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  1. .gitattributes +3 -0
  2. CylinderKnMa/Kn0p32/simulation0662_Kn0p32Ma3p1/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p32/simulation0663_Kn0p32Ma3p2/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p32/simulation0664_Kn0p32Ma3p3/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Grid.pvd +9 -0
  6. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda.pvd +9 -0
  7. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda/Lambda_0_300000.vtu +0 -0
  8. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda/Lambda_1_300000.vtu +27 -0
  9. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda/Lambda_2_300000.vtu +27 -0
  10. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda/Lambda_300000.pvtu +21 -0
  11. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Lambda/Lambda_3_300000.vtu +27 -0
  12. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/Surf.pvd +9 -0
  13. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/gas.vhs +9 -0
  14. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/Grid_0_300000.vtu +0 -0
  15. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/Grid_1_300000.vtu +0 -0
  16. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/Grid_2_300000.vtu +35 -0
  17. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/Grid_300000.pvtu +29 -0
  18. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/Grid_3_300000.vtu +35 -0
  19. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/grid/final-grid.dat +0 -0
  20. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/log.sparta +319 -0
  21. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/mir.txt +3 -0
  22. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/output/grid.300000.dat +0 -0
  23. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/output/lambdakn.300000.dat +0 -0
  24. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/output/surf.300000.dat +209 -0
  25. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/runmpirun128.sh +18 -0
  26. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/runmpirun64.sh +18 -0
  27. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/slurm-28428751.out +205 -0
  28. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/surf/Surf_300000.vtu +38 -0
  29. CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/surf/geom.surf +610 -0
  30. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Grid.pvd +9 -0
  31. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda.pvd +9 -0
  32. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda/Lambda_0_300000.vtu +0 -0
  33. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda/Lambda_1_300000.vtu +27 -0
  34. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda/Lambda_2_300000.vtu +27 -0
  35. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda/Lambda_300000.pvtu +21 -0
  36. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Lambda/Lambda_3_300000.vtu +27 -0
  37. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/Surf.pvd +9 -0
  38. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/config.txt +78 -0
  39. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/cylinder.surf +410 -0
  40. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/gas.species +14 -0
  41. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/gas.vhs +9 -0
  42. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_0_300000.vtu +0 -0
  43. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_1_300000.vtu +0 -0
  44. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_2_300000.vtu +35 -0
  45. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_300000.pvtu +29 -0
  46. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_3_300000.vtu +35 -0
  47. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/final-grid.dat +0 -0
  48. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/log.sparta +319 -0
  49. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/mir.txt +3 -0
  50. CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/output/grid.300000.dat +0 -0
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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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CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/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==AQAAAACAAACgDwAA/QoAAA==eJxlV3s41VkXjqKSEpMupBQpQ01KFGlNFw0ZUSqJ3O9yyT2X45L7LZFzjkPhpzQkTUVFkchEJiWhQia5JQoTMqRvmefbs+f5vr/Ws8/eZ6/Lft93rZ/Pq2vqbacYaNFXqmsPY0BBx5NthPaeL7jcD2CguOKuyUMfBmTl31vYezGwZtaVvi5cn8/UXZDhyYBLqyXLxZ6BHM3AJPUTDGQcqV/Jw/t+zhHauwbPf5EIfbfJhQHR/DxOuwMDNQWFcS/8GRAqnZLd4UPv/85yQ+5rPGdtcvLkIbQeWo5Z1u4MdEDbjyG4v6c1fZ9gMAMrOlbe+oz+5nHLr95FP3M7tqeXe9K4OXFWsv5oN/B9uXwomgE7zRd7Ms4w0BuR0//5NAP5cXt3XPNlQODAGJ+WMwP3my7t7bXD/Q3RN1c7UjtiPVWvivFahlzrbXKieerUapVexri8pwZumGFcL3lX73UGMbA15H2hy2nqX0fGdvRUHANKt/2L9RJofCSPArs5jxSxDpvFO876nqL5BLPa7586yYA0N/vRJ2fqnylU/VJhTdc+/323rmerW8ZZDHx+axQah/de8BfhLMe4BniJTUs9aR53zwd27fSj7/BYyag1CO9TWJryYi7mlTsx46IK+g0xytIa86DvU2n34Q9zVwY2qUnPnfCmce6SW7ZtHsYfJrLm2DDWc+Gs6mfXMX9PGcXvO9BvdcPwTEM8J7N/7as+d4qX2bP9v9uN67Sk/Mr6wwzUa3yQWD+bgaJLC4b9NmZBmHDnqy7jDKhk6eUbdKXBx/d9CVUqqcD0r+8KuM6BBQt/r383xoaP9ZIfZW6zIbjIsTmskA1XuPvgbT8bLgxMb3BgpPh1zFcbDoSY3vGwuMgBYz/lxuRxDqx5OhK4VYsL/c7TledCtm9Kke4QF5QaRq/lK6dCyecftG1iUoEXcPNJ/PtU8AlJ4p9njvnWTXzbakHxd1kaJIsRp6kiczzlnGne2+R/90/H+q4sfbvDHd9pR0rDhSSsZ3zhoTcJbrQOf1hL3pnpRfcXL71R0oh2Y1j62LeT9PxrqYPCiejvpv/wr/p43zpL0coFVgy8qwhJVMS4Br+MFuw0QzyJfIg/hvFVSbkOG+P9hUcKt9VhXLeUvCt08J6jfGUt907S+EzUurdb4f9eVyk3lO5n4HSrJpcxYWCv2U7edfT3oVMYr6I8nrLQOZPjR/HxNSJaUB7ffb2cysrmCMTrYPmMikgGVGptsj1YdJ/4JfldrHi5KxFxckBMzEEyiNrVOXvESpEvP0+tDRpDvkzM7LoVeIb+PqTQ5HYW8eZ3ZrPuS7x/5J7kPCG850Hn3rNF6L9a8HlVTgoDcUq/JrzDOML7ujtc8fywIHd8AZ4/7lw1dQPXs3IXqUvgvnzKYJgp+tW8bdpwOvT/4yX6Q/ioN+vkiv1YbyGbtx+cjiMvROouJyB+SxwvyAzj2lBu8iC4Ul4TXXsnkVEz9S9es6qNC1iok0HZUqqTiB/X27Kz+x2pv765OtszcV+2vnYd25gByRcsp7X7UKf+jBX7vISBJvXWBfNjs0BxKPiGvGEm1HVUVSzcdh40msfeSq/igWz64nu9ClzA4myKl+PA/Lz5+/fcZ8OBxWVlTZfY4NEsNrzzKRvOiVc28i3kgLzZs138ezkQM7N37GsaByrGpgQHGjmg0u31cJUkFypiDK6GG3EhS6xZqeAOF07rv3lUvzwVbEVjrmY6pP6j50pmpsGBiDsb1c1/1SPeJBXTL44jTtsaGqViEYfOfFkjgzYM5DVVKBYiDmKnnv2ejHjm9dVMZmPdDuuoPX6Aedcc4RjwWzKQ9crLVRPt5+jt2iW2DCzSGFQ6ivV7uOuqw5t/6dSPOoa/xvtSHVXNWYulYeB7txvzW0MYcK+qOlCMfSDnPK92PuJk32CE8vdu9L5XiEYxvH/8vNzmN0cw/nbxB9/QJlfvqL5hTnVsy2C2xKg/5T15P7VZCbMkMO7p7hXqSvsL+V259OHRHOSZg5uxsa8p4lD8l6o/LKhukPsJrgnfJq88CryCtnPE/GAA9uHsnsUlYpiPiLygljLalKZ2sWDEr2O2mlQ18lJb66LzJObZqcuK3c/GPm1SLXPnLNY123DmOcy7ca796hMY17kJqRj+QAZ+SpI24QunfNjN1/IgKpDqPDlvLqO9sCCY1pfUlW/p08ws9N+wISf1oxfta3LtJzys/CjfSB7dvgLMWnz/1Nt9aocMUM9kGn1YBxhQTNsmW6SPfUBaQG+VIfYpTfN8V3NqSb3us6LmL0adSkoxE7Taw4Du6NOG4J34/uXHipx2Yd/OjrszQxnreGiyXVQc9cjvoNyWwiyoPZ9qvrU5E4YsLaLSyy7AKs9na5b7pcGtkYagpkkuNLRkxEbEcWA5dhO5NjZYtPn6WOezocPb/mhcCRvaBvwcYmZw4LC4RJTNZg5cMWVdrsHzxQefhuq/4cDEipFmBUUutLdnlv7lywXHMFaO6W3kyzTa56SCWOQJ6eOIA/40j8Gf7Wk+RN9JfyRz1Z2e8Ya9XlS3iO6ReYzgm1iit1lKyb8sCaBzAekjn7wOmrG9qb6ReSpKS/cu15byTDMsSFTZlvKY9PcfWos3Po/Fvj/Y1h2fTPFCdPt/8RTdoH2rJB3zrOpQDUc93i72g15PDAMBfwstPRdz5KOALdp4YfclFrgfKH2qRz+C6j/7elba1xDKB8I7opOkP93I3LiuwZ7mQeY5wvNEx5efor2orse9Sjos6kLnHTK/pNqtufolguqEqVZk5EYWnWfHdS0uhcbQ/kXer+v87mV56NeupFk70IiBfrvri2oQx8I/Znu+N6P8XzAqF9GJ85bK3wnQuYzoIOExqVObYFH7a+Rzz5VOKx+sQ2TVL04DgfT9iM6SfEh/ubbrsV4enns9XY5QOl/g8GmpYE91KNysr0dXE+tQXqPihvNXXqDais36WXCTzf98anYmpOX5W3s0psPS5ClTIREeHPX8sEVjHRdMS7SF+RU48FRxrr5BLRs6FfsdIrG/8OrPRBh0s6HFsWlZ+xIO7NRavDHEmAPnWkY1+q5xwOymiYpaDweaDA0FHJW4wD9Uwnrgwv0Hv0THib5IhhW31WIeeiYJhtJ+FG9EL51Yk5c0AylOyHzvbFS2oRLP8f+ZKL8ylM4BZN4i3yMEN2S+shUbCdXzoLgiekZ4SHBI+jvRZaJvxJ/5kXXPvyL+n7i/6k3PYOC6vhPPB9fPr/Dt2x1PcUXmF8KPI6xlcaLRdN4h6zSp3LI/kX/qqs6/OUQhzgZuzXuC/hjhw0opLDr/k35AcOkv9jL9NPIg0jl2kyTirdmu7q9zVrQOZzV5vrN9qZ+PT1y59okMAHa9Dpy7RhZtwxLgvFhY7V2GeRhe/4IMwH75JI6ljL8X8HqiAjiIwzP6my/GUJ0huhE0oeZWF0XzfKSwUEMe9WMZn3iCNOrCmHvGcgHcXyH+zWdPOJ3DpNQ0Pv0WSr+j8qfhn4S64q8Y2Puv/eVm/rba+D7lc8PnbMVzgmFNlRzMo7Cg3j0zkvZTopulBnxj24PouxGctL+dFnrE3zRdk6ieke/Ro7mZTB/yOT9XUejuOgYMfGyWCBdkwUOZoWHPgkwQ+rsRXIDumi0Cm9TTILNC9OLBfi4cs1Zv14vngHc8e6bGCzZEr72bWn+GDS5RQzxDLzZsOOwQ6xTAhvXPH6qXJ7PhcTnf+uMP2FDakfuTRCsbYt2SD9gs5cB/AIEhfSs=AQAAAACAAACgDwAAxAoAAA==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AQAAAACAAABoHAAA5wYAAA==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CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/gas.vhs ADDED
