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  1. .gitattributes +8 -0
  2. CylinderKnMa/Kn0p28/simulation0558_Kn0p28Ma2p7/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p28/simulation0559_Kn0p28Ma2p8/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p28/simulation0560_Kn0p28Ma2p9/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p28/simulation0561_Kn0p28Ma3p0/output/grid.300000.dat +3 -0
  6. CylinderKnMa/Kn0p28/simulation0562_Kn0p28Ma3p1/output/grid.300000.dat +3 -0
  7. CylinderKnMa/Kn0p28/simulation0563_Kn0p28Ma3p2/output/grid.300000.dat +3 -0
  8. CylinderKnMa/Kn0p28/simulation0564_Kn0p28Ma3p3/output/grid.300000.dat +3 -0
  9. CylinderKnMa/Kn0p28/simulation0565_Kn0p28Ma3p4/output/grid.300000.dat +3 -0
  10. CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/Lambda/Lambda_3_300000.vtu +27 -0
  11. CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/output/lambdakn.300000.dat +0 -0
  12. CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/output/surf.300000.dat +209 -0
  13. CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/surf/Surf_300000.vtu +38 -0
  14. CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/surf/geom.surf +610 -0
  15. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Grid.pvd +9 -0
  16. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda.pvd +9 -0
  17. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda/Lambda_0_300000.vtu +0 -0
  18. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda/Lambda_1_300000.vtu +0 -0
  19. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda/Lambda_2_300000.vtu +0 -0
  20. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda/Lambda_300000.pvtu +21 -0
  21. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Lambda/Lambda_3_300000.vtu +27 -0
  22. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Surf.pvd +9 -0
  23. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/config.txt +78 -0
  24. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/cylinder.surf +410 -0
  25. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/gas.species +14 -0
  26. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/gas.vhs +9 -0
  27. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_0_300000.vtu +0 -0
  28. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_1_300000.vtu +0 -0
  29. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_2_300000.vtu +0 -0
  30. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_300000.pvtu +29 -0
  31. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_3_300000.vtu +0 -0
  32. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/final-grid.dat +0 -0
  33. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/log.sparta +318 -0
  34. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/mir.txt +3 -0
  35. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/output/lambdakn.300000.dat +0 -0
  36. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/output/surf.300000.dat +209 -0
  37. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/runmpirun128.sh +18 -0
  38. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/runmpirun64.sh +18 -0
  39. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/slurm-28429417.out +204 -0
  40. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/surf/Surf_300000.vtu +38 -0
  41. CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/surf/geom.surf +610 -0
  42. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Grid.pvd +9 -0
  43. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda.pvd +9 -0
  44. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda/Lambda_0_300000.vtu +0 -0
  45. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda/Lambda_1_300000.vtu +0 -0
  46. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda/Lambda_2_300000.vtu +0 -0
  47. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda/Lambda_300000.pvtu +21 -0
  48. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Lambda/Lambda_3_300000.vtu +27 -0
  49. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Surf.pvd +9 -0
  50. CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/config.txt +78 -0
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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAAYigAAA==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AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==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eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0624_Kn0p30Ma4p3/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 0.00381 0.083996 -0.083986 0.0013194 -3.8343e-05 -0.0024408 -0.0013198 -1.7615e-05 12.82 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.13164 3.0863 1.9297 -2.4086 0.47065 0.37706 0.037703 -0.031911 585.9 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.00422 0.093877 -0.093773 0.0044222 -3.7537e-05 -0.00079596 -0.0014735 5.6958e-05 15.44 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.12467 2.9061 1.7449 -2.324 0.44819 0.33651 0.034447 -0.031218 537.87 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00373 0.081118 -0.080868 0.0063645 -9.0027e-05 -0.0011439 -0.0012716 8.2001e-05 10.26 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.12141 2.824 1.6238 -2.3104 0.45733 0.32142 0.032689 -0.031242 525.02 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00346 0.075787 -0.075329 0.0083165 -9.0088e-05 -0.000816 -0.0011846 0.00011781 12.406 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.11445 2.653 1.4566 -2.2174 0.4585 0.30118 0.030081 -0.030099 497.37 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00332 0.072559 -0.071836 0.010224 0.00048436 0.0034033 -0.0011207 0.00021404 11.532 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.10924 2.5631 1.3392 -2.1854 0.46922 0.28754 0.028405 -0.02981 497.17 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00361 0.079838 -0.078649 0.013726 -0.00020327 -0.0011647 -0.0012386 0.00019731 11.49 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.10062 2.3507 1.1647 -2.0419 0.45192 0.25777 0.025392 -0.028023 438.84 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00314 0.069798 -0.068348 0.014154 6.0536e-05 0.00029232 -0.0010726 0.00022692 10.273 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.09798 2.2905 1.0718 -2.0243 0.45786 0.24242 0.024027 -0.027988 432.71 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00315 0.065591 -0.063779 0.015312 -0.00010197 -0.00042473 -0.0010034 0.00023384 9.7519 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.09154 2.1164 0.93107 -1.9005 0.46472 0.22767 0.021924 -0.026276 411.86 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00317 0.067483 -0.065091 0.017807 0.00089744 0.0032805 -0.0010083 0.00033123 10.719 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.08625 1.9957 0.82127 -1.8189 0.46428 0.20963 0.020193 -0.025278 380.49 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.00305 0.066154 -0.063229 0.019452 0.00089236 0.0029007 -0.00097915 0.0003511 9.4766 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.0808 1.8669 0.71442 -1.7248 0.44363 0.18376 0.01819 -0.024205 356.92 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00286 