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  1. .gitattributes +4 -0
  2. CylinderKnMa/Kn0p32/simulation0658_Kn0p32Ma2p7/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p32/simulation0659_Kn0p32Ma2p8/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p32/simulation0660_Kn0p32Ma2p9/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p32/simulation0661_Kn0p32Ma3p0/output/grid.300000.dat +3 -0
  6. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_0_300000.vtu +0 -0
  7. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_1_300000.vtu +27 -0
  8. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_2_300000.vtu +27 -0
  9. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_300000.pvtu +21 -0
  10. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_3_300000.vtu +27 -0
  11. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Surf.pvd +9 -0
  12. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/Grid_0_300000.vtu +0 -0
  13. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/Grid_1_300000.vtu +0 -0
  14. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/Grid_2_300000.vtu +35 -0
  15. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/Grid_300000.pvtu +29 -0
  16. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/Grid_3_300000.vtu +35 -0
  17. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/final-grid.dat +0 -0
  18. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/output/grid.300000.dat +0 -0
  19. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/output/lambdakn.300000.dat +0 -0
  20. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/output/surf.300000.dat +209 -0
  21. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/runmpirun64.sh +18 -0
  22. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/slurm-28428726.out +205 -0
  23. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/surf/Surf_300000.vtu +38 -0
  24. CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/surf/geom.surf +610 -0
  25. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Grid.pvd +9 -0
  26. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda.pvd +9 -0
  27. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_0_300000.vtu +0 -0
  28. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_1_300000.vtu +27 -0
  29. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_2_300000.vtu +27 -0
  30. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_300000.pvtu +21 -0
  31. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_3_300000.vtu +27 -0
  32. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Surf.pvd +9 -0
  33. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/config.txt +78 -0
  34. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/cylinder.surf +410 -0
  35. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/gas.species +14 -0
  36. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/gas.vhs +9 -0
  37. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/Grid_0_300000.vtu +0 -0
  38. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/Grid_1_300000.vtu +0 -0
  39. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/Grid_2_300000.vtu +35 -0
  40. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/Grid_300000.pvtu +29 -0
  41. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/Grid_3_300000.vtu +35 -0
  42. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/final-grid.dat +0 -0
  43. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/log.sparta +319 -0
  44. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/mir.txt +3 -0
  45. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/output/grid.300000.dat +0 -0
  46. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/output/lambdakn.300000.dat +0 -0
  47. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/output/surf.300000.dat +209 -0
  48. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/runmpirun128.sh +18 -0
  49. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/runmpirun64.sh +18 -0
  50. CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/slurm-28428728.out +205 -0
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AQAAAACAAADgLgAAMiEAAA==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AQAAAACAAADASwAAlQ8AAA==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AgAAAACAAACAOwAABRMAABMJAAA=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AQAAAACAAADgLgAA5QcAAA==eJw12qGjOYB6BuBzN9vsznZts3sFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQThBEARBEARBEARBEARBEIQTBEEQBGHhPL+vPH/D975v4Ov3ggwxzAijjDHOBJNMMc0Ms8wxzwKLLLHMCqussc4Gm2yxzQ677LHPAYccccxvTjjljHMuuOSKa2645Y57HnjkiWdeeOWNP7zzwSdffPPDr7/8EmCQIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOO+c0Jp5xxzgWXXHHNDbfccc8DjzzxzAuvvPGHdz745Itvfvj1T78EGGSIYUYYZYxxJphkimlmmGWOeRZYZIllVlhljXU22GSLbXbYZY99DjjkiGN+c8IpZ5xzwSVXXHPDLXfc88AjTzzzwitv/OGdDz754psffv3zLwEGGWKYEUYZY5wJJplimhlmmWOeBRZ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CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/Lambda/Lambda_2_300000.vtu ADDED
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+ </UnstructuredGrid>
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_AQAAAACAAACgDwAAtQIAAA==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AQAAAACAAACgDwAATwAAAA==eJy7vOexiKynkMPlUXqUHqUxaM6fC9I3rxqlR+lRGhut7FXdrM8jPEqP0qM0Ftp2duj81bmj9Cg9SmOji68LfXI8P0qP0qM0NhoAXjcwVw==AQAAAACAAACgDwAAjAwAAA==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AQAAAACAAACgDwAAxAsAAA==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AQAAAACAAACgDwAATgwAAA==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AQAAAACAAACgDwAAbAwAAA==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AQAAAACAAACgDwAA6woAAA==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AQAAAACAAACgDwAAIwYAAA==eJxdV1toXFUUTfOaSTKTpMnk0SSdJCa2qfVRkw+12nJBS+MLTREVVBAtWijoPRnvc/xQsIogRsSfFoofFcFXEAs1oF8V9KP1LQYU0YDoR03R1rTpy3pmr7XntJmPuZxzz9l77cfae9+54p5jU73G+2tqrrin03jzB/wtzed8rzC7fSFtN97M4aWxh+3+vr32t9p4n9nlTKvxdtjjx5qM95Y9Pl9nPCtlrnjBx7qF+1ZO5dhU1niDlQONlJcxXrmiwK4nKr827vcb74nK7z/fq6gvNFLvWuOdsmoP26eVesBvoHx778uj9ldvvEl7fNbiENw1lNPIe0u+V/kfq+H5HPfzBvisnaKvw3gVMdutnTWVX5b67H2R/6sP3Md9+Omi75nKgWU+a3j+jA/59in2WT/IfpcBjlGnR+zopD+6ae8G422oHBwy1Xuy7iXuBlP1j9ojctUPg4zTtfTH1XyOGeC8i/F4inE7Qzw3lSDn0cCrqN3yeQQ8exPsf5ri/lAZ9l9Xhr7OMvx9IoXcj1LIfSTFuc4UeH9KgOv1BPlzdwL/ZBLE/+sY/ijHkLc5hv3Xx7B7MQLOvRHuBxHkbeZ+PmK8Q+BeDBHHD0PgCkPYMRni3ngIPd0h9P4c4NyPAfB/EECuzWvRl6f/VzH/u+jHKxin1cznfvJpPeM5vCLPmhmvdsbJxlH83b2CVxnmYT2f//rA1US+tDFuecrtdvkkfj/NvMwyv/ocn5Wvql/kXEkcfcyXTU6/8ldwK59rqfesj/sNzD+b7yKn1lT3xW8XiD9PP9XxfD/l9JG3zcTfxmcd7VK+aV2qI75l1gtbN3Rf/adxUvsFf8HJFzsHyKMhxm2QeLbSH9OMX3YaeO8pwf7HAtyfi2DX8wn0vMp8/yOFvJMp8mMhhb9nU+B8PCUu8uSLBPa8kUDvHQl4nyNvDsewZ38MPLti4Nga415NjPO/RcD5XgR/+RH8fXNEPkaQfyIEviMhzn8cAlc5xL1dfH9nyLwOYce3AXAfCnCuQP9lXR2W/fPMLxsXwXVJnda8Ez+1Mh86mVcdjEvWndN6K3bnGP8eU9Uv8e5hnHqZFwO8N0h5o5Q3zjy7irjHGOdGZ4fmq+pTPCJX9Y3Qnh7mVa/jn/Jb81Ds6nN+0TxW/wjODN/nXF2Xe82Of8p3xad9UPyrfe+Uj7idc/zXfiZ25Mj/1Sv8so54B4ljlHYMOP9K3NdRzzjvT1Ley9qnpiHv/hLy5fYA91+KsDYJ7NiWsp6TL++yv7yWIh9uSOGH3xPoezuBPp/9yEuYLwn0L8TYn4vhzxn2k90x/LExZr+JIfeHiP0hwvpZ8uU24syTJ8fJj+9DyC3z+QD7y43sI+vIk0xI/gdY95jqPAU+u7iKn2pd3ZN+sYZ1KGOq9V783sF61uDmNc13wad5sMw5pZVxz5FfGadP5zI5V0s9S6yfFzmvrHH5LU+7L/Layat+2lF09VvkFoivjfbk3Xyia3muov5mV8dF3FpT5YP2G5WnvNC6rvOgnMs5+7TO6/yk9UXWw4xHgXgHeK9oLvdbwfUf5Y/2LZ33tH/IvvWP4Gt3/hW5fcQ/QR6Vif+aafhtRwnyPNbVFyOcH0nI8wRyR1PYtYnzVRf7yCfM//0J7j3NPnJfAhxj5NtijHz+KgaufZyrpthHOsiLGvaXo+wbB8mTFyLs38L+cpE8OML56lAIf7wSQu42vp/g+0bOXd9xrnonQB4scb5aZB8/5VfzQr8TBHez+67QeeyyvFx2c7nOIVpPNR+UBzqXi790XjjJ9Wn3vaBzkvj5T786D8r789TTQv62Mr+ayIuzLh+UNzr/aF7qd5XO++LfIVenRW4t3xdc/xT53a5/ipwu912g85n4o8Xlo849EodL+op+h8mzhf1lmPrWk3ejtKPX8UznYe2f6l/lqfhrFfO/f0V/H6D8EfJhmP7ZwvvPkGcD0/zeKNGfnM//Yf3dGSNPiuwPO/ld8WSCew+RFz0J/P5LjHgeZJ4/x35xK9dZ9ot55v37zPfd7BMPcr0xgr56ruc5P70ZUm6IuN3Lte0Tcv9vzo3fsD/MBLhXCvB+U8D+E3j/A0TskEs=AQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAABoHAAA5wYAAA==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CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/grid/final-grid.dat ADDED