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+
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+ # species name
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+ # diameter (m)
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+ # omega (unitless)
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+ # tref (referenced temperature)
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+ # alpha (unitless)
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+
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+ Ar 4.17e-10 0.81 273 1.0
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AQAAAACAAACgDwAA0woAAA==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AQAAAACAAACgDwAAWwUAAA==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AQAAAACAAACgDwAAqA0AAA==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AQAAAACAAACgDwAAtwkAAA==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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+NADFJe2aAQAAAACAAABYAgAA8gEAAA==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AQAAAACAAABYAgAAYwIAAA==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AQAAAACAAABYAgAAUAEAAA==eJw1UT8oxHEUF0rdQOkWhpNSdyTLyZ/h9MmCMjgiw9UNShbFprMY3PRelCwnkijbJdMtF51NdMpwA8nNbrAwe997H2/4vd/3+3vv8++XTFrNCU4KVlnBbrQ4U98XHFZ+EplzwWDG3m4EffmvdOlR8Pxk9S5oCfUhqF1upSJtigCT7FG/H1LfG2WfVoSWWFUEmNiaIhUJm8RfEN//598R2DMXPaCua/H5smDWrotvglLMkL4FV0Zf61Cf61LfG1DXP05e42/OLanrjQvWQ80Tz/z/Jmxyk3r2xPeOBYuB6EzwYHCH9zxX6b/B+1b13k3+YfJNKpo2p9T1dtLXCPNN87xBH0IdBeZc5LlMPS+CEE/ukzrbydtLvjh5xhTbIbAVfj8SNCz2/AV57oj7ynzrzN18NP31U/eEej7L7Fnl/6HPU+LfquuqMN+q4g+xkhHYAQAAAACAAABYAgAAZgEAAA==eJxFUT0sg1EUZe0gxFchaSoxUDYRA9LkxtIOHVRM8uH9f2tNknYUE9K5mLCgOlh0kVQ7SESnRr5ROtcgombf63mJu9y8c+8995z7st2iVws11ZNHvXxBk1fLdIs/ilr9lF8uKuBfkrK2LyNpyUZL0FklirygATwsaMhGgyOPcvrMR5PHDH1jjK4L6Vg4vkclu2BhF++OQf+GAf6gsXdGg/dZUSy8KqTXFfjOJfApSZtW2I1AXhQ075eb/Wi/sTHB4aPKoHeNkaWJHbj9twHqiQDzFUNpu6ivydKktjTmOgrvnKL2WxRVCT8rEjyXArxzArpfOf1GqbnDgX8w6GIMeDuAvtmAoqvWk+8Gvkou9zT8bmvat4Iazv+qAt+jhI6chP4L53tSgP+ew0ecY77u7n7n9i0H+MeyoWkLjBjgTxq8Ce3uocAXV6ifSvj7FrjHyf+/D3QfcrJyvCQnW/ZfGP0Bn4cHgg==AQAAAACAAABYAgAAYwIAAA==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AQAAAACAAABYAgAAYQIAAA==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eJxFUTlIQ0EQ9ehSWaTUmEYIWImNIIHRQkVQiI2NChb5jU1s5M+MjcWvPEEQk9KjCWgpUbCJlXhgoyms0gjyBUFJSnH3zxOn2N2ZneO9N5Wys1ulpqTPJ7eV8qnGcSmvlFwp+M9Cc7VMFJfF4ktCJ+63MSgUxYVa5p2p3z+umHxa4RB3iUld2+Y4W14fW/1PiDq1uh3MKSr5dMkqfbjvqFupnVvYrV8KubOV2xK6ablAIDScmNCUBx4z+bL0I1ufU8xdw7wJpg5vWeD8UsuvqvE6+5+/6h+9an0/hRIZXsFzQ0ynZaGK121ADOc3+taZHu6d7cEPwHuMDe/ouuFv//FSyw/V+I0AVxd0eQHfI+BYEYtPi+nbAx3uwLPKht/NT/DPs+1pUynwNov+b9jjBfAr9jsDnp3Q+4lt/gEb3iJb3hD6x6HpcB2avw9/MaRfaqIT/g==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/simulation0813_Kn0p38Ma3p2/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/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 3.2
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 3.2*${sos}
38
+ variable vo equal 3.2*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 985.009805563779*cos(${theta}*2*PI/360)
41
+ variable vx equal 985.009805563779*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 985.009805563779*sin(${theta}*2*PI/360)
44
+ variable vy equal 985.009805563779*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${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}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/985.009805563779/${xncells}/10/4
69
+ variable tstep equal (--5+6)/985.009805563779/115.789473684211/10/4
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 2.41114351002896e-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 = 0.0908443 secs
101
+ create/ghost percent = 1.22355 98.7764
102
+ balance_grid rcb cell
103
+ Balance grid migrated 53160 cells
104
+ CPU time = 0.0866968 secs
105
+ reassign/sort/migrate/ghost percent = 63.011 0.0900022 8.82071 28.0783
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 985.009805563779 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 985.009805563779 0 0.0 temp ${To}
111
+ mixture air Ar vstream 985.009805563779 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 = 0.0947782 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 23.7195 24.4431 2.78702 23.7602 25.2901 6.71652 0.169699
126
+ surf2grid time = 0.0225195 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 13.2848 24.019 4.20382 2.44648 12.4296 6.9333
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 = 0.0479019 secs
134
+ reassign/sort/migrate/ghost percent = 32.8002 0.0633796 17.8351 49.3013
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 = 0.012558 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 10.797643 401060 7969 6761 6 382614.57 10.3613
150
+ 20000 23.702126 401826 8058 6806 6 392241.76 10.46175
151
+ 30000 35.908188 402431 8105 6909 10 395574.61 10.477367
152
+ 40000 47.648765 402729 8093 6865 12 397337.78 10.4876
153
+ 50000 59.630016 402587 8052 6811 9 398467.83 10.4929
154
+ 60000 71.70803 402087 8103 6882 10 399128.93 10.489517
155
+ 70000 84.421358 402137 8034 6803 2 399573.04 10.503171
156
+ 80000 96.52732 402434 8056 6816 13 399960.98 10.501713
157
+ 90000 108.2451 401495 8115 6914 9 400170.73 10.501156
158
+ 100000 120.0783 402686 8122 6913 9 400425.36 10.50227
159
+ 110000 132.09227 403055 8251 7055 9 400635.73 10.512745
160
+ 120000 144.99111 401284 8128 6870 7 400769.89 10.508842
161
+ 130000 157.17578 402104 8099 6876 6 400875.48 10.512785
162
+ 140000 168.86026 402830 8139 6923 11 400965.03 10.517221
163
+ 150000 180.65717 402247 8179 6932 16 401074.92 10.517273
164
+ 160000 192.5916 401596 8075 6884 12 401150.09 10.517719
165
+ 170000 204.78221 402411 8095 6842 13 401214.32 10.516947
166
+ 180000 216.92679 402196 8120 6939 9 401295.99 10.519328
167
+ 190000 228.55896 402400 8085 6853 10 401349.76 10.519621
168
+ 200000 240.36117 402802 8114 6890 6 401401.26 10.52107
169
+ Loop time of 240.362 on 128 procs for 200000 steps with 402802 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.022614 | 9.8972 | 56.75 | 401.2 | 4.12
175
+ Coll | 0.27578 | 9.643 | 69.429 | 412.0 | 4.01
176
+ Sort | 0.095529 | 1.6299 | 9.8547 | 167.2 | 0.68
177
+ Comm | 57.621 | 93.265 | 104.91 | 97.8 | 38.80
178
+ Modify | 0.018565 | 0.11111 | 2.0913 | 92.1 | 0.05
179
+ Output | 0.004218 | 0.0096709 | 0.012289 | 1.0 | 0.00
180
+ Other | | 125.8 | | | 52.34
181
+
182
+ Particle moves = 80280252395 (80.3B)
183
+ Cells touched = 84058920888 (84.1B)
184
+ Particle comms = 192789048 (193M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 19104167 (19.1M)
187
+ SurfColl checks = 102886585 (103M)
188
+ SurfColl occurs = 2104214 (2.1M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 1789975066 (1.79B)
191