0.06095 -0.057664 0.019743 -1.0102e-05 -2.9506e-05 -0.0009059 0.00030964 10.035 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.07438 1.732 0.61221 -1.6202 0.4278 0.16165 0.016336 -0.022909 335.31 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.0029 0.06146 -0.057492 0.021725 0.0019464 0.005151 -0.00087248 0.00042214 10.179 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.07129 1.6385 0.53074 -1.5502 0.45939 0.15728 0.015552 -0.021878 340.67 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00266 0.05348 -0.049409 0.020466 0.0015899 0.0038384 -0.00075111 0.00038176 8.0773 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.06707 1.5327 0.45067 -1.4649 0.43252 0.13306 0.013873 -0.02092 306.65 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00285 0.059707 -0.054418 0.02457 0.0041884 0.0092763 -0.00078896 0.00053164 11.396 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.06219 1.4244 0.37586 -1.3739 0.41855 0.1145 0.012478 -0.019782 284.77 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.00268 0.055676 -0.049999 0.024494 0.0036653 0.0074817 -0.00072778 0.00050226 9.552 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.06015 1.3735 0.32063 -1.3355 0.41833 0.10043 0.011607 -0.0194 270.19 0 0
40
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AQAAAACAAABABgAAAAYAAA==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AQAAAACAAABABgAA9gUAAA==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AQAAAACAAABABgAA/QUAAA==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AQAAAACAAABABgAAGgQAAA==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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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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAATigAAA==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AQAAAACAAABwLwAA7CEAAA==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AQAAAACAAADoSgAAcA8AAA==eJxN0k/oB3ldx/FND11CsejgSZ3AiY1MMw8l6OCCGrRQEhkpKmKQIhUoKJriH9TVDM3CMadkQr5lgzDYwDIiAxILCYEg0qEgCDboEEEKgUKHfML3+Xr95jKP6+f1fj73JcP0g5e+YHrkR99HnjnG//XPPxf/5ldeHF/ve1k8Pv4r8Wef98r4h997LH7rU6+N/+nzj8cve/vr4i+9/PXxjz/rjfEf/vtb4n/5+9+NH/vYO+Kvvv4P4p9+9N3xB//vvfF/fvsD8Vd+/cPxv339o/FP/szH41d/+on4/f/7qXh/05/E//GPn4mf+5LPxY9/8c8f7DnHT77zLx5su8TPf+WXHuy8xp98zpcfbH6Lv//03z7Yf4vf8ORXH9xij5964msP7nLEL3rDkw9udMbzz3/jwb2u+JFHvvngvcP0r8984f2NY0wzmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zXTPOaaZbrvENNOd15hmuvktppnuv8U001vsMc30LkdMM73RGdNM73XFNNP3DtM3vvvo/Y1jTDOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNdM85ppluu8Q0053XmGa6+S2mme6/xTTTW+wxzfQuR0wzvdEZ00zvdcU00/cO019++RfubxxjmtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2mme84xzXTbJaaZ7rzGNNPNbzHNdP8tppneYo9ppnc5Yprpjc6YZnqvK6aZvneYPvCuX7q/cYxpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMppnuOcc0022XmGa68xrTTDe/xTTT/beYZnqLPaaZ3uWIaaY3OmOa6b2umGb63mF602O/fH/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9wzT91CvubxxjmtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2mme84xzXTbJaaZ7rzGNNPNbzHNdP8tppneYo9ppnc5Yprpjc6YZnqvK6aZvneYXvD0q+5vHGOa0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaaZ7zjHNdNslppnuvMY0081vMc10/y2mmd5ij2mmdzlimumNzphmeq8rppm+d5iecbzm/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa6XuH6emP/Nr9jWNMM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM10zzmmmW67xDTTndeYZrr5LaaZ7r/FNNNb7DHN9C5HTDO90RnTTO91xTTT9w7TU6/7jfsbx5hmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omumec0wz3XaJaaY7rzHNdPNbTDPdf4tpprfYY5rpXY6YZnqjM6aZ3uuKaabvHaa/GX7r/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa6XuH6RPf/537G8eYZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrpnnNMM912iWmmO68xzXTzW0wz3X+Laaa32GOa6V2OmGZ6ozOmmd7rimmm7x2m3/uHN9/fOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zD96ufedn/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9w/ToW99+f+MY04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0z3XOOaabbLjHNdOc1pplufotppvtvMc30FntMM73LEdNMb3TGNNN7XTHN9L3D9BO/+Pv3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztM//1j77q/cYxpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMppnuOcc0022XmGa68xrTTDe/xTTT/beYZnqLPaaZ3uWIaaY3OmOa6b2umGb63mH69nfec3/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9w7T/9R/d3zjGNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtNM95xjmum2S0wz3XmNaaab32Ka6f5bTDO9xR7TTO9yxDTTG50xzfReV0wzfe8wfev80P2NY0wzmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zXTPOaaZbrvENNOd15hmuvktppnuv8U001vsMc30LkdMM73RGdNM73XFNNP3DtPXn//R+xvHmGY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia6Z5zTDPddolppjuvMc1081tMM91/i2mmt9hjmuldjphmeqMzppne64pppu8dpr974mP3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztMX/yfT9zfOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zD98W9/6v7GMaYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZrrnHNNMt11imunOa0wz3fwW00z332Ka6S32mGZ6lyOmmd7ojGmm97pimul7h+n93/z0/Y1jTDOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNdM85ppluu8Q0053XmGa6+S2mme6/xTTTW+wxzfQuR0wzvdEZ00zvdcU00/cO0zt/9jP3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztMb/zsn97fOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zA9/oM/u79xjGlG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymme45xzTTbZeYZrrzGtNMN7/FNNP9t5hmeos9ppne5Yhppjc6Y5rpva6YZvreYXrFWz5/f+MY04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0z3XOOaabbLjHNdOc1pplufotppvtvMc30FntMM73LEdNMb3TGNNN7XTHN9L3D9KJvfeH+xjGmGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmma65xzTTLddYprpzmtMM938FtNM999imukt9phmepcjppne6Ixppve6Yprpe4fpeS9e7m8cY5rRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNppnvOMc102yWmme68xjTTzW8xzXT/LaaZ3mKPaaZ3OWKa6Y3OmGZ6ryummb53mJ79hb+6v3GMaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKaZ7jnHNNNtl5hmuvMa00w3v8U00/23mGZ6iz2mmd7liGmmNzpjmum9rphm+t7hR//1/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa0f8PY4jinQ==AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==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eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/Surf.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="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/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 0.3
13
+ variable Ma equal 4.4
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/Kn0p30/simulation0625_Kn0p30Ma4p4/cylinder.surf ADDED
@@ -0,0 +1,410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ surf file from Pointwise
2
+
3
+ 200 points
4
+ 200 lines
5
+
6
+ Points
7
+
8
+ 1 0.5 0.0
9
+ 2 0.499753280182866E+000 -0.157053795390643E-001
10
+ 3 0.499013364214136E+000 -0.313952597646568E-001
11
+ 4 0.497780982301540E+000 -0.470541566592573E-001
12
+ 5 0.496057350657239E+000 -0.626666167821523E-001
13
+ 6 0.493844170297569E+000 -0.782172325201156E-001
14
+ 7 0.491143625364344E+000 -0.936906572928624E-001
15
+ 8 0.487958380969374E+000 -0.109071620698271E+000
16
+ 9 0.484291580564315E+000 -0.124344943582427E+000
17
+ 10 0.480146842838471E+000 -0.139495553019615E+000
18
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The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_300000.pvtu ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="PUnstructuredGrid" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <PUnstructuredGrid GhostLevel="0">