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CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/output/grid.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/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
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+ -5 6
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+ -5 5
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+ -0.5 0.5
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+ 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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+ 139 -0.18406 0.46489 -0.16937 0.47044 0.04593 0.40525 0.14325 -0.37909 0.11207 0.042349 0.0040104 -0.0052893 37.946 0 0
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+ 140 -0.16937 0.47044 -0.15451 0.47553 0.04379 0.38367 0.12428 -0.36299 0.11224 0.038428 0.0037151 -0.0050979 36.971 0 0
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35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.0414 0.35879 0.1055 -0.34293 0.10856 0.033397 0.0033623 -0.0048619 37.9 0 0
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+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00289 0.02277 -0.020753 0.0093702 0.00044637 0.00098859 -0.00031896 0.00016271 0.675 0 0
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156
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157
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158
+ 77 -0.36448 -0.34227 -0.37506 -0.33066 0.08571 0.77288 0.57165 0.52016 0.08324 -0.091479 0.010286 0.0067334 71.29 0 0
159
+ 78 -0.37506 -0.33066 -0.38526 -0.31871 0.0901 0.81349 0.61858 0.52832 0.080262 -0.093975 0.010977 0.0068224 72.486 0 0
160
+ 79 -0.38526 -0.31871 -0.39508 -0.30645 0.09308 0.8348 0.6515 0.52195 0.069784 -0.087104 0.011329 0.0068303 77.698 0 0
161
+ 80 -0.39508 -0.30645 -0.40451 -0.29389 0.0952 0.85813 0.68624 0.51524 0.07207 -0.095988 0.011911 0.0065853 74.101 0 0
162
+ 81 -0.40451 -0.29389 -0.41354 -0.28104 0.09815 0.88744 0.72606 0.51029 0.062479 -0.088899 0.012386 0.0066189 80.763 0 0
163
+ 82 -0.41354 -0.28104 -0.42216 -0.26791 0.10058 0.911 0.76142 0.50016 0.061317 -0.093347 0.012923 0.0063899 83.523 0 0
164
+ 83 -0.42216 -0.26791 -0.43037 -0.25452 0.10358 0.93825 0.79999 0.49023 0.055521 -0.090602 0.013438 0.0062772 83.774 0 0
165
+ 84 -0.43037 -0.25452 -0.43815 -0.24088 0.10373 0.94015 0.81665 0.46581 0.050779 -0.089025 0.013625 0.0059182 84.11 0 0
166
+ 85 -0.43815 -0.24088 -0.4455 -0.227 0.10818 0.97612 0.86266 0.45676 0.04884 -0.092243 0.014317 0.0057255 87.986 0 0
167
+ 86 -0.4455 -0.227 -0.45241 -0.21289 0.11016 0.99307 0.8918 0.43689 0.041162 -0.084022 0.014654 0.0055426 87.975 0 0
168
+ 87 -0.45241 -0.21289 -0.45888 -0.19857 0.11085 1.0036 0.91472 0.41301 0.038789 -0.085908 0.014977 0.0051379 92.073 0 0
169
+ 88 -0.45888 -0.19857 -0.46489 -0.18406 0.11385 1.034 0.95525 0.39568 0.028609 -0.069068 0.015454 0.0051302 91.488 0 0
170
+ 89 -0.46489 -0.18406 -0.47044 -0.16937 0.11536 1.0511 0.98327 0.37155 0.028684 -0.075911 0.015895 0.0046436 98.19 0 0
171
+ 90 -0.47044 -0.16937 -0.47553 -0.15451 0.11788 1.0597 1.0025 0.34324 0.026473 -0.077322 0.016163 0.0041769 90.962 0 0
172
+ 91 -0.47553 -0.15451 -0.48015 -0.1395 0.12265 1.0997 1.0511 0.32337 0.019316 -0.062788 0.016814 0.004093 95.639 0 0
173
+ 92 -0.48015 -0.1395 -0.48429 -0.12434 0.12053 1.0929 1.0541 0.28838 0.015143 -0.055354 0.016796 0.0036602 99.794 0 0
174
+ 93 -0.48429 -0.12434 -0.48796 -0.10907 0.12213 1.1061 1.0755 0.25821 0.010187 -0.042431 0.017053 0.0033893 98.034 0 0
175
+ 94 -0.48796 -0.10907 -0.49114 -0.093691 0.12336 1.1252 1.1018 0.22817 0.0096858 -0.046771 0.017458 0.0028492 99.473 0 0
176
+ 95 -0.49114 -0.093691 -0.49384 -0.078217 0.12618 1.1398 1.1228 0.19596 0.0065617 -0.037597 0.017739 0.0024874 103.42 0 0
177
+ 96 -0.49384 -0.078217 -0.49606 -0.062667 0.12612 1.1463 1.1348 0.16151 0.0051586 -0.036246 0.017906 0.0019676 104.89 0 0
178
+ 97 -0.49606 -0.062667 -0.49778 -0.047054 0.12632 1.1409 1.134 0.1252 0.0020548 -0.018612 0.017845 0.0016742 100.22 0 0
179
+ 98 -0.49778 -0.047054 -0.49901 -0.031395 0.12689 1.1461 1.1425 0.08992 0.00045969 -0.0058409 0.017954 0.0013207 103.87 0 0
180
+ 99 -0.49901 -0.031395 -0.49975 -0.015705 0.12585 1.1477 1.1464 0.054062 0.0010729 -0.02275 0.018023 0.00049183 102.54 0 0
181
+ 100 -0.49975 -0.015705 -0.5 0 0.12814 1.1657 1.1655 0.018309 0.00010524 -0.0066992 0.018309 0.00018236 101.19 0 0
182
+ 101 -0.5 0 -0.49975 0.015705 0.12794 1.1607 1.1606 -0.018232 2.5245e-06 0.0001607 0.018229 -0.00028385 101.21 0 0
183
+ 102 -0.49975 0.015705 -0.49901 0.031395 0.12629 1.1478 1.1465 -0.054069 0.00036336 0.007705 0.018015 -0.00072826 100.91 0 0
184
+ 103 -0.49901 0.031395 -0.49778 0.047054 0.12649 1.1427 1.1392 -0.089657 0.001926 0.024472 0.017924 -0.0010239 103.37 0 0
185
+ 104 -0.49778 0.047054 -0.49606 0.062667 0.12622 1.1439 1.137 -0.12553 0.002573 0.023306 0.0179 -0.0016057 103.99 0 0
186
+ 105 -0.49606 0.062667 -0.49384 0.078217 0.12617 1.1407 1.1293 -0.16073 0.0049242 0.034599 0.017816 -0.0019812 100.28 0 0
187
+ 106 -0.49384 0.078217 -0.49114 0.093691 0.12479 1.1316 1.1147 -0.19455 0.0072076 0.041297 0.017623 -0.0024073 104.84 0 0
188
+ 107 -0.49114 0.093691 -0.48796 0.10907 0.12551 1.1361 1.1125 -0.2304 0.0077045 0.037203 0.017596 -0.0030345 101.91 0 0
189
+ 108 -0.48796 0.10907 -0.48429 0.12434 0.12122 1.0946 1.0643 -0.25552 0.011548 0.048103 0.016899 -0.0032579 100.2 0 0
190
+ 109 -0.48429 0.12434 -0.48015 0.1395 0.11915 1.0813 1.043 -0.28534 0.015262 0.055789 0.016623 -0.0036056 99.882 0 0
191
+ 110 -0.48015 0.1395 -0.47553 0.15451 0.12016 1.0898 1.0416 -0.32044 0.02165 0.070375 0.016701 -0.0039279 95.84 0 0
192
+ 111 -0.47553 0.15451 -0.47044 0.16937 0.11633 1.052 0.99532 -0.34077 0.024227 0.070761 0.016014 -0.0042412 93.901 0 0
193
+ 112 -0.47044 0.16937 -0.46489 0.18406 0.11473 1.0412 0.97399 -0.36804 0.028272 0.074821 0.015743 -0.0046057 92.74 0 0
194
+ 113 -0.46489 0.18406 -0.45888 0.19857 0.11352 1.0294 0.95101 -0.39392 0.033921 0.081894 0.015471 -0.0049011 91.914 0 0
195
+ 114 -0.45888 0.19857 -0.45241 0.21289 0.11089 1.0098 0.92031 -0.41554 0.035458 0.07853 0.015013 -0.0052935 92.584 0 0
196
+ 115 -0.45241 0.21289 -0.4455 0.227 0.10863 0.98006 0.88012 -0.43117 0.043726 0.089256 0.014511 -0.0053705 94.549 0 0
197
+ 116 -0.4455 0.227 -0.43815 0.24088 0.10896 0.98917 0.8742 -0.46286 0.047155 0.089061 0.014472 -0.0058714 89.363 0 0
198
+ 117 -0.43815 0.24088 -0.43037 0.25452 0.105 0.95235 0.82724 -0.47185 0.054257 0.095123 0.013846 -0.0059174 90.227 0 0
199
+ 118 -0.43037 0.25452 -0.42216 0.26791 0.10259 0.92759 0.7909 -0.48466 0.049365 0.080557 0.013198 -0.0063474 82.196 0 0
200
+ 119 -0.42216 0.26791 -0.41354 0.28104 0.09885 0.89412 0.74731 -0.49089 0.063757 0.097061 0.01274 -0.006186 82.069 0 0
201
+ 120 -0.41354 0.28104 -0.40451 0.29389 0.09661 0.87438 0.71538 -0.50277 0.066837 0.095099 0.012286 -0.0064035 80.974 0 0
202
+ 121 -0.40451 0.29389 -0.39508 0.30645 0.09472 0.85384 0.6828 -0.51266 0.072054 0.095968 0.011857 -0.0065452 78.208 0 0
203
+ 122 -0.39508 0.30645 -0.38526 0.31871 0.09125 0.8194 0.63949 -0.51233 0.072866 0.090951 0.011189 -0.0066187 73.73 0 0
204
+ 123 -0.38526 0.31871 -0.37506 0.33066 0.08949 0.80341 0.61092 -0.52177 0.080202 0.093904 0.010856 -0.0067207 74.617 0 0
205
+ 124 -0.37506 0.33066 -0.36448 0.34227 0.08489 0.76421 0.56523 -0.51432 0.085429 0.093885 0.01022 -0.006604 72.071 0 0
206
+ 125 -0.36448 0.34227 -0.35355 0.35355 0.08355 0.75497 0.54217 -0.52539 0.088041 0.090851 0.0098989 -0.0068255 65.414 0 0
207
