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  1. .gitattributes +8 -0
  2. CylinderKnMa/Kn0p28/simulation0566_Kn0p28Ma3p5/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p28/simulation0567_Kn0p28Ma3p6/output/grid.300000.dat +3 -0
  4. CylinderKnMa/Kn0p28/simulation0568_Kn0p28Ma3p7/output/grid.300000.dat +3 -0
  5. CylinderKnMa/Kn0p28/simulation0569_Kn0p28Ma3p8/output/grid.300000.dat +3 -0
  6. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/output/grid.300000.dat +3 -0
  7. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/output/grid.300000.dat +3 -0
  8. CylinderKnMa/Kn0p28/simulation0572_Kn0p28Ma4p1/output/grid.300000.dat +3 -0
  9. CylinderKnMa/Kn0p28/simulation0573_Kn0p28Ma4p2/output/grid.300000.dat +3 -0
  10. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/Lambda/Lambda_300000.pvtu +21 -0
  11. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/Lambda/Lambda_3_300000.vtu +27 -0
  12. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/output/lambdakn.300000.dat +0 -0
  13. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/output/surf.300000.dat +209 -0
  14. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/surf/Surf_300000.vtu +38 -0
  15. CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/surf/geom.surf +610 -0
  16. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Grid.pvd +9 -0
  17. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda.pvd +9 -0
  18. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda/Lambda_0_300000.vtu +0 -0
  19. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda/Lambda_1_300000.vtu +0 -0
  20. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda/Lambda_2_300000.vtu +0 -0
  21. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda/Lambda_300000.pvtu +21 -0
  22. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Lambda/Lambda_3_300000.vtu +27 -0
  23. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Surf.pvd +9 -0
  24. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/config.txt +78 -0
  25. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/cylinder.surf +410 -0
  26. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/gas.species +14 -0
  27. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/gas.vhs +9 -0
  28. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_0_300000.vtu +0 -0
  29. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_1_300000.vtu +0 -0
  30. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_2_300000.vtu +0 -0
  31. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_300000.pvtu +29 -0
  32. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_3_300000.vtu +0 -0
  33. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/final-grid.dat +0 -0
  34. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/log.sparta +318 -0
  35. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/mir.txt +3 -0
  36. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/output/lambdakn.300000.dat +0 -0
  37. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/output/surf.300000.dat +209 -0
  38. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/runmpirun128.sh +18 -0
  39. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/runmpirun64.sh +18 -0
  40. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/slurm-28429450.out +204 -0
  41. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/surf/Surf_300000.vtu +38 -0
  42. CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/surf/geom.surf +610 -0
  43. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Grid.pvd +9 -0
  44. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda.pvd +9 -0
  45. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda/Lambda_0_300000.vtu +0 -0
  46. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda/Lambda_1_300000.vtu +0 -0
  47. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda/Lambda_2_300000.vtu +0 -0
  48. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda/Lambda_300000.pvtu +21 -0
  49. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Lambda/Lambda_3_300000.vtu +27 -0
  50. CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Surf.pvd +9 -0
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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAAYScAAA==eJxdWnlYjd/3RQmVJqKJBlNSKUmoLMpQSSiKSJIGIWUqY0LqzvPQXCSKipQ5JWRIyJQQRUghZArV77z3o+73+f3jPO99rrdz99l77bXWPvWX1FJsDgox3FOVG9pHhGs3+FPEUSJw92kFP+0UwXLxGsbanWKIpSGj8n6J8WHwMaWAcAna43z4DjUS1I8Pnus9S4qw/buzV5yRgg761E3qibhmoDgsLywRJawq9qKXiVifs+/icPckRG7pddWvJAm3zdYpnTVKhl+41cOAuGT89B45+mhDMtZFbz9s7pwCu176Gsb5KVgzOSj8kmYqlGqeV3dtTIWny4RjUWdToftug5NxnzQEeO7c6bQ1DRf7NV1KqU1DLeYrKLmmwwCL9KxPpSPzWxy3rTMdaxNVr7fpZmCR/ZWH4qkZ+HNnvNs9/wyIzMeQr2Qg/7Pl0C+pGZirmBujcTMDxXpaP5f8zsCc/GTPFeMyscdNZLpjbiYsqvI0vAMzUeIpNmZvycSK9CktXuxM/Cks8tp0LhNnjSvWrniUicZ4txHBPzIh3hpmaadzEDrOU6rznQ7Cx3d2s6NIAJfnge+dhwoh6GibuJIrxCuf4w0mP4WYO3x+pfpyEV4OleQWXROh+Hdz7nQLMXoZ7PixPFuMcI9RCtO1JXh6a27nmBgJLHkqd+pbJdg/cH6BMEgK5R/+77KrpBgaorpp76REBCwu+eshSMQtDxxlDE5CeWl/PRtaEq486q0R9T0J1Tc9WzpmJyODrn9Gmp6M4Qszz5/7lYxLh1PG/1iRgrt7F7y4Xp2C80Hxv+ItUlHSOXJa1N5UTNth6SWoT8UEBdGXVps0eM2Y9ahgcxqo06o7lYaXf/SLfAzTcdfN98vAuHRsWWYyNOVyOuo+vZk2RTkDl302t9g6ZmD8EcWrI5dnYB891//9zgwspTeOixVm4NOOh/4FJzKQ9WdK3/baDIzQihNv6coASbqfDUaZCHXQvLptciacqrhmdJ9MuKyLUr+3KxNNdUsW6Agz8c52K605LxPp3tWGSdv4aI4KWHv6Kx83dk7PsFkjgJNymlP+QwFy5qU8b10gREn9g5SLN4QoXpiqttpRBGGtkYCZIkIv7znszUok7glfkny2iDGxZk3W4a9iPG5oMqvzk0CrdNXAoeck4NuvM7EZKsXPG29zt7GlWNfsYNbZKsXH4NnQ80jEjm8H/tzIS0SGx7bNI8yTwNEbtMoyR14H8eE/+t1anwzawyLGmuxkHOWUGe7uTIZryMrGoHkpKJiZ8bsfLwVpK7u2hD9NgX3X1toC+1R4x/qqDs9OReecbOcj71Kh3TD60C2rNKy2fzmfJZHXRaFW2/Es7XSklLv11V6cjmhZONKRm1g8mPsjHVHudSI/qwwUpvnu2rI4A3sejvb4FJWBcxFGI1WkGViTc3FYQ1EGVmlZNmo+z8DiiY877/zIwBYcMYJ+JhpObYk4YZeJ7brXNs49xoOKH236d3U+zpVfWH41ig/a6cDm/A985NVtzTznIYB68XqdvpcF0LYLt4adEJ5zrj+6kS/E0SvKZ3aOFCHk809GLV8EXY7ngPlNItzM7W+nu1gM609K1b7VYjT9Co2ssJKA7aCxNIsmQYpVwMRxzyRIGCT1aJkkxbLpoY47E6X4YbqcU/5diqUkK9ZMTMSG4G+HGoSJOJ1mbHDqQyISXOZdkAYnQZepSXOqTMKfmzqGweOT8XZbRNTj8GQU7i+23lqejL03TOZdVEgBW+nzEJcFKVj2X4Jg/OUc9ZGtKQieYHA3ZHoqWr0WhxqJU/FyQ9HrAy9T8XbzUvd109Lw+e9BxY64NMwaZLpK80oaPJIj9Q4qp6PP5/k5WkHpcFL6+uFrXjqGmVAVnoFc188HJpplYJmfx4AEV4JLNzdquRG8utt2hNW8OQO2226FnU/LgMdHVa9lzVy4STpH5bjzMJOx7t2fHB4+7JfUP+zHR86fXlmTIvnIdzP5Y1TJx90LJzOsTAWQsO60j2ULEBK7NX6wghC/t18KPrdOiHT7y50tz4XYN949W3m2CBJOVlZ8pgh1MynkE6P20/CMPxvFuJOzutfRV2JkMc/2mugmQcjXqkl22RI0j6R2LEX8lftlnDApRhWqF5pLpQicNTA74JcUTb+nGYycmQja/Rf1KgSXatrPVO6uS4S7vtpcBSShZcOAMhxOwhTJq9zC1iSc8QvNf+CSjIFHblfZHSQ4td62hdmajOIFE6YU26QgaSZVuSlYH8m6u7orBXnx4wfNckmF/SvuwCxGKkgRJig/SsXSeTWD/IamQWmPWfjzVWnQDxxzUfFIGqQ/qjoevU4DAVWSiekgRfk0fG86vlx4Lx72Kh2H65oCpxllICByctuxKRlo6TJ4W6nChbig8nN6OBc332nPd7zPRdyKGK10ex5WWcXE3RbxoGjfqaLYm48dWk9S9oXwMab8wBGfW3wsfDKrWsFQgEjBwiCdKAFqDkU4KtcIcP1r/vYyByGWTtrkEpYpxMGRI+9P6hRi7/Ej3v5+IsQT9HA9I0Jv2ugFV03E+GTcsCNmH1kV/xRseiDGLhkASbBSweSq6JQEVX0+qo1VkkIlUGWGmpsUaWM3rwiUSPH8YqSn9KcUry8PubDLOxGZF0beoR9NxOOcr44jNJNg86rPPVO/JLTxhN+sikj8t3tsf/krCSpq40mpJCPdx+G7y75kLB++drbSuWSM3mTemtsvBePK580WBaRgrMkVsxc3UpDse/rx0AGpKFMMr6vySsVOl18vXmakQva156l4+rbi2/5Radjq6U9aZBomx4mUzXLTkLbqkdHtj2n4Gu27u96M1IVf1oIhl9iIVUqeyqFzoPxEbfaHQVyUmn98uJzE37SNc8n1ARdNB41fLzTjYZehmtbBnTwkPJ3E3FvPg+bfDaPsJ/Gx7uSzEFEiH3s0JwZrfecj/TZ+j54nQIQzs+ZirgC94xccDlYX4uqHoGdr1grxSDz1RstlIdQuWSWa6YigHGpCqIkISlqDUfFcBJUAW/GJSWI43T+y+Fu8GEnVnANeb8XYZ22yMHSpBKUUrSqS4MXNI9dPq0rxqB8/So30546QKk5pkRQTZUQlEdZKjyP/LkhEuew4EjGnq7I4tHcSBilrRvRfnATmas4Cm6wkfDebMNVoQDL44lvPND2SoXC3JH4uOxnj/uq+OH5Zjk+HL3o7RbmmYIZp6+tQdgr4fd3rH9elILFGM36dUSoiSbYdiiD8SdFbzaIwFRTqqLekovIRZ97XEWlojbCfaD6bBUIK7MN/saDj98Ig/xEbk6L9RSoiDhZamNBv6nNR4RE9wGMXF5lh38L2kPzfcSDiduEYHtTm1b7vt4WHpJFi4YW7PMhg2JSP3jp3MzJj+Pj6/qSFwh0+ZLRlqACy9rVLgHWOzpODawVYHPZj+jgrIdp/6g5V2y+Ex4zdkzbWCbEplPdxJ0SojT5xLTxdBOtoqnJF6Nhbd2L9QjHGzTci1FSMvmtT5s3pRfBIvaiPs7cE/d95XTc7LIHGh1/jNRSl/3gf4UcPz9x+XyjFoWhR8bwvUsRWTYkeTfrDmzGPpg3jJMIkoS2iz4NE3KxMWlI/Lgkb1J2mX9qfhEn5MTUhd5LwVuzt/