6
+ <PCellData>
7
+ <PDataArray type="Float64" Name="id"/>
8
+ <PDataArray type="Float64" Name="xc"/>
9
+ <PDataArray type="Float64" Name="yc"/>
10
+ <PDataArray type="Float64" Name="f_1[1]"/>
11
+ <PDataArray type="Float64" Name="f_1[2]"/>
12
+ <PDataArray type="Float64" Name="f_1[3]"/>
13
+ <PDataArray type="Float64" Name="f_1[4]"/>
14
+ <PDataArray type="Float64" Name="f_1[5]"/>
15
+ <PDataArray type="Float64" Name="f_1[6]"/>
16
+ <PDataArray type="Float64" Name="f_1[7]"/>
17
+ <PDataArray type="Float64" Name="f_1[8]"/>
18
+ <PDataArray type="Float64" Name="f_1[9]"/>
19
+ <PDataArray type="Float64" Name="f_1[10]"/>
20
+ </PCellData>
21
+ <PPoints>
22
+ <PDataArray type="Float32" Name="Points" NumberOfComponents="3"/>
23
+ </PPoints>
24
+ <Piece Source="Grid_0_300000.vtu"/>
25
+ <Piece Source="Grid_1_300000.vtu"/>
26
+ <Piece Source="Grid_2_300000.vtu"/>
27
+ <Piece Source="Grid_3_300000.vtu"/>
28
+ </PUnstructuredGrid>
29
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/Grid_3_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/log.sparta ADDED
@@ -0,0 +1,318 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 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
11
+ variable L equal 1.0
12
+ variable xmin equal -5.0
13
+ variable xmax equal 6.0
14
+ variable ymin equal -5.0
15
+ variable ymax equal 5.0
16
+ variable Kn equal 0.3
17
+ variable Ma equal 4.4
18
+ variable To equal 273.0
19
+ variable Tb equal 273.0
20
+ variable theta equal 0.0
21
+ variable mfp equal ${Kn}*${L}
22
+ variable mfp equal 0.3*${L}
23
+ variable mfp equal 0.3*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
26
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
27
+ 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}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/4*0.3)
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 4.4*${sos}
38
+ variable vo equal 4.4*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 1354.3884826502*cos(${theta}*2*PI/360)
41
+ variable vx equal 1354.3884826502*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 1354.3884826502*sin(${theta}*2*PI/360)
44
+ variable vy equal 1354.3884826502*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${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)/0.3*${cpmfp}
50
+ variable xncells equal (6--5)/0.3*4
51
+ variable yncells equal (${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)/0.3*${cpmfp}
55
+ variable yncells equal (5--5)/0.3*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 5.7528162356074e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 5.7528162356074e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/146.666666666667/${yncells}
64
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/146.666666666667/133.333333333333
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}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/1354.3884826502/${xncells}/10/4
69
+ variable tstep equal (--5+6)/1354.3884826502/146.666666666667/10/4
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 5.7528162356074e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 1.38438861819844e-06
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 146.666666666667 ${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 146.666666666667 133.333333333333 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 85160 child grid cells
100
+ CPU time = 0.123775 secs
101
+ create/ghost percent = 1.02535 98.9746
102
+ balance_grid rcb cell
103
+ Balance grid migrated 85003 cells
104
+ CPU time = 0.0989973 secs
105
+ reassign/sort/migrate/ghost percent = 54.0823 0.0305564 8.45743 37.4297
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 1354.3884826502 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 1354.3884826502 0 0.0 temp ${To}
111
+ mixture air Ar vstream 1354.3884826502 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
+ 428 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 76068 8664 428 = cells outside/inside/overlapping surfs
122
+ 428 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.106109 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 20.5107 20.9929 2.38039 21.1416 34.9744 6.01506 0.142353
126
+ surf2grid time = 0.0224331 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 15.0486 23.7244 5.60588 1.79336 11.7012 6.63118
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 1177 cells
133
+ CPU time = 0.0466017 secs
134
+ reassign/sort/migrate/ghost percent = 5.59581 0.0551461 19.0387 75.3104
135
+ create_particles air n 0
136
+ Created 582478 particles
137
+ CPU time = 0.0152155 secs
138
+ stats 10000
139
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
140
+ run 200000
141
+ Memory usage per proc in Mbytes:
142
+ particles (ave,min,max) = 1.7666 0 3.375
143
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
144
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
145
+ total (ave,min,max) = 5.55685 3.22189 7.90939
146
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
147
+ 0 0 582478 0 0 0 0 0
148
+ 10000 15.403305 642631 12428 10703 20 608519.75 16.1302
149
+ 20000 33.886556 648277 12583 10894 15 627840.81 16.2851
150
+ 30000 52.236426 649216 12601 10880 16 634833.24 16.341433
151
+ 40000 71.648216 649519 12544 10841 12 638410.46 16.39775
152
+ 50000 90.445357 647960 12587 10882 14 640464.52 16.41208
153
+ 60000 108.86912 649376 12463 10867 20 641904.72 16.431867
154
+ 70000 127.78162 648481 12630 10920 30 642935.7 16.443786
155
+ 80000 146.13966 648392 12564 10908 16 643677.66 16.458125
156
+ 90000 164.94502 649852 12726 11021 16 644282.83 16.465167
157
+ 100000 183.71253 649700 12665 11000 12 644830.67 16.47414
158
+ 110000 202.76152 649118 12621 10877 13 645216.24 16.4742
159
+ 120000 221.59909 649468 12493 10793 16 645539.18 16.48015
160
+ 130000 239.68873 648998 12582 10855 16 645795.5 16.486223
161
+ 140000 260.3619 648456 12381 10802 16 645993.72 16.487336
162
+ 150000 279.25281 648836 12478 10791 17 646212.95 16.484467
163
+ 160000 297.34249 648801 12561 10835 24 646343.99 16.482312
164
+ 170000 317.06805 648789 12583 10899 16 646478.66 16.479888
165
+ 180000 335.76224 648004 12515 10859 18 646594.81 16.482839
166
+ 190000 354.70478 649023 12720 11019 15 646715.97 16.481895
167
+ 200000 373.51068 648850 12699 10965 31 646823.64 16.483235
168
+ Loop time of 373.511 on 128 procs for 200000 steps with 648850 particles
169
+
170
+ MPI task timing breakdown:
171
+ Section | min time | avg time | max time |%varavg| %total
172
+ ---------------------------------------------------------------
173
+ Move | 0.023712 | 15.879 | 102.87 | 522.9 | 4.25
174
+ Coll | 0.43252 | 16.11 | 143.13 | 587.5 | 4.31
175
+ Sort | 0.14521 | 2.8786 | 21.688 | 232.1 | 0.77
176
+ Comm | 66.652 | 100.45 | 112.34 | 96.7 | 26.89
177
+ Modify | 0.018747 | 0.13978 | 2.6105 | 103.4 | 0.04
178
+ Output | 0.0041213 | 0.01559 | 0.017288 | 1.7 | 0.00
179
+ Other | | 238 | | | 63.73
180
+
181
+ Particle moves = 129364727357 (129B)
182
+ Cells touched = 135241517021 (135B)
183
+ Particle comms = 234352467 (234M)
184
+ Boundary collides = 0 (0K)