+ 126 -0.35355 0.35355 -0.34227 0.36448 0.08102 0.72539 0.50481 -0.52092 0.086011 0.08335 0.0092801 -0.006873 67.934 0 0
208
+ 127 -0.34227 0.36448 -0.33066 0.37506 0.07795 0.69885 0.47034 -0.51689 0.094219 0.085732 0.0088677 -0.0067724 65.733 0 0
209
+ 128 -0.33066 0.37506 -0.31871 0.38526 0.0757 0.67699 0.43967 -0.51478 0.095396 0.081476 0.0084044 -0.0068061 66.111 0 0
CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/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/Kn0p38/simulation0804_Kn0p38Ma2p3/slurm-28428726.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 53265 child grid cells
6
+ CPU time = 0.0914763 secs
7
+ create/ghost percent = 1.26852 98.7315
8
+ Balance grid migrated 53160 cells
9
+ CPU time = 0.0842628 secs
10
+ reassign/sort/migrate/ghost percent = 63.3642 0.0384274 9.01254 27.5848
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
+ 336 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 47589 5340 336 = cells outside/inside/overlapping surfs
19
+ 336 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.093324 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 24.0884 24.6735 2.7647 23.9056 24.5678 6.26692 0.183147
23
+ surf2grid time = 0.0223097 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 13.4442 24.5286 4.23348 1.96259 12.0735 6.81262
25
+ Balance grid migrated 988 cells
26
+ CPU time = 0.0330336 secs
27
+ reassign/sort/migrate/ghost percent = 8.45014 0.0921485 26.5506 64.9071
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 363039 versus 363040 (../create_particles.cpp:346)
30
+ Created 363039 particles
31
+ CPU time = 0.0134194 secs
32
+ Memory usage per proc in Mbytes:
33
+ particles (ave,min,max) = 1.6084 0 1.6875
34
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
35
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
36
+ total (ave,min,max) = 4.51242 3.22189 6.03439
37
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
38
+ 0 0 363039 0 0 0 0 0
39
+ 10000 10.960566 393230 10575 8786 9 378165.49 8.6246
40
+ 20000 21.991807 396652 10614 8929 12 386974.72 8.6563
41
+ 30000 33.75767 397308 10710 8907 8 390271.91 8.6698333
42
+ 40000 45.242557 395899 10596 8884 6 391812.72 8.6616
43
+ 50000 56.487603 397392 10640 8910 3 392857.32 8.66954
44
+ 60000 68.39224 396973 10634 8911 9 393522.76 8.6686
45
+ 70000 79.687931 397164 10712 8965 11 393999.52 8.6674714
46
+ 80000 91.592386 396717 10658 8911 7 394354.68 8.6682125
47
+ 90000 103.01213 397153 10722 8891 12 394672.49 8.6732444
48
+ 100000 113.7625 396639 10598 8769 3 394898.62 8.67632
49
+ 110000 126.01824 397716 10733 8963 10 395090.87 8.6767545
50
+ 120000 137.1484 396698 10586 8857 6 395247.76 8.6765417
51
+ 130000 148.3196 396924 10818 9029 7 395395.11 8.6758154
52
+ 140000 159.72592 397224 10704 8912 9 395495.29 8.6787929
53
+ 150000 171.06901 396344 10560 8789 8 395584.81 8.6786667
54
+ 160000 182.8976 396803 10669 8854 9 395645.96 8.680975
55
+ 170000 194.57566 397253 10596 8878 7 395719.84 8.6804353
56
+ 180000 205.45987 397242 10677 8895 3 395771.95 8.6814222
57
+ 190000 216.96472 396468 10633 8933 11 395833.34 8.6811526
58
+ 200000 228.33609 396120 10454 8672 8 395863.22 8.680285
59
+ Loop time of 228.336 on 128 procs for 200000 steps with 396120 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.022627 | 9.8008 | 51.325 | 381.3 | 4.29
65
+ Coll | 0.27861 | 11.143 | 72.166 | 412.8 | 4.88
66
+ Sort | 0.095269 | 1.6204 | 9.0789 | 159.3 | 0.71
67
+ Comm | 53.477 | 87.284 | 97.842 | 93.0 | 38.23
68
+ Modify | 0.018724 | 0.11707 | 2.1478 | 91.9 | 0.05
69
+ Output | 0.0036964 | 0.0092162 | 0.010594 | 1.1 | 0.00
70
+ Other | | 118.4 | | | 51.84
71
+
72
+ Particle moves = 79172643798 (79.2B)
73
+ Cells touched = 83120213621 (83.1B)
74
+ Particle comms = 200353711 (200M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 20426411 (20.4M)
77
+ SurfColl checks = 68901438 (68.9M)
78
+ SurfColl occurs = 1736057 (1.74M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 2312569593 (2.31B)
81
+ Collide occurs = 1785106520 (1.79B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.70888e+06
87
+ Particle-moves/step: 395863
88
+ Cell-touches/particle/step: 1.04986
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00253059
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000257998
93
+ Surface-checks/particle/step: 0.000870268
94
+ Surface-collisions/particle/step: 2.19275e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0292092
97
+ Collisions/particle/step: 0.022547
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 3094.69 ave 17469 max 0 min
101
+ Histogram: 79 15 7 5 6 4 5 3 2 2
102
+ Cells: 416.133 ave 417 max 415 min
103
+ Histogram: 1 0 0 0 0 109 0 0 0 18
104
+ GhostCell: 10796.4 ave 19812 max 593 min
105
+ Histogram: 21 9 14 8 17 3 6 4 16 30
106
+ EmptyCell: 74.8203 ave 218 max 0 min
107
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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) = 1.63477 0 3.375
114
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
115
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
116
+ total (ave,min,max) = 4.67874 3.36171 6.17471
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 396120 10454 8672 0 0 0
119
+ 210000 19.378683 397084 10610 8870 9 397424.58 8.6932
120
+ 220000 38.177305 396136 10630 8865 6 397021.49 8.67205
121
+ 230000 57.35126 397313 10769 8924 15 396996.85 8.6764667
122
+ 240000 76.103917 396736 10677 8887 10 396930.82 8.672725
123
+ 250000 95.295302 395917 10654 8884 5 396899.31 8.67804
124
+ 260000 114.80408 396903 10657 8880 4 396841.62 8.68925
125
+ 270000 133.7183 396981 10582 8817 11 396855.67 8.6880286
126
+ 280000 152.52796 396424 10557 8738 9 396871.82 8.686125
127
+ 290000 171.46246 396592 10555 8786 6 396835.43 8.6902667
128
+ 300000 190.69603 396588 10640 8907 10 396848.13 8.68374
129
+ Loop time of 190.697 on 128 procs for 100000 steps with 396588 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.011894 | 4.9254 | 25.928 | 270.2 | 2.58
135
+ Coll | 0.13797 | 5.5836 | 36.712 | 293.0 | 2.93
136
+ Sort | 0.052244 | 0.81704 | 4.549 | 112.0 | 0.43
137
+ Comm | 31.879 | 48.801 | 54.348 | 63.1 | 25.59
138
+ Modify | 1.9219 | 13.554 | 67.352 | 416.6 | 7.11
139
+ Output | 0.040844 | 0.04252 | 0.045659 | 0.6 | 0.02
140
+ Other | | 117 | | | 61.34
141
+
142
+ Particle moves = 39684812504 (39.7B)
143
+ Cells touched = 41661537164 (41.7B)
144
+ Particle comms = 100317283 (100M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 10228362 (10.2M)
147
+ SurfColl checks = 34459559 (34.5M)
148
+ SurfColl occurs = 868374 (0.868M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 1162656450 (1.16B)
151
+ Collide occurs = 897465500 (897M)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.62582e+06
157
+ Particle-moves/step: 396848
158
+ Cell-touches/particle/step: 1.04981
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00252785
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.00025774
163
+ Surface-checks/particle/step: 0.000868331
164
+ Surface-collisions/particle/step: 2.18818e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.0292973
167
+ Collisions/particle/step: 0.0226148
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 3098.34 ave 17507 max 0 min
171
+ Histogram: 79 15 7 5 6 4 6 2 2 2
172
+ Cells: 416.133 ave 417 max 415 min
173
+ Histogram: 1 0 0 0 0 109 0 0 0 18
174
+ GhostCell: 10796.4 ave 19812 max 593 min
175
+ Histogram: 21 9 14 8 17 3 6 4 16 30
176
+ EmptyCell: 74.8203 ave 218 max 0 min
177
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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 = 53265
184
+ CPU time = 0.00748278 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.00155912 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 0:00:08 (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 0:00:08 (wall clock time)
203
+
204
+ Processing SPARTA surface information.