+ Collide occurs = 1392463350 (1.39B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.60936e+06
197
+ Particle-moves/step: 401401
198
+ Cell-touches/particle/step: 1.04707
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00240145
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000237968
203
+ Surface-checks/particle/step: 0.00128159
204
+ Surface-collisions/particle/step: 2.62109e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0222966
207
+ Collisions/particle/step: 0.017345
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 3146.89 ave 19290 max 0 min
211
+ Histogram: 84 14 4 7 5 5 5 0 0 4
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) = 1.66113 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) = 4.70511 3.36171 6.17471
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 402802 8114 6890 0 0 0
251
+ 210000 19.931901 402141 8140 6987 15 402600.01 10.507
252
+ 220000 41.338444 402037 8234 6972 3 402333.54 10.5043
253
+ 230000 60.937103 401805 8138 6920 14 402161.12 10.514067
254
+ 240000 80.85277 402307 8129 6880 9 402144.44 10.519225
255
+ 250000 101.60567 402164 8047 6868 4 402235.31 10.5181
256
+ 260000 121.28817 402077 8031 6728 6 402229.29 10.52335
257
+ 270000 141.77167 402034 8215 6926 10 402263.43 10.5211
258
+ 280000 162.66606 401959 8139 6838 14 402346.22 10.5204
259
+ 290000 182.19754 401175 7980 6760 11 402302.26 10.511322
260
+ 300000 203.36707 401845 8140 6878 13 402266.63 10.51351
261
+ Loop time of 203.367 on 128 procs for 100000 steps with 401845 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.011489 | 4.9785 | 28.569 | 283.8 | 2.45
267
+ Coll | 0.13765 | 4.8427 | 35.333 | 293.6 | 2.38
268
+ Sort | 0.052825 | 0.82513 | 4.9792 | 118.0 | 0.41
269
+ Comm | 32.722 | 50.812 | 57.28 | 68.4 | 24.99
270
+ Modify | 1.9282 | 13.725 | 74.69 | 438.2 | 6.75
271
+ Output | 0.034695 | 0.036612 | 0.039805 | 0.6 | 0.02
272
+ Other | | 128.1 | | | 63.01
273
+
274
+ Particle moves = 40226662758 (40.2B)
275
+ Cells touched = 42118757701 (42.1B)
276
+ Particle comms = 96539229 (96.5M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 9572623 (9.57M)
279
+ SurfColl checks = 51519530 (51.5M)
280
+ SurfColl occurs = 1051351 (1.05M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 899649606 (900M)
283
+ Collide occurs = 699782008 (700M)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.54534e+06
289
+ Particle-moves/step: 402267
290
+ Cell-touches/particle/step: 1.04704
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00239988
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000237967
295
+ Surface-checks/particle/step: 0.00128073
296
+ Surface-collisions/particle/step: 2.61357e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0223645
299
+ Collisions/particle/step: 0.017396
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 3139.41 ave 19305 max 0 min
303
+ Histogram: 84 14 4 7 6 5 4 0 0 4
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 = 0.00760433 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00324093 secs
CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/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/simulation0813_Kn0p38Ma3p2/output/grid.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/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 0.00262 0.031632 -0.031629 0.00049686 2.3498e-05 0.0014958 -0.00049643 3.13e-05 2.2794 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.08602 1.1324 0.70805 -0.88378 0.17507 0.14026 0.013871 -0.011679 164.19 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.0025 0.028867 -0.028835 0.0013598 -4.4935e-05 -0.00095284 -0.00045362 6.3924e-06 2.815 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.08433 1.1036 0.66264 -0.88256 0.19354 0.14531 0.013448 -0.01158 164.4 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00234 0.028152 -0.028065 0.0022088 -7.0526e-05 -0.00089611 -0.00044193 2.0618e-05 3.5396 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.07848 1.032 0.59339 -0.8443 0.18491 0.12996 0.012225 -0.01122 150.92 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00231 0.026934 -0.026771 0.0029556 2.8762e-05 0.00026052 -0.00042005 5.0516e-05 2.4446 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.07559 0.99342 0.54541 -0.83031 0.19191 0.12606 0.011581 -0.011062 150.8 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00261 0.03187 -0.031552 0.0044905 -8.1439e-05 -0.00057222 -0.00049688 6.1546e-05 2.7111 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.07099 0.92489 0.48326 -0.7886 0.18623 0.11412 0.010516 -0.010594 142.92 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00221 0.025513 -0.025134 0.0043865 0.00014774 0.00084654 -0.00039246 8.2197e-05 2.2035 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.06875 0.89669 0.44427 -0.77889 0.19512 0.11129 0.010043 -0.010486 133.54 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00229 0.02748 -0.026909 0.0055725 0.00015375 0.00074242 -0.00042025 9.9191e-05 1.6846 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.06464 0.84513 0.39546 -0.7469 0.20158 0.10673 0.009378 -0.010055 131.01 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00217 0.024859 -0.024172 0.0058033 -7.8639e-05 -0.00032755 -0.00038092 8.6009e-05 1.488 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.06258 0.81258 0.35748 -0.72971 0.20129 0.098613 0.0087769 -0.0099129 126.23 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00191 0.021233 -0.020481 0.0056029 0.00062122 0.0022708 -0.00031194 0.00012367 2.1593 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.05878 0.76028 0.31286 -0.69292 0.19861 0.089674 0.0080338 -0.0094753 120.41 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.0022 0.025499 -0.024372 0.0074978 0.00029178 0.00094843 -0.00037824 0.00013267 2.108 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.05581 0.72218 0.27637 -0.66721 0.1888 0.078204 0.0073065 -0.0092517 110.9 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00205 0.023651 -0.022376 0.0076609 0.00087051 0.0025425 -0.00033779 0.00016027 1.9057 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.05245 0.67087 0.23714 -0.62756 0.19564 0.073928 0.0067978 -0.0086961 108.01 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.00191 0.02185 -0.020439 0.0077234 0.0014042 0.0037161 -0.00029899 0.00017968 2.2903 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.04914 0.63698 0.20633 -0.60264 0.18127 0.062063 0.0060882 -0.0084911 99.566 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00198 0.023073 -0.021317 0.0088296 0.0006727 0.001624 -0.00032426 0.0001642 1.8805 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.04535 0.58469 0.17192 -0.55884 0.1794 0.05519 0.0055183 -0.007911 93.274 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00211 0.020893 -0.019042 0.0085976 0.0016711 0.0037011 -0.00027284 0.00019318 2.3761 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.04199 0.53311 0.14067 -0.51422 0.17065 0.046683 0.00489 -0.0073437 80.743 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.00197 0.022659 -0.020348 0.0099684 0.0014033 0.0028645 -0.00029757 0.00020157 2.7228 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.04097 0.52591 0.12277 -0.51138 0.18065 0.043369 0.0047659 -0.0073511 83.204 0 0
40
+ 16 0.4455 -0.227 0.43815 -0.24088 0.00196 0.022638 -0.020007 0.010593 0.0014194 0.0026808 -0.00029196 0.0002085 1.7431 0 0
41
+ 144 -0.10907 0.48796 -0.093691 0.49114 0.03824 0.48896 0.099156 -0.47881 0.17475 0.036188 0.0043023 -0.0069523 78.25 0 0
42
+ 17 0.43815 -0.24088 0.43037 -0.25452 0.00216 0.023978 -0.020828 0.01188 0.0031158 0.0054625 -0.00027821 0.00027241 2.3094 0 0