185
+ Boundary exits = 23023568 (23M)
186
+ SurfColl checks = 185082821 (185M)
187
+ SurfColl occurs = 3296647 (3.3M)
188
+ Surf reactions = 0 (0K)
189
+ Collide attempts = 2874761687 (2.87B)
190
+ Collide occurs = 2231286129 (2.23B)
191
+ Reactions = 0 (0K)
192
+ Particles stuck = 0
193
+ Axisymm bad moves = 0
194
+
195
+ Particle-moves/CPUsec/proc: 2.70584e+06
196
+ Particle-moves/step: 646824
197
+ Cell-touches/particle/step: 1.04543
198
+ Particle comm iterations/step: 1
199
+ Particle fraction communicated: 0.00181156
200
+ Particle fraction colliding with boundary: 0
201
+ Particle fraction exiting boundary: 0.000177974
202
+ Surface-checks/particle/step: 0.00143071
203
+ Surface-collisions/particle/step: 2.54834e-05
204
+ Surf-reactions/particle/step: 0
205
+ Collision-attempts/particle/step: 0.0222221
206
+ Collisions/particle/step: 0.017248
207
+ Reactions/particle/step: 0
208
+
209
+ Particles: 5069.14 ave 34247 max 0 min
210
+ Histogram: 84 16 2 10 6 6 0 0 2 2
211
+ Cells: 665.312 ave 666 max 665 min
212
+ Histogram: 88 0 0 0 0 0 0 0 0 40
213
+ GhostCell: 17265 ave 31629 max 955 min
214
+ Histogram: 22 8 12 10 17 3 6 4 16 30
215
+ EmptyCell: 0 ave 0 max 0 min
216
+ Histogram: 128 0 0 0 0 0 0 0 0 0
217
+ Surfs: 200 ave 200 max 200 min
218
+ Histogram: 128 0 0 0 0 0 0 0 0 0
219
+ GhostSurf: 0 ave 0 max 0 min
220
+ Histogram: 128 0 0 0 0 0 0 0 0 0
221
+ compute 1 grid all air n nrho massrho u v w
222
+ compute 2 grid all air trot
223
+ compute 3 tvib/grid all air
224
+ compute 4 thermal/grid all air press temp
225
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
226
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
229
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
230
+ dump_modify 1 format float %12.5g
231
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
232
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
233
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
235
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
236
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump_modify 2 format float %12.5g
238
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
239
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
240
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump_modify 3 format float %12.5g
242
+ run 100000
243
+ Memory usage per proc in Mbytes:
244
+ particles (ave,min,max) = 1.79297 0 5.0625
245
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
246
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
247
+ total (ave,min,max) = 5.80682 3.4453 8.1333
248
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
249
+ 200000 0 648850 12699 10965 0 0 0
250
+ 210000 33.429731 648759 12643 10974 24 648416.28 16.537
251
+ 220000 66.779727 649556 12506 10857 13 649039.24 16.5577
252
+ 230000 99.374786 648978 12603 10925 24 649284.6 16.557333
253
+ 240000 132.90084 648014 12430 10761 22 649178.7 16.54285
254
+ 250000 165.5897 648023 12480 10799 19 649122.17 16.5458
255
+ 260000 199.5976 648742 12554 10889 19 649160.19 16.536983
256
+ 270000 232.61999 648590 12617 10838 15 649093.21 16.528171
257
+ 280000 266.26052 648550 12562 10792 16 649103.88 16.525575
258
+ 290000 299.18288 649134 12553 10884 16 649089.04 16.535933
259
+ 300000 332.66601 647681 12590 10894 20 649071.3 16.53152
260
+ Loop time of 332.666 on 128 procs for 100000 steps with 647681 particles
261
+
262
+ MPI task timing breakdown:
263
+ Section | min time | avg time | max time |%varavg| %total
264
+ ---------------------------------------------------------------
265
+ Move | 0.011864 | 8.0324 | 51.857 | 370.6 | 2.41
266
+ Coll | 0.21503 | 8.1959 | 74.713 | 423.5 | 2.46
267
+ Sort | 0.085663 | 1.4962 | 10.503 | 165.4 | 0.45
268
+ Comm | 37.118 | 55.279 | 61.863 | 69.9 | 16.62
269
+ Modify | 3.044 | 22.221 | 132.59 | 570.7 | 6.68
270
+ Output | 0.060992 | 0.069374 | 0.073742 | 0.9 | 0.02
271
+ Other | | 237.4 | | | 71.35
272
+
273
+ Particle moves = 64907130108 (64.9B)
274
+ Cells touched = 67853408362 (67.9B)
275
+ Particle comms = 117452363 (117M)
276
+ Boundary collides = 0 (0K)
277
+ Boundary exits = 11547909 (11.5M)
278
+ SurfColl checks = 92956064 (93M)
279
+ SurfColl occurs = 1653152 (1.65M)
280
+ Surf reactions = 0 (0K)
281
+ Collide attempts = 1450718642 (1.45B)
282
+ Collide occurs = 1125869746 (1.13B)
283
+ Reactions = 0 (0K)
284
+ Particles stuck = 0
285
+ Axisymm bad moves = 0
286
+
287
+ Particle-moves/CPUsec/proc: 1.52431e+06
288
+ Particle-moves/step: 649071
289
+ Cell-touches/particle/step: 1.04539
290
+ Particle comm iterations/step: 1
291
+ Particle fraction communicated: 0.00180954
292
+ Particle fraction colliding with boundary: 0
293
+ Particle fraction exiting boundary: 0.000177914
294
+ Surface-checks/particle/step: 0.00143214
295
+ Surface-collisions/particle/step: 2.54695e-05
296
+ Surf-reactions/particle/step: 0
297
+ Collision-attempts/particle/step: 0.0223507
298
+ Collisions/particle/step: 0.0173459
299
+ Reactions/particle/step: 0
300
+
301
+ Particles: 5060.01 ave 34450 max 0 min
302
+ Histogram: 84 15 4 11 4 6 0 0 2 2
303
+ Cells: 665.312 ave 666 max 665 min
304
+ Histogram: 88 0 0 0 0 0 0 0 0 40
305
+ GhostCell: 17265 ave 31629 max 955 min
306
+ Histogram: 22 8 12 10 17 3 6 4 16 30
307
+ EmptyCell: 0 ave 0 max 0 min
308
+ Histogram: 128 0 0 0 0 0 0 0 0 0
309
+ Surfs: 200 ave 200 max 200 min
310
+ Histogram: 128 0 0 0 0 0 0 0 0 0
311
+ GhostSurf: 0 ave 0 max 0 min
312
+ Histogram: 128 0 0 0 0 0 0 0 0 0
313
+ write_grid grid/final-grid.dat
314
+ grid cells = 85160
315
+ CPU time = 0.0133515 secs
316
+ write_surf surf/geom.surf
317
+ surf elements = 200
318
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CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/mir.txt ADDED
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+ read_grid grid/final-grid.dat
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/output/surf.300000.dat ADDED
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CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/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/Kn0p30/simulation0625_Kn0p30Ma4p4/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/Kn0p30/simulation0625_Kn0p30Ma4p4/slurm-28429417.out ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 85160 child grid cells
6
+ CPU time = 0.123775 secs
7
+ create/ghost percent = 1.02535 98.9746
8
+ Balance grid migrated 85003 cells
9
+ CPU time = 0.0989973 secs
10
+ reassign/sort/migrate/ghost percent = 54.0823 0.0305564 8.45743 37.4297
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
+ 428 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 76068 8664 428 = cells outside/inside/overlapping surfs
19
+ 428 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.106109 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 20.5107 20.9929 2.38039 21.1416 34.9744 6.01506 0.142353
23
+ surf2grid time = 0.0224331 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 15.0486 23.7244 5.60588 1.79336 11.7012 6.63118
25
+ Balance grid migrated 1177 cells
26
+ CPU time = 0.0466017 secs
27
+ reassign/sort/migrate/ghost percent = 5.59581 0.0551461 19.0387 75.3104
28
+ Creating particles ...