205
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p38/simulation0804_Kn0p38Ma2p3/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
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+ <UnstructuredGrid>
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+ <Piece NumberOfPoints="400" NumberOfCells="200" >
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+ <PointData>
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+ </PointData>
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+ <CellData>
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+ <DataArray type="Float64" Name="id" format="appended" RangeMin="1" RangeMax="200" offset="0" />
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+ <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0.49999998503" RangeMax="0.50000001255" offset="23176" />
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+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="25900" />
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+ </Cells>
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+ </Piece>
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+ </UnstructuredGrid>
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+ <AppendedData encoding="base64">
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AQAAAACAAABABgAAMQYAAA==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AQAAAACAAABABgAANQYAAA==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AQAAAACAAABABgAAMAYAAA==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AQAAAACAAABABgAAFQAAAA==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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AQAAAACAAADASwAAlQ8AAA==eJxN3E+InutBhvGgEBAKQqClIAQClqwGKsFAwJmMHBQKQqBY2pXQMhCIVAIDASWLFKWCRJAjtiPKoZaBwhRhsNAijFrtKqviIiKcjVnpwoCgG3ee2/u+Lr5Z/ZbX4pvnfZ9/7wef+fD01q3v/PjWJ3//+rk/01//5Qv9M7/2F/rbv/mRPjr7K/1P55f6y7//Pf2fH17pb3z3r/Vn/uZaf/8ff6B/9Z9/qP/l3/5W//Z/3ehbt36sP/ik/6f//Zf24/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP0f/Tvf24/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP07/73z8Lftx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfqPf/qn9uP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9H/qJ39iP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9O/8c//GP7cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6v3/1R/bj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/T/3kd/aD9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tv8XPvwD+3H6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+j/7zW/Yj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/7/H1pP+7z96X9uM/fl/bjPn9f2o/7/H1pP+7z96X9uM/fl/bjPn9f2o/7/HphP+7z64X9uM+vF/bjPr9e2I/7/HphP+7z64X9uM+vF/bjPr9e2I87/j+3H3f8f24/7vj/3H7c8f+5/bjj/3P7ccf/5/bjjv/P7ccd/5/bjzt+PrMfd/x8Zj/u+PnMftzx85n9uOPnM/txx89n9uOOn8/sxx0/n9mPO/6c2Y87/pzZjzv+nNmPO/6c2Y87/pzZjzv+nNmPO/6c2Y87/pzZj9P/H7/7W/bj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/R/86tfsR+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfp/9zX/ii/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/T/5/G/Yj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/T/7XP/rr9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/fiD/5+/n9qPO38/tR93/n5qP+78/dR+3Pn7qf248/dT+3Hn76f2487fT+3Hnf8+sh93/vvIftz57yP7cee/j+zHnf8+sh93/vvIftz57yP7cee/j+zHnT8+sB93/vjAftz54wP7ceePD+zHnT8+sB93/vjAftz54wP7ceePD+zHnX8d2Y87/zqyH3f+dWQ/7vzryH7c+deR/bjzryP7cedfR/bjzr+O7Medv9y3H3f+ct9+3PnLfftx5y/37cedv9y3H3f+ct9+3PnLfftx5y/37cd9/79nP+77/z37cd//79mP+/5/z37c9/979uO+/9+zH/f9/579uO//9+zH6f/R13/Bfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6c/i996dP24/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP0/8+v/Lz9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9H/7iz9mP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9OP04/Tj9P/S5/6Wftx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cfpx+nH6cZ+///sP9OM+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+f+s+v97bj/v8em8/7vPrvf24z6/39uM+v97bj/v8em8/7vPrvf24z6/39uOO/+/sxx3/39mPO/6/sx93/H9nP+74/85+3PH/nf244/87+3HH/3f2446fb+3HHT/f2o87fr61H3f8fGs/7vj51n7c8fOt/bjj51v7ccfPt/bjjj9v7Mcdf97Yjzv+vLEfd/x5Yz/u+PPGftzx5439uOPPG/txx5839uOuv93Yj7v+dmM/7vrbjf2462839uOuv93Yj7v+dmM/7vrbjf2462839uOuX13bj7t+dW0/7vrVtf2461fX9uOuX13bj7t+dW0/7vrVtf2461fX9uOu/1zaj7v+c2k/7vrPpf246z+X9uOu/1zaj7v+c2k/7vrPpf246z+X9uOun1zYj7t+cmE/7vrJhf246ycX9uOun1zYj7t+cmE/7vrJhf246ycX9uOuP7y2H3f94bX9uOsPr+3HXX94bT/u+sNr+3HXH17bj7v+8Np+3PWH1/bjzt9f2Y87f39lP+78/ZX9uPP3V/bjzt9f2Y87f39lP+78/ZX9uPP3V/bjzr+e2o87/3pqP+7866n9uPOvp/bjzr+e2o87/3pqP+7866n9uPOvp/bjvj8/sR/3/fmJ/bjvz0/sx31/fmI/7vvzE/tx35+f2I/7/vzEftz35yf24+5fPLQfd//iof24+xcP7cfdv3hoP+7+xUP7cfcvHtqPu3/x0H7c/YuH9uOuP9+1H3f9+a79uOvPd+3HXX++az/u+vNd+3HXn+/aj7v+fNd+3PXnu/bjrh/eth93/fC2/bjrh7ftx10/vG0/7vrhbftx1w9v24+7fnjbftz1w9v2487fP/57+nHn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73Xn73X3j67sx90/urIfd//oyn7c/aMr+3H3j67sx90/urIfd//oyn7c/aMr+3HXD8/tx10/PLcfd/3w3H7c9cNz+3HXD8/tx10/PLcfd/3w3H7c9cNz+3H3L+7Yj7t/ccd+3P2LO/bj7l/csR93/+KO/bj7F3fsx92/uGM/7v7FHftx5+8f/B39uPP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3uvP3ev3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB/3HB7+fk4Pfz8nB7+fk4PdzcvD7OTn4/Zwc/H5ODn4/Jwe/n5OD38/Jwe/n5OD3c3Lw+zk5+P2cHPx+Tg5+Pyf0f/K/YD/e/6/9eP+/9uP9/9qP9/9rP97/r/14/7/24/3/2o83ftqPN37ajzd+2o83ftqPN37ajzd+2o83ftqPN37aj/f8sh/v+WU/3vPLfrznl/14zy/78Z5f9uM9v+zHe37Zj/f+8Jh+vPeHx/TjvT88ph/v/eEx/XjvD4/px3t/eEw/3vvDY/rx3h8e04/3/mY/3vub/Xjvb/bjvb/Zj/f+Zj/e+5v9eO9v9uO9v9mP9/5sP977s/1478/2470/24/3/mw/3vuz/Xjvz/bjvT/bjzd/sR9v/mI/3vzFfrz5i/148xf78eYv9uPNX+zHm7/Yjzd/tB9v/mg/3vzRfrz5o/1480f78eaP9uPNH+3Hmz/ajzd/tx9v/m4/3vzdfrz5u/1483f78ebv9uPN3+3Hm7/bj7d+Yj/e+on9eOsn9uOtn9iPt35iP976if146yf2462f2I+3fmU/3vqV/XjrV/bjrV/Zj7d+ZT/e+pX9eOtX9uOtX9mPt35oP976of1464f2460f2o+3fmg/3vqh/Xjrh/bjrR/aj7d+az/e+q39eOu39uOt39qPt35rP976rf1467f2463f2o+3fm4/3vq5/Xjr5/bjrZ/bj7d+bj/e+rn9eOvn9uOtn9uPt39hP97+hf14+xf24+1f2I+3f2E/3v6F/Xj7F/bj7V/Yj7d/ZD/e/pH9ePtH9uPtH9mPt39kP97+kf14+0f24+0f2Y+3f2c/3v6d/Xj7d/bj7d/Zj7d/Zz/e/p39ePt39uPt39mPt39qP97+qf14+6f24+2f2o+3f2o/3v6p/Xj7p/bj7Z/aj7d/bT/e/rX9ePvX9uPtX9uPt39tP97+tf14+9f24+1f2493fuCUfrzzA6f0450fOKUf7/zAKf145wdO6cc7P3BKP975gVP68c4PnNKPd37DfrzzG/bjnd+wH+/8hv145zfsxzu/YT/e+Q378c5v2I93fsZ+vPMz9uOdn7Ef7/yM/XjnZ+zHOz9jP975Gfvxzs/Yj3d+yX6880v2451fsh/v/JL9eOeX7Mc7v2Q/3vkl+/HOL9mPd37MfrzzY/bjnR+zH+/8mP1458fsxzs/Zj/e+TH78c6P2Y93fs9+vPN79uOd37Mf7/ye/Xjn9+zHO79nP975Pfvxzu/Zj3d+0n6885P2452ftB/v/KT9eOcn7cc7P2k/3vlJ+/HOT9qPd37Vfrzzq/bjnV+1H+/8qv1451ftxzu/aj/e+VX78c6v2o93fth+vPPD9uOdH7Yf7/yw/Xjnh+3HOz9sP975Yfvxzg/bj3d+236889v2453fth/v/Lb9eOe37cc7v20/3vlt+/HOb9uPd37efrzz8/bjnZ+3H+/8vP145+ftxzs/bz/e+Xn78c7P2493f8F+vPsL9uPdX7Af7/6C/Xj3F+zHu79gP979Bfvx7i/Yj3d/xH68+yP2490fsR/v/oj9ePdH7Me7P2I/3v0R+/Huj9iPd3/Hfrz7O/bj3d+xH+/+jv1493fsx7u/Yz/e/R378e7v2I93f8p+vPtT9uPdn7If7/6U/Xj3p+zHuz9lP979Kfvx7k/Zj3d/zX68+2v2491fsx/v/pr9ePfX7Me7v2Y/3v01+/Hur9mPd3/Qfrz7g/bj3R+0H+/+oP149wftx7s/aD/e/UH78e4P2o93f9N+vPub9uPd37Qf7/6m/Xj3N+3Hu79pP979Tfvx7m/aj3d/1n68+7P2492ftR/v/qz9ePdn7ce7P2s/3v1Z+/Huz9qPd3/Zfrz7y/bj3V+2H+/+sv1495ftx7u/bD/e/WX78e4v2493f9x+vPvj9uPdH7cf7/64/Xj3x+3Huz9uP979cfvx7o/bj3d/3368+/v2493ftx/v/r79ePf37ce7v28/3v19+/Hu79uP9/0E+/G+n2A/3vcT7Mf7foL9eN9PsB/v+wn2430/wX687yfYj/f9Cvvxvl9hP973K+zH+36F/Xjfr7Af7/sV9uN9v8J+vO9X2I/3/RD78b4fYj/e90Psx/t+iP143w+xH+/7IfbjfT/Efrzvh9iP9/0W+/G+32I/3vdb7Mf7fov9eN9vsR/v+y32432/xX6877fYj/f9HPvxvp9jP973c+zH+36O/Xjfz7Ef7/s59uN9P8d+vO/n2I//DwyLlFE=AgAAAACAAACAOwAABRMAABMJAAA=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AQAAAACAAADgLgAA5QcAAA==eJw12qGjOYB6BuBzN9vsznZts3sFQRAEQRAEQRAEQRAEQRAEQRAEQRAEQThBEARBEARBEARBEARBEIQTBEEQBGHhPL+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CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Lambda/Lambda_2_300000.vtu ADDED
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+ <Cells>
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+ </Cells>
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+ </Piece>
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+ </UnstructuredGrid>
