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  1. .gitattributes +1 -0
  2. CylinderKnMa/Kn0p26/simulation0529_Kn0p26Ma4p8/output/grid.300000.dat +3 -0
  3. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/Lambda/Lambda_0_300000.vtu +0 -0
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  11. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/grid/Grid_300000.pvtu +29 -0
  12. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/grid/Grid_3_300000.vtu +0 -0
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  14. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/output/lambdakn.300000.dat +0 -0
  15. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/output/surf.300000.dat +209 -0
  16. CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/surf/Surf_300000.vtu +38 -0
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  19. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/Lambda.pvd +9 -0
  20. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/Lambda/Lambda_0_300000.vtu +0 -0
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  24. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/Lambda/Lambda_3_300000.vtu +27 -0
  25. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/Surf.pvd +9 -0
  26. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/config.txt +78 -0
  27. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/cylinder.surf +410 -0
  28. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/gas.species +14 -0
  29. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/gas.vhs +9 -0
  30. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_0_300000.vtu +0 -0
  31. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_1_300000.vtu +0 -0
  32. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_2_300000.vtu +0 -0
  33. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_300000.pvtu +29 -0
  34. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_3_300000.vtu +0 -0
  35. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/final-grid.dat +0 -0
  36. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/log.sparta +319 -0
  37. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/mir.txt +3 -0
  38. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/output/lambdakn.300000.dat +0 -0
  39. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/output/surf.300000.dat +209 -0
  40. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/runmpirun128.sh +18 -0
  41. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/runmpirun64.sh +18 -0
  42. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/slurm-28429237.out +205 -0
  43. CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/surf/Surf_300000.vtu +38 -0
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  45. CylinderKnMa/Kn0p28/simulation0572_Kn0p28Ma4p1/Grid.pvd +9 -0
  46. CylinderKnMa/Kn0p28/simulation0572_Kn0p28Ma4p1/Lambda.pvd +9 -0
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  49. CylinderKnMa/Kn0p28/simulation0572_Kn0p28Ma4p1/Lambda/Lambda_2_300000.vtu +0 -0
  50. CylinderKnMa/Kn0p28/simulation0572_Kn0p28Ma4p1/Lambda/Lambda_300000.pvtu +21 -0
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AQAAAACAAAAoUwAAWwIAAA==eJzt3N1LU3Ecx3E9x7knt9kSqbQWMkMYBRkI3ZStsMBKTYgeLsJQepqZDCFmBF5IYEWjsQZZIdkDwipbQoliYRSMIEgEUTMn60nEspGm6ezpF+d3Vxdy3PnW59y8Lt5/wfmdz+/Y173v9bvicu2/nX72Ifi4k2tHl7VbakeKHlyBctuye7y1TxcvWf5uuunTDi66vN3dMLn67Soot5lVwkVNPfeVoK2zhLnoc+tCu8qc8hDOlz63yZVjEyQLlqUe3ubkov+5O3vMkQ0FcL7V+NP3lLZxp0zD9++IouT/3pcG36y/NijAGBm4/PHm03xR8ohtwjfg4f7r/dzriKWrAsZaa+vMqc/93Jd54nG9NUGSeo/79UCl6O3WHsxwcLfvT961toWr9F6ZZgitdEOlqR5L3VI4w50sDl+v36SSjHUP5yx5tHc5VKp3h4a9gTPcQxVjtcFurtz9bNaooaMZKt2M6ERVKD1Rsr8uWvallDvXPrvx6+hILqSiZ1HCTuMt7tYburzMce7f+rF96ueLX0BqDrlSbm8uUUPinnb8/EAGqRv98fbfU6OBxD3aGN+uMmshcUMdxktrrkLqFvWlnSjJ1kHisr0bpC7bc+khcdmeCVKX7XySIHHZLsYAicv2JZC6bL9hhMRluwlIXbZjMEHisr0BpG75k9lAdXYyJO6KYlXjeRek7mBI72nqhNRl92cXQOKyc1ZIXXYPGFKXnZdD6t6rjjQX2syQuA7tVMMBJ6Su1ffNfbINUnfAmljjFRdC4l4IJFX68yF12XdISF32XwBI3e9CyOnSAQAAAACAAAAoUwAAlUMAAA==eJxNXHk8lP/3tUdIRaR82tOiklSicpC00qakvVTSLloVSZl9f2axU0ohStqoSFkqpUVUtNBCq0SFpN/7mS/m9/njM68ZGTPPfd97zzn33Of8cZ0nYw9SKPm96O3JKAq/gtbFjHwtwZCZJ589mkShuL6r67y7FI7Xu3avqaIQ4l52mrGJwrqK48U6ERQ+p5o+8xkkxaWjBx9e8Jbi1Mht/rwSCl+ss4cZ6Utxrii9/7owKdL3fRLpXJbiZur8xD/2Ugivv/pbv0oKu/EbkvqXSmEfOdgqx0CG+PGlw4dESDF+jkZpaYYUh+sm7og2kcGAMlmqtkgG3SL5/JtVUjy8FLm74LcUkd92L1wtk+Fnw6lBvQpkaLXoP/pBbxmCLe6LpKtlKPoqn7fEQA7G5tkZ3WbIMWn0vkdeQhnmLKs3PFYig1bb94bXYXJopVltPHpZjlDFUf39WnIUbG74lzxYjj4m9zWXvpMjYe6JO/m9Fbi2u+1rxmo53viNe7vtpBx+24+d6zNHgYNRbV9msBWw3HE4ou6NHPc1vnYboaOAzov/FhoIFaC/zcUbCki2z3l3xFYB9RsV1/wXKnDBwD+VU6HAleVizT0TIvBq0ojioOgI1ObuOmrwLgJ37kUufWMVidOL+r3U5USieWzQ8z0vIjFuqX/yxbFRWKox4vyWoChkj3h/sWdWFAZfP/0yXSMa13dd33bKJRqFoz/UhJ+KRp+bBT/rWqKhm+yWrjM8BjmP+H2D7WLwNj+vu/2hGByNWfd0QHEMHNMMe71tjcGSGfxAHbdYzDT3V99ExcLTy/Few5tYHAlwo37pxEH71tVw8z5x6HnUKn7+PPLoO73UNS4O5fP+eTe8jYM8kAp7rhWPWFn/K3eN41FT0Hh0aL94zDWfFH/SKh7hk8ryt42Px7GLNnvy5sRj/bKogl/L4lG1brrhqbXxsFnJfNqfPF55P3jER/JoalpptG+N6vnUK5O3XF4ZD+15A70+eccjqLvGj6Ge8Xi2oWme3fx4POB91JszJR66izf/ciJ/b4aEY3OOL8G5Mw2nm3Ik0Daz2jzWUIKI0d69qiwlUJworNvWIsEso2kpElsKY0d/klSuleCdx8c1f+MkmF7WZ/bX6RS2h/f93ryWnPtF/DvVbRJ0v7ex26zRFOKWJt39zaVQdsecPfkVhZgftW5LPSlM3N2v17+9FBISQo5weklhuH2LfskcKW5Fx7t3i6NQeKW6+nQlhaJg0zmig1KkTdcnR1uKE16aVHgfKdwyZpw2XygF+51VKPVMij55NvditWQwNlz43WSXFFa/5C8lDCkGmUXffG0tQ67284yDK2V4Oz9Hf/kFKd5fOiRo+SxFffl63gGmDFnNboElWTJ8YU6bKBwjw415ixNCl8owuaCX2Yz3JG+q1VoO/ZPBrkVhpBsow7Fv4klWx2Xox7jZ2mucHHsW3d0yNFCOIWsC9wdckeGjooF76I4MRnqv//TNlKPHjpiQ7C9yJJ6JLUaLDOc3d/+wx1qOBwszoo8+lkP79pAVRYMVmO32IC92jwKlkR/+S6pVYOS72VP1XSJQf4RZmsmJwHx/i30XKyNwdJS+ZdvwSCTYSE6bHYxE7KoR97oXRkJ84VzSf22RaLpVEmK0KgrvR6R8PHA8CgnXW/bVZkeh7t0Tn+U60WBF1jYscItGm76W+rjAaIwr/JF2IDcaN7Y8lL9qi0Y546fb2bExsOVZmvmsicHV/NbKh1QMMqcYfXvwIgZ/AqdsjB8Xi1+fPfbG7otFofU18bursSiSHjA9+iMWssHrB5SMiYOBNGR0+oo49H0ulYkOxMENfTwiouJwdmv/H7KUOCS2PLCe/TAOJ4/dG7lVJx5ezgaBX/XjUTzMKjxFMx6/lz0M8tGNxwWZxuO2LvE4m7Rk9cpe8biYeMKi2DIe85MPOfSzjYfvmncb3EfF49yk6OanFvEIoY/N0Hg8+jyvOmBYPJoWX53fXC6Gzf3AdEmLGLd8P79Zs0MMi0971265JMaBxb4Xc/tJIMzWXUTNkcDtvtedwm9iPGl8rl7eS4JjaxZfb/WQIPBg5iQfhQSD47ym/Jwpgf9b04aAaRKcPZGsd/ybBDMrfT5OM6Nw8JvWn/QACcp2ioKysiT4enWu+xE7Cn/HuuzL9KdAV58vjSTPyk6dv25Kwe703zIPisJazkvHlGIKu3KmLYifR+HQXRvvyhAK9OkN/0fht8MZ6zmTpZg9dEpCOcmTLl82/6CeUui/STs8dbsUvyL7mVDHpfi3v8/YmS0UXGTaxiEmUrxIETBuZEtxe3nN6zF/pKg5dX+S4QIpPAOu3pxP8sRi+HcPx6kyzDv3z8FumwzhRR8/N5N8y09J3cYulGLbpFGGradl6PbyixFVQfLI9jnmacoQxVfbbkrya57EfIfVdRmKLXPPtTbLsGuzr+LFQjmShi5xGxwnx7EDLbqtVXJg8pusdW4KCHpPvfxQqoDzZfl4t88KULP3uaQjAj76zt1mKyIQVB5vff1vBD4MqN20eA451wNWqB87EAn/3Wq3V16PRNKHXWEJ3aJQn+A6p9+8KPAM6Q8WhZef7D8eehsFp9emo55qRyPA17L3gwnRoE6W/eHticazxvh4k7PR6NfybYmsNhoFuQ78/iNjoDXQO1dreww+pA19FxYbgy/uBnfu3I7BrvBqvl1zDHyGfvCeZRWLNJ8V/v6esVCWmZOxsHphUTOxKhYvpyuOryB1v/uDJP7i2XFweLAt7/q4OLyP1IiImR6HXB1XppVvHE6r9S6JT4jDS/3irtOz4pDJT7Zkkse5r9RsX98kv0cljDz1mOQNKVLy1jg03GFXfVKLx6fiqUMO1sVhz803kZNb4tAWbLM8Y6QYY0vXHlvuKIbt7fNjtp8Q4eevmuMD34pQ998Ey9srxBg4OD6vR6IYcQGXP58cIYZ5vz/eo6aLcaZHS8SBl2LcXtPWNcJKgjNj3mwI54nR+/w8tfwiMepnDes5d4IECUdu6Q9aKIGEv2tslJoEK22bN0b1lUBaPrt2DEuCWw0Lz9ukk+eNYzMd7CUYxswyctoswY6GgllrX0kQoCyAFCwn/Qx9FyNB9T3XHgsvS3Dx9LxdDHcKmUuVFRznKx9vnlkpQVPVgIF3Sf7cu2F15N9ZVT58+2Jl9HgRBXur45YtBJdZmxfO/KUrRbcbYyNG9pZi1o8VrxlhBF/NTWjok02hQT30QaK7FN//2Fl7kP7xM0H/0/IPFK54fTib1U31usngOR+WvpLC9tWSA9smyjDz5iJSwmRYy7vwUfeNDKQY1R6bLAfroe7EtBA5PE9vH9N2SQ7BQr15tT/lODDBye/0AgVc2eo93eMVYAvmFT0guIU3/Ksh+Wi4o+37/Tep2wuHxbPOVUUAvKCGvpaRGKBsrJFQP3Ev6tbFSLx0pj95JIpXzinqMjUK5b0DXWrjo1B5edmwMYVRKPTNCIkldbv15PRD4snRGPIk4UnD9mhI9ekCG41/A//+2v0mGto331Zk68RgT8Hv/JPkXOsuCx2YsjwG19uGOO49EoNTQTGl8pMx0Nt5wKyuMAafDhz/nGcUi/1LT0+4PzkW15N2vVi0KxZH3c75/82Oxbx+ppvdG2Lh5ZQQ66gRB/c5qVw3xzhw/gjmMjzIefyTOm7RsjgYKRtkHEJ1ohwE7DiM/5k1fU1qHN4mZ9Sx38TBU43r+5DU+cXHv4x+f1CEf58rM82TRIiVs3n7/xOhJTp7pbW7CDvqv8dq3hPBVQkoRFCGYaMIxbJU9k6JCI9705VWjC/jHy/cP1kMrvHqr8MfiOAbEl95qU6Edc+LB545JkZf3yc9cu+Tx/r1drYtIuxPNTd/N0aMGWp2HDN9CdgWg7Z7DCXn/o9a4kR/MazHl/slniTn3zXx1Mq5EogHFpbZHpXg0fIp12pyxQgg1caqUowt5UczC+IlyMkaOZ9zU4I4C23mEXOCkwjaOLZMgpGedmYhFapzvGFSbMnhAxLk7/mt9lKu4g8fDw/5UjGLwm3LcQGh9yQYobWLNdiI5MGOYT2qVlNYs7Tt4PJzFCjjryMTWynohOQoRhMesWP6uiN9bkjxwPbX0L5GMmgqG7oMT2ufqp1MkGHINP5ZX105rHp5ev6dLUfgw6EWBzgEr1d9dSYcAU0TxFpVXRU44nUvJmKNAveLBuztcUoB721tq3e/UyBV7fW2V5MisLhFz/eyLAKZtTFdr9REIK/y23tH+0gUNNFAOxI7XQZqdEuIxLGh/eZPfE+eKwFlFIoNwx1Sd0ZhtxddqaLw9pROZMnVKEQEs/Z9bo3CBtaDcZz50XAeXvd2Ez8a82gYeSsadm/Hu/YiuONx32+DL1vGIOVRivrsaTFIGHXw0Nk5MYjamV68fDd5vSbxNy89Br3aDHaUvYmB20PFrFL9WHw+G3q256hYVJifulTqHAvXsd3G/BcQi+xS3b9zr8fixnUa2MTiwILs1dpNsUhfsSPqTPc4hFzPO/JHPQ7nBB4rm0fFAWd+fdxXLMSNHOegn/VCjKb/PE8I9eaeIyddE2JzzkXjoQNFeDP20QDteSIEnViiuFEuBGm2mew2Icx+6G5P3CrCVfPtuTUMEc6Jhuh6OYlgfXpHxJMN5DzLNKeWXhIhcGXTzeZSET5oyAsjRSIcl31YopsiwuX9bxPHknM8rd9NHjVaDCx0DV98TYTP/Z6vmFAigqP0Saw4QIz5gm3mO2ViFDnFrrzxU4QImp7ZiWGwJHrz6GIxzMaknDN/I0bIHLW2EYvFOOrrIrktFneee3bxijc/lpJ6nRee5HVXjD1btap3/xVjavbBz7fIeS0/sXNq13IJXp/udapuAsHjT4vSMgmOf5vkvyP4FgW/NzMmfPlNgbkpsMt5Wynujo9cwQ2VgkZZP+4SfLL21du5xjKMinu/f+deGZyPVBcqsmXYqkggjFmOkKbmuptbCC7mhs0pjJbjv7vzey36KMcAn2/LXBwV6PtCbavTUQWefkpJ0biowBoBi9rYoIDv2nmTWwkfjH18swWMCOha6I1/VhqBMcpGHgmZ5cn4CoIX9tfMD09WRGLHj+WBGaWRqK/Xe76/bxTwQVMjZlYUjH3oExkF4S/kLrsYBdGI/keSH0VhoIMWX6tPNJKHr9qwbVE03MOO71fjROPgY5bWovvRWGv6wv6+BsEHjGdrqobEQCM7csqGCTHw6xtyPGNtDDQfScadSYgBjc4/Ed6YVfHzsYk6OY/ujc3DBxNcTMNjx1iInb8NmrsgFk9XPtcO2xKLHxmWpk3sWBQoYXYsjg/bsHBaXyFKcosI0xWC57pJq2+5AOTL7vdsFSDaI+Ppe0qIc20la2+Qcyl8qT276ygh5iaOjypcK8SGndQ1h0IhCqqK0z+oiXBMMSf5UpQQyxXH9nhcEKLqwu6d5+xEYNyeKnMMFCGvya8y6rUQ56s+SjMMREgq6XYlTSFCxovWnE9FIpjonYr0niOCnNPkUXxAhDhhzOXzz0XY0PqHq9+F4IqlhfXTToqgtfRdudF51fml5rw1WzVLDAfZ3qeaH0RgOf00GmQuxqXnS1n7l4ix/NOA9C4SMQiYDqt4Qd6HbusTVPiiQ3exPrF0ux85h8brtTwnHaFQa7xfHHifQmTGE6NPg6SI7i2WsJZLERs832wERfDvoE3FQz6pcG1F4vHzv8g5JCDfeMR9GfY2eDIqDeTonxV/Y4MH4V82Dy9ax8sRdfbau5Afcvx+X7znGDmH/Pcrfx7er8DwqF3H9l5VYHDPY7Ld/xTQ8B3h7UF42a6tU6dN2hiBTXtn2jgdj4DZaKeKzWUROODZ+rpHr0jk5y+4yloWiat9aAIUiejTJ6/2yovEbpGLd1NLJLR3zDyhMSwKfuZnfnt6RKH72V7b+AEE116e6ryO8LVDK/qOLGiOwrr4LaudrKPxc2PpQenBaPwuFqtNuBgN15vcEUvfRiNr3OmJk3rE4ET6gPtLx8dAbdKvL1fnxuCVd/ZXlwByPtsUhGmo6mbXE97PHd/FYMt6r2Vun2LgfWr1oFcOAuxXAnEBdD/U1gdF8aGetLc+uJKP1foHdhioC0GZ0oVFiAvTH29vtBFgg5lB2tUTAix4uaJ12zwhbLeMGPA1RIifjT0IBRXg4JrCkNABQjybSSsiQhRlTuR9vShEIm9wWfUiIfJ6zT+dJBCicr2necFLIZbm942YQeqo5kG6EgpxO6jbqBlFQsxa33+991gRthKSfdtbhEF2twybWoUgpGNiXyuCdwNrWDWk7roohTARHkl1Pf8NEKPc90EL5SPGqOBFS8/Ei5HjSitVYozka6bLJkswsfjhvkFMCbrXzn3h+VKCpJqXS+f3pjrxacNk943XCA7Vf3o///UvChW9J1cNnSSFR+njyq5rpXBZYR/kmynF2ZOHM/+py2BodjUpZbIMscNiL2gflaG1eqH00hUZXtj99hL8kWFwQ1F/gylyPJuRlHZQJkfmh8LVy0rlOBJ43u9kdwUqbq0avmayAssMWUGjDinQmBejP/i6AiPVbfTMfihw9dXERQP7RSD9o7PziGURWLaxB/tsfARM9l67s+NRBHL3jF0XpxaJqsw301c7R8IodDCBZJF49vTdiJSPkRCONVi6q1cUBG/c7pqSOhg24cGpwYej8FRa8mPKlSj0uNXN5/HrKCj+vBvuZBINnyezBwrnRCPkZEOV95Fo3Gq8Hv7mCqmDb1v2HvoQjdyBnmeeaxKcudf36yX9GDRWungNmh+D7umfzb6Ex+DcB7WbFlP5uDbkAfs0i49+J6Y07HrGw/XbS8mJ4yPZ0MM15xIf7y/Xrj/zk4+MuXSE+IifgLbHx/lYRk7paC8BuhiIHc8eF8Bye6/bTuV8hJ2iiaUABUzXhA0vBRjE3+3f11CIgUXOerbbBO08i9TL7G9nZ84ldbQhifcpUIgpSuIsaNcHhJBX0wBV2Fn/fl2gCYkIjW++pb7cQ3DojTln5hSIkPBr+eQFPVX99BM1wy7vuBjham+eXagRQ1eNPzVojATmyetG/iW4sd5o+YHxTyVYf+/HbvM2CcYRFr/eQcV3rhG02+MNhdz9/tUp/0nRfUidxd35UvTr7dQ0n/D3iJ/rhg97IUXKIDozCN+mZYZFMjTwamN382SIKX82NPmyDJca/pbu+0gey8z0ttmTera5YmzXI3I8dV5lZJkvR9fD9QZ9WuTo92aWvnyxAiekQaHeCQrUX/PcWkx4tyBFtNLPPAJT17K6UO4RODyaRkYRaPL0CJr9gODAl4OjUj4Q/lL+4o8nqWd322hhIRL6ityz2fsjMWwzrfRGorKbIWdBOeE1q8qtnxlGwUD0pLxhQhToU3lnYRSOfV5wpd+xKHhl+Azunh6F6VFvKy0royCdvtZsulE0FucZH/g7Kxrj3j07EL4zGlue1bF2J0dj/Ojz4/o0cxHcd921D4t54G7c8Ed4mYtljR7alQ1c/DUpDPW6x8MeJTDmY9dL6scyPR6u3Zt+cNhGHm7L+rn/XM/HlpGcvDFsfntf5uGrj3aOoQ0f4anbf0Y38tt5jAB+t4sdBQv48K1ft5YZzQeNWis2CMDSdzoR+FEAj2HPP+2aLYSooQ0eCiFiM3d6H7tP+qxJqv9EwmNoFOBOcN6fjMxFAVdF4NyKWH1fXYx9I3bfmeQmhs3loKvz+GLw6jhzFpN+N3RQKyfeRoITtaGCm2wJ9lTvuqbxSIIJq4f2z9ankFhFXW12ozBjyvP73GAKow6P0OySqTo/itZ5bd/I+Vm7oEeSZCupQ3kbD+tnSBFv9m1F749S+F6yarw5Tobeg5LSa7bI8L+2LEPLalpRleFmG03E5LgypYxrsEul998MXHbZ+aschDwwflsosMr4cZ8wDwV2py48cEquwOHLTjSiw4743MnNjQrMZK2sqBgdgQWvCh9ZeEdgzqA/A+4diUBW4uZsvRsRsJvU4++LlgjMSNcZbjghEtsr1wVv3xSJ3paLtE+IIzG5D6Nl5bNI+MygFdooTOs/ymeaXRS2lozSm78oCkev7Pr1YFMUdn7eHvtJGAUavQtuR0HZhj8SnrHVQeOKNxcG93f38TvDxYFbh96VJXJQRsPWUg5+TDvZw/YPFxHlPRhbB/Bw+UkZc40FFwfrdjxcdYiLeenUBqYPD69z6ITk4eS8lFqj+1woZf4xPFReHfuY84KHfQTFDFzER+G6KM/xZXy4rVDrc3KOAIXZg30uNgtwpnlk87OVQth37a5te0oI/gDpuhlvhQgK31mcMUyEVE7FJrUdIiwqOTL/VaGKz5oqAYQYlq+3BvgcEGO3jtuqstti/NamO4AEz7pbyP0XSTCw+OuqbxIJZuw5uSHxlQRcGo7ZUHh/oGLusX0UXCcKx0wk/cry9oqFF19SYH3u7bXCQopl1uG7jq8k/LFnWO7dBClKwrIv7KmQ4mLLp2Sn0aRvFTaYz98hw+phPzf4JckwYZj1OPUcGTY2Zxn8I/XFYc988lYqXjlwwGoXbYYci4befbXwgRzzF65Rn2uu6KwzRSvMPftGK3DtfPzY4U8UCDQoWlXXpoBO/IKsRusInHLteZ27LgLJo3cLy8MisN9R89P90xHwNp3buoDg+brqv1f+/ojA3C1Pnq3Sj0RdvtnXeheVDsiTbyffJBJjplj36tsQifl/w2aGW0Qh4MazoPrpUTC5sMrgvSsH8ey+lxVxHDzS7B/WK4mNB3+fvr3xlI2c6cPmnP3NQQ0tI1pxYTE0/YHNAA4yEqL+hu7i4Msr9m2tCVyItee+KXvJRcEJT1KheBCLaIGdj1VzbV9+TuSjaTx9oQRI0Vm7/fcuAW6YNr7qrSGElWnOiFRPITbteblcROqEcUNqYi/SXxaW7bA8pS9Cxe/TOavWiuAfKP2WdkGEfW7Lpa6/RFj3K+Nr5gTVObA3+JAuvCrGH0eLIa7NpO8sC4pLdpHg4rgqect+1dzGzfJN+cyeFMYskrwx9KPwUpfDnhRDwcd57fxTjyhMuvGLIEbp/+itkxS5Kzim8duk2K1ubbuV4OVxRSuPjiC8bfrPstwlejIco+nkQhmSL47V/xotQ/qjXfGMFzJMyi3e+92Y8LYzfmZP5sqhHHsGytF3PX1l5Gg+5iWeUq7SGUxv3Cg76aZASpfiaacOqnSyBXMCrI4+VaDnnLsva5oUoMn6eXIulqot0um2KQKJ/qdfHzsbgfoNx3XPVESgiJBHoVEkHn4dynvkoNLPJt7bcCIgOBKJfWimEIk+zdeqz8xgg3E6L67qPhvnJr/cX23PgST11qPFWRzoKQkUF943d0zjlnPh8mjK4enBPIwPt3eOsuCj1NBk37A4fvv8QgCNJ34vdS4K8O1i/O+C7kJs+3d3tmShEPunKkz6xwmhs6GP+5gCIeL7WL8coqPSBQ5G++X9kojQbN1dq+gh4Vls/zZLEzFWOHyY4rOa4M+iEre0KDGU6fZBDOcenN57ekvw6l+hnrGbKr56BTdP+jyQwCyrZ+in/hRe0eiUxNd+VWjtrAjCw89uCoiuoGBR4ut03VwKrTuTGeM8pTj4/cVJQ4YUd96VPhh7UYoe2wpLVz6XQkDL9aaq+G5n1kd67ZYh+N+HfFa6DOypmYJerTIo6cFoOSrcv7s/8ZXjw9JXi40UcnAmFl51v6LCFW0EfVjaKGA5IVP92WEFok8piSPS4v1Y0kcKELB3qPC7AtrbdjUlGkd0znO30GNnrwj0DfPQ/S8wArTq4X8qAn9zF8c6PYuATtiWb7UhLNCqY0gTC8ZZc6pTmez2+RYbuRmPE00COdj33bjLwzoOjJ7cuJpzgIth87zdy415+L37TXSPkzyYKQEYH7T6rUH6+YguF/TGjhJg8Bg13uOTpF6b9sh/R3AjISNqXF8hsi0TJ+jEqOLaNXRFdZEmqddTxiRPd1XpNb/2c38U3xPBw026ttJQjKLBV0QZ08Xo8ZrlbR0uBtte12HZUzF2Hlq6o7SrBMmNxg+2j5ZAu8U1dshKCe5Wtw1+kiBBVVBI2K9PEqTr+fGch1HwnxKn/nMjhfUiy1fqsRToafOqMgpmPmv+KzSS4r7D2z+EUsCSLcyKOSQFAYtDBUlSsNYybD7WSTHv/nudaf/JsO/Qh9R59jLMdm0yc/aR4RVP+5hzhAwXukapjymSwT03XTREV66cxu1wleOf6OPDj/vliJ+Y/CQ0VY6NdNo9l0NaX2c7hvT/kIQZ3NjlCph89myOFZF4spfePneLPKe/VRXhKScvw8wwAqWH1BZGTYrAVb4/ob4ReHQ+bPaKQib+7KU/EAuvan0cBzxktddzNi6Z0AoMG6kaF3tPmctp1ws5GLFCkPdzOBc7evT+3FfBhc2wTZJbw3hge9ONjAdjqxOiEis+PJM0veed5YMcfgKZBJj1M2Y1FS/AMe3fmv16C7H1oOWfGj9VXNODrHoalwpRrTm5TV9LBIOaPhs3TSFxPLy2uWGbCOfp6WmqCNPrR26JqiY8gLZPmIpxdvwI7ZerxOj23CnrWaYYm5bTkVDNB39dHDPBBYQ/Dkz+7rtPgvjqSd83X5XAXuFO3lk1/5NPtJDpEBznPTFg5uYECkOUb6iaS1gm7Qk/M1oKRydagZBidXDSHe5l0rcJSr1RT+L99te9YVYyLIlLOftstQznEysP8QXk+ZH8qmKSv3r+77/EPFLN4XorerEDIYd6LxqJyFFbceRk1mE5LlyghV05SusVoXeb5NivsTZp6QQF/IXd3+svU+C+1PniTZYC66WPba/fVmD8WFqhYuLZ98bZ3uFM3HpitcG8mona0f1eBaxhQV5AD4BZWDQ1IqTRnY2/ficc/itio5Gd1M9vJ6ddx+DgGBXn+SeQi6B57zOT3nFhuuVK0dSVPAxWCgA8FOvuMHJx4uOpU4P93mw+ZkuHB82xF6D/z3KrUVcEcNibvSpykBAp6WldS9cJ8YSw8AkyIVxWH/gT8EiIpClZPUx7ifCZc/RS9BwR9tJ0mC2CREN/zd1cEb7QNOSzCHNvfe9hM1SMmj9n6h+7i3Fon2zA4HgxSl3jBOOqxQhrkdmvNCX111ZZ8XGBHr8GSjC9e02V1ikJJqZ/SjV9JkFPD/1Vjb0omGTeqZJ4UKDVgZ+E3w2foX58TQHpx8pBLwW8CVU/aqTyOczrkhG7jOTvpsHl7iPSpZj5p/+as4TvjVUadki/dVROKGF7c2fa2nAZ9kteH5eclEFJrypkqPY+wj3cVY4/u5Mmz5sih66Wg2TiYjnWLqpZUB8ih/K4XJRjxFCtlE/v5XDoHzu0SJfEtyZvpvVmBrpPoRVxBg5eeXdlkQMTercOD/U6z0TB1jvJunYs5EpooYeF4eUmRV9GsZHucnde8iE2gifSDIuN8k32D6dt46DfgoSsq02c9vkqF5JJ9ICOCz3emrcte3nI17x7akUVDyarjizqP5MPPfOiC/cu8/GFlaaPSQJ8ttDMKM8WYEnEiLM5Q1X65Y/MVtIJhFCOIe4Q/h7eahvWJMQy5cBVhP70lDtIhLT6iC9xSSJkGFaGOrwXweVz1T3XHiq+pU7LACFiFIaNJhRPjMWv+DuXfRfj4rxCH52REixgHp52eqEEixPznrkIJQgYVZfcpViC100vbOdoUIjO0TumO4nC47/RR1q3Uuh7dfvs/QRfMxYGkZZKYRBnfH237xQWrcy9Z9BXiso5fU7sJv33Gk0HQqQIDGa/zU2R4q5ZpvDwI9KH/9OYoWEow2ML7+YNY2VIfLt5vdcyFa//uMHk3uizMpQqugfMekp4ftYP79guBG9Pnd6thx3hZVXaBHoRvK0cxITjw+7bA/vOZGDDjhE1nOsMKI/hECZIUetxez8T79cmlI2vYyImji5ILCjD8IrVPldX1d+UkqdLvlhz4N/PY43DWQ6GfGfE/evHxXal8Yrb7gshOLlr3NmwMB5KKp59mfmThzX96U/IBz0Nmn2fD/GnyTnDnQWI4xcaeBYIICs+fy2lnxChB/V6WawSYlbs5z87hSRPveNfDswX4r972Z/UG4S4dPjhgaZxIgy/estyXIAI34bQxFgEeirp8UiENcJGv15/RTBy+2LsZCNGcjAtSJH++kmTIB6Sr2aHtP0KxPBX+3sjpEEMX33dswxrCQST1mt+XCfB5EnPonqLJWgOTZkSek4C61xHh93vJTDREuyo60NBV0k8KDi5j17+mUPhwO3aq8fSKNDTn4clFD7ttzM10ZFC/0pN8xM3Ka44qFWnrZJ2+iQ8lzz56VouhRcm3hE0SZFoSxdMVR7bEBS9MVTlM+rwFQln0Yaf8Pa+HI4VWgUtOpYMpKyiCz8DHyY3sTRrGbC6kfD9OcnT+ucO5Jsw0Z8eB/VgAYUzOLbhLCRPowemLPQdFZ3Y7MOGhzz/9oe3bCi7xyoOzqn3qL77nAPb8bG61VO4OL24kbExlts+L+bh+dcLd+f68Tr1uX4TRRH6K/hI308bY1R4eMWuyhH/pQow4pzaKQMLIZb+qxm7msT1wU5NR3BV84WRZ4wfuj4TYo3SaCLCzlXRTZtniPBH40DVg2MiHK1/csUuRxVXdkDpI3/Ce56en/0xgvRZnaNlt+QsMZz+iAaN+395S0BbyCySt1TiFkKRJNihFMwloN2He7MkUPOkHTkStMgTx+VbqOryEKscgwImhTC/3o6DTlE4Gv37n38ehdQ7i0jkKIyWDWqePUDlK7vx/ENB41EpmsNCa17dkkJpS6qVoptv8IQuujKsfGWR9rSfDDmPm7+PcpbBeFbjOPb7cNAujzpuOM5uybSoTQjHsUepW/v/CMcrzcDVD7YysOJu87q6fEa734uJSCca+DPb+RATbjpXOWOns7Dx5u3+02NYeO0xKC9Hl40vOQ/co/zZUNKu32xku90L7beZgxn9mKMe3+bA+8/kihN23HZ/CxcHL56YNOM/HrijInz2e/Fwumr5UrtXPIyaQU8MVXEdl1rNKx4sADW/xaVov6DdbyWEkkYS/iodNSyh8RjBwbEuaaUkri/SHna5R+KqtB2qiXBCU3xuxzARxF96bA73FMHxyzGNS2EiLJ1MR1SEIOsbmwRmKny8f4Unh7lOhZODU3ZX77omxg+6jKtLMOxZSEzURAnCXp7btmCjBBqNf9ruc1R46kGb09VtryVYGCrZ9qYbhQU2D14fdaGgU+bfOp/wojYfeoKn8oGUvNhmFvmZwoXPNKGXwrv11LmFzqr6/BTM9UXnpQgYk794w2spQl5+fGBcwuqMV2Nu4Zr8M+EY51i86lJMOL7QdrcH4e38iIEfF0SfT1EMKO0UXxkwp20bc5hI6Fluk3aFiZIezWNOjlHl6Z5JWbP3lrMQufq++hF7Npa7ONY+T2Uj79iUI31HcDCg3qpsp4gD8/cR8lvfOO26KBcX1lQLjj/gwkRJIHgYUftLXLCJxDfnq17PCoKfjiuuqW/kt/uR+Njy/cZIs5ECPFZ7paHHFmDGYmPXl12ECPy25/r46UIUeOzT8zgkhJJmZApxtik8z/ezED1P1Jhm9RRh/HpaKRdBN+2HVp6HCIPdr2lO8RPBb2HXrOuJIiS2Jr5IeiuCvTJhxDDJaJg4cbK400/hcr7X+NHnCa+l5Yd34k6fhNXN6p0fvFT+it3pkpaMTAnuiO1le59KsP/eU4H7D1Vd7ojvvwv0oISC5G/D+DVCCuruRw3npVOgpy2mhP+eFlr9HKUrhcOQi/PH2UvRZnRuyddIIW6+P79xvZCD3s4+lgusmVgW22Wv79dwnP6X2lwvD4fdy4xprbfC2/kQA3p25ovD/RnY98X3vMkdBp4O2TetTy8mVghH3yvfwMSSywc8DrxmwlIpzLNwlrZ5n2RhSXfNlX802PDcuc/ZdTMbSpj4hN2pK31fQDsNOGjNsyItkIOja3PFKUu56PMq9eafZC7+2zu7y+d/XCgv40Ietm+Ob47O5uFHiQGhKnzUWx8pmHucD6UsqCGA5L++oftWCvCFtjc+E2Am84f26CHCdt+vELR65yYWwlU58BPi8kN6ICZE7lC1f9RIEVaZTP1us1SEkTtmTBq9T4QpflUZXaNI/pbEEQYowlAZlV1SL8KeW5emRxB+O+jWyFdFU8To/WBC8cN9YlSGdRdaJ4jxpUU7e22FGEpbuQ7JS/2hkkWTJaBdyw+DJBhXwHh+75IEeSfogZ8EzoQ9LdQl/ZRu2+MolE3Mf3ucxDXiai6JFIXKpylFtYcpXDvb+GJrKoWPX0umTyuQYP0dC4IUCK7/6zZ5exMPxz4GGd70ZyE4v8+YXfMYqPPMnVoSF45AZQEJx9HY+8klT8Pb5yIM0Kep8SQDwRwP2bI6BqYF0s4BJpZ6aW+22cds1ymYGH/NO+z3YhZ2a1cwh2Wz8NHSRjfVgo1QvcqsqCR2u4+A0z6v5cAwqfi+3XEOaFG6SoeLF4Vdg/uu4yJyb/TxwVe5OPztXsTXrrx2Hy0PAWVJFpJ8HoJouXYIH2debD+yI5KP9f3oTsOHhVIgEmDnoJaLP58I4K9t8unjYCGUbsFFQnwS+3PXC4Tg2I0TtN4UIj/t61fZOyE26Y3TGGkhwvJV9IRQhD6nuSFx/iLMvE3NPC4X4UHr5m7di0XIsqYnAiKkd5FUvzVW1WW+wNTXIEAMtSI9Y7cLYjw/dypj+BcxHjku+V7RXQJb5X8q38j/gJYE/0ny/84gfEhJU0icioOUwhfqfwSmDR9KIW437XihULUq4vInB8KHdtJOLApnW1Zsy28TY3Sxy+Qkcl7fu4kuhi/koMyLDggTvyzD362vDsdG04RnXSPDoeuoY+VxMRwLaHvJz3Aow+7FgMfVvOwVtxm4vp0WCpjQl33SWLWRCaVKcomJ/ftKQt/rspC5mJ4gE3ykBMYs0FXctDsbjl+/l6/nsbFz6OSJGvVszLrRcyZrJQdWreYkMznYEXzLNcxEFVddyp38RZKvySPuOFiQuCqN5Tx0VwrFvM55J+3WMxCr6vF89bGxztMEaGpZJu+TIIC9a2X0bB0hcg5mrLviQOruywtpj3ap5uRbxf1JJyDX5eoNoy56ImwM52UE26rie/53NecXydvlzk96Wp1Qzb3H1s9Y4PtNBKVNjcT3t+BYbfdJYqynbavbxbBRCsikXu8bFteP5K0LZb1pIOm7O8/Z2QwZRuJ3cZP6iyWkz/Yddmy/QIKVzaNKwrIl0OxKT/QlmKQsNBQyafuIDdWpW8m6jiSQXNL5OQadOSa+c4aHJbNLGnv6sNr5KQOJBUcVc5LD8WTcwroz0eGgq8Wt0nAo1wtMGPDM9ySUj4HzY109VpcwoIS9k5hwVgpLTGwef7W35mdVX62ix7QJLHTLruvd7w8LUxjjyFuwMY1bfi35Ahs15n/EvC4ccF/8HZm7lwO2A+1I5SBtTJLW7SFcvLbtmdBjKxdRyUHrA55yobT/2fLa/ZI8GHjTjmIeWpP6Dv/uQfgNbedK4INcpAsPmgifpe39Xiqd2OWj/sCavwJsSF7juXCYEFtmzbBXuAsxtbt3ImuvEBftmvLWJ6vytuXL4c9scxEYJppDIp1I3dV06mW3XYSSuWOjtyhI/aVlzOsixBg2++qQenwn4FD/bj3FnXjZMpV33pjgZdpd+lEqxrR3jT8P3hdDOZb6J8aI5UO+uvWXIIjptrc7qctTmDEjAldJcCqzTabWKsHYhv9ab6tTMFQKYhS8SNMs603hyWbayS6Bp1JoEcNLKcwIMUEZCA70lUZtJgRnsjP0dRhYVOz88BGJq/9A2lkQDvPaF2YP9RmYHuooGjaLga6bm0zLeCqcVLT/X/9hc5lIH0wLYEwMD+Ze3aLHgqTwyQ9NLxacR+sYPbnBQutOWpBS4d/cL7FTRpWyoTZxgMsKew58lQsFHCxZFyvNrOaA6UsXQi7uf8xQPDvIRe0W2T23Gi4WXnyp9Xo8D/PpdhnCA+0ucLzDA+tL2Ch9Sz5wW0d/mB8f2m/iFg69y+/0tzTwFwUOkQuQN8/CKadOgNR/AQprayHsh/166LhEiJlbDBwGBgrh1Jy9t1Sh4rULTgjXL6kjcafHqmYiyIY63BvlovIB2v19LjARqXSp9+sMUt82iSCNog1CYugb3s6ImCYG97l4cQ+Stz70j0m/HTWGf9edxGNLjwVzX6lJcLVq75PRkyn8uRvWaGJPYdoA+aQfThSeXfWz9ZhGwWhm3o0YV5WOof1y1ePeZyXw8AkaXUvO1ae7vvv7VfLg1GLpHnac1ck7kzdsOSR6GA5Z7agLuvnhmEtYcOnncHTfw14cNpaBod6MEymBDLiaSOxyixnga9BOJCboU9yDw0Tqf/n2n2qZeDlobJndWBbeZN8nFYWFf3uep09+yYJ5wcsBy6zY+Lm+7ZGdHxv7lMYyNiz/XjfpN54DzZLrjDl8DlIJGhjyhYMxRbL3n8dxETDbaW7eMS6Gfoy8Y1fBRWVDq98JBx5o1qLG52HlogEXLtTycOO33aZ0Rz4St/uu3i/kY0IIPUnk43JwS0lkfwEUrEHkRwKEbfW+MeaWADmznsRv6idEVdfhpU9mCHFwGu2QE6KubV/6sgghVnroMWddIXxXPKX8d5UQftyyBU+1RJ2+pEv0mGyjCN+TCubYsUQIGGmulnyF8N33kl63noqw9XMSQZwiLCsddvWWpRg5wUxDUw8xZn8PHz9yJ4lr1k96cwOhA5zC+f9RePpQZ43mIAoVOfSggwI9fdclr+uvXv154WAKQ+/NGn5ooGqPpCNPNSNiptcR3lagtJ1xcHrBzHEpe5n4RMMlcwaeP5pUWpcXjknKhhWOrkqjJwNblIsrDOzqc7xrSDgDyjWWUgYm+DwK3DOOCWUblqn0pF/6NDJkgd5mvHiKhW0FfsyRrax2/yUbv+9eW1yewMbd22eP9K5lgxy+oyMncCBy0/a4FMrBBHqd8alK5+/QD5319XqvfMXFodojLTJ7Hmpt7tpaM3g4H86pfPKUB/rTTJqt0hGVsqGaoHMOH0FY0I0wAZwvjVotrxJ0+tUeTtvW181ZCHoowyd9lv7/2CjyfGpdwZE8ITaeCAhmvxVCOQ78S/Laxum4nokICYe/rrntqOI/FUlvbTSlIuRc8Tw5Oo3U5Re04VilOyrt/YS3pJWx7tV3pZAyQCfrlzaF53rWN88YUYjynqPu94/UYY9HohVtEizdSSvDqp/7hu4hrYDC27600CbpPEcd/hZ662BYPQsTNWlGQvhK4m3SScLR07SXv8OdcFiNcxig9yccoxVNJ07bMjr13lTa5nOGgZAbo5cm65M+mrB+12ZfJh4GJzINUpjtvkYmhtF0cCcLZ16maxy8zEIW42jdX0M2nrsIw3puZMNLz5Rhm8OGctzSn4PgOwVho/dwUPw9bnt8LgeX6DGpGhfzlxh4dXEiONj1+psn0VwoaW4LF3U7J48f5cZD3/Kcz7a+PPTbudJm8DUeuuVEbX8+kA/usYG7Wg4QHCzTCfa8xcf0X84ybWMBzu3aJPp6UIBqutzdFiCaGbztbyOJO12m+5M6u6bfuPfThLjLiN/cuJnwWaWNUgjl+mmREOfoccF3ISKswjaHGYtQe3zg2wWEBxXc/vD2xXwRBioFWhGCF359Uk/67qeHH/fbmVJ4Y0UnJgXl2pcxhW+Ebb8k8Tlps4xQagohm68FSki/7Njjis9fOGS1HoWVlW6a+0g/nXfPaMV78pzba4IivEoCJf0hfNjiYl7j9XCC5/cVO21o5eBU8CvPxDwmqqdE5wYuYyCoOcbS9lU4voYuWVP0KRyP809JXQYwsDUoe6j9GgYSrnoMHi1lIO7Du+mWbxhYrxHMG+Ks4jOFzwO1dl5g4sEr822Le5K6fs78zZGtJD8ltNDDgvGw35NNtNnIF+/6be/IRi1dRsLY7T5BNnJCGqpve3HgrBz8cTCToMqBVRz0ODVcXmDNRSrf4qMBl/Cb7C/lMd9IXe4emJk8lAdDpSGP14n/hrgMfnaljYfwDw68TE8+xpwNvz1VpvJd7co7+ih1q2oOsM3ivGmchrDTN/p+2vwJByao9GP3cuOVZsGqeZ7+o4E7hzwkuKpPz99LW1T+/P/Snvb7Rfqt0m6lQa7/TJNlZX8lyB3w8cnIFklnXj6qOcG9okbiG+qdMPO3BDc2b8iz/ilBYtuBY9q/Vbh3gUdmivCHBFq8CHKiJRDS6wIlEvRY3GR7zVsC4+gWpx8PRHjmRhu1+HhgdzyobAK7fU+agUdiGtgwoKTDT1T1twMniYwOTExxZMBHb4vPl6MMjBHpP3hDeKrf5FPf7o5htu+FMhHznBbgmLh7sl4/fYhKV/rfngYL3QemRV+3ZeNhzJp/u7ezcVy+YNTlTDbevnlsU2POQYDSWMwBvUU4Qc6Bku5UcBCS9/zsBtJfzVPu/0vhc6FsL++4aDzjI31M8LCxcsCg8kFtWkUjbj6U6vwK1Xz9sUNy5IBPfJAm+sYIAmwJM36UflYAGm3YaBF8JNm6236oEIMbbX9mTRdCs/hgF/gLofVv+R0+j+DitoygH+eEsFHkuHy4K8S6zXP+e/dTiJSvlk2nCB41SB+yP61OguHKCyrB0wFKQ1Tnc7ZYOLOiSbW3PKj6g+c4NVX8BW3yLY/IeaA20xMKCjkxrkdDelCY1OvM2EvdKSjt3M8Jf91o+MrrvRhTlcKAEEpYH8LF6XfRz6b9YeJe+ATp9LUM1PaaN/UxiaNtIrt6cVk4FB6fJ57+G45PaV+O3JxA4u1uOD4ugIFLflvTta8TfsNhrvuhw8Q3J3oTmomhGUYZoxRMxBrwxV41TOxWLhyy0G/r+QpfKQt/pp73kNaxoJTdbFQ4eJRysZSNBLrcWnAwMfAC5m/ngN7u3J3EQRd6DesfByMF249On87F7b3ryjgJXNBfLvcDt90nzsNrbmS6WMDDRno9/A0Pyw5feppqy0d/v53Ll+/jw2Vl4nzTG3x4RPkTZCCAT/D2TXc9BDBfUu/2QSTAv+IBjufeCtD74/Svwb2EyOqegryxwk4/uM4RNXOHPULsVRIcIWhV1atWAho17v0ugXbuPh1DEpftJ9SvafekcKAtc0FMN6qzP848c3RYI4ljB85hVBrYvWmQ4FkZvVAvQfP6/J27SXxvlW19P5T01VC6XZN+6t6LO/uTFoWXtB2ZvP+CLbQiQ+L/uqLRF2IE/PSRtDIFuNk/uC3oO7uTZ7orAT8DD1fX9jhREw6tSdc5v9+GI6tyzelbBCdd1qORDQMfu63z7RrDaJ+vMmBPrx0PYOKSNe28ZOLCiokONUlMzHo+yM+uReV/2KIUPFh43Xea2s4vLDRvnuHPd2BDKfsGqXCw8eGQ2dLhHMD87a0vG1RzOeVlfsOBtXLwS3DSoHnitAgVburQh0tDU5OWrOZBKedk8PCPLmeafBx/Si/i83FT+PRsrIQPpbrfoJrHltnrrE/fK0BbhDMpwQIUfdMYqm0qRPIsGqiS/up583Sih7AT5zCNvjRZk7wJO/Huqb0O1Zl3HXHd+vVQcldDCutmelhu76XCtx1+Jl+lMENBa2rppbUmqvfp8E2sPOPrPJzk+95eC0lrlnT2UXrqMTlXtX83s+qASdoMEQ7ObHr1Op7Xqd95rfqB5mwGUu6NULfRY3Tqv/bT/AujWsMxaoN59VIzBkppO8NSBi7S6wnxDJQcE60q/cbAj/35iwTjmcgPoCeUTOw6pVcxr4Dwmrr5JrXGLNDb0722s8DNds5ckslC2ZkfUwf3YGP6qHXP1s5Vzc0DV/nIT79iYy+9BmjLwfQrd06P43LAd7qWtvocp30vnwurPHc36Vou/mY7BpQlccEvHhwy9jsX9Bbn1GE8ONPrXmt5aK2qyemSzkMcYcORH3gYWaV3vW0IH92X2mcW7eGDlXGZasjnQ7mOYi7A12u8+3xPARbdTz15OFMAy8KYxzdbBJ3XW5TZ91BxF6rTd3w/jTbKUDjHVS5Wozpt1XQ38nPbMwmGueTfL8nKfhDeqtL7jlmyhr4ndfiHi8OUo/USNDZddw8hrw9RbN44mvTZIhdacFXhoQ58pNPgVbTCXPV31BMn+u/KkcDw1NompkKM14ywo8NXC+ElPeSlReqZ+oKxPl3LmOhZOZNkFgPvKi0rrRrC8c5IJzbIiIEksXo8uy/Jy9UPXTTcVP20g6dqiaZH7uvCbL8/CRM/e9KLYExUsOjGwYTFE2PZPidWp//MM9n1+fK7LExTGkDZnf7CzDTaEMlG+XHtTcvvs3E4euza8VYc/LWmX+FgVYKR7HwCB5OVC9AcXLWXGqgt5oKeLh+9xEXQkdt7fqvxULWaRhQ8ZHr/vPqCrdIlOvJ1vXIhho8PXgGH1/BVcc2+PMjG2VCAVcobjAjQ2mfWkZ3kOl/Mn+AoIXk3e7WufR2ph1uyYmuzSJ0VhU7JPvhZgo3KwbAEXkrArMI1HTzET3mDCwp9GxyqhYaqeP9Oc1594I8qrzvq872m3+Zm3VT9tKMud5n235Ir5P1jV1PxOQ8JjlIuKpE6nvHIHvkiOA6hndd8LFEOANjt9wVgIjdoBk/PhtG+n8WAl+JGeXcLBvS7ho3jWTJga/ea+c2bxP34MhJSRicPKjR2jmYYE57bhd6sYkJXmODTRaryKV2jstZbDGZhg3KhiwX/4oxhofdZGNV96ghJHzYIucCEVWwcVK5Hq3Awy3nN+4eDOJ14eGuG21TXExycGHy1ldHIab+PBsnP5fQn5kLuv0jzfg0XgdMW7Wlx5kEvFydnUzw07pD/zXrCg1YNd/hXQz4clINzPnZrROr2jeUjxvrXqoOP+J3Xv2NvpKMOblMOBFR9sAOndvTHDlxLyNXKLXWqOHfcl+IjDQ+bVb/X0YfruJdGsr6o4tUxp+n4dx3nqIfSWCCBh2JspJTUYeW48QbppyeNVs5/LUa23g2/li9CUFu6rt9jyoN0jf+khhQWNKwYps8fMNr94wwoZWZjBjZWljo12DOQbEUviJL+qXfS+mg0A9JuZf/sSZ6aKhf2VHOaefS642EmHOg0qlTxmo58HXTy+ZRuYSxoKo0OLIydtYFtr8tGol3ljO3uqrjGiXR5a96ysYvTfDVhBAdZxjSi4sB7gm5ANsHDG5TCKBdXdC7NPjeJi4qWj9gSzG2/P4pqnnO38uDgLMJffyjXSgnPMXI19avm4SC9rubE76yDHffD6cgXmj1HEv7Yse89NfTcotQ+Kj7Zocsq1x9IvjlqLaZvedPZV/vkLh/gXK/CQaUx9KIZBdq9lkdwbL8zNxo4BEfRauN50m/DvTfVjNdX5ac6Y/7JjUaqf9dR7zvqd3gBnUgS+IwQp2stFCPtSG/CUITIdKA7JgfZa+kbXLE68Q9np2TBht6q/rojVCtfQPL3ZI48L2wjAw0fdp79dY6BLd26Fz96q5qn/u8+T0wc6L0sJjqBiYgnG2zONzOhtMfasuBXaJgTRfrqtWXfirVuscCuLSeVhQWD2mOTzdxUcxuh+4/BwflsJEf/oqKNOfhEy74rOLhL3zaDycFc5Q0ZOHgZGbh8kBkXSnq5RoWHO3TE/+2dq/hraOY/9bJkHkwIe4kjcftvz2ubKb9UfLCjHnboNR06ekd97IhHx/0vOvrctgErty4bQUG+SGNdzzEUGNwZJIMpKG8bQOLfgZfyud9/DdBS6Q0d+kMHTqbZ5TJSdzv6a0ecO3htB9/p2ENbfs7T7Ps4CUZ8OV4bKhB17tkt2Gx9/KuC3TnfTltf6DZgFYnbANpwxsDg3I8rr/VntPuEGQhS3iCLAari19RP6ap56vqS2HulQ5iQluptGrRVpevX2Z+zGKjJQojZpY1W1iwoxxO7WLj87cOZbldZsPRf4ureyMLZSvqGKGxonTGZ3If01ccT21w33GJDuV5ixoGyXS9V6cKOyhtWcCAysL8UO5CL4kv0Qg8XNsobJnCRPixrTOsbLt6MopVSHpTruQR3TNu5glAyCsstb9UfJH2tq3KhSvW843p34JSO69rRBzv4Rsf17eh/Ll8v7zk+RDXXvmdVXrFxJNU5L0td9D7I7f/puR18swP3dOhHHbjaXTlYUOm8c8Q1LY4WVCfeOsOgF+8lmIfTHJO7YtQSNLjyhRDus8qqakfyUFI+Ss57wGq/3xizM34dOlIHP30u3zrl2ToGLDK9+hsdInV6Cu3MYoDpQyM0JpI+08SAiZW3DHjDjjBRZOBZkFPCBPen4dztJqz2vUEWTjje+W+iiIV5jlMvbchVzeM6dJARU5/M4W5gY33Ntq17ElW6cGYgDdRJ3X1zWJa2RjVPn9CF3kAmz7fRkx4u3sWZ3+6yh9vJNzry5dXuieRHVOd9lzpwZYf+2lFnO/hgRz3uwDtZNspv0FmfO/SFjv54N7/30sJ6VZ/t6MsddbfjfXT3tJrpalCIMSrpuV5LhZc66kew0miiwssdc7YOfbHj9/hKG6gEyvVtdzHmOO4xGz5CiMd5G3pN1OR26j7/28Ng4L3L+9X3rBhYOOpyZp+eDBhMXddY6cKAt82K/9T8GRgfE2PAFzNw50Pyfn4RA7yS9f+mGDLRS7lwx0TwgiNtLgJmu8+b2Vnf/507OWPRVBaWKw0ULHwuoBfcWFA0dwuuVmPj9mrJhE8D2fje9It8UzZOPdzPW3lctV/10IfyXdWNg1tX+rpuma7CxVcC1iaaXSR8Z0p99OgBVKd/uiOOF1xE32pIfkRzR5JfVc09lMef5Jc0Rrng35lnzSZqb83HU3A9MZkZQ14fHnrffp8l6aPraURBYbzRGtFqUJ33XTlUYza0lrxOo61+1qr8UtoNB6n4aMfjnd30jd0o2CgHUxSu96HvxEjqQeXhEqPBqtc75uAd36PjXHWco7kW+wdkzFTh38kLevb06yvAq8B73eqNCJ9n92vqncpsv+8iA635tBGb0b53Fd7ph/jffk44lHdBIvU53I52ejFw4eEDP9NNDNyaQRs+VHmsXLf9TPrzEFpoYnbObWkX578AwneccupeX2Pizc+IC47vCf6+X52f152FelrOn6zqwwaDFj4Zc4oF23lpI4TPWfitxfr6TZ2N/wPEeoZjAQAAAACAAAAoUwAAhjUAAA==eJxdXGV4VEmzxt1dF3d358XdYVnc3d0WWdzd3R0Wd1tg3M+4RSYJ7u5wu3oqzH2+/AkJk5HT3VWv1dl3ZMvpD83jUGPbklCxQXHY3vLngNhrIQyov3DJz7RxmFnv3si7K+OgOZokj3VfHEbdXXl7QfU4TMr18eL9/nHon9qSt7M/DmmmD39xOX08aleYofy1Lg63apz9c4E6DqtLv8h4u3I8TlcvkzKqXzx0T7tbW7+Kw4k39vaDssSjxeV3P5wz4tHvwKzRA07GI/h+OLo0i4dt54pueXrHIybn3V/dz8cjfRkc//gkHoufdb76x+J4vKmY+83ubfHIdvjW3dwlE/Dr/N9vz3ZKwM1/+5/Nf1k8brbtQs8P8Zjc9mSRiWsTkOub8aX+fgIm9np94EzhBMzVLE5epGcC0v3zJkO+rwlomXxG+uKlH+CNZ8jqWcsSUGVT9hdlDyUgfrS1fNpOD1CvZrI31+c+QLP/inQ77hOPP6UWV+QBrB1StdSffIAvVR7+WyLhAVIe3FA3afsHMFRVnas47gEy9Ix6WufJA3T6nmeyJ9tD7O2Zbd+RLQ/wfVX61E3OP0CqDxUmXy/vRipvy6P/znbjqunJ+W1eN3aFUs2em8eDFmnW1lzTw4ONSWpcGnPag2DWDun7vfcgdDG2ef/GXlSll13oheXw1RPPb3gh3/Z3L7KXzzR8bg0f/krxq7d+jQ/2+E81f/h8yPtofJMiyfzofnP9zITMfkwt9I+zZAc/Rn37q/uOTX4kmbKtUqX//MiXO9OiIUkDuH7rUJVeLQPYcj7DxFMrA/w+AijUttD1fbcD2NDnZ9OvLwKodLDHuJE1gohZ3rPSkklBBD3zJ7fYFMTp3E3rKP8GMf71Yn23u0EMrDWm9yZfEINu30zy7XEQYycPnnUvWxR6frWl/FAhCs2+vnj2X50oDNrvrv6qdhTo3TSsG4Vy2ece31srCv0b7zg3Qvx8JHPfTjFVo5C6hOnij3JRiHVH1XcVicLf/tAZfb4olL9yMV+2T0HMi+526J43iBqvajdvOSmEqcl2pMm/J4SHj9/8vfNnLBaVrfH3vpwhdN7XMaG1PYRPZ06JnRqH9IPTN87UJoQuLxxvts0PoW39fWMMOeNQr+vjm5qKcfA6Ms1r7w5hUrBMwVO/xN9t0Iwo+GccGjQ7WG/Z7jh8L7gi2ZyicThKx6tJHJKVW5rLZ4lD0sp7Gjf9FYfK3oyBXEPj0OhQzO1Mu+Kw9Vh0Pm9ZsZ8X/mfY3zwe0RuXLu7liEP7nKvaPE0Rj7OTRqx/MTseoxcc+5bkUDy6Xkjda36ReLRv0/jK1urx2DaO/jIeuSrP7z39XTzSiP89Kc5PYbmBxPnp9z7nobwJuPGiyeTdLRIwMPbapwn6eNTT/vP8w8t4pJq/uenKMQm8nxLgPrZ0xJTUCdDkWlBgU+UEHKcfzyWg0dIvb5M9T8DBVI7Ks5sl4L8U46LMXRNwoLSt1NviD3A9x1+b5/z1AJPff77Vfl4Cr38C/rx77FCHRi44B+RbsnaZC3nOX3dNtrtQNj5meJ/abgy+uOZEyWVupJcL6EYXuYAe7PvRKmve7h6cGNCtS6l9Hiy/9DlHkngPlq6Y+LNkDi9e6e83OT3SC+8eOlhemJrPLjXslhcFp7dJ/eyXl/eRDyt/DP2UMNAHn+VT1/jDPjweVe7j1igftpfNs9mZ1o8ltd3qsdX9OJxjyt+tB/uhmj7IvXK/H2tMxeZVfu3H6UrZ/JMqBlDm8Uex0gGU6Fvty7CdAeTP9qnHV1sAzcYtyf/6SwCBO+fth3IEcRTf1hetGsTE2u9O1mkUxKoKKeqUHhiEJ3fasXW2BnG88uUsCfuCuP86a5USG4IouVqpe2JHEOlblBBbNIhjf1EFCaLW8sYDHtiCaO1pd9v3MIh0B14mX+cPYt2nE5vT3gli/dxvdSdYgoh9dz9NSBG/79D3S3lrLJ/LWBw1Dhr0bmIsap5s+Ffvc7HoUcDyfVSmELJ+H1+iXs0Qvg5c9HpEfCyGl99h6ZAqBIeoyj3qhLB15ecOplkhVE2++c2raiHop93em69SCLsrrlxu1obQl57+WQilqqQ5VaBXCPRpFm0Pocy/Zb/9myYORe+XjdbVj0ORZJn2tzGHYPWPzb1DPD68L+OwoWjVU3Gr4/Cvt/+0jn/EIcPG4ws6t46DzdX9eaUbcfg140yv7a/jkE1cncfinNwS1cK5OfHx8dj0MVX/be3ikZVe9rI4d62vD3trjsO3mV3XpxscDyst8854HDqRVlyxeF7feLT+Z0evy+541FrTY2XHpAnwbSp30yjOW0/5gHjI41UzAV9aX+qoHZyAe7lLdk15MB60q/KI8+V0nr90u5UTD2xFA1/mOmEYs651C4sTMR1Xbx2XzYUNWwyBrB1ccI+7Yetz1IWH65FnxksX0gWv7zxa141FNy5MCyx240BzanxuiKK7YFwlD8STjbo5xYPT/61znd7jQcm8dcRR8GCsIWf0g6JeNBLP0qCPF82qX1/o3e7F6uYOnz7WC13jtNXGin0+e2DVPyuX9SHKPqpVcLAP7Rdl7Hhmkw+l3xZ/skPvg3jyjXlS+KEZW/2fK438sNzbM+3zaD/XBz/O/rQOvH3TD9mOn/jx7/zT2cpnCuB91RUPtnQPoK88EAE87LPipEHU/R2zL5hXPwlg1NI7JUc7Axjxr6NV2wcBLBvcN9gieRCtVujOrqgVRC9ZKIMo8rRhkuPie5VzX67W7BbkcxbE1SfWuYeWBTE9sPhh3dVBmKn9zQ5ibzJCIEGub7GoKC90LHa3nfinb3oMv34MJly+VqXBwFisujY67Zd9sZDltFosnvZ+OKVnu1gEBz9pmtsZC/t4QiohpKr0NZ19VSzyrTxyYePdWK7TIaQW3bZIfXEexLtb9S4WjYd+/7YqfQiPfae+9hkbwqk6jSq3XBtCxQJp9MlKhnCs4M8dpzqHMDdJl521BV6id9X8dQjaMnU3GOeFINv5lhD6JO9dokcBgZO2tROVPA6ZLyZr2v1KCJtu1hWVN4TvYrdNnBSHM9vpA8VhJZU10Tf2itU53yIOAhSlWHk/DnHFBrUprsTh3NjOwxa2i4OEK3MFLguZzjxMEo90i9PUXiT6x8gu6UTLjkOL7Rmr6nVx/HsHfix9H/DMd2D1y8LTjtgcOHOubbfxGZ0YkooqsBOld05aPP2aE1knpPlz1EcnMjef33B9KRfo1bf3d0FlyVH3zDkXhkw9+ejQJxfMqbYPSRC4xZV2RNExM9zYvuesKrnBjYVft9Tpm8uDaqGtX2d29SAqdeFLyzd6sKzisfHbHR78vbBSuyPpvCjWhv7lBf31rMNelF/w6/QQrRfxy6jA+7BuWZn1zSr5kPJrsz3F+/r4evpQw95FQFIflvxJAMaHO28uDd8i9rV4043KVfbjXvUMhbJ29YOuUtVJfnSbUnzrr3V+rqN+DG6e8cjAz360oONTOoC/b32LvzUqwH04gNIS4AXwx5eF8x9FB3A2+aNVpQW+ke37UwDuX3VMW3MH+VwGQdUmfeMgBHiosXF8EPk6+/PPFnV+TBE6mTGQLz83BvJll0WjZ+4rSpLoaD5HMdgpqlHutLGMn2PQuBV18Bi0khs3FndP35h5omkslh6lhhDD1ycWt463WnxucSyyjX5TJokmFs0/uP/rLp6nZa+y41vWjgXtOsv3WO6TISyj5Z8ciyO2mav7HoiFh+BSrRD3sxAECBItORZjH307/sYei3FUJhaEsDbvsVXz9or6vn/QrjtpQ4gqTkhB7O8P6afdvxzCuWqLLaPFPj7hfD6/+4AQxOae1WFWhD849lIDF/vx2bFRC44JnJS01di4hBDvHzuq9O/3z5wpdmTsprljvWrHhcZU+RzIPvC+o9xQB+NNB47FVxGdyMHr7ESj9hV6P1vp5H3jxC8qX0lcXL9djPdcfJ1daPhvxpzx313cB9x8Tt0YfaWvKF1uFG8+pIc6vwef8xLw9eDZuD1P1+30QP3nUEFBPJBt7acHAsw0MXT0YoA56YI6s7xIoqk24vFeL2ZNmC5KvhfzBZpa+8mLvALtv23sg9xmM33I+CN7n1PHfDATnAj6MGH2i+0lkvqh4K56RT4/n3s/PNXpjfvRJNwAUN6542HBo348zj5zwxSzH7KMPPWjwdzx4tL6MZDgVJkARDEQJTAAWQ6XBxgvBZgPBbCzMBXgAP4aQAwsgBayAUTheXU6UNG4lnfcf4+WRiHNpE732gWi0F0CtWiEZuX4t2UoGuYe1XfvzhAN1aZWB7Z2jobcbrliuE7FYOgEqmjRmENlOW0M0uZaWu3OjhjUuRazv7QtBm+J7vWIQbsfN8XOjQGhxBS/YrDn1VFN21qiTles7hl5KIb3cwxuddlSZM3UWO5jAqc7FuSekzIWBZOf99xALALBbaLixGLvg5niysdiWxQB5lisrJp/yIb1kX2/otDgl72ahJC73KjKGe/HMq6IhSh+YzYPUFC9bTLR0hW8K0Qdwo4logtd7GaHAIfiCe3ISW3qoh3NcjuvmJ7YuS45MLFBOs/BCQ5M2O4YWuWcg+uak9dP4IFatz+aNjhx0EQb3Ykkf50fXCyLC8G2+Q5O7ebCxEl3mnbe58J60e03RrtQqsPL6aXTuUFdJ3UTN3plXP53+TlujLDZxA5xo+QfnWo+SOeBWNzUm9p7UJlo0VQPqOpcvuZhXu2B4UifUN+KXsbhXqwKjEgyfr4Xuz3eEieueCG3qc0L8WSvJmUVdXQtfSCBi7c3eVRjmo9xog+1JKD04cHVrnV7/PJBlrlMftzSVOx8q4YfxIbvjvOjnbyQfkRloL/wg9BC0zg/bHQcv/m5jwYgy29RUVerdcr7q2EAxGpXdgygmAQ+QZQu/+af82Wi0OrOnIK1hwdRVvNlZyeBC0YYr4lPEgVCP5OCUWi6bmG2YQL/2gtSR4pCMt+zqEKvoiDhTfdo5Itq/Lzd/ijMkgdc8EY6tleiMXrH9F0HisUgXUXa2dF4PpKASzTadewRl3RoDNe/GGxVqx7G+6MZl4n6/Ofek6e9MVjSy3EzPkcsnv9XR7SuGPwpWGWTdTFcR2NZJ4hlPhUDUZwLpy4Ri2dxNfP919uG8QQH19sYp9sw7uuFQmWKKowvFNZdFFDVCaWyI01y662lbe04PTJ64oo1dpCacc1vR0q5kR1Ismq42BkOSBiwyoGF4yqNXOV2wHZ1QuHi6Z0YKoUTJwo+NKY/u9aJUveWiA7ihKRh2V2Q26CPC782by55WOzDdpJAu2D8/ElUQDfjjAgv29Vp2T9Nj7lhfBf78lSUG08JtmXwoGqvln9mb+YBVdlyCzy4O857ufQFD3o8yrPuVrQHXfv8+bFvVi+uprrc5mxtL18PLzZ9I+DlxfQsxKi9aC/Yjea9+P5uTdcpxX2YK3ZBs5E+UNVL2OnDSLGLU6h9MLbZ2MX2QvC3vbGDmmf0cx/2Y3H/p4/aN/fz80bqZqkcRWOznPBj7QQSfvzYQh83EICErU2DSCGJqujv8sAH0PDFa88QgUPlMbsWRM4uc7u8cASYFwVxpTR1qCjo7EsFoolC7c/3hpwYEISfllEdZPwQhUlHpwhIHcU8PQrF3XFdk6yK4r4ejVqyAESjy9unBZKfF4/793XF3G+i0KQ3KTnR+LxsW7bW76OxbhgRs2jmo9GMr6zcF60ovivXzcflbHxubJBPs9uGw+WbFngWZwOpK5lLK5jZ986AWmMU7DpaN2HYVQVPSP54rqBYTmL4dsZLdlSj8nnBjsd3o3reeGFHnoZFjzTL5sBTguV9HBgvBQoH7xcH6yJOjJIPdCJ+wsO/Jv/jxBy5YZ3Y9pUImQtdBhIiEryoTqohZ6a7kKJ7pgrnd7uQSaDUnl4X8lH7yepmfuJGOaLlI0QdlIKFG5uHErNwY4ipb1tdag/uX24uEK7o03WezIkfLb4XoMbgAaFVc1IvyvRZe+9DaYErqayIOhjex140jfujbaHrXjS0pcvQM8oL0ZTvVs/gQ5GNq3YtbuBDD3nwfagnrlLSvT64CxTedlDrQ2ybaWNSxPkgQMWXzE98zAP8fM78kG3gi+jThz6tPtMggDoXdYIx+0HoaWLID3F4V/wYGsCGmQmZU+0JYGbSj8rxcgHkrEtIPABBLmIC7wOshwj8mCJu6o8Fgn9RudAEML1CkULvxwSxnNrPwSAGCzS2OE0Q82fQwgbR0TSryqbsUZAfu08U4/og89Mgvpa62qPzQjO2dSZhxcK81oI3u7d9GFTaCik7XbZCXyhD/U9/2HCpW/SaCb1sjG9smE70P8oGrMryx9gCCkgd2zZSYV1IgSyfLgVbif6mtzPfsePB3KUHGu21I9u9g6uLucX++jlHszi5g/m7A4+S3C3QYK6DebwDs12GRf2fit9r3i8q8YeTz6kTmgNPng+Z4OQ+5kTZv2ZsXfnZieDuS4GK1V0QIEHsEBeyNyPG4ULG2Arr9n12gdD3N8G7w3zeDWJ7HTe44Z5s/6bxuFnv86CJ6Brv8nm4H3og5bGJHhiHz/wjuNsDff23qZNbPRCLkXX0Gw86LxwpdryX9QMv1q5uJKCWF703+QpvXOVFDdlQBC8nWun38r714WmK6llr1vdBypD9fDg6kxqVj8+BD02a/kqRu5yfX9eHjo0ICfvQ4eLJdW83+rn/+TGsA3UqHwZKgcmP3tSeCgZQ3197UAXRXxP7siHfhyyrXwp+X5gQXABvt803fBY8JrEvn46atv9ahwBiPvurtU1mwtuznUSrNEGQ4Fr7s5gZB5vR/1JVwejMmEdy0zILnILtv0hjhS51A9EirZDbNosNG0kGbW+DlJ1X27Dq9oLqg0W/m3y9fMmahRQUGpFyyalxCtcvBQKkLj8bUlBh85Viu3LZ8Y84LY1aie/OkgIR2SE227lcYv+E9VY7Uu35O/PWAg7Gew7WpR1oOLPHsRpmB9YUo0LrhCwfRZ3IIIUAJ2QZ3OmEve6JHYWfOrkuulDubpyoWC7ExKbfeHyBC+JNi4V04URTEvRc6DeYlFk3iKWWb+pGxV9FfnycKvojXaZdbqhKaHfb77rhJbn7oxtUjazlPcgvD6YH699uHLV3jQdVnqk7dbgo9o1cSA+S/DtE26KwF1mKLWqZv6UX9CmTDfBCvp3JXtYJvci+c+3opmovqMpGP/Di5TMCID78zEQE3Qcpqx/z4tDht9MWObysG/kgj+c3H9cFH4htPRf9NFwv/Hx9/Xx+faAuHpVa8JDlZu2l/QbGGUbWDYxI+2Xf8AtPjWhr/kOsoIn5rRmjfL36F79oRn7aZoUtSCOFdQtk+ctnxevg31cTrlqxwEDAwoa31HZa2rClNyFcG4hdZ7xtA6Hytj9skPJgAwU33vbck3q6qDOS2CjoOFVnnpDcDinnN7HjR+UmMy5OtEOQ8QGFdttZTxP7hWSZzA7MlE/oQPc5/14eNM+BN4PpADpYn3KC1HTUEP2JbK82Tkj6KepLWId1Mp9xMo92YkbpdO+X13fx80XqjITt712M99ysa7j5+rqRg+yYQ26MpTJ8z427k1+FNsS7uS960JbgTAkPCO2tqeeBvKxjPZhC5WmPBxJeRXnwsn/PkV3SeTE5KzksXtbRvJDwtauX/Sovhu2rKiqsh/UoD4hVHX7rZd/LhxqDlSnTqnpRnGjo317mj3rIh9/RM+40MM4zQMLrP43M341oINDu/Ywm5ocmdOpqdH8tamacZsarh8czXctrwcAjL8Q7tqBGv+utMySzIseWGY18m6z4OC/TuwcpbSB1bkxDG78PGzoQvLoo+otgH3ee21hvV9hnVNBzZceklfcoyCyJqoIAycnV7RhTMmbM5MF2rut2yDJy045KZA9+EDiG5I2Coi6caXCheCMHCI1+H+/ANS0BOAf7cw48O02CpxOdYqoeNQ5youLpJaoGW5woI4VsJ54WIWbhZJ/JBWK9DZq5WL9zgVDT2aMu/EMfT+CZsF7kwh5qr6XckDJFCzeu3bvRRyX2xYBkV14+PC5+Jnr1yI3jd0m48mCy3JgeVCK5bGhEP7vfkpi34H/Uhm4JXE30eKKW8YgWr/L3/3tYax1/fh1GSICsx8vZtAR6nDpNwrmBcZQBq9ydXSk6G9FNEgkj69om0Cm8edwECYsKmVnHNqNBqVvjZ78wg95t5f4W1gUsjAutyHhyRrF3Oivk8SlsQ9XabfrtHmzDq59EuG38ujZklMRSgfx4VSLr+4JkkxMKlgaJSCrsJ9q5Ltq5ftsh24/djvcdJ5w7kMoBaY9Vd8AkWE6Hvg6kPHZ86Og5DuQ6lHflkQsOOEgOF+sb9iXEeRa7a099J2odKSWgoRNnps+7+ddpJ/KNPH6zYMiJzTsaCGToYn0wgiuWTDrQd/EmF/oeXb/FEHCx7uhGx2LUudzo/qbFw/Vw42HR29kv9HOzju3GxbNHC/7c4caBrQS83IwH3ayve/CXFEo0ULZMFm9Ri8blKg+ZelKLDruCrzrl0LEOoGP/RId3VBaH60HsaPxrPWoW8ixZMdGA0tIgMrD/YIQo6tUWW4yMf0y4c4WEdvGdaEIzMwht3zsYWde0UiixMF60QBRtsTOtWKvNQKURJaRxasMMulztbJBlaIkNm6JJ6LBBymHfbZC2WH4FUg5poaBarvbDSy1UkMNYQZwoBcp6MqYUdDoxp+4f1ezoMO9jseZD7JD0eIsd9KkW6+2s9zgYJzsgbdfODnRpRsqvA6UEKqx+3QFS85s7HKxnOUAqubG0E2N/GQRTdOJCGQKKTvafnZhQgpx8Jwwd6Q244CW7v4ULKqINov+Hz4uL+50LmV9axJV3I2+BEmcsVdwoS/C7hxtJ81j37Z/nxvCd+R3Zt7hZV9Cw763h/anFpkOjRYXSsu6nhZTle+qQRrDLezd0WD8w5/ghf+j59fXszxvwV8nvXTDewHjFgI0FCVEYMY/gtM6IYbIBmHBjLjFuE97QsW0VWVdp76eyYCvZz70tkLLeHQsGvZs49lgeKzLJjW3FVSoXj63Mo22ITkY7wYbuC9Qh0xkbHgwkA9WGCg1T/jcjlYILr1bEziunYOo0OuEKpE2+TZznGuZ6HRQFr+9sfu5NY4eU21ramZfaIeXvbXaup3bWxeyg3be9hAMP3pIA50AnKaQ7uI8Jnrmm3uGFHxyos+uLq0BhJ544N1vfivMb3q8RH07S1FtOrjNOvNMRkXHi6hkS8F0gFF57kAtlC576NXmbi3GJC1T958S7QOxoZB43+sgDoQZVg4uxaoyTxFjDfEmD17ladfqeR8v4RMu5Dy3rwTrWMXT8enr4ZvfJX1ajh7Rlqhgg6dlaAxpFn+xe8rsBpZZdz9xolBESJviNsJLN0taEK0Rv9Sb0DfWtGFfPjFZSeDXjx977s2cmtWD7jS3rMs+y4PnXlDcGBiyYLVDx9DZW9JGBBCufByuSEsyuZMPovWvESRb4nGzW3TaUPZ7d1sxrg/jjAyaxrr4GBKAVzJICoAIPybtLFa6jCiRdfqogJdn9BUU9loTBzrzajku1SAiJnN9wzsHO+8XBONvBORoHvvUZq/553sH1x4HdWSnI42T/zsn+oBPGLNrrV1c7Qeir9XUnCjShCh3xqSpIIc+FfJLguliXdWGMFPxVzIfUKCpQe+wuNSTtSa2BYfm0nF3mavBifvcBuqcazNny6G5UTy2OUcxpv5b1WB2GUnmYrsOhrn81NL7TQdoPQ/R4TR/Lp8flClQxDZiflgxeA8Y8z+bqndyIrEmXdjo8zIgru8iwNMLQjZQ3EwodUAqqLCbOtZixj2SsI2ZUk0a9mfVjC4ZJo9qCbrLhWrFOsIDa86yYLgUpKwgVfCto47yRjXUXG/MBG/ssNrisFKRSGL8qGD1nvW3LZAVOolPHFH5/CvtHdrx536bnktp2rOyWp3eaSXaQm1Z0uZ37pB2SpjrsWEB0R/TfDY+HitLugIyddBPn9lYecWkd3MccKCEf4GDdPcLrm8oC7ISE4f2ckPEUUadvR/940++S4G9Ef+KckLGKgSp8JjtfpcJ0wVaLVVDjNdkGy9XwJyfHXI2ekpBrIOnJKQ3kscquZV89Un8lLW2kw4krJEjqsH//vAUrc+qxS1X47KQRepyZTRtBjygqD10NeHWKCLKBn88ISYNnGvFTvMuqISPEYguKYAJdrU73TFhGdErwIgnrV5vRT5zeKs/MOPuQhA4L6taTnYRxpBXLi8/Kq55kxaWcFFCzYmYxqlw2vj42zs3Z8F6w8aRLbSAWt/SO4FGNrcNcj2wo8qCUq2FBBVu7k7KlsC+gsD6tQHeJjDuF/QEFkuYUtnM9sGOsBNp2rB507bBrnR3HKYZ01460Etjb2bdw4BaVw+YONPo465P2b4Gbp5EC58D3+Fujht6LnOMxtE2zOzln5ESxzzFtpo1xYtrE/BljK6jwrej+Co+6qJBVGtEqjJMCvJr9YjXrFGrmVWr0FuzyQR8N7gwlwUOD3ENr6ooX0+JvaYxrMWPXgWLXvmuhUBsYpcPuxQ1evPboQCrKqMp67LHf/YqlehAKOZigx+a93b5NqW9gfc6AdrJQGPGQbKRxRvbZjbxPTOgpDXUTDp6hN2aGhF2Lzbgg0PIIoxnyMmS0oPFe3cZDoy3Mwy3oJAugFa/+pOSaFc9+FXhoTG9DYbKjBO+R9GSQDZSiardcnNOGBdfmFeeW0GOGBzbO/wk8TDSiq8I5HoWvu4JGUnhSsHoXfeBIXZ4gA5miPq/ue2DWaDukTbpI8KOzoSebz4vzTbaN146RkgA4IO3qqg7uQw68aEnBDQeyULzuqAOUJlp4w4HtRGP8Dl5PFXoe/IMqGeJXU0BKxXxXhXbSaFeB3K8kNdSc91JDNZuQnBrlbxqbzy6l4fOuwY4SSUeN8mlALvjYOloMkwVKyzklHT5P0E88OkXHOogOUg4rp2dfUM86nx6UOh1W3oAlVP4OGVgnj6zrDRcJfkYIEJPxR3ZRf0dS4NGEGrLAmZFOBhHMoLTIFZMZhSrWebkpkwXZ01GnseDFnvrlnTssmCWFdQv7WFYoBFsNVizLSgKn4K/rSwkkbsMw+RXBye/HkHJgY13fxn6Ugh5h4ISkJIOOj+CpML5VeN8qWP6UCKqddQs7+112zoGIOm2eKiiMHXUGEVITuIriJYL/EjvfL+qzhB+9HKwrO+BLo3r2NaX293qFdSEV+/IqDJ5HiEDFdV/FOQU1JFzvoYaUrU6p2YfRYGF1EhY0aCcLmob1Iy3ulJPMF6nlAdBitiQWOuwpM6Xf4K06XNx4t/PbpzrWRfWQtOy84KtkJ/3SQ8p69QzIe/rgibQHDIzbjaz7GUEp45DJyHqfCaQC/LvdhNZSkBG8Rgq2Zohfzr5gNjMesoDSFuapEVwsZe5YC9c7K+cYrKxHW5FdFnobqkmD08Z5Chv3Gxvn8myck1Dws1y59m0aCzz8hQJ2CqpK41Zhf1Xhvhipy4nrK+PIpezcV+z8OnbWpeysI9lZ17WjoAwCmLEgTrvtxhYdltujY9NvVEPGUier8NZcU1BdFeMPFfMhFeakHKk58ESFaxR3qqXGBfshgbjUiCY7874aBWSgTIN/RHessU2D2lk2pGwXq2G9Xcv9RQsZH36pZV1Jh9kk763Qsa6h41yfHtKG3aCHgeKa0XrOWxs4D2fg3IQBX98me36wqxFzJPEzYttB7auxX42gVEC1ESZQCnyGyQQLfazyZhz3EQAwsz5mYT3MAq2HAqEW/KT46BsL82srr4cVj+L9napYrOwX2dgXsbGPZYOUlWbZUD8HJZBsqEFt2G5j38EGN8UUS4n1FN1o+EAFi7IN8//crEDS1FsK+58KZLlMYUemnPHf25e2Yzkd/6p20NWq3caOOFGVM0y0M29TkEWgjFKfBI6XQrQBcnue1yBt/j39yhhVWH+VhCsVKFW/vayK9QYVhsjCpeK+oUazY10upO4l6u2cxwu+blGzj6nm/ISGc4QajB56r9KHd+L5qxFj1qKVNEq0/Do69mt13K8FbtpBQRI9QiSXd9ZD0vO9esi29ljPeT0DZGxijYH1TgMkPGpnZN3aiCTyy8T834Tisu2aIO2hImY0nmPcVXGlGYs+kGBo5vpq4VygBTtPdc6sPLawr2Dl3KcVJzKRQGjlvKONeWOkLme/OrVWx6k2FJIF2cY6lo11SoV1UoVz2wrnAxTGEQo6yCC1wn6QwjxLYV3Uzn3eDkr7HnmhQF72NzbmQaL+jCLFXMf9S43LAQq+qCBj42VUqCmFRhXS1qZkuor3nYpxpRo1p1GlUEPG08Q5Lf7y3Pv96TXcpzScg9ZAxqu/aUDuxFdoQSrhlTNa9g11KC0Jrw5H5pKirsM5ggcZIusaeknAV89+kR4yFtbewPl5Axx0Ob4YIOOUzY3sbxuZT0f0iXBdMuHOjuQpJr83cf8ws09u5pyYBTmlEGIBofzCZy2QspdY380UNypvhYzFTbeCrkae01YUkcF/KyatWJPwtpCN8ZqNz5MNeymOeMSGoynpHdqYv9lQUQa6FBRwZN8yo5HC+FvBJRqn2KFwXknh3IuCe53JoLf/1q1kOVyi/H4f0vaYZgCh806HNYjvSoKqCpl3VTtwUfTTsD+vgpFsUXFOP9wm4VAFaSPFqth3VqNpCmK2auaBasb/kb56lmzKKA0aXijeqH0FLc8DaDG+JU3caNnH1zEP1OHJDn2tQFCHtKLqFsyvR38pxOrZ/9cjvOENnJc0cE7fAGnTpzWiM8U9uhg5V2tkHcaIQRLAmCDh+XIT8sb1yH1FMUF8yFbVcpkRtyG+w5MBZkg6fSNybmOzUELQAmIZV7UW1lGskPHxIVaQehh9xMq5OBumPDUMn/mHjXmdDfdJNhd4Oayv2TgXZUPsfho8sEHS0kzK77pMqfvKLRX2TxUMGp5ut8ersI+vMF9ROBemQMJdo6gHpWdOXZPUjClEb8U5DevSagykON1yFfvzKvZfVNCfXVGr6loV9t+WBAUy3pksgpPOkfwrcNJpKleZNCD200ec0zCe1LBOpYGkffUj+DecI9KyLq+DoRYl3nSYRDHBWB10oiteKKRHcymU6yHLmk7PfMQAYp3qTgaIi4pv6w2sBxggx28KG/k8GTkfbWR+ZmK92YQEao8xJtbnzJCyRm8zUlOMZ5f4mcawBK/dW4gC5BbWUyycM7JwDtCKLFJQiuhSrhlPSlZJY8OP9lQBbHhxo6hokTb232xh+0f022eTiRFHzu35rMs8H0RdDesydkjZP6sdh6uQQ2YHVfFZOey/dYww/1Z+76uwfmfg/K3mN+8M67EqJInPW/19RxWOUPmZpOK8kIp9HxXk2FE9Nee01QisoIESNeslGvZ1NAjTIg3PA2hZR9dyflzL/pUWEg4004FUUtUmHVrKIJSOc9d65JNGgVjP2F1ZD9/SI7oQMUkD82EDSMUpI85rmKcbMFoGi4wYJQmoEYc70MY3QsYwk5sgad6fJsgY6l0T543NnEc0M+82o4ociDFzDsQCQg91+log2/kJC+cGrHAQXGxt5TyLFT9GZcpiUqys/9rw00kLbeNzYuPramO/TeE8r8J1RcHANX/cuSJ+H85BiXUrsKmy46OC26MIoIh6K4Fa5JxGU2yhmBmTyZbZq+N8uRqNnXOSdNmp4nkDFeNGFecRVOgmAaMKM02NxCdRQd1gS8MpjdX4SMd8s5rzhGpIu6KBBs9zP18yVvDU8TKYp8Gjgkcft6qoxciCf7bPMlULuTx3tcgvBwJ1aCeNVx3zeh2W/kxbsbtTh0JycFH/Wz+cWLh4+m3/6SHtpKwGzhEZ2M824PZd2rARHTHMx43MRwTvqU3AxsTnxsT5fRPjMzPnbc2cAxbnlMrNQzNU/TfWeFrEwjlmC1rKARULqpCdrorwHxlP6GeFHD9cacXJzWnvOK0R3VHSsZcK62AKIK72rqDCcyACxxJdcEdyQcRmF3oj/x+eR1TgovIm+FHiPgrnWwycA9TiteqU6ExqjJK/UCGX/IcKdPUL/aPiOUoVzzGpuN+p0UkGXNWQtEyl5r6iYRyo4fkqDYgt/PtaAxkvaKiF3K6rtCh+7fvS9wEtVtEYVBUdTsWR0KHDSlHtv9/VodSneoIZ6CDHE6rpWRfSIz3ZUT496zEGyCpU18A6gIHxpoHn6oxQT6IBVSNcZGtcNXIOxgRCRRW7m3C0KDV2k0yJt7tvYt3GjEs/iGCaIe33/WaM/7Dr/e03Zq5HFsiYzHQL61wWjKIYaYKFc7NWxrVW1icVzqUo7AcpuFifnC8Fb2g8TfwcnoNQuO4rqEvlLEbBKoJDYv3XTKUGouB8xXEHk95UIOmAzwaaDs4/W+y/jWQU6uCj8a1cGs5rqHi+SwUZx56o4rk4FeemVJCx9rRqpJYHSo3D+wKiYkX4jGy3aTWYJgcbNEghB6g06C4PlAYVJUHQMu/T8jnSoosrRedPSXX4r3fhxm8G6zhXp0M9spejdTzPqMf8FOq1VabrOTevhzYrBdQNoOpYsY7hN/6TtsZdA+MQI+RYQSvRV6URbuR8jem3DyDb7ToT4wYT6yAR/di8gQJSET9PHoufZuaNFs71WrhPWnguWeFcpgIJi+0K56YVziUoSFi4x3xC8JawHqLwvGAE9wryLRquwvhNQRqKd50UOPkVHUTBc6XsY0VcAgX7jMx/tZB2bic1MpOtsFSFohPXHs7cN1J/662+2C16jYrzCSrOh6ugGEtv6S146jgZ3Fcj5j4FqtRcZzSQsZKxEV3pGckl+bWcG9DiCx3PTVocIVhp17J+pYOJ5NKxOsyg2OwuHc8d6yDjRaK/hucy9Dynruf5BgP7pQb8pHHR/WKd5eCuASMuPBVX2giaeh7awci5DCOfAyPzNhMkvNpowjSCY2YT3r6qVrFAGjMWXyajxYyWckDYjMzpLx2ZqTWDpsFPfzDznIKF9ReFfV6FdWaFHx/5OZzbUHhuWcFQKtceBdmlMaBwHVLkFOopgXcryMCJAlJDWojv7avSFVTQz1PJmzFggyhq/01+lbjv9Nj5kBqdhvPwKshlFesY5q8qrksqzu2oOJ+g4vybGqdW0uCBGm8pJnFMzXkFNWhKs2YtDXqcbvf58FwN5zk0PB+gBZ1GU98IDg7v24gekenz98pNZuj4fgGROhzWsfTYTvLKCj3PqepZnzSwj2Jgfmzgum/gfm7kfLQRMv581sj8XZzjeUMPty5jYvxiwriRNQTlNiGGbJI4E+fBzZznNHNuywwZZ76n8Jy1wrhKAakuTR4pPL+mQMq7Yr3C+zeCc/rIC6GwbqpAxsTF+oZz/grnRRXOswucROP04vk7SsFe4VxhIs42cf3RgaraE8Ez28tBZRWm0vEap+J5CBXP5aogY3SKinOgatYF1PAXJsKq5vqgRnJJ4NWc29RwTlXDcyAarElKgEqLEWIXHB+qRXc5yB/BwS83ZXL/qqNjHBPx5SR8iNKF5b5GetatI7jpD1q2X3rklsTTgPoyMCDq8bsjYmcaQGg0/pOB8x1GWLRkhEX8WOpSKbOZ+D4QJvZHTCD03dpjQiaS1/KYf+McKXMJ3BL2ESPnLnFdZbxZnLMwPlO4Hyu4S7Hqdwqou9UW38N5JAWpsuWAJhjJTYTziQrnHiJ9tNDu62977lHwiuBpWnFuCfY4LSgshVHDb/0urIeqYREoBqtUv/Xf8PyPinNYKp53UyHqVaccj7OrcXkR3ShDzfkCNe9HwXdp/C6vhs+Nhn1LgZMeXBDIQANpvzfSYtGQpNmvTo345uHcm5Zz2zqeg9JBjk+d12FtV5qI1P3WmeRYx1o9ZFzHpud8gwGL6JjUMECOy84yoJEsZAbI2HO8AZT2+lJe8ByK10w2oocEOkZImPPWiGurU73O1cqEYTJ4ZPp9vaX8GR3JHYd1SwXSNhXrG/Z/Fc5dKsyPI32yrBxkUJBODj4ofF8HhXNhCiZLgSmChxLxUXhuJfI64ftEKJx7sqEd0bAaZmSQH0zUM2rHpTSQyxCngowZzVChcw6aBFRxjkPF+l2kn16cRoPUasixAlF/w/1bjdzyxgdq9sk0rKNqfufPjpEslFTLvqX2d76wpAQMWpSVgwBazs/puB7q8JHSRYd1v/HdRxoLba5nPqnn66GHJWZRkxQFDHhJtnCviC6xMDc5EQZ0Pjws88VkRp6bjKxrOCdiRA4ZMDVCykLBCM7ZQ3aEqIvhOXeF8b6C2gTT/1OQlOKZdyK4JjzPpbAPofB1iax3OE8WOdeJ/GW0LCiRfkrupPgN52YT9UCF+aSCQ2RbXLCyTmeEHCe6p+U8pZrrnAoJRIPWCr4qAYiKdUsV5NiSwMfh+3CoIWV8wYNe0fj2TDUyyYOn5n0VySn1kcFaDftaGuSUg5ZaSFmhtRayvCzWQsa9bkdwcA45CBPBw9dp/OGoLkzrXukgaXVtPfMqgZ9o3ECv57yvgXOzBs5/GyCX0WrAtWUEHI08H2eEpP9zjL+vfzi/EamDYV9GnJdd1MAjODWxP4Z9ZIX1rsg6h/u9wjpE5O/C8+IKz5VG1ivRp0l8XOI+qimNbYXnbhSQe355l4JbqyggYcO3KdRpLFhRn244ocdNsq8eapBKbkg1+8oqnr9Q4S3ZeVdVkONoj1WoIwO7apAatUGcU21bMmAjPo11IRliasj4WAUN6y8aVChJSU8NurTe/U/Je6IuzF9XfkAyLcrIgWktaFf3nxZZ16czSODQcv5Tx/dT0nHd0IHY2SmBm8J4Tc8+qR7Z5AeN+DmJ/LWFvPGOAWXJtj1m4Fy8AU2lkKDgJsmz7gieCc8hRPzrsJ6pcB2J6LIyLiq+h+8jFemr4TyMgmIER8Q5OxekG1sokPHFR5EceNNHNaYtF/1WnZwmXyPnM6zPRR6XWO+HyACLwnNjCuaXJMHShn21cgrqaQJNvcdpdayLa6ClcRaBf8L3ZVHxfI8KtcMDyKyTqjhHoOa51oifmlcOuqmxk+yPz2rQlPn2lhqe79ZA1486s4Z1PA3rAlqsJfo2PuLbyMsZ0mKoFHR0zKt0rD/rIG8zYdFBxsoz6VGw15B6MR31kNtmRURHDOsgEf4a1gkMkHEHsX7heJLC/TiyPol6TaKO/r94NjxvoPB9Z8S6DR/Ysd53hedx7Dw/pHBeXeH7+Cj4uwMlCSN6w9waqUUJEetCtyMSz1u1MN2hIdJf/1dvSuwD4TlsBfFkVxZWeK7OyvlhI8/Xavl+GmrOP6rwleJhG1WQdHyfinPCKs5xCvy0f9eIv4pE/FRpSy5Xc/0TfVYCIg3raRq+nhqMkQFiDfKuuxX9440GNyg+VkoL6n5lxbqmk4NsWmgH06CeFn/LAQ4dKI25fYgOEn5u1sFsoi8d41E982w9z/3qmS/qcSQdbVADP5+CTvIfCkjtyyf6Wvh+R5Gfw3nTCE5JPD+JfVDKX/+PTyb2v7AfKNZfNnQ7UspATcQvC/PZiJ6byDcTcU+ifhS+L47Cc48RnTecE4z4q2F/X+Hcg411fwuWCNT+XwoDZMxU9Lcwb1Wzvh3RkeRtZQQ/tXQtOKlaUIWqcpBSBcdHGvwTPIZoyWg162hqxolq1kk1PO+gYbwieKscyNVwDkDDOFQLStPcGKhlH1SLDtIQiOjCzcvSHaB0kDGVMRE/XcqhL3Scl9LzvIH+N99IPC89JfFU+P5bkXoWntNXGB9F+OAHkgHdEbwjx3SjI/U5fH8Y5TdPDeeYFNZfIn05se4m6rdlyB4TdaCJHOeP4KXE503Ey4nrl+izhftl5O/C+ovCeq6N9UATita6n/Hzd91v3Sc8h6GGHJ87qcJBGgPdpOKck+A1ITIcVZz3UfP9JNSQMHmKGjIWdVPNOqYa5eRgpuZ3fZcxloUa9lPE9/IklAj+Srcvqir4ajgQgZszaSJQy9dLy/VOy7kjHX+OCC5uLQc+dL/nWSidnfpZZB3l8ol1Dc8ZRnyP3YOpQQn+LweVIvmRSpKw2n/XVWnDfVE4v2LH2hMll13PbOf7mdkZ19n5Plf23+dr330JoNhnjvDScI5OgbxNoVgnaxUqvAIH0W3cPkZ+H87jKJz7UTBVEvwIzgrrlwrPOybOjxgRbEGBHx3PN2hYt1H95qvhPKTqdx5Cjt/9o4KUwUR9DufLVTyno4K8HWBONc+vqSHHo7uruV6of/u24T6m5vlyDc+Nafh+WhrI2x4uiPThsD4veBHZyJm1qCa/tPg/bRlfLg==AgAAAACAAACMAgAAZRsAABIBAAA=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AQAAAACAAAAoUwAAzQ0AAA==eJw13KG7OWCYJuCzu3bHzJpdM2tmzazZNTNm1l6XIAiCIAiCIAiCIAiCIAiCIAiCIAgnCIIgCIIgCIIgCIJwgiAIgiAIgrDh3L+v3H/D977P8wa+fl+QIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeE3p5xxzgWXXHHNDbfccc8DjzzxzB9eeOWNdz745Itvfvj1H34JMMgQw4wwyhjjTDDJFNPMMMsc8yywyBLLrLDKGutssMkW2+ywyx77HHDIEcec8JtTzjjngkuuuOaGW+6454FHnnjmDy+88sY7H3zyxTc//PqPvwQYZIhhRhhljHEmmGSKaWaYZY55FlhkiWVWWGWNdTbYZIttdthlj30OOOSIY074zSlnnHPBJVdcc8Mtd9zzwCNPPPOHF155450PPvnimx9+/adfAgwyxDAjjDLGOBNMMsU0M8wyxzwLLLLEMiusssY6G2yyxTY77LLHPgcccsQxJ/zmlDPOueCSK6654ZY77nngkSee+cMLr7zxzgeffPHND78CYJAhhhlhlDHGmWCSKaaZYZY55llgkSWWWWGVNdbZYJMtttlhlz32OeCQI4454TennHHOBZdccc0Nt9xxzwOPPPHMH1545Y13Pvjki29++PWffwkwyBDDjDDKGONMMMkU08wwyxzzLLDIEsussMoa62ywyRbb7LDLHvsccMgRx5zwm1POOOeCS6645oZb7rjngUeeeOYPL7zyxjsffPLFNz/8+i+/BBhkiGFGGGWMcSaYZIppZphljnkWWGSJZVZYZY11Nthki2122GWPfQ445IhjTvjNKWecc8ElV1xzwy133PPAI08884cXXnnjnQ8++eKbH379xS8BBhlimBFGGWOcCSaZYpoZZpljngUWWWKZFVZZY50NNtlimx122WOfAw454pgTfnPKGedccMkV19xwyx33PPDIE8/84YVX3njng0+++OaHfz78AQYZYpgRRhljnAkmmWKaGWaZY54FFllimRVWWWOdDTbZYpsddtljnwMOOeKYE35zyhnnXHDJFdfccMsd9zzwyBPP/OGFV95454NPvvjm58+g5y9/CTDIEMOMMMoY40wwyRTTzDDLHPMssMgSy6ywyhrrbLDJFtvssMse+xxwyBHHnPCbU84454JLrrjmhlvuuOeBR5545g8vvPLGOx988sU3P/z6q18CDDLEMCOMMsY4E0wyxTQzzDLHPAssssQyK6yyxjobbLLFNjvsssc+BxxyxDEn/OaUM8654JIrrrnhljvueeCRJ575wwuvvPHOB5988c0Pv/7rLwEGGWKYEUYZY5wJJplimhlmmWOeBRZZYpkVVlljnQ022WKbHXbZY58DDjnimBN+c8oZ51xwyRXX3HDLHfc88MgTz/zhhVfeeOeDT7745od/Bv0BBhlimBFGGWOcCSaZYpoZZpljngUWWWKZFVZZY50NNtlimx122WOfAw454pgTfnPKGedccMkV19xwyx33PPDIE8/84YVX3njng0+++Obnz4Lnr38JMMgQw4wwyhjjTDDJFNPMMMsc8yywyBLLrLDKGutssMkW2+ywyx77HHDIEcec8JtTzjjngkuuuOaGW+6454FHnnjmDy+88sY7H3zyxTc//PpvvwQYZIhhRhhljHEmmGSKaWaYZY55FlhkiWVWWGWNdTbYZIttdthlj30OOOSIY074zSlnnHPBJVdcc8Mtd9zzwCNPPPOHF155450PPvnimx9+/fdfAgwyxDAjjDLGOBNMMsU0M8wyxzwLLLLEMiusssY6G2yyxTY77LLHPgcccsQxJ/zmlDPOueCSK6654ZY77nngkSee+cMLr7zxzgeffPHND/8s+AMMMsQwI4wyxjgTTDLFNDPMMsc8CyyyxDIrrLLGOhtsssU2O+yyxz4HHHLEMSf85pQzzrngkiuuueGWO+554JEnnvnDC6+88c4Hn3zxzc+fYMff/BJgkCGGGWGUMcaZYJIppplhljnmWWCRJZZZYZU11tlgky222WGXPfY54JAjjjnhN6eccc4Fl1xxzQ233HHPA4888cwfXnjljXc++OSLb3749be/BBhkiGFGGGWMcSaYZIppZphljnkWWGSJZVZYZY11Nthki2122GWPfQ445IhjTvjNKWecc8ElV1xzwy133PPAI08884cXXnnjnQ8++eKbH379j18CDDLEMCOMMsY4E0wyxTQzzDLHPAssssQyK6yyxjobbLLFNjvsssc+BxxyxDEn/OaUM8654JIrrrnhljvueeCRJ575wwuvvPHOB5988c0P/wT7AgwyxDAjjDLGOBNMMsU0M8wyxzwLLLLEMiusssY6G2yyxTY77LLHPgcccsQxJ/zmlDPOueCSK6654ZY77nngkSee+cMLr7zxzgeffPHNz59A59/9EmCQIYYZYZQxxplgkimmmWGWOeZZYJEllllhlTXW2WCTLbbZYZc99jngkCOOOeE3p5xxzgWXXHHNDbfccc8DjzzxzB9eeOWNdz745Itvfvj1978EGGSIYUYYZYxxJphkimlmmGWOeRZYZIllVlhljXU22GSLbXbYZY99DjjkiGNO+M0pZ5xzwSVXXHPDLXfc88AjTzzzhxdeeeOdDz754psffv3PXwIMMsQwI4wyxjgTTDLFNDPMMsc8CyyyxDIrrLLGOhtsssU2O+yyxz4HHHLEMSf85pQzzrngkiuuueGWO+554JEnnvnDC6+88c4Hn3zxzQ//BPoDDDLEMCOMMsY4E0wyxTQzzDLHPAssssQyK6yyxjobbLLFNjvsssc+BxxyxDEn/OaUM8654JIrrrnhljvueeCRJ575wwuvvPHOB5988c3PnyLHP/wSYJAhhhlhlDHGmWCSKaaZYZY55llgkSWWWWGVNdbZYJMtttlhlz32OeCQI4454TennHHOBZdccc0Nt9xxzwOPPPHMH1545Y13Pvjki29++PWPvwQYZIhhRhhljHEmmGSKaWaYZY55FlhkiWVWWGWNdTbYZIttdthlj30OOOSIY074zSlnnHPBJVdcc8Mtd9zzwCNPPPOHF155450PPvnimx9+/a9fAgwyxDAjjDLGOBNMMsU0M8wyxzwLLLLEMiusssY6G2yyxTY77LLHPgcccsQxJ/zmlDPOueCSK6654ZY77nngkSee+cMLr7zxzgeffPHND/8U+QIMMsQwI4wyxjgTTDLFNDPMMsc8CyyyxDIrrLLGOhtsssU2O+yyxz4HHHLEMSf85pQzzrngkiuuueGWO+554JEnnvnDC6+88c4Hn3zxzc+fAuc//RJgkCGGGWGUMcaZYJIppplhljnmWWCRJZZZYZU11tlgky222WGXPfY54JAjjjnhN6eccc4Fl1xxzQ233HHPA4888cwfXnjljXc++OSLb3749b9/CTDIEMOMMMoY40wwyRTTzDDLHPMssMgSy6ywyhrrbLDJFtvssMse+xxwyBHHnPCbU84454JLrrjmhlvuuOeBR5545g8vvPLGOx988sU3P/z6P78EGGSIYUYYZYxxJphkimlmmGWOeRZYZIllVlhljXU22GSLbXbYZY99DjjkiGNO+M0pZ5xzwSVXXHPDLXfc88AjTzzzhxdeeeOdDz754psf/inwBxhkiGFGGGWMcSaYZIppZphljnkWWGSJZVZYZY1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CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/grid/Grid_0_300000.vtu ADDED
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CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/grid/Grid_1_300000.vtu ADDED
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CylinderKnMa/Kn0p28/simulation0570_Kn0p28Ma3p9/grid/Grid_2_300000.vtu ADDED
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AQAAAACAAABABgAA8QUAAA==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AQAAAACAAABABgAACAYAAA==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AQAAAACAAABABgAA/gUAAA==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AQAAAACAAABABgAAHQYAAA==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AQAAAACAAABABgAAOAYAAA==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AQAAAACAAABABgAAMQYAAA==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AQAAAACAAABABgAAKwQAAA==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AQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAABABgAAFQAAAA==eJxjYBgFo2AUjIJRMApIBwAGQAABAQAAAACAAADAEgAA0gMAAA==eJxt2D1IlmEUxvE3KNqKtiJosyWMtoLe5+jm0FJDSbg0Bg3h0odBSFtBVEsfUBC12GIRtPShLS0tQiEVJAoFQURUIhmWdg5enA7PPyH4gfL2vPdzznWf++50On0d/dybXbErY6NP2x4/u2IHrneetb19ZtneP+mH9y79saHBUbjn3W8b2nUb7j25ZB/mJuHx6V/2/cgsvGnhp10eW4anphfsy5sNE20Pn5q3kd3b4Msz32zj8V54et1XezXShRe/f7bFQ/vgyTufbGLlMLxmy0cbHj4K3zk4Z8fGTsDXDr+3C/vPwXM9b+3HjvPwxSev7eHHS/CzrVO2+fRV+MbASxv5dBMe6n9he3behQfWP7en++7BD249tjN2H55f+8j8H+y/N/872D/P/HNh///NnwP25zV/bti/n/n3hH09zNcF9vUzX0fY19t83WF/P+bvCfb3af5eYX//5nUAe72Y1w3s9WVeZ7DXo3ldwl6/5nUMe72b1z3s/dF4n8DeT433Fez913gfwt6vjfctPTjaeJ/DngeN5wLs+dF4jsCeN13PHVg/k21HPpW/T0c+lc9PRz6V50lHPpXn/2fPp/J905FPZX3SkU9lPdORT2X905FP5X2lI5/K+01HPpV6SEc+lfpJRz6VektHPpX6TEc+lXpORz6V+k9HPpV+SUc+lf5KRz6VfkxHPpX+TUc+lX5PRz6VfEhHPpU8SUc+lfxJRz6VvErH70u+pePzSh6m4/8v+ZmO5y15m47vV/I5HetR8jwd61fyPx3rXfaLdLyfsr+k432W/Sgd77/sX+mol7LfpaO+yv6Yjnos+2k66rfsv+mo97Jfp6M/yv6ejn4q80A6+q/MD+no1zJvpKO/y3ySjjwo80w68qPMP+nImzIvpTv/yaryN9229ZlN23oGWM8M6zvCWhNYawhrzWG9I2tb7xRWDcCqGVg1BqsmYdUwrJqH1SOwegpWD8LqWVg9DisTYGUIrMyBlVGwMg1WBsLKTFgZCyuTYWU4rMyHtUfA2lNg7UGw9ixYexysPRHWHgprz4W1R8Pa02HNALBmBlgzBr06k8CaYWDNPLBmJFg/fW1rBrO2NbPBmvFgzYT06gwJa+aENaPCmmlhzcCwZmZYMzasmRzWDA9r5od1RoB1poB1BoF1ZoF1xoF1JoJ1hoJ15oJ1RoN1poN1BoR1ZoR1xoR1JoV1hoV15oV1RoZ1poZ1Bod1Zod1xod1JwDrDgHWnQOsOwpYdxqw7kBg3Zk0beuOBdadDKw7HFh3PrDuiGDdKcG6g+q23SlZ9RcRPekTAQAAAACAAACADAAAkgIAAA==eJw11WODEAYAgOF8l23b7rJ1GdddtnHZtnHVvNpqy21hy7Ztt4Whtmz3oaf3y/MT3kiRPhWZURiV0RidMRjAQMZkLMZmHMZlPMZnAiZkIiZmEiZlMiZnCqZkKqZmGqZlOqZnBmZkJmZmFmZlNmZnDuZkLuZmHuZlPuZnARZkIRZmEQaxKIuxOEuwJEuxNMuwLMuxPCuwIiuxMquwKoNZjdVZgzVZi7VZh3VZj/UZwgYMZRgbshEbswmbshmbswVbshVbsw3bsh3bswM7shM7swu7shvD2Z092JO92Jt92Jf92J8DOJCDOJhDOJTDOJwjOJKjOJpjOJbjOJ4TOJGTOJlTOJXTOJ0zGMGZnMXZ/IJf8it+zW/4Lb/j95zDufyBP3Ie5/Mn/swFXMhFXMwlXMplXM5f+CtXcCVXcTV/4+9cw7Vcx/XcwI3cxM3cwq3cxu3cwZ3cxd3cw73cx/08wIM8xMM8wqM8xuM8wZM8xdM8w7M8x/O8wIu8xMu8wqu8xuv8g3/yBm/yFm/zL/7Nf/gv7/Au/+P/vMf7fMCHfMTHfMKnfMbnfMGXfMXXfMO3fMf3/MDP44/MKIzKaIzOGAxgIGMyFmMzDuMyHuMzARMyERMzCZMyGZMzBVMyFVMzDdMyHdMzAzMyEzMzC7MyG7MzB3MyF3MzD/MyH/OzAAuyEAuzCINYlMVYnCVYkqVYmmVYluVYnhVYkZVYmVVYlcGsxuqswZqsxdqsw7qsx/oMYQOGMowN2YiN2YRN2YzN2YIt2Yqt2YZt2Y7t2YEd2Ymd2YVd2Y3h7M4e7Mle7M0+7Mt+7M8BHMhBHMwhHMphHM4RHMlRHM0xHMtxHM8JnMhJnMwpnMppnM4ZjOBHXq+oSQ==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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AQAAAACAAAAoUwAAWwIAAA==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AgAAAACAAACMAgAAZRsAABIBAAA=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AQAAAACAAAAoUwAAzQ0AAA==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AQAAAACAAABlCgAAGgAAAA==eJztwSEBAAAAwyC9/oVf4hIoAAA4G4r2XY4=
26
+ </AppendedData>
27
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/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/Kn0p28/simulation0571_Kn0p28Ma4p0/config.txt ADDED
@@ -0,0 +1,78 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ variable BZ equal 1.380649E-23
2
+ variable PI equal 3.1415926536
3
+ variable molecular_mass equal 6.63E-26
4
+ variable molecular_diameter equal 4.17E-10
5
+ variable gamma equal 1.666666667
6
+ variable R equal ${BZ}/${molecular_mass}
7
+ variable L equal 1.0
8
+ variable xmin equal -5.0
9
+ variable xmax equal 6.0
10
+ variable ymin equal -5.0
11
+ variable ymax equal 5.0
12
+ variable Kn equal 0.28
13
+ variable Ma equal 4.0
14
+ variable To equal 273.0
15
+ variable Tb equal 273.0
16
+ variable theta equal 0.0
17
+ variable mfp equal ${Kn}*${L}
18
+ variable cpmfp equal 4
19
+ variable nden equal 1.0/sqrt(2.0)/${PI}/${molecular_diameter}/${molecular_diameter}/(${mfp}/${cpmfp}*${Kn})
20
+ variable sos equal sqrt(${gamma}*${R}*${To})
21
+ variable vo equal ${Ma}*${sos}
22
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
23
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
24
+ variable ppc equal 30
25
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
26
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
27
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
28
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
29
+ variable nave equal 10000
30
+ variable noutput equal 300000
31
+ clear
32
+ shell mkdir output
33
+ shell mkdir grid
34
+ shell mkdir surf
35
+ seed 7
36
+ dimension 2
37
+ global nrho ${nden}
38
+ global fnum ${Fnum}
39
+ timestep ${tstep}
40
+ global gridcut 1.0 particle/reorder 100 comm/sort yes
41
+ region 1 cylinder z 0.0 0.0 3.50 -0.5 0.5
42
+ region 2 cylinder z 0.0 0.0 2.00 -0.5 0.5
43
+ region 3 cylinder z 0.0 0.0 0.80 -0.5 0.5
44
+ boundary o o p
45
+ create_box ${xmin} ${xmax} ${ymin} ${ymax} -0.5 0.5
46
+ create_grid ${xncells} ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
47
+ balance_grid rcb cell
48
+ species gas.species Ar
49
+ mixture air Ar frac 1.0
50
+ mixture air Ar vstream ${vx} ${vy} 0.0 temp ${To}
51
+ collide vss air gas.vhs
52
+ collide_modify vremax 100 yes remain no nearcp yes 10
53
+ read_surf cylinder.surf
54
+ surf_collide 1 diffuse 273.0 1.0
55
+ surf_modify all collide 1
56
+ fix in emit/face air xlo ylo yhi
57
+ balance_grid rcb cell
58
+ create_particles air n 0
59
+ stats 10000
60
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
61
+ run 200000
62
+ compute 1 grid all air n nrho massrho u v w
63
+ compute 2 grid all air trot
64
+ compute 3 tvib/grid all air
65
+ compute 4 thermal/grid all air press temp
66
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
67
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
68
+ dump_modify 1 format float %12.5g
69
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
70
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
71
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
72
+ dump_modify 2 format float %12.5g
73
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
74
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
75
+ dump_modify 3 format float %12.5g
76
+ run 100000
77
+ write_grid grid/final-grid.dat
78
+ write_surf surf/geom.surf
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/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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CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/gas.species ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Species data
2
+
3
+ # species name
4
+ # molecular weight (atomic mass units, e.g. 16 for oxygen)
5
+ # molecular mass (kg)
6
+ # rotational degrees of freedom (integer, unitless)
7
+ # inverse rotational relaxtion number (unitless)
8
+ # vibrational degrees of freedom (integer, unitless)
9
+ # inverse vibrational relaxation number (unitless)
10
+ # vibrational temperature (K)
11
+ # species weight (unitless)
12
+ # multiple of electon charge (1 for a proton)
13
+
14
+ Ar 39.95 6.63E-26 0 0.0 0 0.0 0.0 1.0 0.0
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/gas.vhs ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ # VHS collision model parameters for each species
2
+
3
+ # species name
4
+ # diameter (m)
5
+ # omega (unitless)
6
+ # tref (referenced temperature)
7
+ # alpha (unitless)
8
+
9
+ Ar 4.17e-10 0.81 273 1.0
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_0_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_1_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_2_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_300000.pvtu ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="PUnstructuredGrid" version="0.1"
3
+ byte_order="LittleEndian"
4
+ compressor="vtkZLibDataCompressor">
5
+ <PUnstructuredGrid GhostLevel="0">
6
+ <PCellData>
7
+ <PDataArray type="Float64" Name="id"/>
8
+ <PDataArray type="Float64" Name="xc"/>
9
+ <PDataArray type="Float64" Name="yc"/>
10
+ <PDataArray type="Float64" Name="f_1[1]"/>
11
+ <PDataArray type="Float64" Name="f_1[2]"/>
12
+ <PDataArray type="Float64" Name="f_1[3]"/>
13
+ <PDataArray type="Float64" Name="f_1[4]"/>
14
+ <PDataArray type="Float64" Name="f_1[5]"/>
15
+ <PDataArray type="Float64" Name="f_1[6]"/>
16
+ <PDataArray type="Float64" Name="f_1[7]"/>
17
+ <PDataArray type="Float64" Name="f_1[8]"/>
18
+ <PDataArray type="Float64" Name="f_1[9]"/>
19
+ <PDataArray type="Float64" Name="f_1[10]"/>
20
+ </PCellData>
21
+ <PPoints>
22
+ <PDataArray type="Float32" Name="Points" NumberOfComponents="3"/>
23
+ </PPoints>
24
+ <Piece Source="Grid_0_300000.vtu"/>
25
+ <Piece Source="Grid_1_300000.vtu"/>
26
+ <Piece Source="Grid_2_300000.vtu"/>
27
+ <Piece Source="Grid_3_300000.vtu"/>
28
+ </PUnstructuredGrid>