29
+ Created 582478 particles
30
+ CPU time = 0.0152155 secs
31
+ Memory usage per proc in Mbytes:
32
+ particles (ave,min,max) = 1.7666 0 3.375
33
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
34
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
35
+ total (ave,min,max) = 5.55685 3.22189 7.90939
36
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
37
+ 0 0 582478 0 0 0 0 0
38
+ 10000 15.403305 642631 12428 10703 20 608519.75 16.1302
39
+ 20000 33.886556 648277 12583 10894 15 627840.81 16.2851
40
+ 30000 52.236426 649216 12601 10880 16 634833.24 16.341433
41
+ 40000 71.648216 649519 12544 10841 12 638410.46 16.39775
42
+ 50000 90.445357 647960 12587 10882 14 640464.52 16.41208
43
+ 60000 108.86912 649376 12463 10867 20 641904.72 16.431867
44
+ 70000 127.78162 648481 12630 10920 30 642935.7 16.443786
45
+ 80000 146.13966 648392 12564 10908 16 643677.66 16.458125
46
+ 90000 164.94502 649852 12726 11021 16 644282.83 16.465167
47
+ 100000 183.71253 649700 12665 11000 12 644830.67 16.47414
48
+ 110000 202.76152 649118 12621 10877 13 645216.24 16.4742
49
+ 120000 221.59909 649468 12493 10793 16 645539.18 16.48015
50
+ 130000 239.68873 648998 12582 10855 16 645795.5 16.486223
51
+ 140000 260.3619 648456 12381 10802 16 645993.72 16.487336
52
+ 150000 279.25281 648836 12478 10791 17 646212.95 16.484467
53
+ 160000 297.34249 648801 12561 10835 24 646343.99 16.482312
54
+ 170000 317.06805 648789 12583 10899 16 646478.66 16.479888
55
+ 180000 335.76224 648004 12515 10859 18 646594.81 16.482839
56
+ 190000 354.70478 649023 12720 11019 15 646715.97 16.481895
57
+ 200000 373.51068 648850 12699 10965 31 646823.64 16.483235
58
+ Loop time of 373.511 on 128 procs for 200000 steps with 648850 particles
59
+
60
+ MPI task timing breakdown:
61
+ Section | min time | avg time | max time |%varavg| %total
62
+ ---------------------------------------------------------------
63
+ Move | 0.023712 | 15.879 | 102.87 | 522.9 | 4.25
64
+ Coll | 0.43252 | 16.11 | 143.13 | 587.5 | 4.31
65
+ Sort | 0.14521 | 2.8786 | 21.688 | 232.1 | 0.77
66
+ Comm | 66.652 | 100.45 | 112.34 | 96.7 | 26.89
67
+ Modify | 0.018747 | 0.13978 | 2.6105 | 103.4 | 0.04
68
+ Output | 0.0041213 | 0.01559 | 0.017288 | 1.7 | 0.00
69
+ Other | | 238 | | | 63.73
70
+
71
+ Particle moves = 129364727357 (129B)
72
+ Cells touched = 135241517021 (135B)
73
+ Particle comms = 234352467 (234M)
74
+ Boundary collides = 0 (0K)
75
+ Boundary exits = 23023568 (23M)
76
+ SurfColl checks = 185082821 (185M)
77
+ SurfColl occurs = 3296647 (3.3M)
78
+ Surf reactions = 0 (0K)
79
+ Collide attempts = 2874761687 (2.87B)
80
+ Collide occurs = 2231286129 (2.23B)
81
+ Gas reactions = 0 (0K)
82
+ Particles stuck = 0
83
+ Axisymm bad moves = 0
84
+
85
+ Particle-moves/CPUsec/proc: 2.70584e+06
86
+ Particle-moves/step: 646824
87
+ Cell-touches/particle/step: 1.04543
88
+ Particle comm iterations/step: 1
89
+ Particle fraction communicated: 0.00181156
90
+ Particle fraction colliding with boundary: 0
91
+ Particle fraction exiting boundary: 0.000177974
92
+ Surface-checks/particle/step: 0.00143071
93
+ Surface-collisions/particle/step: 2.54834e-05
94
+ Surface-reactions/particle/step: 0
95
+ Collision-attempts/particle/step: 0.0222221
96
+ Collisions/particle/step: 0.017248
97
+ Gas-reactions/particle/step: 0
98
+
99
+ Particles: 5069.14 ave 34247 max 0 min
100
+ Histogram: 84 16 2 10 6 6 0 0 2 2
101
+ Cells: 665.312 ave 666 max 665 min
102
+ Histogram: 88 0 0 0 0 0 0 0 0 40
103
+ GhostCell: 17265 ave 31629 max 955 min
104
+ Histogram: 22 8 12 10 17 3 6 4 16 30
105
+ EmptyCell: 0 ave 0 max 0 min
106
+ Histogram: 128 0 0 0 0 0 0 0 0 0
107
+ Surfs: 200 ave 200 max 200 min
108
+ Histogram: 128 0 0 0 0 0 0 0 0 0
109
+ GhostSurf: 0 ave 0 max 0 min
110
+ Histogram: 128 0 0 0 0 0 0 0 0 0
111
+ Memory usage per proc in Mbytes:
112
+ particles (ave,min,max) = 1.79297 0 5.0625
113
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
114
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
115
+ total (ave,min,max) = 5.80682 3.4453 8.1333
116
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
117
+ 200000 0 648850 12699 10965 0 0 0
118
+ 210000 33.429731 648759 12643 10974 24 648416.28 16.537
119
+ 220000 66.779727 649556 12506 10857 13 649039.24 16.5577
120
+ 230000 99.374786 648978 12603 10925 24 649284.6 16.557333
121
+ 240000 132.90084 648014 12430 10761 22 649178.7 16.54285
122
+ 250000 165.5897 648023 12480 10799 19 649122.17 16.5458
123
+ 260000 199.5976 648742 12554 10889 19 649160.19 16.536983
124
+ 270000 232.61999 648590 12617 10838 15 649093.21 16.528171
125
+ 280000 266.26052 648550 12562 10792 16 649103.88 16.525575
126
+ 290000 299.18288 649134 12553 10884 16 649089.04 16.535933
127
+ 300000 332.66601 647681 12590 10894 20 649071.3 16.53152
128
+ Loop time of 332.666 on 128 procs for 100000 steps with 647681 particles
129
+
130
+ MPI task timing breakdown:
131
+ Section | min time | avg time | max time |%varavg| %total
132
+ ---------------------------------------------------------------
133
+ Move | 0.011864 | 8.0324 | 51.857 | 370.6 | 2.41
134
+ Coll | 0.21503 | 8.1959 | 74.713 | 423.5 | 2.46
135
+ Sort | 0.085663 | 1.4962 | 10.503 | 165.4 | 0.45
136
+ Comm | 37.118 | 55.279 | 61.863 | 69.9 | 16.62
137
+ Modify | 3.044 | 22.221 | 132.59 | 570.7 | 6.68
138
+ Output | 0.060992 | 0.069374 | 0.073742 | 0.9 | 0.02
139
+ Other | | 237.4 | | | 71.35
140
+
141
+ Particle moves = 64907130108 (64.9B)
142
+ Cells touched = 67853408362 (67.9B)
143
+ Particle comms = 117452363 (117M)
144
+ Boundary collides = 0 (0K)
145
+ Boundary exits = 11547909 (11.5M)
146
+ SurfColl checks = 92956064 (93M)
147
+ SurfColl occurs = 1653152 (1.65M)
148
+ Surf reactions = 0 (0K)
149
+ Collide attempts = 1450718642 (1.45B)
150
+ Collide occurs = 1125869746 (1.13B)
151
+ Gas reactions = 0 (0K)
152
+ Particles stuck = 0
153
+ Axisymm bad moves = 0
154
+
155
+ Particle-moves/CPUsec/proc: 1.52431e+06
156
+ Particle-moves/step: 649071
157
+ Cell-touches/particle/step: 1.04539
158
+ Particle comm iterations/step: 1
159
+ Particle fraction communicated: 0.00180954
160
+ Particle fraction colliding with boundary: 0
161
+ Particle fraction exiting boundary: 0.000177914
162
+ Surface-checks/particle/step: 0.00143214
163
+ Surface-collisions/particle/step: 2.54695e-05
164
+ Surface-reactions/particle/step: 0
165
+ Collision-attempts/particle/step: 0.0223507
166
+ Collisions/particle/step: 0.0173459
167
+ Gas-reactions/particle/step: 0
168
+
169
+ Particles: 5060.01 ave 34450 max 0 min
170
+ Histogram: 84 15 4 11 4 6 0 0 2 2
171
+ Cells: 665.312 ave 666 max 665 min
172
+ Histogram: 88 0 0 0 0 0 0 0 0 40
173
+ GhostCell: 17265 ave 31629 max 955 min
174
+ Histogram: 22 8 12 10 17 3 6 4 16 30
175
+ EmptyCell: 0 ave 0 max 0 min
176
+ Histogram: 128 0 0 0 0 0 0 0 0 0
177
+ Surfs: 200 ave 200 max 200 min
178
+ Histogram: 128 0 0 0 0 0 0 0 0 0
179
+ GhostSurf: 0 ave 0 max 0 min
180
+ Histogram: 128 0 0 0 0 0 0 0 0 0
181
+ Writing grid file ...
182
+ grid cells = 85160
183
+ CPU time = 0.0133515 secs
184
+ Writing surface file ...