Gp4MgLNOvafWpYMzpWVncqJybDt1/9t05dkHGhc/cp7Rgqi9oXmhW5KwTCh1YMfBSmYaHFygl57CrQn1yp9Iv3iae8abZtyBsIOa9wYxWYix6MfacEszL4ndX2owkZ1v8on96rYWEaFaRMHZbejPg/qx8WglUs6dy7jYrb04HK9Ui4WMdvsMjV4mL3i8dU+/jwoqYW+Vz7NQ9PcDTGWenw8bhx77P12Poy9Y/YHlPHxqq9mzhoS/1sTrp60DBfARzl2+asbAnw4sJ4xQV+Iw4M373ANFKJqc4Hgd6EQZnmlo3p1CZG4Sm0v21uE/bNmpXgUilComVDzXUWMt7e3xk1bIsae4rCa/UVi5MfmaZmrSaBb+W3u1WUSrDm6kDB3CWr9MrLK+kjBLtxtY+0nxYofLR5RaVJw/ni3XH0jRcLFN83XRydCcP3BVwWfRDjOPGSfkJqIUN9VQRO/JuJl5/KJqx2S8N92knD21qD+9teSQH7MA/efSfjUUjZlhFsyJj+NvNJKeNSB3nPzV5Hz0Mw2lVSMT8FgI+Vg98t0vI85N8RiOgOJc3NPJ9czcMeA72uayAQB9xMcDxa6tsf1/akgj/+05gKfJREcuKlIFj9R5eKVYz/xiNVc6Ad1BK86z4Xn4w2js1V4yOhw0dT15mHUqe3xzzJ5EIbefL2mnfTlhRnzG1352BDiv42bJuejD7KoQBP8N6c6hQB6ZcuMZnwRYG3qWYOC6fI6KLLP0jGqFoJizaamIhiG9j1wPFwE0izdhCWkP7x41UC2BIHBiWM3vcTwH/M9aM0RMTLb9C7M/C6GrL1BgpGPX3n1Ykow4ZbN+Pg6CS5lfq6dOlmKNQvTG9WSpeCffPq3tFmK9I3uQ34aJsKtVd9/R3AiNt//U1GTloj2eAvHpOZEBFZv3jphchJ8Rv/1xIYkkC+57klKwvjSB6srniXh+ZfRpbPGJCOyi/f+3nsazhRRgooO01QXq0e36WilaEQwA6ZjT/TKVmWibiXF/JjQ7DpxeI4XC7Wn3k2wHcD+16fZGCATFBysNzg5JL0PF3aRG0udF3KRRbXPQ1xkD3g2v+IXtyf+D8/alQYc5GG3rCB5sB1If9vbnI9jJylCx0fUxZsbqm/yQchZ/kBtAaL2dtScI7qgMZYCUgH6lkUrDewQwI8Zu8UAQmg2+CuyEoVQOOXE+/ROiIVP9XcGTBDhnXd/8kqyTjF5efihCP2i75cHaYtxaf7izFhSD++DBlda5Ml1g8jloGShuQS549LfbIuQ4PDBkz+iTkswJ89qV4GxFJ+zl21NiJHCqqTjXOgNKVRljTYRzSfKLQxdE3EnMnzQJxHREWv9p49/noipxpvvjTJIwjZFO8/TLkl4xxKUX7xOQ77rmVEbL9NQ7diiZ9GPjphDw+z+htMhQ9OvdGweKeni7mag07T5tuNIJvZdto36VsqEovFS0rJZEEv+zu/8xPrHW9kYQt9MmD4Hdi+G6R6r4mDm/FtZByZxMSIrnMpoxHcOsPR+yEXRLfc1HRY83NGlIsRD5iCnfn7lPLg+ZU17q8BH+URVQ00vPv6DdT4otW/Qwgd/Wt5BTBRAIYpSSALY7lRpqbggwIzzT4o8+wsRspp7ob+XEFNWxDa5knMYZ7V6yzFyDg/HcsL3zxLhoWrOwWiRCInfV5mOeSrCqCrDB08nilGRVSopJzyJyk7f02KseTWs3lVFgntOfWZL7OTnoLS6ICrmogQfnr4Id1WT4ujwNRHLlkmhP7Degpsh/ad7pBh8MiEg3joR3rvyT6+KScTi8kHbO1yZ+NYJD6kV/V+d0NDp/PtjSxkNJ14tWhswhI6N164tPEejQ9FhTUOhMgPJwwcLD4oYUJUdIBNGuwTZM6qZoFR8QiALqkvrmqe8Z6Hj6e/3WMuGRgvLN+A3G0FDVfPPHeKAgP+JRUO56PTf0ugSwsVKkp0zS7joIirAuh8P0xzmcjUX8FA/qGVRexoP6BPzq72Vh52rUs1e2vARPnHPmel7+TC3ZN+a95CPKyS7XNUF8LU4cX2mmwAbNPK01xN9cJOygx4J8FuSNeGagRCUmE3eIERFnALJFCESzk4RqfYSwcZ2wAsu0QuVIduGP08VQZGzoVXvmwiX0vXG140UI/XbpS/FIWJI11b3KSL1sFd/7LKRH8Uwe7WZn2cpwfdzT+kdQRKc+aBBEEACh4NOWzZ9keBiy7obsJH+8wekMN/ttSQng98TjzezecUHPBmYS1Rt6X0auJfpXnlxNGRFHn0Zl0fDNN8U69sD6BhtRz9uK6RjveFX8TFDBmzCwzLaUxjY7n2fvsGU9N2EB66nzzNhWjy0eqs7C9cUbmUvb2DB45Fj4bkwNjwfJu5w+cXG12WbCx9u5/TogGn/GTh4qN5sElfIxc7Htr5q37h4a0gdKA95l86bLWCQNfPhipnPeHhM2S5mfLw71hgYFcuHzsn5va7d4OOmQeWF5t4C3PvEXbVnpgCs4tJTjtsFICLHfX2OAEftDr091iiATFZMEOL1VR97+iYh5j0rLbyfJcTXlvgnKxuEeD92I3UkoNjWXoYIa7xPq2+rFEH15O8RqgpilN5v/2w+Q4wvNatZ2xPEqKLadgXhrR79h20eIIHgWsecbGcJHpHdWm+R4FnYY92XQyWwSlkrzfwihHvHxRyXOB42RryXtjFZKG44MfHzIToaPh0nyp4Gk8O1Dmr7aJCl0SkaZLadBR2JX42P6pXQ4cKwOTBlBgNfmw0UCmsYmPJg/8cxgUwUjDlv+beeCRvW6KGBK1mYbl24mv2Ghd2bm2+FbGPj+CbVGyta2ZjDefnsWwgHl4Z8e6FD+oD/J8GhXqQPbK7mfjEVcdHHnIoEF0PVW6ecMOCh6m2kzm3Ch3SXXP/ifJiHyYMexC3pkPfj36YjLHux+LBOEn10fsHHyvc7o8VGAmgu/mVzcakAw4eEzWsjdSB2HLDuzmUB5mkz3ZoVheBLbE+W+Qjhe26oVJtOnvMsjyheFaJP4C4Fgb4IH2cVDfOKEuHb0O1Xm86J8KrX711db0XIf+K/df5wMUyuP7bZH0j6ssyuFMOi+MWGotdiGIY9s1LeK8GFUVNW+ggkPefT6vAlxcJIiIGLKkrvnuViUpW9RzWPib05wY89p9NxUKV52dvNNBQtdl1tuJqGLX2fJYy5QIP9Ii/+lsl0VN36YbzhHB3PwvduSJrGQJOWWsm1Kwz8OJ2o/MWFiQ9Zl4iiYiLZ8Z6y6lIW+n18dN35JQt/j+ibfvZgQ2ZbPmDDacSTs51+HKx4kpR3sZGDk2t3kK1y0fH8njB1N+FBbYu/nrzARU7pioDLHdx/vhAPZyj7Zy8PUwKlg4pu8vBHdCc+ZADhQzIiyUfVxtqmlHRSB+9b2pPe8HFkGvfiJlMBCKkety9MACffwHsDMgWg3LrBTQIMPa8V22woxOCimw0CDyE+pgTHbo0XItuMcgKE+LNJlhB4UHuz3nSbCI6frZdE5ooQ8kCzrOqVCH8NDC3u6IjRZuQ4S01TgtEUbM6QYOrSsJKXoRIkpMoEMy5SNoChGIOXzXigNU6AyZoUInOwetMjhzmNDMQ6H/U8Rfpr9kytEuYqGq4E71EpDKJh3xAtq/MXafDZerk+yZ4ORkNzr4QLdMTeVR+xfw7jn25jwKG2irl7IhN65OlPLhMOE7yTu3xYiPd6+1a/mgX/DyW/DjqyIfB5p8MtYSNwd3joLQ8OHgSnHDhazoEsXUdw8TJ+335Tfy4oNbaA8CF3O9qMlW+4KKNoMtHFpueujJ6wiejjuy77Z5/g4f6bqgbfjzwMCY1ysZ7ORzFN8GcYnY+X6zYFbi/nY3kvvcNzu/gwYysUiO0FiMlKUD0WLYAwPs73AeFH19VFs29/EMB6dEfJ4OFCHHCgEF0IGbwmCyF1pwpFiMaSsKDy8SKUa6mbJm8kq8fJMA2ik9XqPqgLn4lhx/pYnPFTjO+Py7wHDJEgPsytUG2OBANZY/ZOVpdAm/sDZb6inn3OMLvGrshkw9nQPNDZjgH9YMoJpiFUaNkx15OG3kNXV37dQoPMHq2j4a1RU+jiuXRYO0UXRV6l43ifYh0HdwaqRsRYfX7OgMy2nsZExMyfx9cWMRFQMfh45CQWYou6ej/OZSF0hdqFVh02Vrjb1LVksRGxvGLewIkc5Nj85H4u4cAv4ZFhgA4Xq32TK374ctHMjySSnYsvq+1sfldzYb2jdutTPR70x1nfdfPl4ZxueNm7eB6+XenfUF3Bg8HIme17h/ChNX7+O7onH7q9tdlGIoJLKo6rvj3n48z9tadOapE+cF9XZxvBI3ZT2haWmOCSjNgIKNJBkEuA+nHnvyeRc3jXEZNiFSAEIWG9fx0Rgu5YxNH+K4YC9bP/iLGqjH9sCXkOMU+646EkwQFFpuUGotOWeji9VzGW4NgOKrMlkNnBZ8XIYBxgzskR9vA55bbhIUknWAjSm2dZUUaH0r61n5piCN8kbKjWkYaqaZyFadtocPhWl2deLccfK3WqA9AR+vambd8JDBTcsTbaUcLAhxf0q4q2JO42ZX1rC5ngTqAMKBamyBKfhd8zKAOIjVmBH1vXcdjoM3DSluHaBP/3pU/es5vzz7/moG1R/HNVOy5+RdyMPLKZC06E7+eDBVxQXcTvOxeNa9pjjznwsFVmGPBQO5IaufAgbllvo/2Kh76UTTWMj82f3UP5S0g/OLPj3Hw2H41Pth+IuM3H09dxm6P7CxD5ekjbJmcBiic0SH5vE2B105MZo/IFGJR2r2X+K4IPGpTjIoaH86KGu5/FoKpv4Q8xpjhHXk8mcddPWzG2UkOCwNELx/+aKEFZ/uj5S+dJ4LTj+5d7URJUnhvsI9olwVdlimGLYSozJoSYGGsyxPoD+V1dfWbsUWVh/dJLlldi6BgmSwzaP71M++dv02Dcd76xTzMNeu0XX+XMoePvpKvRVcfoeGp/KM/NhPAlnxOs6EIGvu8775JpzsTwoY9MDkqZ8F/pbXq/gwmnaoc9s3az8GRP/8g3H1hQ+WDNu7+OjehkOw/lNjbONp71mrqEg33rqI3I62DckNKxxxcRHspflC3dx8Xla3Q99ytcqH8M7Fs6kIcttw7pByzkQWdU0/hvCTzc5Aa26BJ9cJJuN4Hzl4dtx3V1Gy35GKcjejgglI/Q6FNKrcf4mHdRgTBnPu4kyIxjHOobuqxqKtFtsoOX44Wtsmvwd5LP3f7DoJ0PvtX2lkCzYv7rFyT+xRFL46p6SbDTf3nufYI337wM+q2wIHE/YCuaFSBB9ZaGfu+8yPlQbXyWGIuzyp84cQW4uHXNlYA13H9+DhP9du7WMXag49zlBfHa4bQe/G/ter1/wCEa1lesSTD7S4ObdM0fH286rnx992zvYTqmzBk2zGcEA1/2U4YxAzJ6M5wJ0ZPikjo+E94XRmfZKrHwlJouxLGQNy9wh0UTCyd16/euW8rGS50+ewLvsmFA0efZHEw6Ns1n2UkO8vfqND0lekAm11250Ko7lV+9kfTfjzP635BwseOQt/RSDRdOTsLxocY8qN/bNeWUHw8mHT+21Kfw8JleHzOulgfrkWMGnVHmQ//h7rVj3OR41D0/yFb3W/ByggQVYW1duSMksK42IpkmgaP5w6S3wySQ2RsmEtQ/rnN4RD4P0+7ITBwj6emzL4x8xz01kMA+Z/xce1t5/ke+K3cZH0by/l7UmcKZEtzpRQG5CM7X1cwihwnwiXSJCg0ultxYrrtInwlzDcexAj062vpajFxJeM+ggCsPxpG+uztXeeDVQhqi0qLvDNGgI+fQ67DVPnTIbN5TdLw8kntvujkDhMSXVrMZ2LVc36yinYHR3rNHpPswkUDZGzeZUBXk7F3oysK0yrb6T8dZUCtNDq81ZmOpDGjZyMtuoeWrcKAy63y6F5cD5reDW0uVubjgTO+tNY+L/UHrmoJjuGCef/ziSC7RxZSd8YLo5bCDd+fo8sD/oBl2YBEPje6DTqczeaCmBjOKebC0v4p170l9tOhLn5vyIfvzdWI0UWMGgit7gg67jm0Sg2e+ss+ZT2KM0ChoGfqB8EM7yvASQzb+I5+3dJ7UGNZHgo3x81a/Jeu2re2P47+Lwcm5UKhCcP+EHXXSEhjIDAIJ4ooqMgY5SXD9/FmWEsGto5tmjTVeIsLFaISX7uRjwUT1lTx/DkZxtS6cPsOAtF1t96teJJ4Jn5bWhMp5T/z8qbyVuTT8jeBqvFGho4uyBVbSsXzLsNyxN+m45D7YeIs9A4v+vtTUzmXAjbIpdJn4ZbCEzY1lwv310BWuX+U86KTXcb3951joq0dNVNnwI6efksDGpvFW4wa/ZyPT0tNUhezrv/kyB99rxpmfNeTiQJPGZK1QLq7H+lSmJnJxihrPX+Yiy8uHHCkXRzckPgiy5sFWoq+6ZQkPHqemk6Pj9cRNl1TTPRVJjy5qVFdK20HwRfYvyd9Fr6ZWaBNd1Lnj89PDA0nfrBzb25p8b0y7Zukq8jx16wJai44EyrIBj/w9h5/vYnMI7hywW/v5kpkE7KU/Rp6YIsEQ7cipN71JvUx4vf5wDekTKQm713cI8eVzmkKlJh+2Me5rHzxh4/Kbk8GruUQ3fRKqPe6iIfYEFSAaKHYVF0WD6cwy40U5NKw2nqrIVqRjSJkwVsGXDvPHEbwd5+ko6N95hj+Mgenu5XEOexkonTVmbt5PBvaYSq37rmVi7onJKe2PmIjeuaMmYzwL/13DYEF2TH9Z2EvBaggbsmsCz9iw06GcKQ7c/7ztbI7k4K7F5lF9HnGQuNf2TjbhpfN9zx4vD+ZiYdlWq1XpXOxRX3GhqIELx8dM1Y1DebigPj1s0Bh5PIYGTboxcoQ8zt15eupjka0JiWdi7K1fP3Xl69gC0T0/LQkOjv69U0jwRSYfCQ6dXXpxiQ7BoeOd/a/sGSV/v4X016GjNkRfOVEEUgL92Gi/0pUSmLvYDJkXItdbuYGi+zYlQlx/+rJ2eAkPvQePmPt2CRsHzbLvz5/MwG/l+8xZD2iQ2jzT/r2Shj27KCSnQdXE84FlNg1U9Eo6aD35f/IoZTDQcdnTr6xSlYGAI0ts+29igJqmrHzLwB+TTIt3nkwYuhU1fiE8dKXAttlYj4UFrzaNeRLDQroDBbAsaIzy/RrtS/iv7IIO6cMna3nN9hxgGEf3KJODJ/cP5aTd5qBRI3KTmS4XwXUYctCZi53OlBLjwnTnaTW1H3L86PaHF7bXfL31P2uex50Fz7+Ie/rmy0Od2+P6yvPYhKWcsk5B0sMnhdrUjRIJYrTrL1Tpy/tBd/xltGG4fDWm2oEd6csy416CnF+lI8wWkf7A6hs3I1EMbb2EIBui5+w6ajmDeYQ3M6xmnZvI7vHti5pSlc++o8G2dfKsORvlurfbf3Of/G2IeivBI+3wdhc3Ojb+yho0IJsOu4YViWeaCR/6+7O+eC4DW+mL91kRPHvwfWZNnCETV2buG+wQx0TuJ17V4jYmrkVQF5ZYsB19dblnMQtzHTLW3dJmY60Zo9ySzsbKN/dMnrWzobV/XMaC+Ry4me4ybrzI6elXq9oi1x8lefv32rqbuf0J7zDf1bT3txjvXSy7jDvEuBLLJSchx4/u57qP29fQCU+Zs+houGWnGN5BL7Uvd4l7+Hx3HwhgnXrfn/SFDWnN3OQGMYyp9vpO3IPj3XjW/b5u/lNb5Nm/kdRNybUrO9TMSfy1s1ttFeW8h7odc8pIiMI5R3U9Z/Ego7WPSR8cHD0mfTijh192661fkRPneO+mISAh5VJaAQ06Kw1Tz3+loYXZypi7WM5/Qqg20S6P/3/3XRigXPNJCkxQw6ljS5jQnLLL0f8UE8XtMY94I1hg8ykBzIKNl2/M3F5s6HWETg4Kl89nrnGsoyyMOT15S4pMP4jE9/xzarBA8HWCj9IjJTmudz/Lxma95fHxV1ZP8iN69ISi56qaH3K9RH7M0rR+kp41QnZxStzz/6zSZjh3KcrfGyvdr7KNPHf322x/kxdTyfmdGV0nvtFXrreGx1/+q034FCOCuhAg6fGtKTVT3yaA1qF3Q85r8eA/ms49n8r6NwdmYNQ0amJBA1VtY+JoOGCw7K4Gm8S74sKIwGK57989d7HdkaGtU0iHpcN4bf02uQ6OGR/XryqfgQbq+pMis4cHEVGqZXWeCTplC/RhYeTO1g33VrAwT13ZrfoWCy6TfKo2zWTjHmVnkHrXarc8bKkrx+GHLnPffCT5/nNpUHZRpzyO3TqoO49HOrPzQsj3rtkNnraMxPUTBfNknVdWwBvZX94/u+NoWHOAHknedyvCIb03waldBcaTLV+L8fx88pGpjWJcU6QmB+KeOrNoKrFWJOezPeeJtIbsa0TZe7+LhhLYxGcNcRstQfToia7mYyVo3jHwciTZf3/ZgEiMS5Rt8V0A7gHKiOGhdDV1Y4L1774Woye/RRb8rYc4NIT9/rCnhU5D0u3oZe+L5b6zVvw6I791dMjG0Wfo/+5T0CGziRcwUK7DMy06zvh3746cx/ezG4o9mJCN748z0Wvx87PX/jLhsbP40OQ5LKyTZn6ZqSHX7VVLqUmdpOfz/sZG/k4kvzIitq5NVJXrnO61Wyd166ZSl1AiOeX1f0mbNiDsl7jnHFY6flqWR/rvRX7juUvq8vdMlg2i5f337nPn8p/kXLrzvBtnVm8dUuJF6ksm+0j81cqUZiaMk+DHgAydpRPkOmCYsQKhqGKkvLj2J/KPCJ5p26Z8uCSArB3+5ULNfM6N6Wly/Inbee/U0u+0f/MwGnR75Z6NZNDQX2aA0+CtoeD3pw8d36wNNZrc6ci+t43ld5AO2djrDx3+M5JOhk5lQFyoGnmcIe/D3X6QzKZOZvb8Hk9zqpLkv7c77irK+yawSB4tyNEK2jBWzjNMzb/sKSTPTtJnJc/HyHXma2aAxz3y/Uf5A9awZsj9rgmKva6nOUp65oOu54O/Vk2SwD2iWeGmvQQTz+97kugk54uZjyeSzkfOg7CkKDeCX5T9uoDwpXdrxZWzJUgqyjk/Z5Kc38dWBlbdMJKvXfl3U76T/VG3dNaT93XX45gM2omG9yLYdRVM3ZglwFuHQP/CL1z8bXhX2q+AhWrxprH5ZgwMGvPTfnBfor8mq74t4NJgfVqjMWO/XH9RrtnwPzTc6tymZ+VCh0lnwFwO6b//zenpqH9K43NdGLj7o2ngT548/leLNnwqIPxguIxgy3Glm0935103H/QhqlaD8MS0j9X6nwhvrLHOPzvZTF7X3fk2uKDw9Fjy3kjFnMH2enI+353Xbsrme8YqyPG7e07Y3b+75yDUrccmwu+7caVbX3Xzn25+371v6jZdka78mariYcPk/KhPkFf+Z0t5/4g8ZqR0/ocI3jWZSfzjRPeuoAYcPFAqqK6SBU797FtDXBk4tnWey1pVOp647lr6lPD9q7cp440Gmf1zjoZxdX4hzp3yuXvf521/1xyi43cgZVTSEe5ZveQP0WHdvuv/j3P3vtWdjwlsCD7w/HFVSUXOR26PLjvxt12+dvss3TxnWkF0M+9/dOajHWZHHP+Hh1c+/k0YL8H5NrbX5pHyc1tXueeU0FzOI+9fyxY5kXxdcd5Vtc84OV4EP384vY3k7dN31rdsxkt6fIlr7zxOdE2VQFG/prSF8HzZtUhrCc4xk/VCp8l9/vfjKMIvr79uv+jF9WqDpe0iLHPOKUk4LYC5+n2vRg0e/LyMTp1qYqH4SGPKE2cGBKx+yz78kOve70/EvUPG0nC5V3m8loXcj+6osw9zT6D9uxdOw/8Be9ssUw==AQAAAACAAABwLwAAVCEAAA==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AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==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eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
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+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0633_Kn0p30Ma5p2/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 0.00346 0.097685 -0.097673 0.0015344 -5.6094e-05 -0.0035707 -0.0015351 -3.1986e-05 17.757 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.1486 4.3942 2.7475 -3.4294 0.65859 0.52763 0.0535 -0.045579 1037 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.00359 0.098444 -0.098335 0.0046374 -0.00021386 -0.0045348 -0.0015479 1.6104e-06 18.954 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.13896 4.1223 2.4751 -3.2965 0.61049 0.45837 0.048466 -0.044579 965.29 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00329 0.088134 -0.087863 0.006915 5.9298e-05 0.00075345 -0.0013792 0.00012045 17.607 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.13208 3.9148 2.251 -3.2029 0.62976 0.4426 0.045249 -0.043356 923.64 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00318 0.088009 -0.087478 0.0096576 0.00024032 0.0021768 -0.0013703 0.00018589 15.562 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.12902 3.7883 2.0799 -3.1663 0.65672 0.43138 0.042984 -0.042958 908.96 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00338 0.092014 -0.091096 0.012965 6.5478e-05 0.00046007 -0.0014298 0.00021087 16.594 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.12324 3.6425 1.9032 -3.1057 0.68225 0.41808 0.04061 -0.042216 869.87 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00322 0.089708 -0.088373 0.015423 -0.00026252 -0.0015042 -0.0013922 0.00021863 19.263 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.11351 3.3524 1.661 -2.912 0.6435 0.36705 0.036197 -0.039975 799.25 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00328 0.088902 -0.087055 0.018028 0.00045285 0.0021867 -0.0013603 0.00031752 16.39 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.10842 3.1883 1.4919 -2.8177 0.65944 0.34916 0.033791 -0.038774 771.19 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00327 0.089786 -0.087306 0.02096 5.6725e-05 0.00023628 -0.0013704 0.00033294 19.486 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.10308 3.0416 1.3381 -2.7315 0.69475 0.34035 0.031931 -0.037558 755.11 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00306 0.082947 -0.080007 0.021888 0.00023545 0.00086064 -0.001253 0.00035731 18.413 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.09636 2.8171 1.1593 -2.5675 0.65994 0.29798 0.028575 -0.035648 686.57 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.0029 0.076033 -0.072672 0.022357 0.0022681 0.0073726 -0.0011059 0.00046697 15.454 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.0923 2.7133 1.0383 -2.5067 0.67891 0.28121 0.026973 -0.034957 666.08 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00298 0.081382 -0.076994 0.026361 -0.00049782 -0.001454 -0.0012172 0.00039122 18.341 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.08377 2.4451 0.8643 -2.2873 0.61341 0.23179 0.023211 -0.032287 608.76 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.00253 0.065091 -0.060889 0.023008 0.0027961 0.0073997 -0.00091249 0.00047763 14.83 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.07907 2.3237 0.7527 -2.1985 0.64337 0.22027 0.021929 -0.031072 590.04 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00272 0.072039 -0.066555 0.027568 0.004611 0.011132 -0.00097298 0.00060788 14.898 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.0726 2.1419 0.62979 -2.0472 0.61243 0.18841 0.019512 -0.029196 558.84 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00264 0.071013 -0.064721 0.029223 0.0048029 0.010637 -0.00094116 0.0006261 16.051 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.06758 1.9954 0.52652 -1.9246 0.62339 0.17054 0.018062 -0.027552 524.73 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.00278 0.074242 -0.066671 0.032662 0.0086547 0.017666 -0.00091128 0.00079052 16.39 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.06343 1.8346 0.42829 -1.7839 0.59045 0.14175 0.016002 -0.025794 486.26 0 0
40
+ 16 0.4455 -0.227 0.43815 -0.24088 0.00244 0.061264 -0.054143 0.028667 0.0098493 0.018602 -0.00069574 0.00074247 16.341 0 0
41
+ 144 -0.10907 0.48796 -0.093691 0.49114 0.06085 1.7524 0.35537 -1.716 0.56048 0.11607 0.014386 -0.025131 454.89 0 0
42
+ 17 0.43815 -0.24088 0.43037 -0.25452 0.00284 0.073124 -0.063518 0.03623 0.011218 0.019668 -0.00082148 0.000878 17.012 0 0
43
+ 145 -0.093691 0.49114 -0.078217 0.49384 0.05655 1.6446 0.28275 -1.6201 0.55483 0.096833 0.013156 -0.023926 441.99 0 0
44
+ 18 0.43037 -0.25452 0.42216 -0.26791 0.00283 0.070592 -0.06019 0.036884 0.011903 0.019424 -0.00075845 0.00088446 15.34 0 0
45
+ 146 -0.078217 0.49384 -0.062667 0.49606 0.05018 1.4563 0.2052 -1.4418 0.50503 0.071877 0.011156 -0.021518 386.39 0 0
46