43
+ 145 -0.093691 0.49114 -0.078217 0.49384 0.03524 0.44726 0.076897 -0.4406 0.16544 0.028873 0.0038064 -0.0064671 71.467 0 0
44
+ 18 0.43037 -0.25452 0.42216 -0.26791 0.00223 0.02418 -0.020617 0.012634 0.0035261 0.0057541 -0.00026845 0.00028883 2.4468 0 0
45
+ 146 -0.078217 0.49384 -0.062667 0.49606 0.0345 0.43282 0.060984 -0.4285 0.16643 0.023686 0.003572 -0.0063585 72.166 0 0
46
+ 19 0.42216 -0.26791 0.41354 -0.28104 0.00235 0.025883 -0.021633 0.01421 0.003698 0.0056297 -0.00028171 0.00031163 3.2797 0 0
47
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+ 85 -0.43815 -0.24088 -0.4455 -0.227 0.13575 1.7944 1.5858 0.83965 0.084125 -0.15888 0.02623 0.010693 254.56 0 0
167
+ 86 -0.4455 -0.227 -0.45241 -0.21289 0.13871 1.8449 1.6568 0.81167 0.07682 -0.15681 0.027231 0.010286 259.66 0 0
168
+ 87 -0.45241 -0.21289 -0.45888 -0.19857 0.14154 1.8703 1.7046 0.76967 0.067778 -0.15011 0.02784 0.0097316 263.97 0 0
169
+ 88 -0.45888 -0.19857 -0.46489 -0.18406 0.14478 1.9303 1.7833 0.73868 0.061194 -0.14774 0.028972 0.0092821 277.59 0 0
170
+ 89 -0.46489 -0.18406 -0.47044 -0.16937 0.14896 1.9863 1.8581 0.70212 0.057662 -0.1526 0.030091 0.0086314 285.75 0 0
171
+ 90 -0.47044 -0.16937 -0.47553 -0.15451 0.14971 1.9938 1.8863 0.64582 0.042285 -0.1235 0.030293 0.0082043 285.08 0 0
172
+ 91 -0.47553 -0.15451 -0.48015 -0.1395 0.15111 2.0174 1.9282 0.59319 0.038257 -0.12436 0.030888 0.0073641 282.37 0 0
173
+ 92 -0.48015 -0.1395 -0.48429 -0.12434 0.15332 2.0499 1.9773 0.54092 0.027681 -0.10119 0.031492 0.006907 289.18 0 0
174
+ 93 -0.48429 -0.12434 -0.48796 -0.10907 0.15672 2.0915 2.0337 0.48826 0.02421 -0.10084 0.032325 0.0060853 287.97 0 0
175
+ 94 -0.48796 -0.10907 -0.49114 -0.093691 0.15706 2.0831 2.0398 0.42242 0.019256 -0.092984 0.032342 0.0051746 287.11 0 0
176
+ 95 -0.49114 -0.093691 -0.49384 -0.078217 0.1577 2.1102 2.0788 0.36281 0.013485 -0.077266 0.032864 0.0044851 296.33 0 0
177
+ 96 -0.49384 -0.078217 -0.49606 -0.062667 0.16306 2.1694 2.1478 0.30567 0.0077806 -0.05467 0.033858 0.0039426 307.01 0 0
178
+ 97 -0.49606 -0.062667 -0.49778 -0.047054 0.15926 2.1189 2.1061 0.23251 0.0058215 -0.052731 0.033172 0.0028239 293.53 0 0
179
+ 98 -0.49778 -0.047054 -0.49901 -0.031395 0.16303 2.1774 2.1707 0.17084 0.0030457 -0.038699 0.034144 0.0020755 303.59 0 0
180
+ 99 -0.49901 -0.031395 -0.49975 -0.015705 0.16461 2.1763 2.1739 0.10252 0.00062248 -0.0132 0.034155 0.0014029 301 0 0
181
+ 100 -0.49975 -0.015705 -0.5 0 0.16259 2.1577 2.1574 0.033891 0.00015244 -0.0097038 0.033889 0.00037992 301.14 0 0
182
+ 101 -0.5 0 -0.49975 0.015705 0.16262 2.1516 2.1513 -0.033796 0.00024367 0.015512 0.033796 -0.0002872 290.66 0 0
183
+ 102 -0.49975 0.015705 -0.49901 0.031395 0.16271 2.1613 2.1589 -0.10181 0.00087945 0.018649 0.033924 -0.0013063 295.2 0 0
184
+ 103 -0.49901 0.031395 -0.49778 0.047054 0.16449 2.1842 2.1775 -0.17137 0.0022741 0.028895 0.034238 -0.0022379 305.8 0 0
185
+ 104 -0.49778 0.047054 -0.49606 0.062667 0.16091 2.1438 2.1308 -0.23525 0.0052808 0.047833 0.033553 -0.0029438 294.97 0 0
186
+ 105 -0.49606 0.062667 -0.49384 0.078217 0.16271 2.1579 2.1364 -0.30406 0.010252 0.072037 0.033718 -0.0036444 299.29 0 0
187
+ 106 -0.49384 0.078217 -0.49114 0.093691 0.15788 2.1042 2.0729 -0.36178 0.011172 0.064012 0.032735 -0.0046771 297.6 0 0
188
+ 107 -0.49114 0.093691 -0.48796 0.10907 0.15771 2.1027 2.059 -0.42641 0.018656 0.090087 0.032635 -0.0052827 300.35 0 0
189
+ 108 -0.48796 0.10907 -0.48429 0.12434 0.15895 2.1101 2.0518 -0.4926 0.022657 0.094373 0.032585 -0.0062551 292.16 0 0
190
+ 109 -0.48429 0.12434 -0.48015 0.1395 0.15242 2.0278 1.956 -0.53509 0.030375 0.11103 0.0312 -0.0066608 281.06 0 0
191
+ 110 -0.48015 0.1395 -0.47553 0.15451 0.15151 2.0211 1.9318 -0.5943 0.034694 0.11278 0.030888 -0.0075634 291.21 0 0
192
+ 111 -0.47553 0.15451 -0.47044 0.16937 0.14996 2.0031 1.8951 -0.64885 0.042684 0.12467 0.030438 -0.0082335 282.02 0 0
193
+ 112 -0.47044 0.16937 -0.46489 0.18406 0.14724 1.9608 1.8342 -0.69309 0.053374 0.14125 0.029649 -0.0086679 277.88 0 0
194
+ 113 -0.46489 0.18406 -0.45888 0.19857 0.1436 1.9115 1.766 -0.73151 0.06822 0.1647 0.028811 -0.0089031 271.84 0 0
195
+ 114 -0.45888 0.19857 -0.45241 0.21289 0.13961 1.866 1.7007 -0.76789 0.075522 0.16726 0.027899 -0.0094342 268.08 0 0
196
+ 115 -0.45241 0.21289 -0.4455 0.227 0.13873 1.8422 1.6544 -0.81047 0.076149 0.15544 0.027182 -0.010289 254.59 0 0
197
+ 116 -0.4455 0.227 -0.43815 0.24088 0.13406 1.7796 1.5728 -0.83275 0.078546 0.14835 0.025938 -0.01075 256.43 0 0
198
+ 117 -0.43815 0.24088 -0.43037 0.25452 0.13206 1.7499 1.52 -0.867 0.096769 0.16965 0.025395 -0.010953 247.8 0 0
199
+ 118 -0.43037 0.25452 -0.42216 0.26791 0.13022 1.7346 1.479 -0.90634 0.10089 0.16463 0.024816 -0.01165 245.58 0 0
200
+ 119 -0.42216 0.26791 -0.41354 0.28104 0.12698 1.6712 1.3968 -0.91751 0.11147 0.1697 0.02369 -0.011746 238.35 0 0
201
+ 120 -0.41354 0.28104 -0.40451 0.29389 0.11984 1.589 1.3001 -0.9137 0.1157 0.16462 0.022238 -0.011766 217.82 0 0
202
+ 121 -0.40451 0.29389 -0.39508 0.30645 0.11626 1.5496 1.2392 -0.93041 0.13771 0.18341 0.021627 -0.011733 223.29 0 0
203
+ 122 -0.39508 0.30645 -0.38526 0.31871 0.11327 1.4981 1.1691 -0.93666 0.13515 0.16869 0.020487 -0.012063 213.19 0 0
204
+ 123 -0.38526 0.31871 -0.37506 0.33066 0.1085 1.4298 1.0872 -0.92859 0.1539 0.18019 0.019495 -0.011755 202.92 0 0
205
+ 124 -0.37506 0.33066 -0.36448 0.34227 0.10764 1.422 1.0517 -0.95702 0.1567 0.17221 0.018981 -0.012327 207.61 0 0
206
+ 125 -0.36448 0.34227 -0.35355 0.35355 0.10206 1.3489 0.96869 -0.93872 0.14959 0.15436 0.017565 -0.01232 198.03 0 0
207
+ 126 -0.35355 0.35355 -0.34227 0.36448 0.0996 1.3101 0.91174 -0.94085 0.16859 0.16337 0.016969 -0.012212 190.56 0 0
208
+ 127 -0.34227 0.36448 -0.33066 0.37506 0.09394 1.2465 0.83889 -0.92193 0.17319 0.15759 0.015897 -0.012006 190.97 0 0
209
+ 128 -0.33066 0.37506 -0.31871 0.38526 0.09087 1.1927 0.77457 -0.9069 0.17895 0.15284 0.014977 -0.011844 171.89 0 0
CylinderKnMa/Kn0p38/simulation0813_Kn0p38Ma3p2/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/simulation0813_Kn0p38Ma3p2/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/simulation0813_Kn0p38Ma3p2/slurm-28428751.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 = 0.0908443 secs
7
+ create/ghost percent = 1.22355 98.7764
8
+ Balance grid migrated 53160 cells
9
+ CPU time = 0.0866968 secs
10
+ reassign/sort/migrate/ghost percent = 63.011 0.0900022 8.82071 28.0783
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 = 0.0947782 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 23.7195 24.4431 2.78702 23.7602 25.2901 6.71652 0.169699
23
+ surf2grid time = 0.0225195 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 13.2848 24.019 4.20382 2.44648 12.4296 6.9333
25
+ Balance grid migrated 988 cells
26
+ CPU time = 0.0479019 secs
27
+ reassign/sort/migrate/ghost percent = 32.8002 0.0633796 17.8351 49.3013
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 363039 versus 363040 (../create_particles.cpp:346)
30
+ Created 363039 particles
31
+ CPU time = 0.012558 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 10.797643 401060 7969 6761 6 382614.57 10.3613
40
+ 20000 23.702126 401826 8058 6806 6 392241.76 10.46175
41
+ 30000 35.908188 402431 8105 6909 10 395574.61 10.477367
42
+ 40000 47.648765 402729 8093 6865 12 397337.78 10.4876
43
+ 50000 59.630016 402587 8052 6811 9 398467.83 10.4929
44
+ 60000 71.70803 402087 8103 6882 10 399128.93 10.489517
45
+ 70000 84.421358 402137 8034 6803 2 399573.04 10.503171
46