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_AQAAAACAAACgDwAAtQIAAA==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AQAAAACAAACgDwAATwAAAA==eJy7vOexiKynkMPlUXqUHqUxaM6fC9I3rxqlR+lRGhut7FXdrM8jPEqP0qM0Ftp2duj81bmj9Cg9SmOji68LfXI8P0qP0qM0NhoAXjcwVw==AQAAAACAAACgDwAASAwAAA==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AQAAAACAAABoHAAA5wYAAA==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CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/Surf.pvd ADDED
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+ <?xml version="1.0"?>
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+ <VTKFile type="Collection" version="0.1"
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+ byte_order="LittleEndian"
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+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/config.txt ADDED
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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 0.38
13
+ variable Ma equal 2.4
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
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+ 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/Kn0p38/simulation0805_Kn0p38Ma2p4/cylinder.surf ADDED
@@ -0,0 +1,410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ surf file from Pointwise
2
+
3
+ 200 points
4
+ 200 lines
5
+
6
+ Points
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+
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_AQAAAACAAACgDwAAtQIAAA==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AQAAAACAAACgDwAATwAAAA==eJy7vOexiKynkMPlUXqUHqUxaM6fC9I3rxqlR+lRGhut7FXdrM8jPEqP0qM0Ftp2duj81bmj9Cg9SmOji68LfXI8P0qP0qM0NhoAXjcwVw==AQAAAACAAACgDwAAcQcAAA==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AQAAAACAAACgDwAA2gsAAA==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AQAAAACAAACgDwAAyAUAAA==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AQAAAACAAACgDwAAewwAAA==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AQAAAACAAACgDwAA+wUAAA==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AQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAABoHAAA5wYAAA==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+ _AQAAAACAAABYAgAAiQAAAA==eJwtx9s1AwEAANHpwzuVaCIIitkKEKIHBOki+hDx6AJn7/zcM1XDw/q4/x5HB/bk2dKzZ89ePHv1bOXZu2cbzz4823r26dmXZ9+e/fidt/F3Rwe259m+ZweeHXp25NnEs6lnJ56denbm2cyzc88uPLv0V/7asxvP5p7denbn2cKze//nL3yyhvI=AQAAAACAAABYAgAAiAAAAA==eJw7eGqh67ZiIYeMza+Kt14Vcvi0ISB7lrmww6yZICDs4Nj06PiM38IOZ+o99tfGijgUZ0x8W7NfxOFf6vcniYqiDkv9HIQTm0UdDHyWcbk9FXW4bvLewtVdzCHRKNRAa6WYA6f0vDhNbnGHyRIsYXy54g7WjL4tvOfFHQ6O2jus7QUAd63urQ==AQAAAACAAABYAgAANgAAAA==eJy7vOexiKynkMNlOtOcPxekb15Ff1rZq7pZn0eY7rTt7ND5q3PpTxdfF/rkeJ7+NADFJe2aAQAAAACAAABYAgAA7AEAAA==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AQAAAACAAABYAgAAYwIAAA==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AQAAAACAAABYAgAATwEAAA==eJxFUUEoRFEUne3Mgs0UNt8GjRU1K/J1Nho2QkryS83CihpZ+WQ19y2kvmw0VlMjysJOM6UsRqKwEymlbycsLPwkC+/2Trmb+9695517z3lJLoiaacFa9rgQ9wji0B76BLc3NoYFUfMrF8wJGp68TS4KAi1sCEYVGBG3zf6BYEoPJwI/81ArXQh69cEd7zHzJ+u/ZcfTJji3Y6IuwYLGEHnHOX/2f35eY5W4Le5RIb7Kfp1zLjnnibpeBVZlmP0pu9wi2C9ZZIdgWR/0CxL1Y4x5hjxLxK1Q/6bAVv2M1W+3aniHzKesX5PvXrBXsfEseLdtSfPeLUhpDPJdQdCph2nWi9x7nf1d+lOlv3Xiz8j7SJ8+BPqN4Tf77Qa6vr9D3iP2G/Tzir690JeE+lLG7dlqXN8zjj9v3LwB43wfMY5nwjjd88bpLhr8AQuHGlA=AQAAAACAAABYAgAA/gEAAA==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AQAAAACAAABYAgAAYwIAAA==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AQAAAACAAABYAgAAVwEAAA==eJwtUTFIA0EQ/ErwG5uAoqBICmMjiI0IkW3EIFi8EUR4UQRTWEV+94pEbCS2EbQxKVSinVgrxCa2ERGLFyzkC0GMrRYi6J0722x+b2ZnZ1Io2JozVOkEV4Pdhqqtz0z4LLSV9ePGidCEq1WhD/tcWRIKHYCEake2hkXxv6zzNlNSSl3OXjPFjWLWrzPl3KDM4DMt2NYZR182yuszNOp+/IjyHkV5B0K+GxjM80Keqwzu8IRurXz1HTr3TEPOSA16u6w+QiZ7VVKaZPqy8FZglDdmdG+P0fmr0F3bVlOwBz43oJsTOrPwuFf0Xqv/v/cJesfIYZup7B7Wcc80K3/NKG7AqK8u5J5A9wJ79+E/jz6Cdw/fL8j5gRV/CF8r6PNM7qxUP6vPPeS1iDynkGca97+Bd8Pk4iqeg7+D+zehN4Oc09j7HSk+icj9nUEz0rxOI/oDGb4RGg==AQAAAACAAABYAgAADwAAAA==eJxjYBgFo4D6AAACWAABAQAAAACAAABYAgAADwAAAA==eJxjYBgFo4D6AAACWAABAQAAAACAAACABAAAJQEAAA==eJw9zj1KwnEAh/Efzs3ewLGxMTCkGwReoE1q8QThBcLojeKPiiAkgiQEQUMvB5CWCFrcXBoCL5DP8Hyf6TM+rXq/ufNx3izbvhuX8cneTVw7vIuvj6p493gYv3fHcbs3iX/7D/HZaBbXH+fx9G0RH3w+xV+r57jz9xKX8hq3tv/7y4v8a/41/5p/zb/mX/Ov+df8a/41/5p/zb/mX/Ov+T/9ucq/5l/zr/nX/Gv+Nf+af82/5l/zr/nX/Gv+Nf+a/2p9m3/Nv+Zf86/51/xr/jX/mn/Nv+Zf86/51/xr/jX/y819/jX/mn/Nv+Zf86/51/xr/jX/mn/Nv+Zf86/51/yXMsi/5l/zr/nX/Gv+Nf+af82/5l/zr/nX/Gv+Nf/6H0+7Z2I=AQAAAACAAABgCQAAhgEAAA==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eJwtxRF0QgEAAMD2XhAEQfAhCIIgCIJgEARBEATBIAiCIAiCIBgEQRAEQRAEQRAMgiAYBEEQBINBEARBEATdyYVDbxFHHXPcgRNOOuW0M84657w/XXDRJZddcdU1f7nuhptuue2Ou+65728PPPTIY0889cxzL7z0ymv/eOOtd/713gcfffLZf/73xVfffPfDT4c+3oUdcdQxxx044aRTTjvjF05rIJU=AQAAAACAAABLAAAADAAAAA==eJzj5KQaAABkfQKk
34
+ </AppendedData>
35
+ </VTKFile>
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/log.sparta ADDED
@@ -0,0 +1,319 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0.38
17
+ variable Ma equal 2.4
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 0.38*${L}
23
+ variable mfp equal 0.38*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${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}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.38/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.38/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.38/4*0.38)
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 2.4*${sos}
38
+ variable vo equal 2.4*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 738.757354172834*cos(${theta}*2*PI/360)
41
+ variable vx equal 738.757354172834*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 738.757354172834*sin(${theta}*2*PI/360)
44
+ variable vy equal 738.757354172834*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${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)/0.38*${cpmfp}
50
+ variable xncells equal (6--5)/0.38*4
51
+ variable yncells equal (${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)/0.38*${cpmfp}
55
+ variable yncells equal (5--5)/0.38*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 3.58555028535087e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 3.58555028535087e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(5--5)/30/115.789473684211/${yncells}
64
+ variable Fnum equal 3.58555028535087e+19*(6--5)*(5--5)/30/115.789473684211/105.263157894737
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}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/738.757354172834/${xncells}/10/4
69
+ variable tstep equal (--5+6)/738.757354172834/115.789473684211/10/4
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 3.58555028535087e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417638e+16
83
+ timestep ${tstep}
84
+ timestep 3.21485801337194e-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 115.789473684211 ${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 115.789473684211 105.263157894737 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 53265 child grid cells
100
+ CPU time = 0.0887942 secs
101
+ create/ghost percent = 1.22991 98.7701
102
+ balance_grid rcb cell
103
+ Balance grid migrated 53160 cells
104
+ CPU time = 0.0837596 secs
105
+ reassign/sort/migrate/ghost percent = 63.1433 0.0418698 8.96017 27.8546
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 738.757354172834 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 738.757354172834 0 0.0 temp ${To}
111
+ mixture air Ar vstream 738.757354172834 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
+ 336 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 47589 5340 336 = cells outside/inside/overlapping surfs
122
+ 336 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.0902972 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.959 24.8241 2.88141 24.7157 25.6198 6.87707 0.209264
126
+ surf2grid time = 0.0223176 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 13.7785 25.3281 4.3057 1.78043 12.1394 6.68968
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 988 cells
133
+ CPU time = 0.0323043 secs
134
+ reassign/sort/migrate/ghost percent = 8.38064 0.0859917 24.8515 66.6819
135
+ create_particles air n 0
136
+ WARNING: Created unexpected # of particles: 363039 versus 363040 (../create_particles.cpp:346)
137
+ Created 363039 particles