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AQAAAACAAABABgAA4gUAAA==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AQAAAACAAABABgAABgYAAA==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AQAAAACAAABABgAAKAYAAA==eJwllI0/E4gfxz0kUelyfhW5cw4pyZr0SsRXKELJujxdUZ4fLqTolfJ8SZ6zSdKv0ZabIrPtaueh7yFMRpltzcM023LXqaTTNdVdx+v+gu/7+/68Xu/6yk3vfVxOYMsOo/X/IydjNGm+OJLpCUTVH1UGouOQvafyWvMGZxjQrTmlfysQTetKqE9fHAeHcMEDJ5UNbDC9fah8mw9wVxGcW20zYJDEuv6jIABTpWoSC98MsLHPxVB+HnzaqTU/wrsA/E3qRJ215ZBRUromegUZWiOMzUbMKGDapE2WK8hQyLjkbBJJAdVJOw//jCpI9KRpWI7VAE29TUvfgAq+V4JndozWglPr+emuNBq0/b7Zc9uaW3Axv/pQnxoDSnfMb3EIug1mEu4/R6n10Cvi3eUUNcCvHIZv8sVGoNRa1Ql8mYD3XzzOoDNhp+vSty/fNoON5iI5ExSkinvcHjYcpgVoUvJYEHWgsvvhFBuozhqZqnk28K/mbB+sYwNnqjc0WMiGFOdd3mWrOaB+81iPQUMz6GYfkfM0m8G3Oe6LqdQmGOrfeOX74EY42e9BEnQxwKe7p63Eng6t13ZFbj9KBbekIz37V1JgWX3WN8/558CMkZJaPHsemzhFwnNWlejDkL3JyafjlMuqn0+sa0R6EVfNzouNn5/wyfG6XDT0n907dRkxfPluPa+rHZhIiC0S+3VjwUDvz7VhPFxZUh7w2zo+7pG8mfMKGsSnBMnKsTVDqIh/bK1zcBiLj95Miz8mQrWi6CdP4CkK6jdTn5+V4JxRq/s78SheP5if5cYYx01a0hDBOin6p9+9F5YpRad9LVFvB2RYRjyzxdRkEnVXJy07HDeJswtX8uzlyCCxtYOz5ai+9cZut88K/PZSyN7mcQXalNWoiFMKPLLeqme+WomPulwbYycUmBSqPWjop0Rbp+OXtClKXHt/SG1CQ4l3TE+W3lqlwEZpau0vBxRYK7absd8jxzBxYe31mEm0zG9Z5RInwxAbuSNx+hlmb+jM+ylgHJe+23KqxXoc6Z0S17JcCVo0DRK/OTeM9l+Ua/nIhGgSo5XXkDCAVZLzCyiP8ObGJ5ZvzbsxYPeK06+WM/Fa4IVkD3oy0jTLmYmWtcBp+NjrepoJLiOUzW39HUAovLRguA+Es1ezH6n6wVazYnZmmxBYe53caY5ieJl3otB2vQQ0dQpMwl9LIGT1p0QLxwnoKHyQYxf+DFYafbDkBspALTF7SXepAp7rziSvfiYDYc7adK1YBZTl6keN/qOAhTGSzoiVsEgtliqhUn6bNVOggGDJsF7mfiXY9vGCJMMKWNZgHBzhqID//lJCLk0p2rlUAR1losYbZDn4X6PkJBDk4FjM+W6iZBKqIpTWL2MnISHBxzdQLoORaanJzGEprInb/2fJoXFgZemMt1SPwUOL3v8LOkahvD/TE9OfgtVldwItUAyz4fyj3jwRkKzvc4z0h+E595BD4OchSDPsTva+MwCGxovG+RBE+/rm0Fc8oKV8t323sBseUI0IUvNOkLxbntp1rx3mEqw3rnjEBcFPh+cuRrGAFLnuTG5MI6T5CwoSN9JhIQpfsv68AhZRDS9PVqTDsfZqbtSuczhC/nXKeBcZ/zre7rfZh4qW8UI3uxYaeh/p5crlDIyLGwkONW/ACj3x5538JjQyVNp0GjRjikmWcMMBFmb09eRuSWXji7BhL9MyNopC3MeIKWwkWpT/HmnAxuWhodMkMw5aaIggP4KDLoTxX7YK2GieHrwnM4aFf49+eAHxLBRdceBNdzBxsVaGRc1I/7gYOiby3L3YZ43vosErK/on+m2Modj87U2qR6pTZf2+N3Vo1Ensv7GkDv/zUYfpaT/0Z7Hp2E/IJX7rV4NahV1VoQO1aDJmK7O2qEa9HyPUv+RWoP7HJQeLrKtwQOOV3qalV9Gu/WFgXWg5plx2DVJ9KEClU/syHe1sVKXvOy2bSkDDEPqdx1uzcHGl94EF2EZpWShuLLq9/0s1eDYa9xp/0ivVi8Uizry5sUkY6r0+Zfqx2w++92Dy79kk4VwP3chvewquXxLtcKEuFV+TPT0mJtKwpi9oW3H2MfwXUnk3Pg==AQAAAACAAABABgAAMwYAAA==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AQAAAACAAABABgAA5wMAAA==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eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==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AQAAAACAAABABgAAXwEAAA==eJwtxVFEHAAAANBKkpzkJEmSJMlJkiRJkiRJkiQnSXKSJEmSJEmSJEmSZGYmmUmSJEmSJEkyMzOTzCRJkmT20Xs/LybqXazjHO8EB5zoJAed7BSnOs3pznCms5ztHOc6zyHnu8CFLnKxS1zqMpe7wpWucrVrXOs617vBjW5ys1vc6rDb3O4Od7rLEXe7x73uc78HPOghD3vEox7zuCc86SlPe8aznvO8F7zoJS97xate8wd/9Cd/9ro3/MVfvektb3vHu97zvg986CMf+8SnPvO5L3zpK1/7m7/7h3/6l3/7xrf+47++870f/OgnP/vFr37zP0dFvxfjWMc53gkOONFJDjrZKU51mtOd4UxnOds5znWeQ853gQtd5GKXuNRlLneFK13late41nWud4Mb3eRmt7jVYbe53R3udJcj7naPe93nfg940EMe9ohHPeZxT3jSU572jGc95/9sSVRSAQAAAACAAADIAAAADAAAAA==eJxjZh4eAADsVAJZ
37
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38
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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAAoycAAA==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AQAAAACAAABwLwAAISEAAA==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AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==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eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/Surf.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/config.txt ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ variable BZ equal 1.380649E-23
2
+ variable PI equal 3.1415926536
3
+ variable molecular_mass equal 6.63E-26
4
+ variable molecular_diameter equal 4.17E-10
5
+ variable gamma equal 1.666666667
6
+ variable R equal ${BZ}/${molecular_mass}
7
+ variable L equal 1.0
8
+ variable xmin equal -5.0
9
+ variable xmax equal 6.0
10
+ variable ymin equal -5.0
11
+ variable ymax equal 5.0
12
+ variable Kn equal 0.3
13
+ variable Ma equal 5.300000000000001
14
+ variable To equal 273.0
15
+ variable Tb equal 273.0
16
+ variable theta equal 0.0
17
+ variable mfp equal ${Kn}*${L}
18
+ variable cpmfp equal 4
19
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
20
+ variable sos equal sqrt(${gamma}*${R}*${To})
21
+ variable vo equal ${Ma}*${sos}
22
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
23
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
24
+ variable ppc equal 30
25
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
26
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
27
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
28
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
29
+ variable nave equal 10000
30
+ variable noutput equal 300000
31
+ clear
32
+ shell mkdir output
33
+ shell mkdir grid
34
+ shell mkdir surf
35
+ seed 7
36
+ dimension 2
37
+ global nrho ${nden}
38
+ global fnum ${Fnum}
39
+ timestep ${tstep}
40
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
41
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
42
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
43
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
44
+ boundary o o p
45
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
46
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
47
+ balance_grid rcb cell
48
+ species gas.species Ar
49
+ mixture air Ar frac 1.0
50
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
51
+ collide vss air gas.vhs
52
+ collide_modify vremax 100 yes remain no nearcp yes 10
53
+ read_surf cylinder.surf
54
+ surf_collide 1 diffuse 273.0 1.0
55
+ surf_modify all collide 1
56
+ fix in emit/face air xlo ylo yhi
57
+ balance_grid rcb cell
58
+ create_particles air n 0
59
+ stats 10000
60
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
61
+ run 200000
62
+ compute 1 grid all air n nrho massrho u v w
63
+ compute 2 grid all air trot
64
+ compute 3 tvib/grid all air
65
+ compute 4 thermal/grid all air press temp
66
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
67
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
68
+ dump_modify 1 format float %12.5g
69
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
70
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
71
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
72
+ dump_modify 2 format float %12.5g
73
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
74
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
75
+ dump_modify 3 format float %12.5g
76
+ run 100000
77
+ write_grid grid/final-grid.dat
78
+ write_surf surf/geom.surf
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/cylinder.surf ADDED
@@ -0,0 +1,410 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ surf file from Pointwise
2
+
3
+ 200 points
4
+ 200 lines
5
+
6
+ Points
7
+
8
+ 1 0.5 0.0
9
+ 2 0.499753280182866E+000 -0.157053795390643E-001
10
+ 3 0.499013364214136E+000 -0.313952597646568E-001
11
+ 4 0.497780982301540E+000 -0.470541566592573E-001
12
+ 5 0.496057350657239E+000 -0.626666167821523E-001
13
+ 6 0.493844170297569E+000 -0.782172325201156E-001
14
+ 7 0.491143625364344E+000 -0.936906572928624E-001
15
+ 8 0.487958380969374E+000 -0.109071620698271E+000
16
+ 9 0.484291580564315E+000 -0.124344943582427E+000
17
+ 10 0.480146842838471E+000 -0.139495553019615E+000
18
+ 11 0.475528258147577E+000 -0.154508497187474E+000
19
+ 12 0.470440384477113E+000 -0.169368960122646E+000
20
+ 13 0.464888242944126E+000 -0.184062276342339E+000
21
+ 14 0.458877312841991E+000 -0.198573945317390E+000
22
+ 15 0.452413526233010E+000 -0.212889645782536E+000
23
+ 16 0.445503262094184E+000 -0.226995249869774E+000
24
+ 17 0.438153340021932E+000 -0.240876837050858E+000
25
+ 18 0.430371013501972E+000 -0.254520707875186E+000
26
+ 19 0.422163962751007E+000 -0.267913397489498E+000
27
+ 20 0.413540287137281E+000 -0.281041688926065E+000
28
+ 21 0.404508497187474E+000 -0.293892626146237E+000
29
+ 22 0.395077506187845E+000 -0.306453526826488E+000
30
+ 23 0.385256621387895E+000 -0.318711994874345E+000
31
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+ <PDataArray type="Float64" Name="f_1[8]"/>
18
+ <PDataArray type="Float64" Name="f_1[9]"/>
19
+ <PDataArray type="Float64" Name="f_1[10]"/>
20
+ </PCellData>
21
+ <PPoints>
22
+ <PDataArray type="Float32" Name="Points" NumberOfComponents="3"/>
23
+ </PPoints>
24
+ <Piece Source="Grid_0_300000.vtu"/>
25
+ <Piece Source="Grid_1_300000.vtu"/>
26
+ <Piece Source="Grid_2_300000.vtu"/>
27
+ <Piece Source="Grid_3_300000.vtu"/>
28
+ </PUnstructuredGrid>
29
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/Grid_3_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/log.sparta ADDED