185
+ surf elements = 200
186
+ CPU time = 0.00162719 secs
187
+ Processing 4 grid chunk(s).
188
+ Completed grid chunk number: 3
189
+ Completed grid chunk number: 2
190
+ Completed grid chunk number: 1
191
+ Completed grid chunk number: 0
192
+
193
+ Done in 0:00:13 (wall clock time)
194
+
195
+ Processing 4 grid chunk(s).
196
+ Completed grid chunk number: 3
197
+ Completed grid chunk number: 2
198
+ Completed grid chunk number: 1
199
+ Completed grid chunk number: 0
200
+
201
+ Done in 0:00:11 (wall clock time)
202
+
203
+ Processing SPARTA surface information.
204
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p30/simulation0625_Kn0p30Ma4p4/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <DataArray type="Float64" Name="f_2[1]" format="appended" RangeMin="0.00246" RangeMax="0.27133" offset="4512" />
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+ <DataArray type="Float64" Name="f_2[6]" format="appended" RangeMin="-0.47829" RangeMax="0.46846" offset="14788" />
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+ <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0.49999998503" RangeMax="0.50000001255" offset="22712" />
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+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="24040" />
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AQAAAACAAABABgAAAAYAAA==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AQAAAACAAABABgAA5QUAAA==eJwtk3s81GkUxhnzC13k0kWpUKmQrNpIsef36ea2sdsOZaNNF7WVUKGMaSkq5X6/JxINFaloKuenQQqZGElpkiS3Fs3sRBg7Q+8/7x/n/Zz3PM/zPfSX//ZqCR0hKb517vkzR2H3GoaOigcTpthOXRmofw7sdPR8PTwvgOJ928J1aaFwkqWtop4VBrdK6hgXrWLAzDygIZQeCwMhzxjleXFgVU1muJbFg5Gif54HNwl813Fs/ZqTwTC079zKaQmgxlXZ1/A+FVyHyof5RSkQeqckTliZAdeZ6fzEnHT4ztS3OOSbDoZrmZmzNbPgYPogy/tGFowrcQP1dlwFd409OyUB2ZC34/nqiqLrUGZ3w67KhA33bbbs+9JfAKlm9lOFi26DQ6c2K/Z6EViW8OKVGMWwQhhZZtN4F0KYBwTs3lJo+nzYUJz4GPzKrsw3fkfB14Zte1V3loPAoknXjFsJjuV51+zJp3B743MHNus5zFugd/uFSS08ePLQpcLvBZzXGhh2C24Axsec1AodPuTJadZnXm2Cm8bqb46vagFe6iWG5q5WEI3xT3Uvew9Md5vAlD/b4Ne2V+8smtrB5VlEOPmoA6LOqbu/kXyCnZ81ox4LusDM+zhu+r0H+AdMioZL+2DFmbAHR5QHgD0oG3AQnnJKw6cMDEJxz4l7s0e+wqev2g2ef4nA8SeDGxq8/6DkpZyApvwN6jbYv4x2GQIL2SUZhuCMOnZ90wjcsvrgP+vWKFQrWqY6XhmDS4UfuuPvjMMjQn0WVMmRc2UNzsqTnXDtfZkKjTQ+li3/KIRG+tBSlLQyaGS6Ws7j8kQFkm94YF77awVy7ILobXMQnZzIw4Qge9tN51O7CHK3newH4kedICOmDMyx5hGkTM1wJkHm1zc59d0myJNSFTXHCDIta8mDUUWClP2aVkwnZ6atybprRiflpWouXVEgx3XHxD5tNNL9W4fbVScauT6m5h9rkCcbBW3TYvXlSMupzdlelhKoTnqYEDVz9Icvw1A83bvg8lsx6GqLjjqxRDD0ZD97D2MQON+8nnnn9sEeX4dFcw53Qpm4NkZubRucWbju4Oa/GyGTG3BaXvwAFnMNBNUWHJzs14gLXoweVlFtw09flHIJ407U2aoUaRrRh2+0AtxWOw7iJA8i1K9RlUYlRmnzbcKIYeSndC7M7RpBiQwHEwkueTt85rCFHDWpU56SpZFvRKNCG2SCaFRIAK/Y2UeB2s2xmU4zpFPSFAj1ODq1KLfLetU4nTKYMICgvLVmtBlFEdS22WG2PcEExW2UBrOXoLqlr3RZBDXJNUFN8kxQ0iGlpNApJxWjO+medGqCk1YFagKfzQqUqr9p/i/NNGrdhFE0KiVZduSpSZ/kKamYP04ulaMmeRrHyf2RIFdm6/YxlELUei94BBPuyIz/jr+c3pm3tm4I+yjzJbZMMU7MSRfjq0qPnwNLRMiCtVKJQkw20IznK3/F6JmyAQbQaNMCKUr9qLok2ErLqg/DXbP8j+zpxZiTdR2pHl0oS3tqcydmd/ft94rrwCOD+nJVaz7i91JTRpiwDcPvMgQRXgJ8qiartGKvU71Nv1YLdtZMK4y0b8JT/jkrNy1owtdBJ7bGiRvwIc8lNzqBh7viWnRiw2pxdOElGmtxDbbosUum3K/CpROLWIlhB3k8KC9Hpc6uQWZq2Y86B+k7TtPcckvRJ/LorZTPd7HQzGTpco1izO/2z+p9UozzBQXlI+wCPLGyn61Ym49V9qeU7Vl5KHTOUPQ7mINBuvm7LJfnYDu8I4P8stCaq39CGJ6J0UwOZ19ROka1OIvsiTQMFs0y39yahr+ZL7s8EpmGzhWF3CRRIh7tsTCQ9CfhR0/m2QrfRGQe0jYc8khC6DDaAabJuEyyoqfWMg4/8BVj/FTiUee+6BB7eSxuUVEzy42JxpsXjDW2WIejpmJihHn9RdzwcaR9e/x5DKldf3GjVxAGvOT6mBxnokPpDOdYV0/k1H4XDNQ64v9KqjAZAQAAAACAAABABgAAFgYAAA==eJwdk3s8lAkbhm0ok8iqJtsvhyIRoqTQ6FbOlkqUFbWbvmVmVohVJEoqKocUUQ1rkpBIiFi8FSa+5NgczDTvzFI/TZpPIRWWT/3//HE/133dW6M2T21NX4YSnUOzmo66xFLVmg/2qR+2pdU4GKam6+A4p4CzfHoRclcl7fzQ7g41YZVCN9cRo6b7B6WXnXD2vPXD2rMbiBJtPx/dAl/wJUdsRm9qExQNzQPFUiq8ghI9NkYEQVfjrYfQ5wg4DLOjF9qiMLCW4bfHLRpjP6vLfrALxZe8zbtWLI5F4FKl9LCRi+AVJ9H/XJCBf4MC6zVK05He8ryv7lYmLpuzzzSrXkHux7ijJe43MXEtokkQy0Zswb6cJn4+uvLZp85cKkSNx/nBr4uKITlCWht3lOLCsJZvwMoyDE57Gjk+foCquDb/8vhqSN2PhSgNPETrivURu3xrYZM9cLdypB7WTRMdVxQawbqlXzed9BhDVscuvCt7gp1O61LMlJ7AY0VBlI9VKxKUWtM3HG/FntF3KxUrnyGuvCbwVH87+lVahieVn2N1SPSJQtMuOLlEfF0s68K8BmXNpeiF3vaPhzsO9IDPuF04eqwXJmQbbXQBF0VvXdfPruJiqES97qdQHt4cYvM2jfDwv0x13qyNAM6dT/OOfemHPVm6z3BaAGlwgLXGFRFCDCUhkYeFMEmi9nd+FqF6nsO+2hOvoK1Yyf8bJGjJucZ/HhTjezw7CZ5qLja6GSHG7aeCHZcTSeiN7DVZEi+BqyXVM3itFPciF7UdHCFxN85Wx3K3BKyLPlr+KiT8vBIZWttI/GPvSrVIEOPENHkHU2KkiOgKYQmvMGNi4um+XYSy0E+s8aZ+yOIG/+gy7Yf+F8kcaQHOHa9LHmnhYbiYeaZ4iodt5WrLBqf7EK6ylzlh3wdzscH8hKwelHkt7nmr3o0bO76R78Rr4e4NnZI2aMhq7ysOtSGHMeW770YL9NlmQ3sCnmDuODB