29
+ </VTKFile>
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/Grid_3_300000.vtu ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/grid/final-grid.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/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 0.28
17
+ variable Ma equal 4.0
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 0.28*${L}
23
+ variable mfp equal 0.28*1
24
+ variable cpmfp equal 4
25
+ variable nden equal 1.0/sqrt(2.0)/${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}*${Kn})
29
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/${cpmfp}*${Kn})
30
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*${Kn})
31
+ variable nden equal 1.0/sqrt(2.0)/3.1415926536/4.17e-10/4.17e-10/(0.28/4*0.28)
32
+ variable sos equal sqrt(${gamma}*${R}*${To})
33
+ variable sos equal sqrt(1.666666667*${R}*${To})
34
+ variable sos equal sqrt(1.666666667*208.242684766214*${To})
35
+ variable sos equal sqrt(1.666666667*208.242684766214*273)
36
+ variable vo equal ${Ma}*${sos}
37
+ variable vo equal 4*${sos}
38
+ variable vo equal 4*307.815564238681
39
+ variable vx equal ${vo}*cos(${theta}*2*PI/360)
40
+ variable vx equal 1231.26225695472*cos(${theta}*2*PI/360)
41
+ variable vx equal 1231.26225695472*cos(0*2*PI/360)
42
+ variable vy equal ${vo}*sin(${theta}*2*PI/360)
43
+ variable vy equal 1231.26225695472*sin(${theta}*2*PI/360)
44
+ variable vy equal 1231.26225695472*sin(0*2*PI/360)
45
+ variable ppc equal 30
46
+ variable xncells equal (${xmax}-${xmin})/${mfp}*${cpmfp}
47
+ variable xncells equal (6-${xmin})/${mfp}*${cpmfp}
48
+ variable xncells equal (6--5)/${mfp}*${cpmfp}
49
+ variable xncells equal (6--5)/0.28*${cpmfp}
50
+ variable xncells equal (6--5)/0.28*4
51
+ variable yncells equal (${ymax}-${ymin})/${mfp}*${cpmfp}
52
+ variable yncells equal (5-${ymin})/${mfp}*${cpmfp}
53
+ variable yncells equal (5--5)/${mfp}*${cpmfp}
54
+ variable yncells equal (5--5)/0.28*${cpmfp}
55
+ variable yncells equal (5--5)/0.28*4
56
+ variable Fnum equal ${nden}*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
57
+ variable Fnum equal 6.60399822965135e+19*(${xmax}-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
58
+ variable Fnum equal 6.60399822965135e+19*(6-${xmin})*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
59
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(${ymax}-${ymin})/${ppc}/${xncells}/${yncells}
60
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5-${ymin})/${ppc}/${xncells}/${yncells}
61
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/${ppc}/${xncells}/${yncells}
62
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/${xncells}/${yncells}
63
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/${yncells}
64
+ variable Fnum equal 6.60399822965135e+19*(6--5)*(5--5)/30/157.142857142857/142.857142857143
65
+ variable tstep equal (-${xmin}+${xmax})/${vx}/${xncells}/10/4
66
+ variable tstep equal (--5+${xmax})/${vx}/${xncells}/10/4
67
+ variable tstep equal (--5+6)/${vx}/${xncells}/10/4
68
+ variable tstep equal (--5+6)/1231.26225695472/${xncells}/10/4
69
+ variable tstep equal (--5+6)/1231.26225695472/157.142857142857/10/4
70
+ variable nave equal 10000
71
+ variable noutput equal 300000
72
+ clear
73
+ Running on 128 MPI task(s)
74
+ shell mkdir output
75
+ shell mkdir grid
76
+ shell mkdir surf
77
+ seed 7
78
+ dimension 2
79
+ global nrho ${nden}
80
+ global nrho 6.60399822965135e+19
81
+ global fnum ${Fnum}
82
+ global fnum 1.07865304417639e+16
83
+ timestep ${tstep}
84
+ timestep 1.42130564801708e-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 157.142857142857 ${yncells} 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
98
+ create_grid 157.142857142857 142.857142857143 1 levels 4 region 2 1 2 2 1 region 3 2 2 2 1 region 4 3 2 2 1 clump xyz
99
+ Created 97600 child grid cells
100
+ CPU time = 0.124474 secs
101
+ create/ghost percent = 1.01919 98.9808
102
+ balance_grid rcb cell
103
+ Balance grid migrated 97380 cells
104
+ CPU time = 0.10468 secs
105
+ reassign/sort/migrate/ghost percent = 55.3025 0.0495413 8.05456 36.5934
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 1231.26225695472 ${vy} 0.0 temp ${To}
110
+ mixture air Ar vstream 1231.26225695472 0 0.0 temp ${To}
111
+ mixture air Ar vstream 1231.26225695472 0 0.0 temp 273
112
+ collide vss air gas.vhs
113
+ collide_modify vremax 100 yes remain no nearcp yes 10
114
+ read_surf cylinder.surf
115
+ 200 lines
116
+ -0.5 0.5 xlo xhi
117
+ -0.5 0.5 ylo yhi
118
+ 0 0 zlo zhi
119
+ 0.0157073 min line length
120
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
121
+ 87188 9956 456 = cells outside/inside/overlapping surfs
122
+ 456 = surf cells with 1,2,etc splits
123
+ 109.215 109.215 = cell-wise and global flow volume
124
+ CPU time = 0.122092 secs
125
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.642 18.5844 2.56502 20.3133 36.8953 36.517 0.0144473
126
+ surf2grid time = 0.0248009 secs
127
+ map/comm1/comm2/comm3/comm4/split percent = 14.8387 23.4287 5.35748 1.69299 11.4461 8.24091
128
+ surf_collide 1 diffuse 273.0 1.0
129
+ surf_modify all collide 1
130
+ fix in emit/face air xlo ylo yhi
131
+ balance_grid rcb cell
132
+ Balance grid migrated 1938 cells
133
+ CPU time = 0.0469227 secs
134
+ reassign/sort/migrate/ghost percent = 6.73958 0.0527037 19.8041 73.4036
135
+ create_particles air n 0
136
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
137
+ Created 668660 particles
138
+ CPU time = 0.0173073 secs
139
+ stats 10000
140
+ stats_style step cpu np nattempt ncoll nscoll npave nscollave
141
+ run 200000
142
+ Memory usage per proc in Mbytes:
143
+ particles (ave,min,max) = 1.79297 0 3.375
144
+ grid (ave,min,max) = 4.297 1.51379 7.13879
145
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
146
+ total (ave,min,max) = 6.11057 3.22189 8.84689
147
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
148
+ 0 0 668660 0 0 0 0 0
149
+ 10000 16.38402 734483 16423 14028 15 695465.09 16.2978
150
+ 20000 38.124891 741665 16619 14271 18 717756.56 16.4886
151
+ 30000 59.323338 743118 16743 14398 20 726018.37 16.5531
152
+ 40000 80.545465 741903 16774 14364 18 730234.18 16.5878
153
+ 50000 102.27449 742425 16832 14483 13 732610.91 16.6011
154
+ 60000 123.98502 742687 16767 14356 14 734300.03 16.619183
155
+ 70000 144.55496 742499 16784 14358 19 735500.75 16.619071
156
+ 80000 166.33196 742938 16733 14399 9 736393.14 16.64375
157
+ 90000 187.96225 742621 16688 14335 17 737143.85 16.645744
158
+ 100000 208.62772 742311 16625 14293 15 737670.47 16.65375
159
+ 110000 230.8332 742585 16774 14436 11 738095.46 16.647664
160
+ 120000 252.36626 742965 16716 14400 13 738462.69 16.655033
161
+ 130000 273.44089 742071 16736 14346 15 738736.96 16.654969
162
+ 140000 295.16596 742104 16726 14406 15 738988.8 16.652279
163
+ 150000 316.86282 742589 16794 14535 20 739212.4 16.658947
164
+ 160000 338.11467 741744 16831 14527 16 739430.98 16.658838
165
+ 170000 359.93607 742049 16750 14341 15 739604.24 16.655429
166
+ 180000 381.01771 742140 16677 14331 22 739732.8 16.651467
167
+ 190000 402.87367 742305 16744 14402 8 739878.61 16.6517
168
+ 200000 425.99347 742528 16700 14290 13 740015.12 16.65522
169
+ Loop time of 425.994 on 128 procs for 200000 steps with 742528 particles
170
+
171
+ MPI task timing breakdown:
172
+ Section | min time | avg time | max time |%varavg| %total
173
+ ---------------------------------------------------------------
174
+ Move | 0.024508 | 18.188 | 114.17 | 559.1 | 4.27
175
+ Coll | 0.48303 | 20.358 | 176.41 | 663.8 | 4.78
176
+ Sort | 0.15841 | 3.5034 | 27.35 | 263.4 | 0.82
177
+ Comm | 69.473 | 102.74 | 114.5 | 94.6 | 24.12
178
+ Modify | 0.020593 | 0.16566 | 3.3539 | 114.7 | 0.04
179
+ Output | 0.0059557 | 0.020203 | 0.022175 | 1.9 | 0.00
180
+ Other | | 281 | | | 65.97
181
+
182
+ Particle moves = 148003023218 (148B)
183
+ Cells touched = 154806239492 (155B)
184
+ Particle comms = 253225027 (253M)
185
+ Boundary collides = 0 (0K)
186
+ Boundary exits = 24994241 (25M)
187
+ SurfColl checks = 169798309 (170M)
188
+ SurfColl occurs = 3331044 (3.33M)
189
+ Surf reactions = 0 (0K)
190
+ Collide attempts = 3803583625 (3.8B)
191
+ Collide occurs = 2940009800 (2.94B)
192
+ Reactions = 0 (0K)
193
+ Particles stuck = 0
194
+ Axisymm bad moves = 0
195
+
196
+ Particle-moves/CPUsec/proc: 2.71429e+06
197
+ Particle-moves/step: 740015
198
+ Cell-touches/particle/step: 1.04597
199
+ Particle comm iterations/step: 1
200
+ Particle fraction communicated: 0.00171094
201
+ Particle fraction colliding with boundary: 0
202
+ Particle fraction exiting boundary: 0.000168877
203
+ Surface-checks/particle/step: 0.00114726
204
+ Surface-collisions/particle/step: 2.25066e-05
205
+ Surf-reactions/particle/step: 0
206
+ Collision-attempts/particle/step: 0.0256994
207
+ Collisions/particle/step: 0.0198645
208
+ Reactions/particle/step: 0
209
+
210
+ Particles: 5801 ave 38227 max 0 min
211
+ Histogram: 84 14 4 8 8 4 2 0 1 3
212
+ Cells: 762.5 ave 763 max 762 min
213
+ Histogram: 64 0 0 0 0 0 0 0 0 64
214
+ GhostCell: 19721.7 ave 36096 max 1099 min
215
+ Histogram: 20 10 12 10 18 2 6 4 16 30
216
+ EmptyCell: 0 ave 0 max 0 min
217
+ Histogram: 128 0 0 0 0 0 0 0 0 0
218
+ Surfs: 200 ave 200 max 200 min
219
+ Histogram: 128 0 0 0 0 0 0 0 0 0
220
+ GhostSurf: 0 ave 0 max 0 min
221
+ Histogram: 128 0 0 0 0 0 0 0 0 0
222
+ compute 1 grid all air n nrho massrho u v w
223
+ compute 2 grid all air trot
224
+ compute 3 tvib/grid all air
225
+ compute 4 thermal/grid all air press temp
226
+ fix 1 ave/grid all 1 ${nave} ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
227
+ fix 1 ave/grid all 1 10000 ${nave} c_1[*] c_4[*] c_2[*] c_3[*] ave running
228
+ fix 1 ave/grid all 1 10000 10000 c_1[*] c_4[*] c_2[*] c_3[*] ave running
229
+ dump 1 grid all ${noutput} output/grid.*.dat id xc yc f_1[*]
230
+ dump 1 grid all 300000 output/grid.*.dat id xc yc f_1[*]
231
+ dump_modify 1 format float %12.5g
232
+ compute 5 surf all air n press px py shx shy fx fy etot erot evib
233
+ fix 2 ave/surf all 1 ${nave} ${nave} c_5[*] ave running
234
+ fix 2 ave/surf all 1 10000 ${nave} c_5[*] ave running
235
+ fix 2 ave/surf all 1 10000 10000 c_5[*] ave running
236
+ dump 2 surf all ${noutput} output/surf.*.dat id v1x v1y v2x v2y f_2[*]
237
+ dump 2 surf all 300000 output/surf.*.dat id v1x v1y v2x v2y f_2[*]
238
+ dump_modify 2 format float %12.5g
239
+ compute 6 lambda/grid f_1[2] f_1[8] Ar kx
240
+ dump 3 grid all ${noutput} output/lambdakn.*.dat id xc yc c_6[*]
241
+ dump 3 grid all 300000 output/lambdakn.*.dat id xc yc c_6[*]
242
+ dump_modify 3 format float %12.5g
243
+ run 100000
244
+ Memory usage per proc in Mbytes:
245
+ particles (ave,min,max) = 1.87207 0 5.0625
246
+ grid (ave,min,max) = 4.297 1.51379 7.13879
247
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
248
+ total (ave,min,max) = 6.4459 3.47786 9.10336
249
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
250
+ 200000 0 742528 16700 14290 0 0 0
251
+ 210000 37.993242 741890 16766 14373 20 742922.39 16.6916
252
+ 220000 75.769123 742754 16810 14467 15 742938.8 16.6951
253
+ 230000 114.31239 743099 16720 14307 14 743051.07 16.714533
254
+ 240000 152.59187 742634 16620 14271 17 742968.92 16.7068
255
+ 250000 191.05862 742083 16732 14400 17 742927.46 16.6962
256
+ 260000 228.91868 741945 16573 14231 17 742892.65 16.692517
257
+ 270000 266.80151 742505 16729 14345 17 742857.48 16.686186
258
+ 280000 305.20677 741720 16614 14307 14 742787.82 16.67955
259
+ 290000 343.29684 742274 16731 14420 11 742740.03 16.6802
260
+ 300000 380.87227 741835 16735 14391 5 742687.13 16.68183
261
+ Loop time of 380.873 on 128 procs for 100000 steps with 741835 particles
262
+
263
+ MPI task timing breakdown:
264
+ Section | min time | avg time | max time |%varavg| %total