+ 80000 96.52732 402434 8056 6816 13 399960.98 10.501713
47
+ 90000 108.2451 401495 8115 6914 9 400170.73 10.501156
48
+ 100000 120.0783 402686 8122 6913 9 400425.36 10.50227
49
+ 110000 132.09227 403055 8251 7055 9 400635.73 10.512745
50
+ 120000 144.99111 401284 8128 6870 7 400769.89 10.508842
51
+ 130000 157.17578 402104 8099 6876 6 400875.48 10.512785
52
+ 140000 168.86026 402830 8139 6923 11 400965.03 10.517221
53
+ 150000 180.65717 402247 8179 6932 16 401074.92 10.517273
54
+ 160000 192.5916 401596 8075 6884 12 401150.09 10.517719
55
+ 170000 204.78221 402411 8095 6842 13 401214.32 10.516947
56
+ 180000 216.92679 402196 8120 6939 9 401295.99 10.519328
57
+ 190000 228.55896 402400 8085 6853 10 401349.76 10.519621
58
+ 200000 240.36117 402802 8114 6890 6 401401.26 10.52107
59
+ Loop time of 240.362 on 128 procs for 200000 steps with 402802 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.022614 | 9.8972 | 56.75 | 401.2 | 4.12
65
+ Coll | 0.27578 | 9.643 | 69.429 | 412.0 | 4.01
66
+ Sort | 0.095529 | 1.6299 | 9.8547 | 167.2 | 0.68
67
+ Comm | 57.621 | 93.265 | 104.91 | 97.8 | 38.80
68
+ Modify | 0.018565 | 0.11111 | 2.0913 | 92.1 | 0.05
69
+ Output | 0.004218 | 0.0096709 | 0.012289 | 1.0 | 0.00
70
+ Other | | 125.8 | | | 52.34
71
+
72
+ Particle moves = 80280252395 (80.3B)
73
+ Cells touched = 84058920888 (84.1B)
74
+ Particle comms = 192789048 (193M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 19104167 (19.1M)
77
+ SurfColl checks = 102886585 (103M)
78
+ SurfColl occurs = 2104214 (2.1M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 1789975066 (1.79B)
81
+ Collide occurs = 1392463350 (1.39B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.60936e+06
87
+ Particle-moves/step: 401401
88
+ Cell-touches/particle/step: 1.04707
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00240145
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000237968
93
+ Surface-checks/particle/step: 0.00128159
94
+ Surface-collisions/particle/step: 2.62109e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0222966
97
+ Collisions/particle/step: 0.017345
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 3146.89 ave 19290 max 0 min
101
+ Histogram: 84 14 4 7 5 5 5 0 0 4
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) = 1.66113 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) = 4.70511 3.36171 6.17471
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 402802 8114 6890 0 0 0
119
+ 210000 19.931901 402141 8140 6987 15 402600.01 10.507
120
+ 220000 41.338444 402037 8234 6972 3 402333.54 10.5043
121
+ 230000 60.937103 401805 8138 6920 14 402161.12 10.514067
122
+ 240000 80.85277 402307 8129 6880 9 402144.44 10.519225
123
+ 250000 101.60567 402164 8047 6868 4 402235.31 10.5181
124
+ 260000 121.28817 402077 8031 6728 6 402229.29 10.52335
125
+ 270000 141.77167 402034 8215 6926 10 402263.43 10.5211
126
+ 280000 162.66606 401959 8139 6838 14 402346.22 10.5204
127
+ 290000 182.19754 401175 7980 6760 11 402302.26 10.511322
128
+ 300000 203.36707 401845 8140 6878 13 402266.63 10.51351
129
+ Loop time of 203.367 on 128 procs for 100000 steps with 401845 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.011489 | 4.9785 | 28.569 | 283.8 | 2.45
135
+ Coll | 0.13765 | 4.8427 | 35.333 | 293.6 | 2.38
136
+ Sort | 0.052825 | 0.82513 | 4.9792 | 118.0 | 0.41
137
+ Comm | 32.722 | 50.812 | 57.28 | 68.4 | 24.99
138
+ Modify | 1.9282 | 13.725 | 74.69 | 438.2 | 6.75
139
+ Output | 0.034695 | 0.036612 | 0.039805 | 0.6 | 0.02
140
+ Other | | 128.1 | | | 63.01
141
+
142
+ Particle moves = 40226662758 (40.2B)
143
+ Cells touched = 42118757701 (42.1B)
144
+ Particle comms = 96539229 (96.5M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 9572623 (9.57M)
147
+ SurfColl checks = 51519530 (51.5M)
148
+ SurfColl occurs = 1051351 (1.05M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 899649606 (900M)
151
+ Collide occurs = 699782008 (700M)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.54534e+06
157
+ Particle-moves/step: 402267
158
+ Cell-touches/particle/step: 1.04704
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00239988
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.000237967
163
+ Surface-checks/particle/step: 0.00128073
164
+ Surface-collisions/particle/step: 2.61357e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.0223645
167
+ Collisions/particle/step: 0.017396
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 3139.41 ave 19305 max 0 min
171
+ Histogram: 84 14 4 7 6 5 4 0 0 4
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 = 0.00760433 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.00324093 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 0:00:09 (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/simulation0813_Kn0p38Ma3p2/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
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+ <VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
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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="Float64" Name="v1x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="432" />
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+ <DataArray type="Float64" Name="v1y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="1468" />
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+ <DataArray type="Float64" Name="v2x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="2476" />
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+ <DataArray type="Float64" Name="v2y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="3508" />
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+ <DataArray type="Float64" Name="f_2[1]" format="appended" RangeMin="0.00171" RangeMax="0.16461" offset="4512" />
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+ <DataArray type="Float64" Name="f_2[2]" format="appended" RangeMin="0.018764" RangeMax="2.1842" offset="6448" />
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+ <DataArray type="Float64" Name="f_2[3]" format="appended" RangeMin="-0.039959" RangeMax="2.1775" offset="8516" />
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+ <DataArray type="Float64" Name="f_2[4]" format="appended" RangeMin="-0.95702" RangeMax="0.94715" offset="10636" />
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+ <DataArray type="Float64" Name="f_2[5]" format="appended" RangeMin="-0.00023842" RangeMax="0.20158" offset="12736" />
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+ <DataArray type="Float64" Name="f_2[6]" format="appended" RangeMin="-0.17711" RangeMax="0.18341" offset="14840" />
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+ <DataArray type="Float64" Name="f_2[7]" format="appended" RangeMin="-0.00050164" RangeMax="0.034238" offset="16988" />
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+ <DataArray type="Float64" Name="f_2[11]" format="appended" RangeMin="0" RangeMax="0" offset="22928" />
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+ </CellData>
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+ <Points>
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+ <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0.49999998503" RangeMax="0.50000001255" offset="22980" />
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+ </Points>
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+ <Cells>
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+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="24308" />
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+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="25212" />