@@ -0,0 +1,318 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ variable BZ equal 1.380649E-23
4
+ variable PI equal 3.1415926536
5
+ variable molecular_mass equal 6.63E-26
6
+ variable molecular_diameter equal 4.17E-10
7
+ variable gamma equal 1.666666667
8
+ variable R equal ${BZ}/${molecular_mass}
9
+ variable R equal 1.380649e-23/${molecular_mass}
10
+ variable R equal 1.380649e-23/6.63e-26
11
+ variable L equal 1.0
12
+ variable xmin equal -5.0
13
+ variable xmax equal 6.0
14
+ variable ymin equal -5.0
15
+ variable ymax equal 5.0
16
+ variable Kn equal 0.3
17
+ variable Ma equal 5.300000000000001
18
+ variable To equal 273.0
19
+ variable Tb equal 273.0
20
+ variable theta equal 0.0
21
+ variable mfp equal ${Kn}*${L}
22
+ variable mfp equal 0.3*${L}
23
+ variable mfp equal 0.3*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
26
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
27
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
28
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(${mfp}/${cpmfp}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.3/4*0.3)
32
+ variable sos equal sqrt(${gamma}*${R}*${To})
33
+ variable sos equal sqrt(1.666666667*${R}*${To})
34
+ variable sos equal sqrt(1.666666667*208.242684766214*${To})
35
+ variable sos equal sqrt(1.666666667*208.242684766214*273)
36
+ variable vo equal ${Ma}*${sos}
37
+ variable vo equal 5.3*${sos}
38
+ variable vo equal 5.3*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 1631.42249046501*cos(${theta}*2*PI/360)
41
+ variable vx equal 1631.42249046501*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 1631.42249046501*sin(${theta}*2*PI/360)
44
+ variable vy equal 1631.42249046501*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
47
+ variable xncells equal (6-${xmin})/${mfp}*${cpmfp}
48
+ variable xncells equal (6--5)/${mfp}*${cpmfp}
49
+ variable xncells equal (6--5)/0.3*${cpmfp}
50
+ variable xncells equal (6--5)/0.3*4
51
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
52
+ variable yncells equal (5-${ymin})/${mfp}*${cpmfp}
53
+ variable yncells equal (5--5)/${mfp}*${cpmfp}
54
+ variable yncells equal (5--5)/0.3*${cpmfp}
55
+ variable yncells equal (5--5)/0.3*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 5.7528162356074e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 5.7528162356074e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/146.666666666667/${yncells}
64
+ variable Fnum equal 5.7528162356074e+19*(6--5)*(5--5)/30/146.666666666667/133.333333333333
65
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
66
+ variable tstep equal (--5+${xmax})/${vx}/${xncells}/10/4
67
+ variable tstep equal (--5+6)/${vx}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/1631.42249046501/${xncells}/10/4
69
+ variable tstep equal (--5+6)/1631.42249046501/146.666666666667/10/4
70
+ variable nave equal 10000
71
+ variable noutput equal 300000
72
+ clear
73
+ Running on 128 MPI task(s)
74
+ shell mkdir output
75
+ shell mkdir grid
76
+ shell mkdir surf
77
+ seed 7
78
+ dimension 2
79
+ global nrho ${nden}
80
+ global nrho 5.7528162356074e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 1.14930375850437e-06
85
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
86
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
87
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
88
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
89
+ boundary o o p
90
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
91
+ create_box -5 ${xmax} ${ymin} ${ymax} -0.5 0.5
92
+ create_box -5 6 ${ymin} ${ymax} -0.5 0.5
93
+ create_box -5 6 -5 ${ymax} -0.5 0.5
94
+ create_box -5 6 -5 5 -0.5 0.5
95
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
96
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
97
+ create_grid 146.666666666667 ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
98
+ create_grid 146.666666666667 133.333333333333 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
99
+ Created 85160 child grid cells
100
+ CPU time = 0.124032 secs
101
+ create/ghost percent = 0.993764 99.0062
102
+ balance_grid rcb cell
103
+ Balance grid migrated 85003 cells
104
+ CPU time = 0.0956886 secs
105
+ reassign/sort/migrate/ghost percent = 58.0429 0.0303276 8.83928 33.0875
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 1631.42249046501 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 1631.42249046501 0 0.0 temp ${To}
111
+ mixture air Ar vstream 1631.42249046501 0 0.0 temp 273
112
+ collide vss air gas.vhs
113
+ collide_modify vremax 100 yes remain no nearcp yes 10
114
+ read_surf cylinder.surf
115
+ 200 lines
116
+ -0.5 0.5 xlo xhi
117
+ -0.5 0.5 ylo yhi
118
+ 0 0 zlo zhi
119
+ 0.0157073 min line length
120
+ 428 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 76068 8664 428 = cells outside/inside/overlapping surfs
122
+ 428 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.101455 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 20.3698 22.7827 2.686 23.5864 30.575 6.37302 0.148459
126
+ surf2grid time = 0.0239296 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 14.9707 23.8843 4.99897 2.18653 11.6733 6.87317
128
+ surf_collide 1 diffuse 273.0 1.0
129
+ surf_modify all collide 1
130
+ fix in emit/face air xlo ylo yhi
131
+ balance_grid rcb cell
132
+ Balance grid migrated 1177 cells
133
+ CPU time = 0.0416897 secs
134
+ reassign/sort/migrate/ghost percent = 5.91114 0.0611662 22.7494 71.2783
135
+ create_particles air n 0
136
+ Created 582478 particles
137
+ CPU time = 0.0160829 secs
138
+ stats 10000
139
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
140
+ run 200000
141
+ Memory usage per proc in Mbytes:
142
+ particles (ave,min,max) = 1.7666 0 3.375
143
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
144
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
145
+ total (ave,min,max) = 5.55685 3.22189 7.90939
146
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
147
+ 0 0 582478 0 0 0 0 0
148
+ 10000 13.255792 645372 10537 9122 20 608893.12 18.2064
149
+ 20000 31.403423 649424 10703 9369 18 628994.7 18.3988
150
+ 30000 49.519869 650793 10725 9394 21 636238.75 18.481767
151
+ 40000 67.966896 650397 10799 9437 21 639954.56 18.532025
152
+ 50000 85.914921 649942 10588 9277 21 642041.66 18.56308
153
+ 60000 104.63269 650798 10765 9388 14 643506.41 18.5797
154
+ 70000 123.08121 650256 10733 9354 14 644525.34 18.589757
155
+ 80000 140.71042 650752 10651 9305 23 645321.54 18.6141
156
+ 90000 159.03057 651207 10643 9324 16 645945.92 18.621767
157
+ 100000 177.68011 651056 10700 9360 26 646459.88 18.61986
158
+ 110000 195.98529 650360 10718 9282 19 646810.34 18.622564
159
+ 120000 213.98274 650239 10675 9343 16 647136.54 18.625883
160
+ 130000 232.317 650035 10670 9343 21 647361.3 18.6285
161
+ 140000 250.97994 650999 10630 9270 15 647531.45 18.6313
162
+ 150000 269.18012 650988 10703 9315 17 647736.75 18.631673
163
+ 160000 287.39726 650644 10625 9304 19 647912 18.629006
164
+ 170000 305.74708 650296 10778 9454 21 648073.62 18.635259
165
+ 180000 323.32119 650915 10621 9288 29 648216.05 18.637339
166
+ 190000 341.9799 650742 10751 9454 15 648342.16 18.640705
167
+ 200000 360.33729 651110 10684 9366 22 648463.46 18.6434
168
+ Loop time of 360.338 on 128 procs for 200000 steps with 651110 particles
169
+
170
+ MPI task timing breakdown:
171
+ Section | min time | avg time | max time |%varavg| %total
172
+ ---------------------------------------------------------------
173
+ Move | 0.024345 | 15.88 | 104.29 | 525.6 | 4.41
174
+ Coll | 0.42585 | 14.969 | 135.86 | 563.3 | 4.15
175
+ Sort | 0.14522 | 2.8684 | 20.65 | 230.3 | 0.80
176
+ Comm | 61.836 | 95.798 | 106.87 | 91.6 | 26.59
177
+ Modify | 0.021392 | 0.13979 | 2.6777 | 105.1 | 0.04
178
+ Output | 0.0038932 | 0.014393 | 0.01655 | 1.6 | 0.00
179
+ Other | | 230.7 | | | 64.01
180
+
181
+ Particle moves = 129692691502 (130B)