265
+ ---------------------------------------------------------------
266
+ Move | 0.012814 | 9.2002 | 57.384 | 395.9 | 2.42
267
+ Coll | 0.24163 | 10.416 | 95.144 | 483.3 | 2.73
268
+ Sort | 0.095516 | 1.8292 | 12.176 | 187.6 | 0.48
269
+ Comm | 41.807 | 58.412 | 64.712 | 63.8 | 15.34
270
+ Modify | 3.4404 | 25.249 | 149.74 | 607.8 | 6.63
271
+ Output | 0.03907 | 0.040604 | 0.044526 | 0.6 | 0.01
272
+ Other | | 275.7 | | | 72.39
273
+
274
+ Particle moves = 74268712763 (74.3B)
275
+ Cells touched = 77679683164 (77.7B)
276
+ Particle comms = 126932160 (127M)
277
+ Boundary collides = 0 (0K)
278
+ Boundary exits = 12538396 (12.5M)
279
+ SurfColl checks = 84988574 (85M)
280
+ SurfColl occurs = 1668183 (1.67M)
281
+ Surf reactions = 0 (0K)
282
+ Collide attempts = 1919876508 (1.92B)
283
+ Collide occurs = 1483742606 (1.48B)
284
+ Reactions = 0 (0K)
285
+ Particles stuck = 0
286
+ Axisymm bad moves = 0
287
+
288
+ Particle-moves/CPUsec/proc: 1.52341e+06
289
+ Particle-moves/step: 742687
290
+ Cell-touches/particle/step: 1.04593
291
+ Particle comm iterations/step: 1
292
+ Particle fraction communicated: 0.00170909
293
+ Particle fraction colliding with boundary: 0
294
+ Particle fraction exiting boundary: 0.000168825
295
+ Surface-checks/particle/step: 0.00114434
296
+ Surface-collisions/particle/step: 2.24615e-05
297
+ Surf-reactions/particle/step: 0
298
+ Collision-attempts/particle/step: 0.0258504
299
+ Collisions/particle/step: 0.019978
300
+ Reactions/particle/step: 0
301
+
302
+ Particles: 5795.59 ave 38487 max 0 min
303
+ Histogram: 84 14 4 8 8 5 1 0 1 3
304
+ Cells: 762.5 ave 763 max 762 min
305
+ Histogram: 64 0 0 0 0 0 0 0 0 64
306
+ GhostCell: 19721.7 ave 36096 max 1099 min
307
+ Histogram: 20 10 12 10 18 2 6 4 16 30
308
+ EmptyCell: 0 ave 0 max 0 min
309
+ Histogram: 128 0 0 0 0 0 0 0 0 0
310
+ Surfs: 200 ave 200 max 200 min
311
+ Histogram: 128 0 0 0 0 0 0 0 0 0
312
+ GhostSurf: 0 ave 0 max 0 min
313
+ Histogram: 128 0 0 0 0 0 0 0 0 0
314
+ write_grid grid/final-grid.dat
315
+ grid cells = 97600
316
+ CPU time = 0.0137574 secs
317
+ write_surf surf/geom.surf
318
+ surf elements = 200
319
+ CPU time = 0.00150818 secs
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/mir.txt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ dimension 2
2
+ create_box -5.0 6.0 -5.0 5.0 -0.5 0.5
3
+ read_grid grid/final-grid.dat
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/output/lambdakn.300000.dat ADDED
The diff for this file is too large to render. See raw diff
 
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/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 0.00432 0.092199 -0.092187 0.0014482 -1.7502e-05 -0.0011141 -0.0014483 5.247e-06 10.868 0 0
11
+ 129 -0.31871 0.38526 -0.30645 0.39508 0.135 3.0477 1.9056 -2.3785 0.40652 0.32568 0.036317 -0.032245 480.58 0 0
12
+ 2 0.49975 -0.015705 0.49901 -0.031395 0.00423 0.091541 -0.09144 0.0043122 -4.3879e-05 -0.00093045 -0.001437 5.3118e-05 12.975 0 0
13
+ 130 -0.30645 0.39508 -0.29389 0.40451 0.12889 2.9386 1.7644 -2.3499 0.43076 0.32343 0.03448 -0.031831 481.51 0 0
14
+ 3 0.49901 -0.031395 0.49778 -0.047054 0.00392 0.081905 -0.081653 0.0064262 -0.00013865 -0.0017617 -0.0012847 7.3267e-05 9.6628 0 0
15
+ 131 -0.29389 0.40451 -0.28104 0.41354 0.12115 2.7664 1.5907 -2.2634 0.42701 0.30011 0.031693 -0.030837 443.89 0 0
16
+ 4 0.49778 -0.047054 0.49606 -0.062667 0.00422 0.092188 -0.091632 0.010116 0.00017201 0.001558 -0.0014366 0.00018337 12.863 0 0
17
+ 132 -0.28104 0.41354 -0.26791 0.42216 0.11903 2.7028 1.4839 -2.259 0.4366 0.28679 0.030166 -0.030979 433.56 0 0
18
+ 5 0.49606 -0.062667 0.49384 -0.078217 0.00422 0.093596 -0.092662 0.013188 0.00017763 0.0012481 -0.0014527 0.00022675 12.053 0 0
19
+ 133 -0.26791 0.42216 -0.25452 0.43037 0.11299 2.569 1.3423 -2.1904 0.46539 0.28519 0.028394 -0.029926 416.25 0 0
20
+ 6 0.49384 -0.078217 0.49114 -0.093691 0.00382 0.07992 -0.07873 0.013741 -5.2635e-05 -0.00030159 -0.0012375 0.00021109 8.5245 0 0
21
+ 134 -0.25452 0.43037 -0.24088 0.43815 0.10523 2.3848 1.1816 -2.0715 0.44216 0.25221 0.025505 -0.028577 394.8 0 0
22
+ 7 0.49114 -0.093691 0.48796 -0.10907 0.00419 0.089309 -0.087454 0.018111 -0.000184 -0.00088852 -0.0013766 0.00027052 10.64 0 0
23
+ 135 -0.24088 0.43815 -0.227 0.4455 0.10023 2.2728 1.0635 -2.0086 0.4657 0.24657 0.024019 -0.027676 381.03 0 0
24
+ 8 0.48796 -0.10907 0.48429 -0.12434 0.00382 0.08439 -0.082058 0.019701 0.00066017 0.0027498 -0.0012785 0.00035263 10.438 0 0
25
+ 136 -0.227 0.4455 -0.21289 0.45241 0.09076 2.0522 0.90286 -1.843 0.41646 0.20402 0.020723 -0.025743 345.02 0 0
26
+ 9 0.48429 -0.12434 0.48015 -0.1395 0.00374 0.077415 -0.074671 0.020428 0.00045201 0.0016523 -0.0011658 0.00034682 8.9179 0 0
27
+ 137 -0.21289 0.45241 -0.19857 0.45888 0.08859 2.0049 0.82503 -1.8272 0.42975 0.19404 0.019709 -0.025653 334.3 0 0
28
+ 10 0.48015 -0.1395 0.47553 -0.15451 0.00394 0.083413 -0.079725 0.024527 0.00019307 0.00062757 -0.0012492 0.00039511 10.433 0 0
29
+ 138 -0.19857 0.45888 -0.18406 0.46489 0.08322 1.8733 0.71689 -1.7307 0.43371 0.17965 0.018073 -0.024363 308.02 0 0
30
+ 11 0.47553 -0.15451 0.47044 -0.16937 0.00364 0.07557 -0.071495 0.024478 0.00020265 0.0005919 -0.0011198 0.00039379 10.335 0 0
31
+ 139 -0.18406 0.46489 -0.16937 0.47044 0.07751 1.7327 0.61247 -1.6209 0.44435 0.16791 0.0166 -0.022822 299.85 0 0
32
+ 12 0.47044 -0.16937 0.46489 -0.18406 0.00338 0.073476 -0.068733 0.025972 0.0018375 0.0048629 -0.0010507 0.00048433 9.1504 0 0
33
+ 140 -0.16937 0.47044 -0.15451 0.47553 0.07318 1.6542 0.53583 -1.565 0.42545 0.14566 0.015099 -0.022294 285.18 0 0
34
+ 13 0.46489 -0.18406 0.45888 -0.19857 0.00303 0.060958 -0.056317 0.023327 0.002931 0.0070759 -0.00083856 0.00047756 9.7218 0 0
35
+ 141 -0.15451 0.47553 -0.1395 0.48015 0.06908 1.5406 0.45299 -1.4725 0.40001 0.12306 0.013398 -0.021196 264.33 0 0
36
+ 14 0.45888 -0.19857 0.45241 -0.21289 0.00303 0.062036 -0.05654 0.025529 0.0017049 0.003776 -0.00086131 0.0004603 9.4875 0 0
37
+ 142 -0.1395 0.48015 -0.12434 0.48429 0.06519 1.4698 0.38783 -1.4177 0.40125 0.10977 0.012394 -0.020544 253.88 0 0
38
+ 15 0.45241 -0.21289 0.4455 -0.227 0.003 0.057526 -0.05166 0.025308 0.0055541 0.011337 -0.00072419 0.0005756 8.7016 0 0
39
+ 143 -0.12434 0.48429 -0.10907 0.48796 0.05984 1.3437 0.31367 -1.3065 0.40802 0.097957 0.011336 -0.018984 234.27 0 0
40
+ 16 0.4455 -0.227 0.43815 -0.24088 0.00282 0.058111 -0.051356 0.027192 0.0059573 0.011251 -0.0007131 0.00060384 8.6226 0 0
41
+ 144 -0.10907 0.48796 -0.093691 0.49114 0.05609 1.2544 0.25438 -1.2284 0.36581 0.075755 0.0097415 -0.018104 223.98 0 0
42
+ 17 0.43815 -0.24088 0.43037 -0.25452 0.00303 0.059194 -0.051418 0.029328 0.0087553 0.01535 -0.00067012 0.00070177 8.8926 0 0
43
+ 145 -0.093691 0.49114 -0.078217 0.49384 0.05218 1.1701 0.20117 -1.1526 0.38006 0.066331 0.0091295 -0.017063 208.23 0 0
44
+ 18 0.43037 -0.25452 0.42216 -0.26791 0.00333 0.065312 -0.055688 0.034126 0.0094811 0.015472 -0.00072579 0.00077904 12.19 0 0
45
+ 146 -0.078217 0.49384 -0.062667 0.49606 0.0503 1.1062 0.15587 -1.0952 0.36559 0.052031 0.0081907 -0.016386 197.85 0 0
46
+ 19 0.42216 -0.26791 0.41354 -0.28104 0.00285 0.053675 -0.044862 0.029469 0.010743 0.016354 -0.00053593 0.00071976 8.2494 0 0
47
+ 147 -0.062667 0.49606 -0.047054 0.49778 0.04517 0.99977 0.10971 -0.99374 0.35957 0.039697 0.0073711 -0.014985 178.86 0 0
48
+ 20 0.41354 -0.28104 0.40451 -0.29389 0.00305 0.057847 -0.047328 0.033262 0.011766 0.016742 -0.00055858 0.00078543 9.5752 0 0
49
+ 148 -0.047054 0.49778 -0.031395 0.49901 0.04258 0.94004 0.073755 -0.93714 0.32309 0.025428 0.0062334 -0.014321 164.43 0 0
50
+ 21 0.40451 -0.29389 0.39508 -0.30645 0.003 0.058054 -0.046425 0.034857 0.015876 0.021145 -0.00047985 0.00087963 11.086 0 0
51
+ 149 -0.031395 0.49901 -0.015705 0.49975 0.03888 0.85791 0.040413 -0.85695 0.31453 0.014833 0.0055751 -0.013227 158.36 0 0
52
+ 22 0.39508 -0.30645 0.38526 -0.31871 0.00311 0.058627 -0.045754 0.036656 0.019578 0.024438 -0.00041115 0.00095962 9.6153 0 0
53
+ 150 -0.015705 0.49975 0 0.5 0.03587 0.7886 0.012387 -0.7885 0.30124 0.0047323 0.0049263 -0.012311 146.43 0 0
54
+ 23 0.38526 -0.31871 0.37506 -0.33066 0.00303 0.059934 -0.045574 0.038924 0.018698 0.021893 -0.00042215 0.00095528 10.703 0 0
55
+ 151 0 0.5 0.015705 0.49975 0.03304 0.71941 -0.0113 -0.71932 0.28515 -0.0044795 0.0043015 -0.011369 129.79 0 0
56
+ 24 0.37506 -0.33066 0.36448 -0.34227 0.00326 0.061264 -0.045313 0.041231 0.021513 0.023643 -0.00037382 0.001019 11.471 0 0
57
+ 152 0.015705 0.49975 0.031395 0.49901 0.03085 0.66961 -0.031543 -0.66887 0.27719 -0.013072 0.0038584 -0.010711 121.89 0 0
58
+ 25 0.36448 -0.34227 0.35355 -0.35355 0.0036 0.070605 -0.050703 0.049135 0.02989 0.030844 -0.00032692 0.0012563 12.714 0 0
59
+ 153 0.031395 0.49901 0.047054 0.49778 0.02844 0.61519 -0.048267 -0.6133 0.27082 -0.021314 0.0034956 -0.009968 122.2 0 0
60
+ 26 0.35355 -0.35355 0.34227 -0.36448 0.00401 0.076722 -0.053391 0.055096 0.033289 0.032259 -0.00031576 0.0013721 14.281 0 0
61
+ 154 0.047054 0.49778 0.062667 0.49606 0.02614 0.56197 -0.061668 -0.55858 0.25125 -0.027738 0.0029778 -0.0092095 108.73 0 0
62
+ 27 0.34227 -0.36448 0.33066 -0.37506 0.00428 0.080402 -0.054111 0.059468 0.038748 0.035258 -0.00024131 0.0014879 17.556 0 0
63
+ 155 0.062667 0.49606 0.078217 0.49384 0.02487 0.53578 -0.075492 -0.53044 0.23625 -0.033624 0.0025251 -0.0088599 103.07 0 0
64
+ 28 0.33066 -0.37506 0.31871 -0.38526 0.00461 0.087104 -0.05657 0.066234 0.042709 0.036477 -0.00021772 0.0016133 17.187 0 0
65
+ 156 0.078217 0.49384 0.093691 0.49114 0.02143 0.46225 -0.079474 -0.45537 0.21134 -0.036884 0.0020712 -0.0077319 89.969 0 0
66
+ 29 0.31871 -0.38526 0.30645 -0.39508 0.00502 0.095583 -0.059762 0.074596 0.047492 0.038048 -0.00019273 0.0017693 20.151 0 0
67
+ 157 0.093691 0.49114 0.10907 0.48796 0.0201 0.43278 -0.087763 -0.42379 0.20282 -0.042003 0.0018073 -0.0073164 89.349 0 0
68
+ 30 0.30645 -0.39508 0.29389 -0.40451 0.00555 0.1093 -0.065625 0.087404 0.060208 0.045205 -8.509e-05 0.0020829 25.805 0 0
69
+ 158 0.10907 0.48796 0.12434 0.48429 0.01832 0.38486 -0.089843 -0.37422 0.18869 -0.0453 0.0015526 -0.0065896 75.729 0 0
70
+ 31 0.29389 -0.40451 0.28104 -0.41354 0.00607 0.11912 -0.068495 0.097459 0.065551 0.04607 -4.625e-05 0.0022545 25.008 0 0
71
+ 159 0.12434 0.48429 0.1395 0.48015 0.01626 0.3468 -0.091512 -0.33451 0.17502 -0.04788 0.0013117 -0.0060063 72.532 0 0
72
+ 32 0.28104 -0.41354 0.26791 -0.42216 0.00681 0.1332 -0.073132 0.11133 0.073657 0.048383 8.2506e-06 0.0025087 29.398 0 0
73
+ 160 0.1395 0.48015 0.15451 0.47553 0.01613 0.33763 -0.099277 -0.32271 0.16066 -0.049425 0.00096413 -0.0058452 60.817 0 0
74
+ 33 0.26791 -0.42216 0.25452 -0.43037 0.00712 0.14189 -0.074136 0.12098 0.076115 0.046643 3.109e-05 0.0026329 28.283 0 0
75
+ 161 0.15451 0.47553 0.16937 0.47044 0.01456 0.30179 -0.097756 -0.28552 0.15408 -0.052755 0.00088476 -0.0053134 58.844 0 0
76
+ 34 0.25452 -0.43037 0.24088 -0.43815 0.00785 0.15535 -0.076969 0.13494 0.084722 0.048324 0.00012177 0.0028786 31.135 0 0
77
+ 162 0.16937 0.47044 0.18406 0.46489 0.01281 0.26259 -0.092819 -0.24564 0.13808 -0.052176 0.00071091 -0.0046779 53.558 0 0
78
+ 35 0.24088 -0.43815 0.227 -0.4455 0.00932 0.18897 -0.088424 0.167 0.10266 0.054358 0.00022367 0.003477 39.968 0 0
79
+ 163 0.18406 0.46489 0.19857 0.45888 0.01116 0.23196 -0.088767 -0.2143 0.12426 -0.051472 0.00055756 -0.0041746 49.029 0 0
80
+ 36 0.227 -0.4455 0.21289 -0.45241 0.00948 0.19213 -0.084525 0.17254 0.10946 0.053623 0.00039164 0.0035524 41.045 0 0
81
+ 164 0.19857 0.45888 0.21289 0.45241 0.01098 0.22921 -0.094322 -0.2089 0.11776 -0.053172 0.0003682 -0.0041165 46.361 0 0
82
+ 37 0.21289 -0.45241 0.19857 -0.45888 0.01079 0.22047 -0.090725 0.20093 0.12058 0.054446 0.00046902 0.0040113 49.078 0 0