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+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="25704" />
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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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AQAAAACAAABABgAA/AUAAA==eJw9lHk41Akcxmd2NR3aPI5tlVZCqSYl5Fr1/mpIckXCZkOyVqhQjmY1orZEpX3MmDGuyV1KrsgtqeQoZ2hSsSQix8jZsMM+z/71/ev7fd/nfT/PdzT++1pJdR6WriiJtIiIw41og9IPzbHQl+YqRzFjoO3tU06z5OFd7QUfGQUuLJRUWQVKsagJFSeSzkXjjKFzyNoyFqSSrQbd1nHxUV6qbFKbA5uEjPvtjlHozM1s9OGxMcp4y5h2YUFMrq38swYbVo5Rfu7b2fAiTGyzbnCQynhnnVzJQVLf6iKpYC6OlTtpe9ozsaItyWv3Cja66DKZRl1c3HJvH77my4Uny8lbRxADxTU+nrtpPGQQqhSJ5nhw0s4abtnAw7Gn3dp/sRJRt9WT8kghCbFJN29m/J2C9gNSl6m8dBSpNYW/Ed6Fs0lEKl39AdT1J3LtwrKgVdcQoBiaC+HlXPtalzwwtHw6mWP5mPz5yQmEF6JTmRLMohUjXa/H9RG5HEWTXi+80yrwsjLeb8qjEksqAig/CJ+gc++g6W3BM5RkOmbJ5b/AvNhPVHe1FzhtamHXTa6HJ+2pTpB0A1ZG3gmxNG5EmL5xketYIyZ9P8RKpjRju+4X5qrXrZiJORR6kdaOafM6+k5mB1ZWOwzPBfARLWU8rh7WiRqLuxf05N+Dr2HyXUvLB2xZ0unQJNuNB/5BJbb3u6FWrCIh/rAHAu9T6bJ9vWjTHNYxNOqDPbNDIfL6J3T4G46Us/phU0A3p78fwHVVMd3NxwexOnlNeGruEGT3KKYaSA2DOsHujLw6Aq2MPbb22aPgLpOLd9gyhqp990++8xagf9POZffWjePYHG1m6PM4VHPihtOefYXyZsb1Qo8JWP+TElOlMImFFukyU4iUFbNZpTqNuZYA0eo09ucLhC0BMwg5u585QZlFzvbTSeSSWUjJ4Nnbh9+wOC4LAZF6+aAQmhcLiJDuOSyO5/NI0N/Wwv04Dw+uf2yiEonYfos3tdOVRIhMHXUpJhGBx9WPqN0lEVYqvGtZXSRiedj6E1+OkonQcX5bsIBE5P45lnXIg0wYGvlMS5wkE5qJedpT+8hESdQtCboWmVCpvJJme5BMFDgHdew+TyYuWRrHXdxEJlLMCenjNDIhPbb3lWsriXjictfJuolErLV8Ixco0nG+/VqUOIlgL5yhkoj2kfGDv1JIRO/Y+qYzjvPYsQjUHPY59TYo8oXYLGor9bwQ/+l8Q8Thc8rs+VksxloxiyWaklr6pTNYoDDccxrLF2qonUKsxoLhScxvEE74fpjADfZpqUsVEyhTi94qy/qKP1r73KkT4wj37Ju9MyrAJMWRY/qjAJntjn4W8mNYULNLH8WhxcURBKq4lsrWDKOloK4/hzOEqpcyeg+yB8Gu4Uuai3+GnNG9p2mkAezMnn6kZf0JOW56v6xZ14fCyuLfqvx7sYtzpUvJuQc7wq/VP3/YDT3f3lx9iS4k+Vrv2ivilG1D65Y3eQ/SMw23TwlvIYI83SqXj9fpV93OLe2AZemurCMhbbi6uuPl5OFWDAy6eDFLWhC1KYXHN2lC+1dxvyf5jaAtgvsKG+vyhFRqPQKHojeS3Wvwu6gGK5VqhNg4VQ/YPEc9JdqlZ1sVNui/0XFWrcSBxYfyGGJnx6dKzcpxR7rBoH2kBAt09LOK0OrPsAyZy0flycfhZSF5aAh82ZnJzoGxkS7H7GI2Ln8NWiXovY+VtkuJt60ZaNLczF2alQ5Z5QtHDYPSYeJL3qHhmYzsREqzWmAy7HTzqv3KEkA95X3jlUsCIr8Td6qpuA2niGtMVwEHJpFWDUMb43CEbzZi1iz6l1sWkuHARzv0dZgWG20y1YPb6GzMH3UofKcViVW3D9bLu7FQYiermPaAjeCB9QbqPRyE68S5ZNRGQ0FE9749HDS02gzuKGZjjnvPUqKRg7Da0RUGFlzoS1ad1wmOBn+mHx6MWDw3TXQYORmPBmo8Pflx3P9+JRjdpJkLCVDgnvcxC44Fg2QVo1OYAGknu7lAex7+BUohCls=AQAAAACAAABABgAAIwYAAA==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AQAAAACAAABABgAAFQYAAA==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AQAAAACAAABABgAANwYAAA==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AQAAAACAAABABgAALgYAAA==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AQAAAACAAABABgAANgYAAA==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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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AQAAAACAAADASwAAlQ8AAA==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AgAAAACAAACAOwAABRMAABMJAAA=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CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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==AQAAAACAAACgDwAA3AoAAA==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AQAAAACAAACgDwAAbAoAAA==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AQAAAACAAABoHAAA5wYAAA==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CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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">
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+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
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+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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 3.3
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/simulation0814_Kn0p38Ma3p3/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==AQAAAACAAACgDwAAEAcAAA==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AQAAAACAAACgDwAALgsAAA==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AQAAAACAAACgDwAAVQ0AAA==eJxFl3k81OsXx0WL9kKpJCUlqeQKkTrfuC3abslt1SrZRglptZVCKUqlpOsqhKRCUeSErMk2ltFYZjDG2GcsWYrf9zvfua/f88fMa2ae7zPPc855n8/nkd7q0NDk3gw+L/cX/PF1AGIidhHyJ/qg+2ucoolBPo5uaMz0CkxDa3fdCbLNeeDEJbbOXv0dMhWX7h33gg85Dms8k4lB8Lp4BI/rcyFTq68nSpWNfl3UAq147PGOWf7buGgTlKX+bGsgWkWaarT016BGx9T01U/K8JcZnF1uxkdv5jVFt3FM0JMWfnLPKEJ1bdm4+YcKoYMl+7VtiInzP6wsYARWwMf68GUl6o1wdirj4fFzLJi/5E2R9kI2dK3dtMVpENG4fUd4z7xcXLlUT6XqZgMo/XNU49sMEdRHz4rq0o2AxjMzXs9y+AeInSsPt93mYJpL0Jyx+ziY9CpQFGTHhf7SGM0wHg8ci3zL6jgCkIrZGVrTxQGtoUll/pvaoFz42KtgoBWGUvTM/Xu6YM1MPaPPZkKIFz5pD3v5HvNVphj9XMBHhRb5S/ddqnCebUyaMjcN41MvxZqcKUEj32caLkeL4avxa9u6c70w/MjgyGz5dgDzedqJxS/wn40mo2MV2cjXdfVrfc3FrFSLrxdONuDuJtMy3SAehHs/+eBhVQs3880zoiM4yJR/dJGo5uJyqRwdm5YK/EhtSz8DdV5+O3mypxXv30t8+1I5B/HaGstSlyYgk5vF1OyFo59Mp0hrjsA5pamclYFjiPNUGKb1w2y7nT1397aAARXO6Q2QPZJwRfT2BzAuXo5cYVIGtRupAPOh/+Qy9R9/56FywNxof492JB9emfCsG29M+B5/RfM5LLk8N9tpeyPc/Dvt3qUmHpwwN1P/128EyJe3XMFkYt6eH0pXTygTx3klquyPWkT8Fu5lhQ0bCVHZzpMz5uwhFKgvkiyICMf1k6r2niIsAjL7lhXaEOSnF47n7YmQJ+TIZxAqN9r2pExzIMxSFtxoEzLodeIZxFbqbTqDyOpbZhFw2Z4gn1o/iW9HnKbGGTt6/V5b4hy14G5bQoP6g2gbglpuwVQb+v9CrQlxWMMaMcq9zjwisxE/xranqg4gOL57Pp65mgW2Dcoc08n7cWnT9X++x5aiSfZaD/lJ2UhtZ09KBx5oGGO1N74ZVf2Obn5X0wYOzpaXM+XY8HQtVck1EOysEb98uAicbe51XAURnhlKVNFQZeILQfspxwedOHKc8/Gn43vcbHy4KDIlE81WJCfNk8sFY3HgqqDbgXqgFcQ/x5ZA7XvvqyWJTCThObWuvh5ru3aTpViNgyuKr6cmNgMYnuclGrHhsZbWlw2Gr/FdTZnd1hom/nnmplL3YDW8WUIdRAgZBou3Xbn+C6b99eaBla9QkjcOzDhgkJTn2gyfc1bt+azLgoflE21UGYXwbboFb6L3Ttz8qPBd2qu7EH32CdNKm4kmBX/Fuhmy8OG1M1q2/ulYsYGqmHKoyzMSTZBphEjtQ1v+lmcBtTtXNge2uzLGNpznQ/UTYxKB7/DmiqacfHk0KqhyZsSWlyBVvVZzeRgcXTePtTwbKy4KlmrLslCkJgjJ129DX8sjNZtl8nD1jqhJm5fUQ7IH1XHygMPutQazcnhx3lx3Yzkbx4nB5eBpU8+QQx94MPl91KVcjR4o7MzPMn4thHE34870hZZAlHDyG7VL8XA8r3VfsWku0n2vDmZnjO5LsGSDp5tLa4E1E06GV67pWtuAhrFgfaHjAl6LHpaK0GsDvv8ystP1wS3XWWbuZgIYt/d7XKRnCoxf3lhvbfED/jq4s0r+iA+2f6ECXgUUdYLknyAdIku2rvFEUMy1PabKc+h6P7icKP65tzGSbUCclbv+pYBrSkhRY+1+ut6DT9AcCE4TOtRYZ0fzMlXCwScG8b2QHBEMgpq+xVbCiQqDoMrdItme5meVPc3BQzuaizl29Hw/W3q9Dht6nqsNsXjGmzbF9hx8dXFxT55KDd7iPdpn0sDCWfpntEE/BddgiMxY5xK8HZUYlLGHj8euUBGvRDoPGchddun83TF8Sb/vwdVyP5xWZXOQcfhB9cKgL2gkp/UX/1YCrnBruTb0qAVuLFmwW49XDmut/7Q9210KlYEpuWU+P5COLw+tV4QU7RrfjP4KBZlVVbcgTPq40fWbJdDBpBp3B1hSbWsrE3J27yJbfhdMJNVlXGoFXv5VFwXD2Wic4Dmx5lMXUDS1p3ZBj7lPzRT9RnCzUFqeM1gKm2UD9O4eqMZwuSrt+JQk8GfbSJ31EgKVjvBNv8F7KgVMD4QF1emn97eCOJ1vmqDz2EFbs0kd2CI37XN21itcWs8gOwEfvPs8pvXwakHGZF/y5V0CmFvgR56EBS8WPC9V/tqEJKQksJX4utY1/OOub5AT9dB4YXAKWNXPIiswB8XytLsEEiePr6opTcQznGnlGgEJUDWkGr6SnwPqQ1cfzFSMRhL66Z1FRRI9+IHkpsdmB/DQeKKOw2hBAXr0L9506gAHs7wCVxyX7oLdv+Y4V8n1gLjOwpnwdqT4RHoaCzu89pEoZGJD4ZtmqYw8SCKzKmpNQ4PQwYr5C7mwfP/F4NsDlbBr03L/lWNLMbfXm8xYOUY9MjNidjvBSfbzwvFPWrF5HGvLy3guPhO6nYvZ9hTe6w9knorthcPMtEYFZRGY52BxiiMXyhp/6v2uboPKaB8blwlt8Gxm5OcMxVaQ7APouvkFLpsf9I+vn04UHtmeN+HZIuJDz+/yi7Y6RGPk068LHU0IqmzXy5nRHJkeofXg7Cm6zu9IdGWcPV3nByU8eDBojiwZRDs57cY6Bq0Lv+xp3TlqT/PRYkevc96O5pFlK+HLlpB9sPNjZmoeWIob1iegdYGHYrns6kVW32TXrA9NWOlcNpxTxYUD2UpPtozwQCzTVgK0bfR9P6DwAyZtN/qXUdAA00N1nifpFwLhMyiSbq9Ffb+NpBLwsYJvp9kfTOpB1GFXX4+3MNHBILghlgsnfodlXb3UDGSRkREXQJwBsXpLABe8TKLNEic0o3rkv+zt8xpwTITeOSdkYpGjzAbwbwDSxKwy6ORI9KQbaH/UCblCk8iZOskgbo+97uCpWf1upq8Aehn73OI/dIJY1v/ohi8yMrIq25JQ1ml35g42ou8qShEq0PKxfFI+NxGNF0lPC99WAf/pjq7YkPRCcNwtx6A9kTDnqKnIoj4Zt01oG53fLAC9oLMfPmoLIN1n+911kR0QsmSMnV11M0yc7aODIaU4SzmfBxFDKKcAOTXvB1DcHm7w8J66adWO9AoU52lZCabjxit9whQU20nTAigzjA1Z2NoG58UNVwQDd96sT1QTQOjstBbNrQ3g3W1DZqAGaF4agNYdDm7qxoftrDrwv8YRdhxJhEdjo2Os7CORinpCVytq1aqN93o4gqOUfdo9hFR5Hnvcj