138
+ CPU time = 0.0124616 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) = 1.6084 0 1.6875
144
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
145
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
146
+ total (ave,min,max) = 4.51242 3.22189 6.03439
147
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
148
+ 0 0 363039 0 0 0 0 0
149
+ 10000 10.785244 394255 10189 8486 5 378854.27 8.7986
150
+ 20000 23.301905 396801 10338 8690 9 387614.97 8.81695
151
+ 30000 36.077724 397957 10353 8606 5 390866.28 8.8337667
152
+ 40000 48.409282 396598 10322 8590 16 392480.89 8.834725
153
+ 50000 60.99637 397903 10337 8648 9 393536.99 8.8453
154
+ 60000 72.992076 397892 10355 8625 17 394192.28 8.8446333
155
+ 70000 85.484738 398356 10233 8622 10 394652.03 8.8463857
156
+ 80000 98.412095 397248 10243 8516 8 395017.18 8.8500875
157
+ 90000 110.29756 397303 10274 8623 9 395283.41 8.8521
158
+ 100000 122.95509 397544 10222 8490 8 395497.58 8.85777
159
+ 110000 135.0778 398238 10203 8456 8 395701.03 8.859
160
+ 120000 147.22353 397259 10210 8478 13 395860.63 8.8603
161
+ 130000 159.0648 397772 10418 8687 11 396002.79 8.8648462
162
+ 140000 171.63551 397783 10404 8709 7 396098.14 8.8678357
163
+ 150000 184.11993 397023 10236 8536 7 396188.5 8.8694267
164
+ 160000 196.15983 397605 10319 8597 9 396252.05 8.8699938
165
+ 170000 209.1519 397799 10256 8587 16 396328.42 8.8695235
166
+ 180000 220.79794 397883 10358 8612 7 396400.46 8.8729667
167
+ 190000 233.2238 397265 10343 8608 5 396451.23 8.8724421
168
+ 200000 245.95825 396625 10223 8488 7 396495.13 8.872795
169
+ Loop time of 245.959 on 128 procs for 200000 steps with 396625 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.022569 | 9.799 | 53.282 | 385.5 | 3.98
175
+ Coll | 0.27969 | 10.925 | 73.05 | 415.5 | 4.44
176
+ Sort | 0.095621 | 1.6092 | 9.2779 | 159.8 | 0.65
177
+ Comm | 59.812 | 99.301 | 111.81 | 102.7 | 40.37
178
+ Modify | 0.017458 | 0.1158 | 2.1225 | 91.4 | 0.05
179
+ Output | 0.0041521 | 0.0099492 | 0.011398 | 1.1 | 0.00
180
+ Other | | 124.2 | | | 50.50
181
+
182
+ Particle moves = 79299025586 (79.3B)
183
+ Cells touched = 83217924279 (83.2B)
184
+ Particle comms = 199039303 (199M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 20227494 (20.2M)
187
+ SurfColl checks = 72377347 (72.4M)
188
+ SurfColl occurs = 1774559 (1.77M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 2233742406 (2.23B)
191
+ Collide occurs = 1726072796 (1.73B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.51881e+06
197
+ Particle-moves/step: 396495
198
+ Cell-touches/particle/step: 1.04942
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00250998
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000255079
203
+ Surface-checks/particle/step: 0.000912714
204
+ Surface-collisions/particle/step: 2.23781e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0281686
207
+ Collisions/particle/step: 0.0217666
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 3098.63 ave 18022 max 0 min
211
+ Histogram: 78 17 7 4 6 5 7 0 2 2
212
+ Cells: 416.133 ave 417 max 415 min
213
+ Histogram: 1 0 0 0 0 109 0 0 0 18
214
+ GhostCell: 10796.4 ave 19812 max 593 min
215
+ Histogram: 21 9 14 8 17 3 6 4 16 30
216
+ EmptyCell: 74.8203 ave 218 max 0 min
217
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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) = 1.63477 0 3.375
246
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
247
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
248
+ total (ave,min,max) = 4.67874 3.36171 6.17471
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 396625 10223 8488 0 0 0
251
+ 210000 19.654389 397705 10240 8586 12 397693.93 8.8734
252
+ 220000 40.6007 397261 10310 8635 13 397599.53 8.8795
253
+ 230000 60.74676 397738 10257 8562 8 397574.89 8.887
254
+ 240000 80.534372 396990 10273 8650 5 397619.82 8.88815
255
+ 250000 101.16532 397283 10229 8497 12 397526.04 8.89936
256
+ 260000 121.1539 397641 10242 8529 11 397503.65 8.9030667
257
+ 270000 141.08579 397813 10341 8636 10 397575.54 8.9037286
258
+ 280000 162.05227 397033 10269 8536 5 397613.15 8.9059625
259
+ 290000 182.11843 397610 10258 8560 12 397578.83 8.9030333
260
+ 300000 202.09501 397300 10350 8656 15 397568.98 8.89597
261
+ Loop time of 202.095 on 128 procs for 100000 steps with 397300 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.011929 | 4.9457 | 26.882 | 273.1 | 2.45
267
+ Coll | 0.13879 | 5.4912 | 37.12 | 295.3 | 2.72
268
+ Sort | 0.052472 | 0.81375 | 4.7073 | 112.8 | 0.40
269
+ Comm | 33.446 | 54.584 | 61.213 | 73.0 | 27.01
270
+ Modify | 1.9335 | 13.616 | 69.842 | 420.9 | 6.74
271
+ Output | 0.033653 | 0.037108 | 0.057594 | 3.2 | 0.02
272
+ Other | | 122.6 | | | 60.67
273
+
274
+ Particle moves = 39756897916 (39.8B)
275
+ Cells touched = 41720101789 (41.7B)
276
+ Particle comms = 99708865 (99.7M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 10130988 (10.1M)
279
+ SurfColl checks = 36297079 (36.3M)
280
+ SurfColl occurs = 889597 (0.89M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 1124001859 (1.12B)
283
+ Collide occurs = 868495439 (868M)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.5369e+06
289
+ Particle-moves/step: 397569
290
+ Cell-touches/particle/step: 1.04938
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00250796
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000254823
295
+ Surface-checks/particle/step: 0.000912976
296
+ Surface-collisions/particle/step: 2.23759e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0282719
299
+ Collisions/particle/step: 0.0218452
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 3103.91 ave 18150 max 0 min
303
+ Histogram: 81 15 5 5 6 6 6 0 2 2
304
+ Cells: 416.133 ave 417 max 415 min
305
+ Histogram: 1 0 0 0 0 109 0 0 0 18
306
+ GhostCell: 10796.4 ave 19812 max 593 min
307
+ Histogram: 21 9 14 8 17 3 6 4 16 30
308
+ EmptyCell: 74.8203 ave 218 max 0 min
309
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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 = 53265
316
+ CPU time = 0.00754782 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00284012 secs
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/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/Kn0p38/simulation0805_Kn0p38Ma2p4/output/grid.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/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 0.00344 0.029855 -0.029852 0.00046895 6.0109e-07 3.8263e-05 -0.00046888 7.9669e-06 1.1471 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.07421 0.69693 0.43575 -0.5439 0.11172 0.089506 0.0085993 -0.0071373 68.357 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.0035 0.030434 -0.030401 0.0014337 6.7376e-05 0.0014287 -0.00047645 4.496e-05 0.85995 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.07021 0.65344 0.39234 -0.52255 0.10757 0.080768 0.0078523 -0.0069392 65.101 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00305 0.026652 -0.02657 0.0020911 0.00011054 0.0014045 -0.00041561 5.4907e-05 1.1809 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.06893 0.64505 0.37091 -0.52775 0.11096 0.077981 0.0075688 -0.0070646 64.246 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.0032 0.028264 -0.028094 0.0031016 -7.5183e-06 -6.81e-05 -0.00044139 4.7648e-05 0.62542 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.06707 0.62544 0.34338 -0.52274 0.11876 0.078012 0.007259 -0.0069855 61.32 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00345 0.029284 -0.028992 0.0041261 -0.00015243 -0.001071 -0.00045778 4.7988e-05 1.3379 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.06266 0.58268 0.30445 -0.49681 0.11169 0.068445 0.0065364 -0.0067285 59.823 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00305 0.026363 -0.025971 0.0045327 -0.000207 -0.0011861 -0.00041118 5.2566e-05 1.0409 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.061 0.56665 0.28075 -0.49221 0.12198 0.069577 0.0063259 -0.0066385 59.485 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00317 0.027529 -0.026957 0.0055826 -0.00014068 -0.00067932 -0.00042564 7.7018e-05 1.7384 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.05695 