4YSOM5WpNFtfrYbygimJhWoXukakt5jsrwGaOM0/3FsP/YB252ZsNShI58I9dNjav1lqQnXYB3ik58euJaBjQnHXMIsMQ9Kutqf8lChFkdJXQS1uI3Yf8P4V5hkNF17369cez6PvkyD+nmwKPyVhjGv0afq3JrXFWKkDM6uSx8HmlaKiw8o9pv49lK5J/t0yqhvZ9+/HbHjWoCb6mVFzSiJzJGO+Mhc0QzmRlGRa24I1cpUjZnAOX/evCXKzbMaJ4WdjS/gK8yN4pDr8Tpt+w1vaAGy0z3KDSi7pwPQPVnJeQC76JxsWStZ+3LlUWYMZhUj78mA+di/PiVu8S4Pv8qEJ8TUwYIptFoJf3uf78RgQLgZqI+vsrcIoUtLry5/pjs+i+q8R4QLzPo5mScFz+srZDRuKvihbF/94hoTzpmGdwQIpu+ZrUHlsSqSeSnY9riCFqaf9X3UmCLc1qX6YtSNAz5CdhRWKjtfvB3MMkzqzMtOibEMNP/M5GFidCX3+71ChGBIcyBnn0ohCe34fejyHb1GofUoDQUI9dvwzw4XKvdS4ZH8HcIabJBBdGj36ZM4+Hk/Faq2jCl1hI/7asPnArHza50nugb5ZVq8/qRaKXW+5pw26clF9f8wOzEyyCck7FugPpzxb5cIjniA1yO31jPwfX12llvaS0IbZxarCR2YqjEYSDV34zIr8/8AQZ3LK8qyQBZQbnlux9A6hMz7E07wZ0psRvYm2sRX6hicCZ9hCRQu+Ine8f4FGHrDJHUIG7rIlM1pJS/MEtpzBSSxD14vXNI6H3MBuw6T8090KEsk1+LJ5hY+X8Md+2ADZKmsYuaY5nIzMkymaN3jV8Vh5/mqt6FVul9YHO0Zfwd05QPd8tFbx7lk3m68/BuSPD9s7VBOw4+duzUwmnodf8U2W4a8ycR7avqrzpKMoh/ahFUbhjlM0xb9iDj6qWB7RtadieTCQYuOxF6ofl5vL5LljDkIfEV1oTIwUEjfKjFfGIHdm2fMYOTqXM6gGaESSCG+5XjLcQXuHUzvefKIRbr53Soxcbidk+hXdRRcuImQr3NOmwAvF/4IMPaA==AQAAAACAAABABgAAKwYAAA==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AQAAAACAAABABgAAOQYAAA==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AQAAAACAAABABgAACwQAAA==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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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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAAeSgAAA==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AQAAAACAAABwLwAAZyEAAA==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AQAAAACAAADoSgAAcA8AAA==eJxN0k/oB3ldx/FND11CsejgSZ3AiY1MMw8l6OCCGrRQEhkpKmKQIhUoKJriH9TVDM3CMadkQr5lgzDYwDIiAxILCYEg0qEgCDboEEEKgUKHfML3+Xr95jKP6+f1fj73JcP0g5e+YHrkR99HnjnG//XPPxf/5ldeHF/ve1k8Pv4r8Wef98r4h997LH7rU6+N/+nzj8cve/vr4i+9/PXxjz/rjfEf/vtb4n/5+9+NH/vYO+Kvvv4P4p9+9N3xB//vvfF/fvsD8Vd+/cPxv339o/FP/szH41d/+on4/f/7qXh/05/E//GPn4mf+5LPxY9/8c8f7DnHT77zLx5su8TPf+WXHuy8xp98zpcfbH6Lv//03z7Yf4vf8ORXH9xij5964msP7nLEL3rDkw9udMbzz3/jwb2u+JFHvvngvcP0r8984f2NY0wzmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zXTPOaaZbrvENNOd15hmuvktppnuv8U001vsMc30LkdMM73RGdNM73XFNNP3DtM3vvvo/Y1jTDOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNdM85ppluu8Q0053XmGa6+S2mme6/xTTTW+wxzfQuR0wzvdEZ00zvdcU00/cO019++RfubxxjmtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2mme84xzXTbJaaZ7rzGNNPNbzHNdP8tppneYo9ppnc5Yprpjc6YZnqvK6aZvneYPvCuX7q/cYxpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMppnuOcc0022XmGa68xrTTDe/xTTT/beYZnqLPaaZ3uWIaaY3OmOa6b2umGb63mF602O/fH/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9wzT91CvubxxjmtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2mme84xzXTbJaaZ7rzGNNPNbzHNdP8tppneYo9ppnc5Yprpjc6YZnqvK6aZvneYXvD0q+5vHGOa0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaaZ7zjHNdNslppnuvMY0081vMc10/y2mmd5ij2mmdzlimumNzphmeq8rppm+d5iecbzm/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa6XuH6emP/Nr9jWNMM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM10zzmmmW67xDTTndeYZrr5LaaZ7r/FNNNb7DHN9C5HTDO90RnTTO91xTTT9w7TU6/7jfsbx5hmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omumec0wz3XaJaaY7rzHNdPNbTDPdf4tpprfYY5rpXY6YZnqjM6aZ3uuKaabvHaa/GX7r/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa6XuH6RPf/537G8eYZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrpnnNMM912iWmmO68xzXTzW0wz3X+Laaa32GOa6V2OmGZ6ozOmmd7rimmm7x2m3/uHN9/fOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zD96ufedn/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9w/ToW99+f+MY04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0z3XOOaabbLjHNdOc1pplufotppvtvMc30FntMM73LEdNMb3TGNNN7XTHN9L3D9BO/+Pv3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztM//1j77q/cYxpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMppnuOcc0022XmGa68xrTTDe/xTTT/beYZnqLPaaZ3uWIaaY3OmOa6b2umGb63mH69nfec3/jGNOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM91zjmmm2y4xzXTnNaaZbn6Laab7bzHN9BZ7TDO9yxHTTG90xjTTe10xzfS9w7T/9R/d3zjGNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtNM95xjmum2S0wz3XmNaaab32Ka6f5bTDO9xR7TTO9yxDTTG50xzfReV0wzfe8wfev80P2NY0wzmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zXTPOaaZbrvENNOd15hmuvktppnuv8U001vsMc30LkdMM73RGdNM73XFNNP3DtPXn//R+xvHmGY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia6Z5zTDPddolppjuvMc1081tMM91/i2mmt9hjmuldjphmeqMzppne64pppu8dpr974mP3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztMX/yfT9zfOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zD98W9/6v7GMaYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZrrnHNNMt11imunOa0wz3fwW00z332Ka6S32mGZ6lyOmmd7ojGmm97pimul7h+n93/z0/Y1jTDOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNdM85ppluu8Q0053XmGa6+S2mme6/xTTTW+wxzfQuR0wzvdEZ00zvdcU00/cO0zt/9jP3N44xzWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TTTPeeYZrrtEtNMd15jmunmt5hmuv8W00xvscc007scMc30RmdMM73XFdNM3ztMb/zsn97fOMY0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG00z3nGOa6bZLTDPdeY1pppvfYprp/ltMM73FHtNM73LENNMbnTHN9F5XTDN97zA9/oM/u79xjGlG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymme45xzTTbZeYZrrzGtNMN7/FNNP9t5hmeos9ppne5Yhppjc6Y5rpva6YZvreYXrFWz5/f+MY04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0z3XOOaabbLjHNdOc1pplufotppvtvMc30FntMM73LEdNMb3TGNNN7XTHN9L3D9KJvfeH+xjGmGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmma65xzTTLddYprpzmtMM938FtNM999imukt9phmepcjppne6Ixppve6Yprpe4fpeS9e7m8cY5rRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNppnvOMc102yWmme68xjTTzW8xzXT/LaaZ3mKPaaZ3OWKa6Y3OmGZ6ryummb53mJ79hb+6v3GMaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKaZ7jnHNNNtl5hmuvMa00w3v8U00/23mGZ6iz2mmd7liGmmNzpjmum9rphm+t7hR//1/sYxphlNM5pmNM1omtE0o2lG04ymGU0zmmY0zWia0TSjaUbTjKYZTTOaZjTNaJrRNKNpRtOMphlNM5pmuucc00y3XWKa6c5rTDPd/BbTTPffYprpLfaYZnqXI6aZ3uiMaab3umKa0f8PY4jinQ==AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==eJw12qGjOYB6BuBzN9vsznZts3sFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAEQRAE4QRBEIQTBGHhPL+vPH/D975v4Ov3ggwxzAijjDHOBJNMMc0Ms8wxzwKLLLHMCqussc4Gm2yxzQ677LHPAYccccwJp5xxzgWXXHHNDbfccc8DjzzxzAuvvPGbdz745Is/fPPDr7/8EmCQIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeGUM8654JIrrrnhljvueeCRJ5554ZU3fvPOB5988Ydvfvj1T78EGGSIYUYYZYxxJphkimlmmGWOeRZYZIllVlhljXU22GSLbXbYZY99DjjkiGNOOOWMcy645IprbrjljnseeOSJZ1545Y3fvPPBJ1/84Zsffv3zLwEGGWKYEUYZY5wJJplimhlmmWOeBRZZYpkVVlljnQ022WKbHXbZY58DDjnimBNOOeOcCy654pobbrnjngceeeKZF1554zfvfPDJF3/45odfATDIEMOMMMoY40wwyRTTzDDLHPMssMgSy6ywyhrrbLDJFtvssMse+xxwyBHHnHDKGedccMkV19xwyx33PPDIE8+88Mobv3nng0+++MM3P/z6l18CDDLEMCOMMsY4E0wyxTQzzDLHPAssssQyK6yyxjobbLLFNjvsssc+BxxyxDEnnHLGORdccsU1N9xyxz0PPPLEMy+88sZv3vngky/+8M0Pv/71lwCDDDHMCKOMMc4Ek0wxzQyzzDHPAossscwKq6yxzgabbLHNDrvssc8BhxxxzAmnnHHOBZdccc0Nt9xxzwOPPPHMC6+88Zt3Pvjkiz9888Ovf/slwCBDDDPCKGOMM8EkU0wzwyxzzLPAIksss8Iqa6yzwSZbbLPDLnvsc8AhRxxzwilnnHPBJVdcc8Mtd9zzwCNPPPPCK2/85p0PPvniD9/88M/DH2CQIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeGUM8654JIrrrnhljvueeCRJ5554ZU3fvPOB5988Ydvfv4EPf/+S4BBhhhmhFHGGGeCSaaYZoZZ5phngUWWWGaFVdZYZ4NNtthmh1322OeAQ4445oRTzjjngkuuuOaGW+6454FHnnjmhVfe+M07H3zyxR+++eHXX38JMMgQw4wwyhjjTDDJFNPMMMsc8yywyBLLrLDKGutssMkW2+ywyx77HHDIEceccMoZ51xwyRXX3HDLHfc88MgTz7zwyhu/eeeDT774wzc//PqPXwIMMsQwI4wyxjgTTDLFNDPMMsc8CyyyxDIrrLLGOhtsssU2O+yyxz4HHHLEMSeccsY5F1xyxTU33HLHPQ888sQzL7zyxm/e+eCTL/7wzQ//BP0BBhlimBFGGWOcCSaZYpoZZpljngUWWWKZFVZZY50NNtlimx122WOfAw454pgTTjnjnAsuueKaG265454HHnnimRdeeeM373zwyRd/+ObnT8Hzn78EGGSIYUYYZYxxJphkimlmmGWOeRZYZIllVlhljXU22GSLbXbYZY99DjjkiGNOOOWMcy645IprbrjljnseeOSJZ1545Y3fvPPBJ1/84Zsffv3XLwEGGWKYEUYZY5wJJplimhlmmWOeBRZZYpkVVlljnQ022WKbHXbZY58DDjnimBNOOeOcCy654pobbrnjngceeeKZF1554zfvfPDJF3/45odff/slwCBDDDPCKGOMM8EkU0wzwyxzzLPAIksss8Iqa6yzwSZbbLPDLnvsc8AhRxxzwilnnHPBJVdcc8Mtd9zzwCNPPPPCK2/85p0PPvniD9/88E/BH2CQIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeGUM8654JIrrrnhljvueeCRJ5554ZU3fvPOB5988Ydvfv4MO/77lwCDDDHMCKOMMc4Ek0wxzQyzzDHPAossscwKq6yxzgabbLHNDrvssc8BhxxxzAmnnHHOBZdccc0Nt9xxzwOPPPHMC6+88Zt3Pvjkiz9888Ov//klwCBDDDPCKGOMM8EkU0wzwyxzzLPAIksss8Iqa6yzwSZbbLPDLnvsc8AhRxxzwilnnHPBJVdcc8Mtd9zzwCNPPPPCK2/85p0PPvniD9/88Ot/fwkwyBDDjDDKGONMMMkU08wwyxzzLLDIEsussMoa62ywyRbb7LDLHvsccMgRx5xwyhnnXHDJFdfccMsd9zzwyBPPvPDKG79554NPvvjDNz/8M+wLMMgQw4wwyhjjTDDJFNPMMMsc8yywyBLLrLDKGutssMkW2+ywyx77HHDIEceccMoZ51xwyRXX3HDLHfc88MgTz7zwyhu/eeeDT774wzc/fwad//dLgEGGGGaEUcYYZ4JJpphmhlnmmGeBRZZYZoVV1lhng0222GaHXfbY54BDjjjmhFPOOOeCS6645oZb7rjngUeeeOaFV974zTsffPLFH7754dfffwkwyBDDjDDKGONMMMkU08wwyxzzLLDIEsussMoa62ywyRbb7LDLHvsccMgRx5xwyhnnXHDJFdfccMsd9zzwyBPPvPDKG79554NPvvjDNz/8+scvAQYZYpgRRhljnAkmmWKaGWaZY54FFllimRVWWWOdDTbZYpsddtljnwMOOeKYE04545wLLrnimhtu+f8SvykLAQAAAACAAADuBQAAFAAAAA==eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/Surf.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="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0626_Kn0p30Ma4p5/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 0.3
13
+ variable Ma equal 4.5
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