0VzqQiJ8P2s4W+d+bmoRlKXrcST+LFuuG8xYjLUMQwBe13Ugkd/gWXS3dilvmWw/oTfhAc72ThKYSjFh+IZdw/2qwVKOBXBfzx+Nsz1bO8rQ3G/z8xAJmmnDhhUgtCSIuMnLHrrZHPPQYYo9NiKbrWKhKbP7Oqi5GVE6NeFb51a1hHKlG3Q2k7cZvCHYwQSXXkp0ZV3p2kOlCS6ki3xVfoSHQn4Py9iDlZL/Fm6Pc2NkT39e6od7bvW2hH0eYrhPsy52Lm+DLaRLvfsBy5Eqa8QeiY0ge7mtmi7a+VA+9d2HLH62XQivA2lkzubY6axwXdm5os7i6OQ5qIGZR4Ku3RWNQEVvUB3EdxzHzZ0LGqBOaf3z/hY7IkDjvnnXro0Q9IG3uMVnYlgHlMt8/pLIVjp5aktti5Fj+r1lw5EVyBp9pnV+Y0w1NFGKnM3KIa0rSkzY6Nn+dJdnRea8OLJytvhoTX4KGHKubjbiVhSsa9dK7UCxNsY6IPBf60TW51/Q5jRivKQ5n6JvxPCS9JlGN7PgmELB9JaV8FUsXBWoMXCjWSKhJhBqsiHHpFkPRHOXu11+EJPIR6a6neF1EAgzRbpxBvB24tfl7VEAC5iI5eDlCu5b9iBismlUnXS3agzePqpEpON4eYKcrXnjMGkYcF2lU8dYO92r+SRMxv4hneSzOs6UeyDFdqxddE80tqnIF1OA0BuWvmljRQhLnvtn6A7RWXmXtVGAJeNbt9Ci2Dqb3mLuOjvMFJOGbByUL6hW192xQW0r/65w0+NC/mu6WHztARIn79Ucj/owYhMlnHg9WZkeTmTSJSAGIsdn2HPzzHkTbQcqNOa+wgw/+0t/T8C+Ei5i5sO9fj077BXr1nFePMQJRAtKL6urCxB95wbMosOfsGFYmGuBPr+J0BWd++2g+T9b4kYbAHWPHvPXrUmF2s0lONGnblgSNlbrT7Y0FIdN2TRA/Zhd3OnmJdIePoFVPfIN5chaN+rQIj7a7IqEUXKyVvRH4S4j681IUpV2YPuxma0Xhw7Qte73in63nLFhtYHlkQXoiUcqDHo+s+T8HPBXnJ/kfisg//NtyX+B/8NFTg=AQAAAACAAACgDwAAug0AAA==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AQAAAACAAACgDwAATgkAAA==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AQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAACgDwAAGwAAAA==eJztwTEBAAAAwqD1T20MH6AAAACAtwEPoAABAQAAAACAAABoHAAA5wYAAA==eJxF1Hto1XUYx/EldmNZDMTKiopCCLLbGGV4dE2iWjAqWuLCkpL9UWSXRdiFhq2lbDblRIYVEf1TEVTIsJKmdqGCiiFi0QVMIs4fhRQJNsLI9+D9eQ4ceP33g+f7PO+2ttf3nvbZ1u6247/7/3w1/u6X7fF1+7bF73zyQrxgx9Z4wxvPx380x+KVIxvjT4eejRev3RBf/vZT8dyxx+If73sofv/m++LnLl0b3znvrvjKwyvjk6dvjX9+7+Z4x9br400PL49X37Yk7uzsjE+dvzg+eGRRPHnggnhs58J4zUvz4671p8ftq06JDy2ZEz/Q9c8efdbR3+PPPzwYP/jE/vicpV/GXxzbFT+y5934vA1vxF/1bIsfnTsWL9v8ZPziGffGfzR74xULropf3n52/Oe5J8QT3ft266U3vB7vf+Wu3TXbmSl9y+SBj2s+Ew2957JLltUbjcZzx3bGK0dacf+y05br/3ZdHL95dWN5fas/nrliXbzvjpF40Ytb4if2vxJPd7wVX3zLZLx+Ym/87TffxBe2/xA/dtNv8dcb/4rP/+JYfO2cE7v12gvbu2ueHfFHd58Z//r0efG81y6Kr5m6JL7n58vjzf92xR8sXBofWtITt6+6Me5a3xeveen2eGznQDx5YE188MhgfOr8B+LOzqF49W2Px5seHo5XffVMfPj70XiktSk+6+h4/O5JW+IVC5px2/F2NaZfmDXt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0656u96YdmnapWmXpl2admnaVbOdmdK0q+Yz0dC0q95oNKZdtUutmHZp2qVpV32rP6ZdmnZp2qVpl6ZdmnZp2qVpl6ZdmnZp2qVpl6ZdmnbVPDti2qVpl6ZdmnZp2qVpl6ZdtXs9Me2qt+iLaVft9kBMu+oWBmPaVbczFNOuurXhmHZp2qVpV93CeEy7aiebMe1a99O2WdMuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07aq3641pl6ZdmnZp2qVpl6ZdNduZKU27aj4TDU276o1GY9pVu9SKaZemXZp21bf6Y9qlaZemXZp2adqlaZemXZp2adqlaZemXZp2adqlaVfNsyOmXZp2adqlaZemXZp2adpVu9cT0656i76YdtVuD8S0q25hMKZddTtDMe2qWxuOaZemXZp21S2Mx7SrdrIZ067XWttnTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0S9MuTbs07dK0q96uN6ZdmnZp2qVpl6ZdmnbVbGemNO2q+Uw0NO2qNxqNaVftUiumXZp2adpV3+qPaZemXZp2adqlaZemXZp2adqlaZemXZp2adqlaZemXTXPjph2adqlaZemXZp2adqlaVftXk9Mu+ot+mLaVbs9ENOuuoXBmHbV7QzFtKtubTimXZp2adpVtzAe067ayWZMu6b/fnXWtEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk27NO3StEvTLk276u16Y9qlaZemXZp2adqlaVfNdmZK066az0RD0656o9GYdtUutWLapWmXpl31rf6YdmnapWmXpl2admnapWmXpl2admnapWmXpl2admnaVfPsiGmXpl2admnapWmXpl2adtXu9cS0q96iL6ZdtdsDMe2qWxiMaVfdzlBMu+rWhmPapWmXpl11C+Mx7aqdbMa06/h/1rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5NuzTt0rRL0y5Nu+rtemPapWmXpl2admnapWlXzXZmStOums9EQ9OueqPRmHbVLrVi2qVpl6Zd9a3+mHZp2qVpl6ZdmnZp2qVpl6ZdmnZp2qVpl6ZdmnZp2lXz7Ihpl6ZdmnZp2qVpl6ZdmnbV7vXEtKveoi+mXbXbAzHtqlsYjGlX3c5QTLvq1oZj2qVpl6ZddQvjMe2qnWzG/wNxdAkZAQAAAACAAACAPgAAxwoAAA==eJxd21UTAEQZRmF26e7u7u7u7m5QUUBAQEGQ7g6DxgADDEJJk7IRpEUJKRNUFGkU8MJ9Lr53b96ZM+cPnG9mJ5jg/6+N3X/sR4L3sQeEh0849sDw8InGfjQ8fOKxB4WHTzL24PDwScceEh4+2diPhYdPPvbQ8PApxh4WHj7l2MPDw6ca+/Hw8KnHfiI8fJqxR4SHTzv2yPDw6cZ+Mjx8+rFHhYfPMPbo8PAZx34qPHymsceEh8889tjw8FnGHhcePuvY48PDZxt7Qnj47GNPDA+fY+xJ4eFzjj05PHyusaeEh8899tTw8HnGnhYePu/Y08PD5xt7Rnj4/GPPDA9fYOxZ4eELjj07PHyhseeEhy889tzw8EXGnhcevujY88PDFxt7QXj44mMvDA9fYuynw8OXHPuZ8PClxn42PHzpsZ8LD19m7EXh4cuOvTg8fLmxl4SHLz/20vDwFcZeFh6+4tjLw8NXGntFePjKY68MD19l7OfDw1cd+4Xw8NXGfjE8fPWxXwoPX2PsVeHha469Ojx8rbFfDg9fe+xXwsPXGfvV8PB1x34tPHy9sdeEh68/9trw8A3Gfj08fMOx3wgP32jsN8PDNx77rfDwTcZeFx6+6djrw8M3G3tDePjmY28MD99i7LfDw7cc+53w8K3G3hQevvXYm8PDtxl7S3j4tmNvDQ/fbuxt4eHbj709PHyHsd8ND99x7PfCw3ca+/3w8J3H/iA8fJexPwwP33Xsj8LDdxt7R3j47mPvDA/fY+xd4eF7jr07PHyvsfeEh+899sfh4fuM/Ul4+L5jfxoe/oGxPwsP/+DYn4eHf2jsL8LD9xv7y/DwD4+9NzydoSfuG/ur4Hri/vBwPfHr8HA98UB4uJ54MDxcTzwUHq4nHg4P1xOPhIfriUfDw/XEY+HheuI34eF64vHwcD3x2/BwPfG78HA98UR4uJ54MjxcTzwVHq4nng4P1xO/Dw/XE8+Eh+uJZ8PD9cRz4eF64vnwcD3xQni4nvhDeLie+GN4+Mlj/xQerif+HB6uJ/4SHq4n/hoerideDA/XEy+Fh+uJv4WH64m/h4friX+Eh+uJl8PD9cQ/w8P1xL/Cw/XEK+HheuLf4eF64tXwcD3xWni4nng9PFxPvBEerifeDA/XE2+Fh+uJt8PD9cQ74eF64j/h4Xriv+HheuLd8HA98V54uJ54PzxcTziIvR9cT7RWPVxP9FY9XE9M2KqH64mJWvXwq8dO3KqH64lJWvVwPTFpqx6uJyZr1cP1xOSteriemKJVD9cTU7bq4XpiqlY9XE9M3aqH64lpWvVwPTFtqx6uJ6Zr1cP1xPSteriemKFVD9cTM7bq4XpiplY9XE/M3KqH64lZWvVwPTFrqx6uJ2Zr1cP1xOyteriemKNVD9cTc7bq4XpirlY9XE/M3aqH64l5WvVwPTFvqx6uJ+Zr1cP1xPyterieWKBVD9cTC7bq4XpioVY9/O6xC7fq4XpikVY9XE8s2qqH64nFWvVwPbF4qx6uJ5Zo1cP1xJKterieWKpVD9cTS7fq4bpjmVY9naEnlht82Va5nli+VQ/XEyu06uF6YsVWPVxPrNSqh+uJlVv1cD2xSqseridWbdXD9cRqrXq4nli9VQ/XE2u06uF6Ys1WPVxPrNWqh+uJtVv1cD2xTqserifWbdXD9cR6rXq4nli/VQ/XExu06uF6YsNWPVxPbNSqhz83duNWPfz5sZu06uF6YtNWPVxPbNaqh+uJzVv1cD2xRaserie2bNXD9cRWrXq4nti6VQ/XE9u06uF6YttWPVxPbNeqh+uJ7Vv1cD2xQ6serid2bNXD9cROrXq4nti5VQ/XE7u06uF6YtdWPVxP7Naqh+uJ3Vv1cD2xR6serif2bNXD9cRerXq4nti7VQ/XE/u06uF6Yt9WPVxPfKBVD9cTH2zVw/XEh1r1cD2xX6seric+3KqH64mPtOrhemL/8HA9cUB4uJ44MDxcT3w0PFxPHBQericODg/XE4eEh+uJj4WH64lDw8P1xGHh4Xri8PBwPfHx8HA98YnwcD1xRHi4njgyPFxPfDI8XE8cFR6uJ44OD9cTnwoP1xPHhIfriWPDw/XEceHheuL48HA9cUJ4uJ44MTxcT5wUHq4nTg4P1xOnhIfriVPDw/XEaeHheuL08HA9cUZ4uJ44MzxcT5wVHq4nzg4P1xPnhIfriXPDw/XEeeHheuL88HA9cUF4uJ64MDxcT3w6PFxPfCY8XE98NjxcT3wuPFx3XBSeztATl4y9OLieuDQ8XE9cFh6uJy4PD9cTV4SH64krw8P1xOfDw/XEF8LD9cQXw8P1xJfCw/XEVeHheuLq8HA98eXwcD3xlfBwPfHV8HA98bXwcD1xTXi4nrg2PFxPfD08XE98IzxcT3wzPFxPfCs8XE9cFx6uJ64PD9cTN4SH64kbw8P1xLfDw/XEd8LD9cRN4eF64ubwcD1xS3i4nrg1PFxP3BYeriduDw/XE98ND9cT3wsP1xPfDw/XEz8ID9cTPwwP1xM/Cg/XE3eEh+uJO8PD9cRd4eF64u7wcD1xT3i4nvhxeLie+El4uJ74aXi4nvhZeLie+Hl4uJ74RXi4nvhleLieuDc8XE/8KjxcT9wXHq4n7g8P1xO/Dg/XEw+Eh+uJB8PD9cRD4eF64uHwcD3xSHi4nng0PFxPPBYerid+Ex6uJx4PD9cTvw0P1xO/Cw/XE0+Eh+uJJ8PD9cRT4eF64unwcD3x+/BwPfFMeLieeDY8XE88Fx6uJ54PD9cTL4SH64k/hIfriT+Gh+uJP4WH64k/h4frib+Eh+uJv4aH64kXw8P1xEvh4Xrib+HheuLv4eF64h/h4Xri5fBwPfHP8HA98a/wcD3xSni4nvh3eLieeDU8XE+8Fh6uJ14PD9cTb4SH64k3w8N1x1vh6Qw98c7Yt4Prif+Eh+uJ/4aH64l3w8P1xHvh4Xri/fBwPeFDyvvB9UTr1cP1RO/Vw/XEhL16uJ6YqFcP1xMT9+rhemKSXj1cT0zaq4fricl69XA9MXmvHq4npujVw/XElL16uJ6YqlcP1xNT9+rhemKaXj1cT0zbq4friel69XA9MX2vHq4nZujVw/XEjL16uJ6YqVcP1xMz9+rhemKWXj1cT8zaq4fridl69XA9MXuvHq4n5ujVw/XEnL16uJ6Yq1cP1xNz9+rhemKeXj1cT8zbq4frifl69XA9MX+vHq4nFujVw/XEgr16uJ5YqFcP1xML9+rhemKRXj1cTyzaq4fricV69XA9sXivHq4nlujVw/XEkr16uJ5YqlcP1xNL9+rhemKZXj1cTyzbq4frieV69XA9sXyvHq4nVujVw/XEir16uJ5YqVcP1xMr9+rhemKVXj1cT6zaq4fridV69XA9sXqvHq4n1ujVw/XEmr16uJ5Yq1cP1xNr9+rhemKdXj1cT6zbq4frifV69XA9sX6vHq4nNujVw/XEhr16uJ7YqFcP1xMb9+rhemKTXj1cT2zaq4fric169XA9sXmvHq4ntujVw/XElr16uJ7YqlcP1xNb9+rhemKbXj1cT2zbq4frie169XA9sX2vHq4ndujVw/XEjr16uJ7YqVcP1xM79+rhemKXXj1cT+zaq4frid169XA9sXuvHq4n9ujVw/XEnr16uJ7Yq1cP1x179+r9Dyrg49M=AQAAAACAAACgDwAAyAIAAA==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AQAAAACAAAD0AQAADgAAAA==eJzj5BwFIw0AADZFEZU=
34
+ </AppendedData>
35
+ </VTKFile>
CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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
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CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/Grid_3_300000.vtu ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