0.52403 0.24521 -0.46312 0.11646 0.061663 0.0056809 -0.0063058 53.097 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00312 0.026412 -0.025682 0.0061658 0.0001193 0.00049693 -0.00040153 0.00010465 1.2675 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.05416 0.49873 0.21941 -0.44788 0.1156 0.056632 0.0052621 -0.0061454 52.214 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00305 0.025006 -0.02412 0.0065984 0.00030425 0.0011122 -0.00037408 0.00012111 1.4332 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.05089 0.46691 0.19214 -0.42554 0.11769 0.053137 0.0048665 -0.0058495 46.332 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.00337 0.02909 -0.027804 0.0085535 0.00021284 0.00069186 -0.00043338 0.00014522 1.1517 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.04888 0.45127 0.17269 -0.41692 0.11607 0.048077 0.0045357 -0.0057935 47.491 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00301 0.025528 -0.024152 0.0082689 0.000553 0.0016152 -0.00037067 0.00015525 1.1567 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.04563 0.41989 0.14842 -0.39278 0.11576 0.043742 0.0041496 -0.0054825 45.377 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.00312 0.026209 -0.024517 0.0092641 0.0011847 0.0031353 -0.00036648 0.00019476 1.1374 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.04348 0.39978 0.1295 -0.37823 0.10829 0.037075 0.0037349 -0.0053586 37.227 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00294 0.024806 -0.022918 0.0094928 0.00048451 0.0011697 -0.00035236 0.00016748 1.2763 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.04283 0.39148 0.11511 -0.37417 0.11743 0.036125 0.0036525 -0.0053098 38.525 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00244 0.019834 -0.018077 0.0081619 0.001078 0.0023876 -0.000267 0.0001657 1.3571 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.03851 0.35268 0.093063 -0.34018 0.11043 0.03021 0.0031963 -0.0048688 38.688 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.00279 0.021634 -0.019428 0.0095178 0.0012566 0.0025651 -0.00028543 0.00018979 0.86681 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.03654 0.33518 0.078247 -0.32592 0.10738 0.025779 0.0029157 -0.0047145 34.365 0 0
40
+ 16 0.4455 -0.227 0.43815 -0.24088 0.00278 0.021875 -0.019332 0.010236 0.0016206 0.0030608 -0.0002782 0.00020886 0.96969 0 0
41
+ 144 -0.10907 0.48796 -0.093691 0.49114 0.03437 0.31571 0.064021 -0.30915 0.10876 0.022524 0.002714 -0.0045021 34.752 0 0
42
+ 17 0.43815 -0.24088 0.43037 -0.25452 0.00263 0.021622 -0.018781 0.010713 0.002155 0.003778 -0.00026115 0.00022761 1.233 0 0
43
+ 145 -0.093691 0.49114 -0.078217 0.49384 0.03225 0.28854 0.049608 -0.28424 0.098447 0.017182 0.0023255 -0.0041948 29.18 0 0
44
+ 18 0.43037 -0.25452 0.42216 -0.26791 0.00277 0.022701 -0.019356 0.011861 0.002449 0.0039964 -0.00026556 0.00024908 1.2989 0 0
45
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164
+ 83 -0.42216 -0.26791 -0.43037 -0.25452 0.10606 1.0025 0.85479 0.52382 0.059922 -0.097784 0.014368 0.0066918 96.545 0 0
165
+ 84 -0.43037 -0.25452 -0.43815 -0.24088 0.10704 1.014 0.88076 0.50238 0.054087 -0.094825 0.014684 0.0064016 96.009 0 0
166
+ 85 -0.43815 -0.24088 -0.4455 -0.227 0.1108 1.0528 0.93044 0.49264 0.051658 -0.097564 0.015426 0.0062056 99.003 0 0
167
+ 86 -0.4455 -0.227 -0.45241 -0.21289 0.11076 1.0523 0.94502 0.46296 0.043785 -0.089376 0.015531 0.005868 100.28 0 0
168
+ 87 -0.45241 -0.21289 -0.45888 -0.19857 0.11512 1.0957 0.99863 0.4509 0.039743 -0.08802 0.01631 0.0056998 104.86 0 0
169
+ 88 -0.45888 -0.19857 -0.46489 -0.18406 0.11867 1.1192 1.034 0.4283 0.034866 -0.084174 0.016789 0.0054052 105.05 0 0
170
+ 89 -0.46489 -0.18406 -0.47044 -0.16937 0.11981 1.1454 1.0715 0.40487 0.031052 -0.082177 0.017318 0.0050686 112.69 0 0
171
+ 90 -0.47044 -0.16937 -0.47553 -0.15451 0.12096 1.153 1.0908 0.37348 0.022844 -0.066722 0.017493 0.0048183 108.94 0 0
172
+ 91 -0.47553 -0.15451 -0.48015 -0.1395 0.1247 1.1887 1.1361 0.34951 0.018939 -0.061563 0.018143 0.0045229 107.81 0 0
173
+ 92 -0.48015 -0.1395 -0.48429 -0.12434 0.12364 1.1652 1.1239 0.30747 0.016541 -0.060465 0.017914 0.0038798 113.97 0 0
174
+ 93 -0.48429 -0.12434 -0.48796 -0.10907 0.12873 1.2214 1.1876 0.28512 0.014145 -0.058919 0.018876 0.003553 111.34 0 0
175
+ 94 -0.48796 -0.10907 -0.49114 -0.093691 0.12753 1.2236 1.1981 0.24812 0.010341 -0.049936 0.018982 0.003113 117.23 0 0
176
+ 95 -0.49114 -0.093691 -0.49384 -0.078217 0.12725 1.216 1.1978 0.20906 0.0071019 -0.040692 0.018927 0.0026446 115.86 0 0
177
+ 96 -0.49384 -0.078217 -0.49606 -0.062667 0.12915 1.2275 1.2153 0.17296 0.0049265 -0.034616 0.019166 0.002173 117.83 0 0
178
+ 97 -0.49606 -0.062667 -0.49778 -0.047054 0.13185 1.2511 1.2435 0.13729 0.0025182 -0.022809 0.019572 0.0017981 117.63 0 0
179
+ 98 -0.49778 -0.047054 -0.49901 -0.031395 0.13183 1.2533 1.2494 0.098331 0.0018608 -0.023643 0.019654 0.0011731 115.64 0 0
180
+ 99 -0.49901 -0.031395 -0.49975 -0.015705 0.12939 1.2325 1.2311 0.058057 0.0008825 -0.018713 0.019351 0.00061798 118.47 0 0
181
+ 100 -0.49975 -0.015705 -0.5 0 0.13178 1.2444 1.2443 0.019546 1.9663e-05 -0.0012517 0.019544 0.00028736 113.78 0 0
182
+ 101 -0.5 0 -0.49975 0.015705 0.1319 1.2666 1.2665 -0.019895 6.2998e-05 0.0040103 0.019894 -0.00024951 119.6 0 0
183
+ 102 -0.49975 0.015705 -0.49901 0.031395 0.13039 1.249 1.2476 -0.058837 0.00055111 0.011686 0.019606 -0.00074061 122.84 0 0
184
+ 103 -0.49901 0.031395 -0.49778 0.047054 0.13122 1.2482 1.2444 -0.097937 0.0016619 0.021117 0.019572 -0.0012066 121.24 0 0
185
+ 104 -0.49778 0.047054 -0.49606 0.062667 0.13068 1.242 1.2345 -0.13629 0.0034181 0.03096 0.019444 -0.0016544 114.54 0 0
186
+ 105 -0.49606 0.062667 -0.49384 0.078217 0.12766 1.2211 1.2089 -0.17205 0.0055922 0.039293 0.019077 -0.0020853 116.03 0 0
187
+ 106 -0.49384 0.078217 -0.49114 0.093691 0.12796 1.2178 1.1997 -0.20937 0.0073794 0.042282 0.018959 -0.0026246 112.29 0 0
188
+ 107 -0.49114 0.093691 -0.48796 0.10907 0.12942 1.229 1.2034 -0.24922 0.0088791 0.042875 0.019042 -0.0032411 114.27 0 0
189
+ 108 -0.48796 0.10907 -0.48429 0.12434 0.12628 1.2027 1.1694 -0.28076 0.01385 0.057688 0.018586 -0.0035038 116.92 0 0
190
+ 109 -0.48429 0.12434 -0.48015 0.1395 0.12463 1.1931 1.1508 -0.31484 0.01473 0.053844 0.018308 -0.0040995 115.71 0 0
191
+ 110 -0.48015 0.1395 -0.47553 0.15451 0.12152 1.1545 1.1034 -0.33946 0.020791 0.067582 0.017659 -0.0042705 111.29 0 0
192
+ 111 -0.47553 0.15451 -0.47044 0.16937 0.12207 1.1672 1.1042 -0.37806 0.024987 0.072981 0.017737 -0.004792 110.83 0 0
193
+ 112 -0.47044 0.16937 -0.46489 0.18406 0.11907 1.1238 1.0513 -0.39725 0.031994 0.084668 0.017015 -0.0049098 104.97 0 0
194
+ 113 -0.46489 0.18406 -0.45888 0.19857 0.11868 1.1273 1.0414 -0.43138 0.037551 0.090657 0.016948 -0.0053519 110.37 0 0
195
+ 114 -0.45888 0.19857 -0.45241 0.21289 0.11502 1.0905 0.99384 -0.44874 0.041274 0.091411 0.016259 -0.0056126 103.4 0 0
196
+ 115 -0.45241 0.21289 -0.4455 0.227 0.11328 1.0824 0.97203 -0.47619 0.043489 0.088773 0.015951 -0.0060854 104.13 0 0
197
+ 116 -0.4455 0.227 -0.43815 0.24088 0.11038 1.0514 0.92915 -0.49196 0.050917 0.096165 0.015394 -0.0062169 102.04 0 0
198
+ 117 -0.43815 0.24088 -0.43037 0.25452 0.10866 1.0367 0.90048 -0.51363 0.053665 0.094084 0.014987 -0.0065899 100.96 0 0
199
+ 118 -0.43037 0.25452 -0.42216 0.26791 0.10378 0.98696 0.84152 -0.51569 0.059492 0.097082 0.014153 -0.0065752 95.153 0 0
200
+ 119 -0.42216 0.26791 -0.41354 0.28104 0.10502 0.99575 0.83226 -0.54669 0.066264 0.10088 0.014113 -0.0070025 96.33 0 0
201
+ 120 -0.41354 0.28104 -0.40451 0.29389 0.09879 0.93868 0.76798 -0.53974 0.066317 0.09436 0.013105 -0.0069958 90.817 0 0
202
+ 121 -0.40451 0.29389 -0.39508 0.30645 0.09842 0.93069 0.74425 -0.5588 0.077626 0.10339 0.01291 -0.0071533 93.084 0 0
203
+ 122 -0.39508 0.30645 -0.38526 0.31871 0.09394 0.88764 0.69274 -0.55499 0.078212 0.097624 0.01211 -0.007184 87.198 0 0
204
+ 123 -0.38526 0.31871 -0.37506 0.33066 0.09135 0.86618 0.65864 -0.56254 0.081887 0.095877 0.011632 -0.0073299 83.783 0 0
205
+ 124 -0.37506 0.33066 -0.36448 0.34227 0.08989 0.84764 0.62694 -0.57047 0.090887 0.099884 0.011275 -0.0073917 82.27 0 0