182
+ Cells touched = 135491241660 (135B)
183
+ Particle comms = 231821342 (232M)
184
+ Boundary collides = 0 (0K)
185
+ Boundary exits = 22498343 (22.5M)
186
+ SurfColl checks = 226675812 (227M)
187
+ SurfColl occurs = 3728680 (3.73M)
188
+ Surf reactions = 0 (0K)
189
+ Collide attempts = 2496629041 (2.5B)
190
+ Collide occurs = 1944023792 (1.94B)
191
+ Reactions = 0 (0K)
192
+ Particles stuck = 0
193
+ Axisymm bad moves = 0
194
+
195
+ Particle-moves/CPUsec/proc: 2.81187e+06
196
+ Particle-moves/step: 648463
197
+ Cell-touches/particle/step: 1.04471
198
+ Particle comm iterations/step: 1
199
+ Particle fraction communicated: 0.00178747
200
+ Particle fraction colliding with boundary: 0
201
+ Particle fraction exiting boundary: 0.000173474
202
+ Surface-checks/particle/step: 0.00174779
203
+ Surface-collisions/particle/step: 2.87501e-05
204
+ Surf-reactions/particle/step: 0
205
+ Collision-attempts/particle/step: 0.0192503
206
+ Collisions/particle/step: 0.0149895
207
+ Reactions/particle/step: 0
208
+
209
+ Particles: 5086.8 ave 35793 max 0 min
210
+ Histogram: 84 16 6 10 2 6 0 1 1 2
211
+ Cells: 665.312 ave 666 max 665 min
212
+ Histogram: 88 0 0 0 0 0 0 0 0 40
213
+ GhostCell: 17265 ave 31629 max 955 min
214
+ Histogram: 22 8 12 10 17 3 6 4 16 30
215
+ EmptyCell: 0 ave 0 max 0 min
216
+ Histogram: 128 0 0 0 0 0 0 0 0 0
217
+ Surfs: 200 ave 200 max 200 min
218
+ Histogram: 128 0 0 0 0 0 0 0 0 0
219
+ GhostSurf: 0 ave 0 max 0 min
220
+ Histogram: 128 0 0 0 0 0 0 0 0 0
221
+ compute 1 grid all air n nrho massrho u v w
222
+ compute 2 grid all air trot
223
+ compute 3 tvib/grid all air
224
+ compute 4 thermal/grid all air press temp
225
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
226
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
229
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
230
+ dump_modify 1 format float %12.5g
231
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
232
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
233
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
235
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
236
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump_modify 2 format float %12.5g
238
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
239
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
240
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump_modify 3 format float %12.5g
242
+ run 100000
243
+ Memory usage per proc in Mbytes:
244
+ particles (ave,min,max) = 1.79297 0 5.0625
245
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
246
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
247
+ total (ave,min,max) = 5.80682 3.4453 8.1333
248
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
249
+ 200000 0 651110 10684 9366 0 0 0
250
+ 210000 32.546272 650235 10773 9442 25 650537.41 18.6637
251
+ 220000 66.041763 650867 10697 9391 20 650763.9 18.65725
252
+ 230000 98.728312 650914 10591 9230 13 650887.58 18.6483
253
+ 240000 132.24111 650501 10770 9442 20 650794.82 18.668425
254
+ 250000 166.78527 650260 10718 9412 17 650708.48 18.66786
255
+ 260000 201.30997 649934 10581 9241 13 650731.26 18.653533
256
+ 270000 234.7779 649769 10753 9385 18 650620.76 18.646271
257
+ 280000 268.52246 650704 10682 9333 15 650700.71 18.644462
258
+ 290000 301.89488 650656 10525 9238 16 650722.24 18.655489
259
+ 300000 335.16925 650059 10506 9218 21 650734.52 18.65262
260
+ Loop time of 335.169 on 128 procs for 100000 steps with 650059 particles
261
+
262
+ MPI task timing breakdown:
263
+ Section | min time | avg time | max time |%varavg| %total
264
+ ---------------------------------------------------------------
265
+ Move | 0.012667 | 8.0209 | 52.509 | 372.6 | 2.39
266
+ Coll | 0.21246 | 7.6051 | 71.57 | 406.7 | 2.27
267
+ Sort | 0.085242 | 1.4883 | 9.9576 | 163.5 | 0.44
268
+ Comm | 40.743 | 58.726 | 64.955 | 61.8 | 17.52
269
+ Modify | 3.0106 | 21.998 | 136.03 | 571.0 | 6.56
270
+ Output | 0.037622 | 0.039097 | 0.043097 | 0.5 | 0.01
271
+ Other | | 237.3 | | | 70.80
272
+
273
+ Particle moves = 65073451906 (65.1B)
274
+ Cells touched = 67980744717 (68B)
275
+ Particle comms = 116198338 (116M)
276
+ Boundary collides = 0 (0K)
277
+ Boundary exits = 11286655 (11.3M)
278
+ SurfColl checks = 113612905 (114M)
279
+ SurfColl occurs = 1865262 (1.87M)
280
+ Surf reactions = 0 (0K)
281
+ Collide attempts = 1259713017 (1.26B)
282
+ Collide occurs = 980745154 (981M)
283
+ Reactions = 0 (0K)
284
+ Particles stuck = 0
285
+ Axisymm bad moves = 0
286
+
287
+ Particle-moves/CPUsec/proc: 1.5168e+06
288
+ Particle-moves/step: 650735
289
+ Cell-touches/particle/step: 1.04468
290
+ Particle comm iterations/step: 1
291
+ Particle fraction communicated: 0.00178565
292
+ Particle fraction colliding with boundary: 0
293
+ Particle fraction exiting boundary: 0.000173445
294
+ Surface-checks/particle/step: 0.00174592
295
+ Surface-collisions/particle/step: 2.86639e-05
296
+ Surf-reactions/particle/step: 0
297
+ Collision-attempts/particle/step: 0.0193583
298
+ Collisions/particle/step: 0.0150714
299
+ Reactions/particle/step: 0
300
+
301
+ Particles: 5078.59 ave 35885 max 0 min
302
+ Histogram: 84 16 6 10 2 6 0 2 0 2
303
+ Cells: 665.312 ave 666 max 665 min
304
+ Histogram: 88 0 0 0 0 0 0 0 0 40
305
+ GhostCell: 17265 ave 31629 max 955 min
306
+ Histogram: 22 8 12 10 17 3 6 4 16 30
307
+ EmptyCell: 0 ave 0 max 0 min
308
+ Histogram: 128 0 0 0 0 0 0 0 0 0
309
+ Surfs: 200 ave 200 max 200 min
310
+ Histogram: 128 0 0 0 0 0 0 0 0 0
311
+ GhostSurf: 0 ave 0 max 0 min
312
+ Histogram: 128 0 0 0 0 0 0 0 0 0
313
+ write_grid grid/final-grid.dat
314
+ grid cells = 85160
315
+ CPU time = 0.0122156 secs
316
+ write_surf surf/geom.surf
317
+ surf elements = 200
318
+ CPU time = 0.00157598 secs
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/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/Kn0p30/simulation0634_Kn0p30Ma5p3/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/output/surf.300000.dat ADDED
@@ -0,0 +1,209 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ITEM: TIMESTEP
2
+ 300000
3
+ ITEM: NUMBER OF SURFS
4
+ 200
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+ ITEM: BOX BOUNDS oo oo pp
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+ -5 6
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CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/runmpirun128.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 2
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 128 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/runmpirun64.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 1
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 64 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/slurm-28429450.out ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ Running on 128 MPI task(s)
4
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
5
+ Created 85160 child grid cells
6
+ CPU time = 0.124032 secs
7
+ create/ghost percent = 0.993764 99.0062
8
+ Balance grid migrated 85003 cells
9
+ CPU time = 0.0956886 secs
10
+ reassign/sort/migrate/ghost percent = 58.0429 0.0303276 8.83928 33.0875
11
+ Reading surface file ...
12
+ 200 lines
13
+ -0.5 0.5 xlo xhi
14
+ -0.5 0.5 ylo yhi
15
+ 0 0 zlo zhi
16
+ 0.0157073 min line length
17
+ 428 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 76068 8664 428 = cells outside/inside/overlapping surfs
19
+ 428 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.101455 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 20.3698 22.7827 2.686 23.5864 30.575 6.37302 0.148459
23
+ surf2grid time = 0.0239296 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 14.9707 23.8843 4.99897 2.18653 11.6733 6.87317
25
+ Balance grid migrated 1177 cells
26
+ CPU time = 0.0416897 secs
27
+ reassign/sort/migrate/ghost percent = 5.91114 0.0611662 22.7494 71.2783
28
+ Creating particles ...
