/step: 0.0132423
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 5822.96 ave 40953 max 0 min
171
+ Histogram: 84 16 6 10 2 5 1 2 0 2
172
+ Cells: 762.5 ave 763 max 762 min
173
+ Histogram: 64 0 0 0 0 0 0 0 0 64
174
+ GhostCell: 19721.7 ave 36096 max 1099 min
175
+ Histogram: 20 10 12 10 18 2 6 4 16 30
176
+ EmptyCell: 0 ave 0 max 0 min
177
+ Histogram: 128 0 0 0 0 0 0 0 0 0
178
+ Surfs: 200 ave 200 max 200 min
179
+ Histogram: 128 0 0 0 0 0 0 0 0 0
180
+ GhostSurf: 0 ave 0 max 0 min
181
+ Histogram: 128 0 0 0 0 0 0 0 0 0
182
+ Writing grid file ...
183
+ grid cells = 97600
184
+ CPU time = 0.0146445 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.00162296 secs
188
+ Processing 4 grid chunk(s).
189
+ Completed grid chunk number: 3
190
+ Completed grid chunk number: 2
191
+ Completed grid chunk number: 1
192
+ Completed grid chunk number: 0
193
+
194
+ Done in 0:00:13 (wall clock time)
195
+
196
+ Processing 4 grid chunk(s).
197
+ Completed grid chunk number: 3
198
+ Completed grid chunk number: 2
199
+ Completed grid chunk number: 1
200
+ Completed grid chunk number: 0
201
+
202
+ Done in 0:00:12 (wall clock time)
203
+
204
+ Processing SPARTA surface information.
205
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Grid.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Grid/Grid_300000.pvtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Lambda/Lambda_300000.pvtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda/Lambda_0_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0601_Kn0p30Ma2p0/Lambda/Lambda_1_300000.vtu ADDED
The diff for this file is too large to render. See raw diff