206
+ 125 -0.36448 0.34227 -0.35355 0.35355 0.08499 0.79927 0.57398 -0.55622 0.09058 0.093471 0.010438 -0.0072686 75.693 0 0
207
+ 126 -0.35355 0.35355 -0.34227 0.36448 0.0831 0.78249 0.54455 -0.56193 0.09436 0.091442 0.010036 -0.0073901 79.034 0 0
208
+ 127 -0.34227 0.36448 -0.33066 0.37506 0.08145 0.76648 0.51585 -0.56691 0.1036 0.094264 0.0097298 -0.007424 76.186 0 0
209
+ 128 -0.33066 0.37506 -0.31871 0.38526 0.07835 0.73064 0.47451 -0.55558 0.10621 0.090711 0.0091216 -0.0073019 74.721 0 0
CylinderKnMa/Kn0p38/simulation0805_Kn0p38Ma2p4/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/Kn0p38/simulation0805_Kn0p38Ma2p4/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/Kn0p38/simulation0805_Kn0p38Ma2p4/slurm-28428728.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 53265 child grid cells
6
+ CPU time = 0.0887942 secs
7
+ create/ghost percent = 1.22991 98.7701
8
+ Balance grid migrated 53160 cells
9
+ CPU time = 0.0837596 secs
10
+ reassign/sort/migrate/ghost percent = 63.1433 0.0418698 8.96017 27.8546
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
+ 336 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 47589 5340 336 = cells outside/inside/overlapping surfs
19
+ 336 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.0902972 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.959 24.8241 2.88141 24.7157 25.6198 6.87707 0.209264
23
+ surf2grid time = 0.0223176 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 13.7785 25.3281 4.3057 1.78043 12.1394 6.68968
25
+ Balance grid migrated 988 cells
26
+ CPU time = 0.0323043 secs
27
+ reassign/sort/migrate/ghost percent = 8.38064 0.0859917 24.8515 66.6819
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 363039 versus 363040 (../create_particles.cpp:346)
30
+ Created 363039 particles
31
+ CPU time = 0.0124616 secs
32
+ Memory usage per proc in Mbytes:
33
+ particles (ave,min,max) = 1.6084 0 1.6875
34
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
35
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
36
+ total (ave,min,max) = 4.51242 3.22189 6.03439
37
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
38
+ 0 0 363039 0 0 0 0 0
39
+ 10000 10.785244 394255 10189 8486 5 378854.27 8.7986
40
+ 20000 23.301905 396801 10338 8690 9 387614.97 8.81695
41
+ 30000 36.077724 397957 10353 8606 5 390866.28 8.8337667
42
+ 40000 48.409282 396598 10322 8590 16 392480.89 8.834725
43
+ 50000 60.99637 397903 10337 8648 9 393536.99 8.8453
44
+ 60000 72.992076 397892 10355 8625 17 394192.28 8.8446333
45
+ 70000 85.484738 398356 10233 8622 10 394652.03 8.8463857
46
+ 80000 98.412095 397248 10243 8516 8 395017.18 8.8500875
47
+ 90000 110.29756 397303 10274 8623 9 395283.41 8.8521
48
+ 100000 122.95509 397544 10222 8490 8 395497.58 8.85777
49
+ 110000 135.0778 398238 10203 8456 8 395701.03 8.859
50
+ 120000 147.22353 397259 10210 8478 13 395860.63 8.8603
51
+ 130000 159.0648 397772 10418 8687 11 396002.79 8.8648462
52
+ 140000 171.63551 397783 10404 8709 7 396098.14 8.8678357
53
+ 150000 184.11993 397023 10236 8536 7 396188.5 8.8694267
54
+ 160000 196.15983 397605 10319 8597 9 396252.05 8.8699938
55
+ 170000 209.1519 397799 10256 8587 16 396328.42 8.8695235
56
+ 180000 220.79794 397883 10358 8612 7 396400.46 8.8729667
57
+ 190000 233.2238 397265 10343 8608 5 396451.23 8.8724421
58
+ 200000 245.95825 396625 10223 8488 7 396495.13 8.872795
59
+ Loop time of 245.959 on 128 procs for 200000 steps with 396625 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.022569 | 9.799 | 53.282 | 385.5 | 3.98
65
+ Coll | 0.27969 | 10.925 | 73.05 | 415.5 | 4.44
66
+ Sort | 0.095621 | 1.6092 | 9.2779 | 159.8 | 0.65
67
+ Comm | 59.812 | 99.301 | 111.81 | 102.7 | 40.37
68
+ Modify | 0.017458 | 0.1158 | 2.1225 | 91.4 | 0.05
69
+ Output | 0.0041521 | 0.0099492 | 0.011398 | 1.1 | 0.00
70
+ Other | | 124.2 | | | 50.50
71
+
72
+ Particle moves = 79299025586 (79.3B)
73
+ Cells touched = 83217924279 (83.2B)
74
+ Particle comms = 199039303 (199M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 20227494 (20.2M)
77
+ SurfColl checks = 72377347 (72.4M)
78
+ SurfColl occurs = 1774559 (1.77M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 2233742406 (2.23B)
81
+ Collide occurs = 1726072796 (1.73B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.51881e+06
87
+ Particle-moves/step: 396495
88
+ Cell-touches/particle/step: 1.04942
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00250998
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000255079
93
+ Surface-checks/particle/step: 0.000912714
94
+ Surface-collisions/particle/step: 2.23781e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0281686
97
+ Collisions/particle/step: 0.0217666
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 3098.63 ave 18022 max 0 min
101
+ Histogram: 78 17 7 4 6 5 7 0 2 2
102
+ Cells: 416.133 ave 417 max 415 min
103
+ Histogram: 1 0 0 0 0 109 0 0 0 18
104
+ GhostCell: 10796.4 ave 19812 max 593 min
105
+ Histogram: 21 9 14 8 17 3 6 4 16 30
106
+ EmptyCell: 74.8203 ave 218 max 0 min
107
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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) = 1.63477 0 3.375
114
+ grid (ave,min,max) = 2.88342 1.51379 4.32629
115
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
116
+ total (ave,min,max) = 4.67874 3.36171 6.17471
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 396625 10223 8488 0 0 0
119
+ 210000 19.654389 397705 10240 8586 12 397693.93 8.8734
120
+ 220000 40.6007 397261 10310 8635 13 397599.53 8.8795
121
+ 230000 60.74676 397738 10257 8562 8 397574.89 8.887
122
+ 240000 80.534372 396990 10273 8650 5 397619.82 8.88815
123
+ 250000 101.16532 397283 10229 8497 12 397526.04 8.89936
124
+ 260000 121.1539 397641 10242 8529 11 397503.65 8.9030667
125
+ 270000 141.08579 397813 10341 8636 10 397575.54 8.9037286
126
+ 280000 162.05227 397033 10269 8536 5 397613.15 8.9059625
127
+ 290000 182.11843 397610 10258 8560 12 397578.83 8.9030333
128
+ 300000 202.09501 397300 10350 8656 15 397568.98 8.89597
129
+ Loop time of 202.095 on 128 procs for 100000 steps with 397300 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.011929 | 4.9457 | 26.882 | 273.1 | 2.45
135
+ Coll | 0.13879 | 5.4912 | 37.12 | 295.3 | 2.72
136
+ Sort | 0.052472 | 0.81375 | 4.7073 | 112.8 | 0.40
137
+ Comm | 33.446 | 54.584 | 61.213 | 73.0 | 27.01
138
+ Modify | 1.9335 | 13.616 | 69.842 | 420.9 | 6.74
139
+ Output | 0.033653 | 0.037108 | 0.057594 | 3.2 | 0.02
140
+ Other | | 122.6 | | | 60.67
141
+
142
+ Particle moves = 39756897916 (39.8B)
143
+ Cells touched = 41720101789 (41.7B)
144
+ Particle comms = 99708865 (99.7M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 10130988 (10.1M)
147
+ SurfColl checks = 36297079 (36.3M)
148
+ SurfColl occurs = 889597 (0.89M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 1124001859 (1.12B)
151
+ Collide occurs = 868495439 (868M)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.5369e+06
157
+ Particle-moves/step: 397569
158
+ Cell-touches/particle/step: 1.04938
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00250796
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.000254823
163
+ Surface-checks/particle/step: 0.000912976
164
+ Surface-collisions/particle/step: 2.23759e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.0282719
167
+ Collisions/particle/step: 0.0218452
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 3103.91 ave 18150 max 0 min
171
+ Histogram: 81 15 5 5 6 6 6 0 2 2
172
+ Cells: 416.133 ave 417 max 415 min
173
+ Histogram: 1 0 0 0 0 109 0 0 0 18
174
+ GhostCell: 10796.4 ave 19812 max 593 min
175
+ Histogram: 21 9 14 8 17 3 6 4 16 30
176
+ EmptyCell: 74.8203 ave 218 max 0 min
177
+ Histogram: 40 1 16 7 26 15 8 11 2 2
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 = 53265
184
+ CPU time = 0.00754782 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.00284012 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 0:00:08 (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 0:00:08 (wall clock time)
203
+
204
+ Processing SPARTA surface information.
205
+ Processing dump result file: ['./output/surf.300000.dat']