29
+ Created 582478 particles
30
+ CPU time = 0.0160829 secs
31
+ Memory usage per proc in Mbytes:
32
+ particles (ave,min,max) = 1.7666 0 3.375
33
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
34
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
35
+ total (ave,min,max) = 5.55685 3.22189 7.90939
36
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
37
+ 0 0 582478 0 0 0 0 0
38
+ 10000 13.255792 645372 10537 9122 20 608893.12 18.2064
39
+ 20000 31.403423 649424 10703 9369 18 628994.7 18.3988
40
+ 30000 49.519869 650793 10725 9394 21 636238.75 18.481767
41
+ 40000 67.966896 650397 10799 9437 21 639954.56 18.532025
42
+ 50000 85.914921 649942 10588 9277 21 642041.66 18.56308
43
+ 60000 104.63269 650798 10765 9388 14 643506.41 18.5797
44
+ 70000 123.08121 650256 10733 9354 14 644525.34 18.589757
45
+ 80000 140.71042 650752 10651 9305 23 645321.54 18.6141
46
+ 90000 159.03057 651207 10643 9324 16 645945.92 18.621767
47
+ 100000 177.68011 651056 10700 9360 26 646459.88 18.61986
48
+ 110000 195.98529 650360 10718 9282 19 646810.34 18.622564
49
+ 120000 213.98274 650239 10675 9343 16 647136.54 18.625883
50
+ 130000 232.317 650035 10670 9343 21 647361.3 18.6285
51
+ 140000 250.97994 650999 10630 9270 15 647531.45 18.6313
52
+ 150000 269.18012 650988 10703 9315 17 647736.75 18.631673
53
+ 160000 287.39726 650644 10625 9304 19 647912 18.629006
54
+ 170000 305.74708 650296 10778 9454 21 648073.62 18.635259
55
+ 180000 323.32119 650915 10621 9288 29 648216.05 18.637339
56
+ 190000 341.9799 650742 10751 9454 15 648342.16 18.640705
57
+ 200000 360.33729 651110 10684 9366 22 648463.46 18.6434
58
+ Loop time of 360.338 on 128 procs for 200000 steps with 651110 particles
59
+
60
+ MPI task timing breakdown:
61
+ Section | min time | avg time | max time |%varavg| %total
62
+ ---------------------------------------------------------------
63
+ Move | 0.024345 | 15.88 | 104.29 | 525.6 | 4.41
64
+ Coll | 0.42585 | 14.969 | 135.86 | 563.3 | 4.15
65
+ Sort | 0.14522 | 2.8684 | 20.65 | 230.3 | 0.80
66
+ Comm | 61.836 | 95.798 | 106.87 | 91.6 | 26.59
67
+ Modify | 0.021392 | 0.13979 | 2.6777 | 105.1 | 0.04
68
+ Output | 0.0038932 | 0.014393 | 0.01655 | 1.6 | 0.00
69
+ Other | | 230.7 | | | 64.01
70
+
71
+ Particle moves = 129692691502 (130B)
72
+ Cells touched = 135491241660 (135B)
73
+ Particle comms = 231821342 (232M)
74
+ Boundary collides = 0 (0K)
75
+ Boundary exits = 22498343 (22.5M)
76
+ SurfColl checks = 226675812 (227M)
77
+ SurfColl occurs = 3728680 (3.73M)
78
+ Surf reactions = 0 (0K)
79
+ Collide attempts = 2496629041 (2.5B)
80
+ Collide occurs = 1944023792 (1.94B)
81
+ Gas reactions = 0 (0K)
82
+ Particles stuck = 0
83
+ Axisymm bad moves = 0
84
+
85
+ Particle-moves/CPUsec/proc: 2.81187e+06
86
+ Particle-moves/step: 648463
87
+ Cell-touches/particle/step: 1.04471
88
+ Particle comm iterations/step: 1
89
+ Particle fraction communicated: 0.00178747
90
+ Particle fraction colliding with boundary: 0
91
+ Particle fraction exiting boundary: 0.000173474
92
+ Surface-checks/particle/step: 0.00174779
93
+ Surface-collisions/particle/step: 2.87501e-05
94
+ Surface-reactions/particle/step: 0
95
+ Collision-attempts/particle/step: 0.0192503
96
+ Collisions/particle/step: 0.0149895
97
+ Gas-reactions/particle/step: 0
98
+
99
+ Particles: 5086.8 ave 35793 max 0 min
100
+ Histogram: 84 16 6 10 2 6 0 1 1 2
101
+ Cells: 665.312 ave 666 max 665 min
102
+ Histogram: 88 0 0 0 0 0 0 0 0 40
103
+ GhostCell: 17265 ave 31629 max 955 min
104
+ Histogram: 22 8 12 10 17 3 6 4 16 30
105
+ EmptyCell: 0 ave 0 max 0 min
106
+ Histogram: 128 0 0 0 0 0 0 0 0 0
107
+ Surfs: 200 ave 200 max 200 min
108
+ Histogram: 128 0 0 0 0 0 0 0 0 0
109
+ GhostSurf: 0 ave 0 max 0 min
110
+ Histogram: 128 0 0 0 0 0 0 0 0 0
111
+ Memory usage per proc in Mbytes:
112
+ particles (ave,min,max) = 1.79297 0 5.0625
113
+ grid (ave,min,max) = 3.76965 1.51379 6.20129
114
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
115
+ total (ave,min,max) = 5.80682 3.4453 8.1333
116
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
117
+ 200000 0 651110 10684 9366 0 0 0
118
+ 210000 32.546272 650235 10773 9442 25 650537.41 18.6637
119
+ 220000 66.041763 650867 10697 9391 20 650763.9 18.65725
120
+ 230000 98.728312 650914 10591 9230 13 650887.58 18.6483
121
+ 240000 132.24111 650501 10770 9442 20 650794.82 18.668425
122
+ 250000 166.78527 650260 10718 9412 17 650708.48 18.66786
123
+ 260000 201.30997 649934 10581 9241 13 650731.26 18.653533
124
+ 270000 234.7779 649769 10753 9385 18 650620.76 18.646271
125
+ 280000 268.52246 650704 10682 9333 15 650700.71 18.644462
126
+ 290000 301.89488 650656 10525 9238 16 650722.24 18.655489
127
+ 300000 335.16925 650059 10506 9218 21 650734.52 18.65262
128
+ Loop time of 335.169 on 128 procs for 100000 steps with 650059 particles
129
+
130
+ MPI task timing breakdown:
131
+ Section | min time | avg time | max time |%varavg| %total
132
+ ---------------------------------------------------------------
133
+ Move | 0.012667 | 8.0209 | 52.509 | 372.6 | 2.39
134
+ Coll | 0.21246 | 7.6051 | 71.57 | 406.7 | 2.27
135
+ Sort | 0.085242 | 1.4883 | 9.9576 | 163.5 | 0.44
136
+ Comm | 40.743 | 58.726 | 64.955 | 61.8 | 17.52
137
+ Modify | 3.0106 | 21.998 | 136.03 | 571.0 | 6.56
138
+ Output | 0.037622 | 0.039097 | 0.043097 | 0.5 | 0.01
139
+ Other | | 237.3 | | | 70.80
140
+
141
+ Particle moves = 65073451906 (65.1B)
142
+ Cells touched = 67980744717 (68B)
143
+ Particle comms = 116198338 (116M)
144
+ Boundary collides = 0 (0K)
145
+ Boundary exits = 11286655 (11.3M)
146
+ SurfColl checks = 113612905 (114M)
147
+ SurfColl occurs = 1865262 (1.87M)
148
+ Surf reactions = 0 (0K)
149
+ Collide attempts = 1259713017 (1.26B)
150
+ Collide occurs = 980745154 (981M)
151
+ Gas reactions = 0 (0K)
152
+ Particles stuck = 0
153
+ Axisymm bad moves = 0
154
+
155
+ Particle-moves/CPUsec/proc: 1.5168e+06
156
+ Particle-moves/step: 650735
157
+ Cell-touches/particle/step: 1.04468
158
+ Particle comm iterations/step: 1
159
+ Particle fraction communicated: 0.00178565
160
+ Particle fraction colliding with boundary: 0
161
+ Particle fraction exiting boundary: 0.000173445
162
+ Surface-checks/particle/step: 0.00174592
163
+ Surface-collisions/particle/step: 2.86639e-05
164
+ Surface-reactions/particle/step: 0
165
+ Collision-attempts/particle/step: 0.0193583
166
+ Collisions/particle/step: 0.0150714
167
+ Gas-reactions/particle/step: 0
168
+
169
+ Particles: 5078.59 ave 35885 max 0 min
170
+ Histogram: 84 16 6 10 2 6 0 2 0 2
171
+ Cells: 665.312 ave 666 max 665 min
172
+ Histogram: 88 0 0 0 0 0 0 0 0 40
173
+ GhostCell: 17265 ave 31629 max 955 min
174
+ Histogram: 22 8 12 10 17 3 6 4 16 30
175
+ EmptyCell: 0 ave 0 max 0 min
176
+ Histogram: 128 0 0 0 0 0 0 0 0 0
177
+ Surfs: 200 ave 200 max 200 min
178
+ Histogram: 128 0 0 0 0 0 0 0 0 0
179
+ GhostSurf: 0 ave 0 max 0 min
180
+ Histogram: 128 0 0 0 0 0 0 0 0 0
181
+ Writing grid file ...
182
+ grid cells = 85160
183
+ CPU time = 0.0122156 secs
184
+ Writing surface file ...
185
+ surf elements = 200
186
+ CPU time = 0.00157598 secs
187
+ Processing 4 grid chunk(s).
188
+ Completed grid chunk number: 3
189
+ Completed grid chunk number: 2
190
+ Completed grid chunk number: 1
191
+ Completed grid chunk number: 0
192
+
193
+ Done in 0:00:12 (wall clock time)
194
+
195
+ Processing 4 grid chunk(s).
196
+ Completed grid chunk number: 3
197
+ Completed grid chunk number: 2
198
+ Completed grid chunk number: 1
199
+ Completed grid chunk number: 0
200
+
201
+ Done in 0:00:11 (wall clock time)
202
+
203
+ Processing SPARTA surface information.
204
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p30/simulation0634_Kn0p30Ma5p3/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <PointData>
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+ <DataArray type="Float64" Name="f_2[1]" format="appended" RangeMin="0.00227" RangeMax="0.3086" offset="4512" />
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+ <DataArray type="Float64" Name="f_2[6]" format="appended" RangeMin="-0.69515" RangeMax="0.67896" offset="14704" />
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+ <DataArray type="Float64" Name="f_2[11]" format="appended" RangeMin="0" RangeMax="0" offset="22504" />
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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="22556" />
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+ <Cells>
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+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="23884" />