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+ <UnstructuredGrid>
4
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30
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31
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+ </AppendedData>
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+ </VTKFile>
CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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 3.3
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 3.3*${sos}
38
+ variable vo equal 3.3*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 1015.79136198765*cos(${theta}*2*PI/360)
41
+ variable vx equal 1015.79136198765*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 1015.79136198765*sin(${theta}*2*PI/360)
44
+ variable vy equal 1015.79136198765*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${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}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/1015.79136198765/${xncells}/10/4
69
+ variable tstep equal (--5+6)/1015.79136198765/115.789473684211/10/4
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 2.33807855517959e-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 = 0.159012 secs
101
+ create/ghost percent = 0.71553 99.2845
102
+ balance_grid rcb cell
103
+ Balance grid migrated 53160 cells
104
+ CPU time = 0.085816 secs
105
+ reassign/sort/migrate/ghost percent = 60.8986 0.107391 11.0514 27.9427
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 1015.79136198765 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 1015.79136198765 0 0.0 temp ${To}
111
+ mixture air Ar vstream 1015.79136198765 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 = 0.0949922 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 24.6906 23.1016 2.68513 23.9717 25.5509 7.2779 0.174182
126
+ surf2grid time = 0.0227712 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 13.1976 24.169 4.18138 1.95855 11.7577 6.86154
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 = 0.0776216 secs
134
+ reassign/sort/migrate/ghost percent = 5.0702 0.0532829 10.7588 84.1177
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 = 0.0200551 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 12.010648 401432 7849 6691 13 383090.06 10.5827
150
+ 20000 24.713169 402283 7951 6767 8 392680.72 10.62665
151
+ 30000 37.405937 402318 7883 6706 11 395941.63 10.6432
152
+ 40000 50.515468 402854 7909 6781 10 397706.21 10.67575
153
+ 50000 63.828157 402445 7881 6727 14 398825.33 10.68084
154
+ 60000 76.470713 402450 7783 6603 10 399472.11 10.6847
155
+ 70000 88.643712 403197 7915 6726 10 399962.81 10.695857
156
+ 80000 102.29086 402615 7963 6800 10 400358.26 10.703738
157
+ 90000 115.24922 402704 7895 6674 6 400598.78 10.702711
158
+ 100000 128.34055 402848 7878 6687 7 400844.14 10.70813
159
+ 110000 141.2004 403082 8047 6830 10 401050.27 10.711145
160
+ 120000 153.92001 402295 7788 6643 10 401206.96 10.710275
161
+ 130000 166.98463 402546 7926 6728 6 401297.48 10.708777
162
+ 140000 180.13964 402756 7843 6651 19 401377.49 10.712271
163
+ 150000 193.06732 402605 7920 6729 13 401492.21 10.716513
164
+ 160000 205.76852 402079 7888 6671 11 401568.93 10.716794
165
+ 170000 218.34306 402406 7829 6668 10 401625.26 10.718218
166
+ 180000 231.4245 402160 7892 6659 12 401700.75 10.720594
167
+ 190000 243.77728 402689 7899 6764 7 401752.11 10.717079
168
+ 200000 256.9458 402932 7824 6677 11 401808.03 10.71482
169
+ Loop time of 256.946 on 128 procs for 200000 steps with 402932 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.022248 | 9.8926 | 56.289 | 401.8 | 3.85
175
+ Coll | 0.27757 | 9.5159 | 67.314 | 409.9 | 3.70
176
+ Sort | 0.095334 | 1.631 | 9.7472 | 167.4 | 0.63
177
+ Comm | 72.715 | 111.86 | 126.41 | 107.3 | 43.54
178
+ Modify | 0.018542 | 0.11129 | 2.085 | 91.7 | 0.04
179
+ Output | 0.0059497 | 0.010763 | 0.013945 | 0.9 | 0.00
180
+ Other | | 123.9 | | | 48.23
181
+
182
+ Particle moves = 80361606488 (80.4B)
183
+ Cells touched = 84127903318 (84.1B)
184
+ Particle comms = 192246466 (192M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 19003071 (19M)
187
+ SurfColl checks = 106626270 (107M)
188
+ SurfColl occurs = 2142964 (2.14M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 1747753527 (1.75B)
191
+ Collide occurs = 1360550908 (1.36B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.44341e+06
197
+ Particle-moves/step: 401808
198
+ Cell-touches/particle/step: 1.04687
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00239227
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.00023647
203
+ Surface-checks/particle/step: 0.00132683
204
+ Surface-collisions/particle/step: 2.66665e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0217486
207
+ Collisions/particle/step: 0.0169304
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 3147.91 ave 19361 max 0 min
211
+ Histogram: 84 14 4 8 4 4 6 0 0 4
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) = 1.66113 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) = 4.70511 3.36171 6.17471
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 402932 7824 6677 0 0 0
251
+ 210000 21.429352 403456 7887 6715 7 402921.8 10.7775
252
+ 220000 42.676165 402207 7902 6792 11 402662.78 10.73555
253
+ 230000 63.988755 402454 8023 6826 10 402622.64 10.734067
254
+ 240000 85.209571 402561 7925 6755 17 402593.51 10.740625
255
+ 250000 106.49203 402693 7795 6657 7 402662.17 10.74552
256
+ 260000 127.71517 402729 7933 6751 4 402745.31 10.745933
257
+ 270000 149.28861 402917 7868 6711 20 402787.5 10.750143
258
+ 280000 169.98534 402790 7946 6704 7 402881.04 10.748062
259
+ 290000 191.08182 402248 7911 6699 3 402854.12 10.741856
260
+ 300000 212.65152 402716 7866 6659 9 402812.59 10.74289
261
+ Loop time of 212.652 on 128 procs for 100000 steps with 402716 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.011778 | 4.9796 | 28.416 | 284.1 | 2.34
267
+ Coll | 0.13849 | 4.7908 | 34.377 | 292.2 | 2.25
268
+ Sort | 0.051251 | 0.82593 | 4.9547 | 118.4 | 0.39
269
+ Comm | 41.543 | 61.307 | 68.877 | 74.0 | 28.83
270
+ Modify | 1.9347 | 13.763 | 75.137 | 440.0 | 6.47
271
+ Output | 0.045264 | 0.047129 | 0.051576 | 0.6 | 0.02
272
+ Other | | 126.9 | | | 59.69
273
+
274
+ Particle moves = 40281258888 (40.3B)
275
+ Cells touched = 42168130158 (42.2B)
276
+ Particle comms = 96316398 (96.3M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 9522075 (9.52M)
279
+ SurfColl checks = 53530847 (53.5M)
280
+ SurfColl occurs = 1074289 (1.07M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 879583767 (880M)
283
+ Collide occurs = 684672059 (685M)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.47987e+06
289
+ Particle-moves/step: 402813
290
+ Cell-touches/particle/step: 1.04684
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.0023911
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.00023639
295
+ Surface-checks/particle/step: 0.00132893
296
+ Surface-collisions/particle/step: 2.66697e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0218361
299
+ Collisions/particle/step: 0.0169973
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 3146.22 ave 19043 max 0 min
303
+ Histogram: 84 14 4 7 5 5 5 0 0 4
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 = 0.00812062 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00311593 secs
CylinderKnMa/Kn0p38/simulation0814_Kn0p38Ma3p3/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/simulation0814_Kn0p38Ma3p3/output/grid.300000.dat ADDED
The diff for this file is too large to render. See raw diff