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+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="24788" />
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+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="25280" />
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+ </Cells>
33
+ </Piece>
34
+ </UnstructuredGrid>
35
+ <AppendedData encoding="base64">
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lh6qGQWhcJWU/Rg6FlrLMFYZhOOjViS6fSTcxnnLaULUpEmRrRYmJ3FvLfUbhlxIjXYp19HLdWEhx2th0n03CiN75M9MXkkHWJVKO770LtYCZyVXjKaj6Od9k/fA9JHR2ST4oTsO5og7BdXYZSM+PpOlOPkCtq3lHUHImNAyK3XXcs/EipFncRz4HFN1jCb5SeSgjrZu5Y5mP3rvN3DEWBXjdnfnlvF0hpF5SElseFINwOuu9rGApxNKO8FcGl6GoV3Z/p3UF3C6HcnaPVaLDYFyuXPoR6PZcqgimIjvGM78lhAY+YYP/NF/RkJDANkQNBn32dOfy1eKSnHVwh24dpnYyL5murUdTqYDhba8GvMK9gaqNj5EU+exA1fRj+HOJGlNXP4Hjvj7f1k1NCBXvGDrN0YzUnvQXClwtQIT0sc7jdDyfquEZZNChNzm6jSuvFVP6AX3rD7TBWLCYwfGsDar5EmonZBiYC/jQ2+nHwE7fp1I6EwzYm66lC+sywRHhPatiz0Rg50delxomEsomjRPXsmDHtgU/CwlWhqLD0ixsGxtS2kplgRZUdXX/RRYGJzL7XZJZ6F+7q/BGJGvZryy87/MoeVnCwvAke0AWBLVPfRVyYkErbKD3gyULzupeTfGyLJR708hdRczle2FirO6UTsFDJthd9YgwMeEJRZMyBhZFNy9NZcCoTiRGa74NOfOtZlUVbeBULLT9V64Nw2950tcsvonqGgurBVsR92p7+mttOv7MUY6faW9GnybXFV6JZjC319IFVJowGyG2N3OoEV/1cGm3XiO+lCjpB08t6v+PgcbQjgYE1cSYtnDWY/91Ommcvw6aS4atwQK7LEcN2Fsh99PgfYmVNKdNQ7G5T/fvblSYdMp1beitWtahEr190Yk5teWo1mUvpgz75r3qr3OVIqxhvX79o2K0XN5qnVFRiKLPAhwvhAsgEbUQ7j2bhy9vx6gHxfOW/ZSDuDCSRp1yNlS4LbJdfTLB9ddhiv98BkIWVWkPv4/U3zQWV5MGEbl+CW6/VAycDIki86dAjpzCWbHmLsz09aTv3LgDFaOFEXnTREzkfkjmHY3DNCNjT9JwDDy9hUQP90QjnMJP7Jm/jaWzZkVCjW3H+Eh8avBof3ItDKOqHBkn4oMh00opz3cJgrPmrWlu00A83UMQHjIKQCf/pHor8TrmaFSnd4NXUSpMpo4E+KEuTMFVRtQHFW4PNMj+HjjmG3u2qMMVV0oD39Vmui7zzhlLWGE44JTS8E/vHB3Aw7bVrN0KV0fzors8z5PAvqoIWOHoxw6qwToifJMcBtW0f3BzrjtMIMKMgOfxfKmVNEtkdZm6nNxBXOFpRNwgt6e3FeSXDGO5wtXv3P6e/1uflmDTdMs9a0guAY+0wtVv85DA69EzmN34g/8nD31VNE4hr8T71cePJ0/Z4H/Z+AmWAQAAAACAAABABgAA6QUAAA==eJwtlHk01GsYx5G9hYYw1pmpy1yTTrSpo/s8TXWJkiWiO5WQFoX2PZVLytpCiOJqosUyM80isla4iDKI6tjKoCJbDddy55fef97z/vN8v8/3+zlvpp4kkEl9CnIThcFDHSJQHtpawSKLgKl+h5ktFkLsvoP67/zzYN9W6iezBgFUMI+qGqqJoPbiJ1VyhRBiAvv1h9lCWDHRHKN9jQ+k0PhjUx5PIHHEm27WwoGE7zUTDZ1cWNJ+a+yUKweMp5QuyZG5kEhn9h7/zgHVnO0tQR5cyAxMrN9tyYHWolOHOh5lwY3lbj8qurjQC/7nx9kcIIXJO2R7c8D2RoRlbjQPTg72cCxmcCG4JOCtgJ4PXtnrZ9YJBKBTMuXO9ckHo66qmbkxhSDpbHGyfFUEipJI+tfZpRCds5q3AF+A07jekSbSSyigSVvtj1fAE8+RvJarNVB/d8JuLrkOiuO+vFV9/hpKMu85olY9FCsGfKhxbQA5l9vWea1NMHzA/Vy2oAUGmnyjToe/h5v3/E1bD7SBeCDh4r/Sdoi+vlWiF9sB98gR93k3PkF1X2UZM+sTxHkdsh561A2TXm15P4K+gIWak2tVYz84Rd0KIIV8g/EHA282eQ9C4eJEc724Iei3Xm97eHQYTH4K/gDf6u0OFSqjIMJLHeUJY/A46+3O45sn4Mjea1/PwiSovCI7txjIY1k9YzfZWwFJ7GcluqYzkJvSn/HSQRGV0q+vkt+kjF/v2CwUJ6kgsYX3lComP6fkHu5Wx7s1D2sb3Gdh8P6CozcSZmNa41KZBQ108AyzbgzVxPpN3poeo5rIXRSQLm9DwtPshWsNGVpIqC8z1ka9OLHa3g5tDG5efcqDroORef5qo206aL7qelVwuC7yV0hLfR/qTfuJIOPKxhcHl17Qx7O7rNwW7zdAFmXNgE+kIZIV3edYWBkh5U3gTpU5xkg8uSnGeHLmAvr5ahNk1vo1SOwpGB66rb4ghIInCSPvKShbqsvIlopmqVdy2/dRkXhmZFIxKVF2RqnYVjgneYkCDavS1guGJqjY3fx4jBVFwzaL2FTpIRoqLZ273OYcDRUYl3Wao2mYcsG0NOwyDc0faNWtE9EwQdZSsT0NlUna8JJFw2cu8dRoNxquJGrSoGHyP/PzxsupeNvt7qOst1RUb0oPWn2VijP590+Vq1Fx1xYXs9RKCj7dMEuBkUHBzTk3d4fPp2A/gccDE5T8JwMgyhhlw3qEOcbYRMRva4Qyd5ZdAYZYdM7Ies86g+lcU/VRv/gvyhoOGa26sn/7GKKHunYysod0kYixOkVnWrdnHs5Qu2ri06c9nbecNpbqmroqdZLQ78fHXWk6JBwYtvcMk2rii0numcEgTbwVq3F6OUMD+7Yx/+j2nI3POUThs371ro4aPwerYkzpCJ0Vo/LrVsY5BLgJipiwcV6kfa8CziAKm5Sf7qtXDi/YERtMgc/PweNAtNqfMQYF8YSgFJJOEAl+BxmVMoRGwODOjt+rNIdB6trJvv18AMbOE2B+g9aJgR18qz7w7VmrKxZ+htCzdTzPkW7421deS3RMAjvP+G24kNQFmXJ6talpnRC3O9xn+/t2mOa7HeIlJR8889+BI31dMXVLM1QlL4q4UtMEZxxPt0pbGiDA2S9ko5UY1gWEGXwbfQ3xcabs1He1QGCduawGFklcWG7fK6FpjJZmIXkJ2b7lf1J2lMHNEufBXsMyUBCLufzCYviyMW1IP78AbBxfX2NN5oP6HmF8ZZAIamzy7qOfEI47DxeXe/FhbFDhS7orD7wvW/b0s3MhkPFsuQGDA8wcuFKbkgsTFvLSjC0c4G8u91E250GDqWPfCToXnDIzYopNuKDyecqwq4oHy7gMi/kjXIht9hx2VHoCPnVqaSGL+PBNS6Wu/z8eCNcnzraqfAJ+aSn1C+sEcIn4xnlCEM6ydqg8LADlgupc50YhFJ3leotWiWAFu8CdeSIP9l8uMvUX5wNp8dOClSwhQATbrpAkgv8Bu4b3Vg==AQAAAACAAABABgAAEQYAAA==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AQAAAACAAABABgAA7QUAAA==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AQAAAACAAABABgAAAAYAAA==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AQAAAACAAABABgAANQYAAA==eJwVkok/1AkfgLNj7CqqdVXoNYaQLLnl+qrEMo5IjnLEypGjDdPO0ItmtkaOyH3OjBlz/KwiReT4ykaNevso7dpSivqUXSm0YnPsu/sfPM/zedyeV9vHMiede3ama26y14VDT1tehrVqwn617vY5BRP4PpahYd5oBYLsXBvFX81gXLna9Ui7HVAKLLW4Tg4w1Vi8K57tAAczdH2DSS6gdfnTvOUzJ7Bm+gjDjQ9AJsX2XqaLK3DUSPrVLnvhnempYx767uBssDlzuc4TVD54PktT94LYnlT2yh1vGE6WV2tx9gSzZKFcN9kfWpIjWSh/CEKCDVoH9P1h7nzhq0lSIFyzkhtO+SMAZFm2JTt2RsCTb8K0ZwOOgvpn8jFpXCS4DV7P1vzqO3h8VFrfqhAN3RadUUXkGGjebq7TZnoC0OZhnUZKAhgmkaXyOsmgUcOvqOk8BdNfdOrQ0lNh/JdIhwd6dFi12Kqncu40MEatPOdvMOCGspDCOJMBrm+np5ILMiGnK1192/ZsuG3ZSr/pxALWsvhw1QM2SLhdqgKLH2FCsWdN35kDq0u0vM/6HDBuzBR9nLgAR26ykm6XFMBQwC1pg08RbGtmHo5tK4ItNYHrz4YWQ5zz2+RRWgkIwmfjfcJLIaXBcvd8azk405v808UVMMo361ntrISR+OcKbS+q4ecOLdeEAzVA18l+bODDBadfdG1/VuaDrGl84nQcH3Ce4nRgYz0wQwPycqIEsMDyTZ2nCiEyNyV+pV8I0ytjx/F9A7hy+i65sEXQR8XMHGUxUGp9t/9UKoauasfj1mES2JY6qjK/VwKsvy9H33GTwiRFd2jLdSnsMYoUz0QQ4EtS8OAFEnAqqJkqJDXCO6tH/kyHRqjfbTQSl0KA7eHFu28aCSCP5Rh2VRHQnj2cvmRBgPeJOFKLHAH8gOU0xxgpWFD94kIWJZDu1xVBXhLDukD3i2kKYjANWGvrThEBfd3tsJ7cBmCki0z2awthl3UGX32rAJhlbd5zHXwIs7F/K7Hjwv1081LVmWpo9zr/6i+lSrh45dFzuk0p4KYvFf16i6DF4Tlzck8eeOlpx0eYsWGg0vfqgIwBA8alDtSyYFhhKsrPkkORJ+TdJJ0/jQnBdW9MR7JxPO3exrlNedgzaOrXY12E13b8RH0WXYq3JshfE/EV+Lr770JVg1p0JzE26Btxkaerfe2dBx81BPtffsuvR6PsqGiZthBL+7vvVBk0YOBOpxFavghDRH96mZmLsb7TR++bMgnWXO5+nTUvwYHUf0tJsXwfpcJuXorFP2w8Ls0lUHMsYe+HAgLXOQr20VMJtGIVTL9YIFBpyKo6NJ/A2aciZY6IwFvBL/peuxHIG4r3le0hcLRH/YLiCSlqJv7haLwmweF5xw6HBAlGzBg98HOXoPLnR2O8PDFGF1+61iIRYczTtbIyAxGqXZdNlPg0YNuk1nreZSF+9b8rGbtUhPidUtOrpXoBKoEdzz6wHjdseP+l424+1vZ7ktUP87DM3bZfQOWihRy6FVNqsejEbx8u0KvxRf/Nufd3KzGv71/ActxzQ3FxqLsMA7M/2JysLcWqj/n/lQ2W4I43IR67ei9hwUwbf3GwCBk1tj7rP17EIQ2Prod38vGkidE/yrnon+k/MzKXg7uNCdVh1xwMbpqgLqadw2TaBVmfmI3cgdqrzBg2Tt3nXC33ysLEhby7MatncLPWw0lzaQYumx6tX4tn4mN1ajm78AfUuWp470gAHavkttc6z6Ugh9Vb5UQ/hUWERmJm0Ekkxyf8fromEb29tkQtuMehWt5OwfecWNRrP+ZDT4rGxcJzU5vtonDJZvVJoVokNnovlkUJQtFwJohG+xSCLsFn/jpUFYwrSX/+erwuEJtNTFSfRPnj04lmmebCQRTs+62uieKLQV/kjxm89kIlWsunLMODqJHkFSQz9sB/piX9OOCO4Xp+Zz9V0LC9xFpK/tYdH28ddKGpu2I+3l826tiHeuwu2+asvagiH2Z9heSCncwxy6lROwzHrtnxJkck0jhZXEN7XIsZP5uoY4kFrX2+JKEpXgplneEyjfB3o//4Zp3VwI51tDdKB2d76ZUvp6YZn3v/D1X8D1E=AQAAAACAAABABgAA5QMAAA==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AQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==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AQAAAACAAABABgAAXwEAAA==eJwtxVFEHAAAANBKkpzkJEmSJMlJkiRJkiRJkiQnSXKSJEmSJEmSJEmSZGYmmUmSJEmSJEkyMzOTzCRJkmT20Xs/LybqXazjHO8EB5zoJAed7BSnOs3pznCms5ztHOc6zyHnu8CFLnKxS1zqMpe7wpWucrVrXOs617vBjW5ys1vc6rDb3O4Od7rLEXe7x73uc78HPOghD3vEox7zuCc86SlPe8aznvO8F7zoJS97xate8wd/9Cd/9ro3/MVfvektb3vHu97zvg986CMf+8SnPvO5L3zpK1/7m7/7h3/6l3/7xrf+47++870f/OgnP/vFr37zP0dFvxfjWMc53gkOONFJDjrZKU51mtOd4UxnOds5znWeQ853gQtd5GKXuNRlLneFK13late41nWud4Mb3eRmt7jVYbe53R3udJcj7naPe93nfg940EMe9ohHPeZxT3jSU572jGc95/9sSVRSAQAAAACAAADIAAAADAAAAA==eJxjZh4eAADsVAJZ
37
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38
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_AQAAAACAAABwLwAAnwcAAA==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AQAAAACAAABwLwAA0QEAAA==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eJzt1k0rAwAAxvHUMGZmxomDw9LchaYxpB28Hb0kMlZrJbF5mzGr0awxbDUJpSwTNlGUpEwj5eBkzfKyD4ASRy/zJZ7Tc/p9g39/x2c8ajMK1A4K0aNMabKLUynI9eni8Ow2RRm4aiifq0mjIM1lu1WtcYpyxnJ31D6cTkG6w1+KTomQglwVFmx07VCU/ma1rKcug4IMeXWO3keK8vTB+a0bzaQgI0UHQ3qpiIL0LUqSa0hRbt6XvlfUZ1GQe4UdfZXPFOWJ1hpTjYspyIvAVvIMsynI27eby9p9ijL6n3GNhIJMmPOCmgRFOfKyJvWrcijI7pXGfJeTokxOYct8lKI0PA06F+RSCnLAJoi4BylKk9z3u3hOUZqvS5TLolwK0mo4M3naKEq7uCXk9VOU/ZGfw4kPitKoX3q1VMsoyDGRXDHloigng8daa4yi/AOXsFq+AQAAAACAAABwLwAAyicAAA==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AQAAAACAAABwLwAAbiEAAA==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AQAAAACAAADoSgAAcA8AAA==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AgAAAACAAADAPQAATRQAAO4JAAA=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AQAAAACAAABwLwAA+wcAAA==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eJzj5BwFo2AUjIJRMPwBAGORNV8=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p30/simulation0635_Kn0p30Ma5p4/Surf.pvd ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="Collection" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <Collection>
6
+ <DataSet timestep="300000" group="" part="0"
7
+ file="Surf/Surf_300000.vtu"/>
8
+ </Collection>
9
+ </VTKFile>