83
+ 165 0.21289 0.45241 0.227 0.4455 0.00971 0.19122 -0.084125 -0.17172 0.11365 -0.055677 0.00046377 -0.0035718 42.139 0 0
84
+ 38 0.19857 -0.45888 0.18406 -0.46489 0.01188 0.24751 -0.094718 0.22867 0.12577 0.052097 0.00048778 0.0044101 47.01 0 0
85
+ 166 0.227 0.4455 0.24088 0.43815 0.00883 0.17924 -0.083874 -0.15841 0.096152 -0.05091 0.00019287 -0.0032879 37.92 0 0
86
+ 39 0.18406 -0.46489 0.16937 -0.47044 0.01257 0.26382 -0.093252 0.24678 0.1343 0.050748 0.00064478 0.0046734 53.145 0 0
87
+ 167 0.24088 0.43815 0.25452 0.43037 0.00788 0.16028 -0.079413 -0.13923 0.086973 -0.049609 0.00011876 -0.0029661 35.72 0 0
88
+ 40 0.16937 -0.47044 0.15451 -0.47553 0.01472 0.31075 -0.10066 0.294 0.15662 0.053624 0.00087905 0.0054602 66.094 0 0
89
+ 168 0.25452 0.43037 0.26791 0.42216 0.0071 0.14189 -0.07414 -0.12099 0.079073 -0.048456 7.7485e-05 -0.0026615 31.415 0 0
90
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CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/runmpirun128.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 2
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 128 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/runmpirun64.sh ADDED
@@ -0,0 +1,18 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -J Cylinder——Kn
3
+ #SBATCH -p wzhcnormal
4
+ #SBATCH -N 1
5
+ #SBATCH --ntasks-per-node=64
6
+
7
+
8
+ module purge
9
+ module load compiler/devtoolset/7.3.1
10
+ module load mpi/hpcx/2.11.0/gcc-7.3.1
11
+ export UCX_WARN_UNUSED_ENV_VARS=n
12
+ /public/software/mpi/hpcx/v2.11.0/gcc-7.3.1/bin/mpirun -n 64 ../../../sep2024-sparta/src/spa_mpi <config.txt
13
+
14
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Grid -r ./output/grid.*.dat
15
+
16
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/grid2paraview.py mir.txt Lambda -r ./output/lambdakn.*.dat
17
+
18
+ /work/home/ackp4pog0w3/WANGXU/ParaView-5.11.1-MPI-Linux-Python3.9-x86_64/bin/pvpython ../../../sep2024-sparta/tools/paraview/surf2paraview.py ./surf/geom.surf Surf -r ./output/surf.*.dat
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/slurm-28429237.out ADDED
@@ -0,0 +1,205 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ SPARTA (4 Sep 2024)
2
+ Running on 128 MPI task(s)
3
+ Running on 128 MPI task(s)
4
+ Created orthogonal box = (-5 -5 -0.5) to (6 5 0.5)
5
+ Created 97600 child grid cells
6
+ CPU time = 0.124474 secs
7
+ create/ghost percent = 1.01919 98.9808
8
+ Balance grid migrated 97380 cells
9
+ CPU time = 0.10468 secs
10
+ reassign/sort/migrate/ghost percent = 55.3025 0.0495413 8.05456 36.5934
11
+ Reading surface file ...
12
+ 200 lines
13
+ -0.5 0.5 xlo xhi
14
+ -0.5 0.5 ylo yhi
15
+ 0 0 zlo zhi
16
+ 0.0157073 min line length
17
+ 456 0 = cells overlapping surfs, overlap cells with unmarked corner pts
18
+ 87188 9956 456 = cells outside/inside/overlapping surfs
19
+ 456 = surf cells with 1,2,etc splits
20
+ 109.214731023047 109.214731023047 = cell-wise and global flow volume
21
+ CPU time = 0.122092 secs
22
+ read/check/sort/surf2grid/ghost/inout/particle percent = 21.642 18.5844 2.56502 20.3133 36.8953 36.517 0.0144473
23
+ surf2grid time = 0.0248009 secs
24
+ map/comm1/comm2/comm3/comm4/split percent = 14.8387 23.4287 5.35748 1.69299 11.4461 8.24091
25
+ Balance grid migrated 1938 cells
26
+ CPU time = 0.0469227 secs
27
+ reassign/sort/migrate/ghost percent = 6.73958 0.0527037 19.8041 73.4036
28
+ Creating particles ...
29
+ WARNING: Created unexpected # of particles: 668660 versus 668661 (../create_particles.cpp:346)
30
+ Created 668660 particles
31
+ CPU time = 0.0173073 secs
32
+ Memory usage per proc in Mbytes:
33
+ particles (ave,min,max) = 1.79297 0 3.375
34
+ grid (ave,min,max) = 4.297 1.51379 7.13879
35
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
36
+ total (ave,min,max) = 6.11057 3.22189 8.84689
37
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
38
+ 0 0 668660 0 0 0 0 0
39
+ 10000 16.38402 734483 16423 14028 15 695465.09 16.2978
40
+ 20000 38.124891 741665 16619 14271 18 717756.56 16.4886
41
+ 30000 59.323338 743118 16743 14398 20 726018.37 16.5531
42
+ 40000 80.545465 741903 16774 14364 18 730234.18 16.5878
43
+ 50000 102.27449 742425 16832 14483 13 732610.91 16.6011
44
+ 60000 123.98502 742687 16767 14356 14 734300.03 16.619183
45
+ 70000 144.55496 742499 16784 14358 19 735500.75 16.619071
46
+ 80000 166.33196 742938 16733 14399 9 736393.14 16.64375
47
+ 90000 187.96225 742621 16688 14335 17 737143.85 16.645744
48
+ 100000 208.62772 742311 16625 14293 15 737670.47 16.65375
49
+ 110000 230.8332 742585 16774 14436 11 738095.46 16.647664
50
+ 120000 252.36626 742965 16716 14400 13 738462.69 16.655033
51
+ 130000 273.44089 742071 16736 14346 15 738736.96 16.654969
52
+ 140000 295.16596 742104 16726 14406 15 738988.8 16.652279
53
+ 150000 316.86282 742589 16794 14535 20 739212.4 16.658947
54
+ 160000 338.11467 741744 16831 14527 16 739430.98 16.658838
55
+ 170000 359.93607 742049 16750 14341 15 739604.24 16.655429
56
+ 180000 381.01771 742140 16677 14331 22 739732.8 16.651467
57
+ 190000 402.87367 742305 16744 14402 8 739878.61 16.6517
58
+ 200000 425.99347 742528 16700 14290 13 740015.12 16.65522
59
+ Loop time of 425.994 on 128 procs for 200000 steps with 742528 particles
60
+
61
+ MPI task timing breakdown:
62
+ Section | min time | avg time | max time |%varavg| %total
63
+ ---------------------------------------------------------------
64
+ Move | 0.024508 | 18.188 | 114.17 | 559.1 | 4.27
65
+ Coll | 0.48303 | 20.358 | 176.41 | 663.8 | 4.78
66
+ Sort | 0.15841 | 3.5034 | 27.35 | 263.4 | 0.82
67
+ Comm | 69.473 | 102.74 | 114.5 | 94.6 | 24.12
68
+ Modify | 0.020593 | 0.16566 | 3.3539 | 114.7 | 0.04
69
+ Output | 0.0059557 | 0.020203 | 0.022175 | 1.9 | 0.00
70
+ Other | | 281 | | | 65.97
71
+
72
+ Particle moves = 148003023218 (148B)
73
+ Cells touched = 154806239492 (155B)
74
+ Particle comms = 253225027 (253M)
75
+ Boundary collides = 0 (0K)
76
+ Boundary exits = 24994241 (25M)
77
+ SurfColl checks = 169798309 (170M)
78
+ SurfColl occurs = 3331044 (3.33M)
79
+ Surf reactions = 0 (0K)
80
+ Collide attempts = 3803583625 (3.8B)
81
+ Collide occurs = 2940009800 (2.94B)
82
+ Gas reactions = 0 (0K)
83
+ Particles stuck = 0
84
+ Axisymm bad moves = 0
85
+
86
+ Particle-moves/CPUsec/proc: 2.71429e+06
87
+ Particle-moves/step: 740015
88
+ Cell-touches/particle/step: 1.04597
89
+ Particle comm iterations/step: 1
90
+ Particle fraction communicated: 0.00171094
91
+ Particle fraction colliding with boundary: 0
92
+ Particle fraction exiting boundary: 0.000168877
93
+ Surface-checks/particle/step: 0.00114726
94
+ Surface-collisions/particle/step: 2.25066e-05
95
+ Surface-reactions/particle/step: 0
96
+ Collision-attempts/particle/step: 0.0256994
97
+ Collisions/particle/step: 0.0198645
98
+ Gas-reactions/particle/step: 0
99
+
100
+ Particles: 5801 ave 38227 max 0 min
101
+ Histogram: 84 14 4 8 8 4 2 0 1 3
102
+ Cells: 762.5 ave 763 max 762 min
103
+ Histogram: 64 0 0 0 0 0 0 0 0 64
104
+ GhostCell: 19721.7 ave 36096 max 1099 min
105
+ Histogram: 20 10 12 10 18 2 6 4 16 30
106
+ EmptyCell: 0 ave 0 max 0 min
107
+ Histogram: 128 0 0 0 0 0 0 0 0 0
108
+ Surfs: 200 ave 200 max 200 min
109
+ Histogram: 128 0 0 0 0 0 0 0 0 0
110
+ GhostSurf: 0 ave 0 max 0 min
111
+ Histogram: 128 0 0 0 0 0 0 0 0 0
112
+ Memory usage per proc in Mbytes:
113
+ particles (ave,min,max) = 1.87207 0 5.0625
114
+ grid (ave,min,max) = 4.297 1.51379 7.13879
115
+ surf (ave,min,max) = 0.0205994 0.0205994 0.0205994
116
+ total (ave,min,max) = 6.4459 3.47786 9.10336
117
+ Step CPU Np Natt Ncoll Nscoll Npave Nscollave
118
+ 200000 0 742528 16700 14290 0 0 0
119
+ 210000 37.993242 741890 16766 14373 20 742922.39 16.6916
120
+ 220000 75.769123 742754 16810 14467 15 742938.8 16.6951
121
+ 230000 114.31239 743099 16720 14307 14 743051.07 16.714533
122
+ 240000 152.59187 742634 16620 14271 17 742968.92 16.7068
123
+ 250000 191.05862 742083 16732 14400 17 742927.46 16.6962
124
+ 260000 228.91868 741945 16573 14231 17 742892.65 16.692517
125
+ 270000 266.80151 742505 16729 14345 17 742857.48 16.686186
126
+ 280000 305.20677 741720 16614 14307 14 742787.82 16.67955
127
+ 290000 343.29684 742274 16731 14420 11 742740.03 16.6802
128
+ 300000 380.87227 741835 16735 14391 5 742687.13 16.68183
129
+ Loop time of 380.873 on 128 procs for 100000 steps with 741835 particles
130
+
131
+ MPI task timing breakdown:
132
+ Section | min time | avg time | max time |%varavg| %total
133
+ ---------------------------------------------------------------
134
+ Move | 0.012814 | 9.2002 | 57.384 | 395.9 | 2.42
135
+ Coll | 0.24163 | 10.416 | 95.144 | 483.3 | 2.73
136
+ Sort | 0.095516 | 1.8292 | 12.176 | 187.6 | 0.48
137
+ Comm | 41.807 | 58.412 | 64.712 | 63.8 | 15.34
138
+ Modify | 3.4404 | 25.249 | 149.74 | 607.8 | 6.63
139
+ Output | 0.03907 | 0.040604 | 0.044526 | 0.6 | 0.01
140
+ Other | | 275.7 | | | 72.39
141
+
142
+ Particle moves = 74268712763 (74.3B)
143
+ Cells touched = 77679683164 (77.7B)
144
+ Particle comms = 126932160 (127M)
145
+ Boundary collides = 0 (0K)
146
+ Boundary exits = 12538396 (12.5M)
147
+ SurfColl checks = 84988574 (85M)
148
+ SurfColl occurs = 1668183 (1.67M)
149
+ Surf reactions = 0 (0K)
150
+ Collide attempts = 1919876508 (1.92B)
151
+ Collide occurs = 1483742606 (1.48B)
152
+ Gas reactions = 0 (0K)
153
+ Particles stuck = 0
154
+ Axisymm bad moves = 0
155
+
156
+ Particle-moves/CPUsec/proc: 1.52341e+06
157
+ Particle-moves/step: 742687
158
+ Cell-touches/particle/step: 1.04593
159
+ Particle comm iterations/step: 1
160
+ Particle fraction communicated: 0.00170909
161
+ Particle fraction colliding with boundary: 0
162
+ Particle fraction exiting boundary: 0.000168825
163
+ Surface-checks/particle/step: 0.00114434
164
+ Surface-collisions/particle/step: 2.24615e-05
165
+ Surface-reactions/particle/step: 0
166
+ Collision-attempts/particle/step: 0.0258504
167
+ Collisions/particle/step: 0.019978
168
+ Gas-reactions/particle/step: 0
169
+
170
+ Particles: 5795.59 ave 38487 max 0 min
171
+ Histogram: 84 14 4 8 8 5 1 0 1 3
172
+ Cells: 762.5 ave 763 max 762 min
173
+ Histogram: 64 0 0 0 0 0 0 0 0 64
174
+ GhostCell: 19721.7 ave 36096 max 1099 min
175
+ Histogram: 20 10 12 10 18 2 6 4 16 30
176
+ EmptyCell: 0 ave 0 max 0 min
177
+ Histogram: 128 0 0 0 0 0 0 0 0 0
178
+ Surfs: 200 ave 200 max 200 min
179
+ Histogram: 128 0 0 0 0 0 0 0 0 0
180
+ GhostSurf: 0 ave 0 max 0 min
181
+ Histogram: 128 0 0 0 0 0 0 0 0 0
182
+ Writing grid file ...
183
+ grid cells = 97600
184
+ CPU time = 0.0137574 secs
185
+ Writing surface file ...
186
+ surf elements = 200
187
+ CPU time = 0.00150818 secs
188
+ Processing 4 grid chunk(s).
189
+ Completed grid chunk number: 3
190
+ Completed grid chunk number: 2
191
+ Completed grid chunk number: 1
192
+ Completed grid chunk number: 0
193
+
194
+ Done in 0:00:13 (wall clock time)
195
+
196
+ Processing 4 grid chunk(s).
197
+ Completed grid chunk number: 3
198
+ Completed grid chunk number: 2
199
+ Completed grid chunk number: 1
200
+ Completed grid chunk number: 0
201
+
202
+ Done in 0:00:12 (wall clock time)
203
+
204
+ Processing SPARTA surface information.
205
+ Processing dump result file: ['./output/surf.300000.dat']
CylinderKnMa/Kn0p28/simulation0571_Kn0p28Ma4p0/surf/Surf_300000.vtu ADDED
@@ -0,0 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <?xml version="1.0"?>
2
+ <VTKFile type="UnstructuredGrid" version="0.1" byte_order="LittleEndian" header_type="UInt32" compressor="vtkZLibDataCompressor">
3
+ <UnstructuredGrid>
4
+ <Piece NumberOfPoints="400" NumberOfCells="200" >
5
+ <PointData>
6
+ </PointData>
7
+ <CellData>
8
+ <DataArray type="Float64" Name="id" format="appended" RangeMin="1" RangeMax="200" offset="0" />
9
+ <DataArray type="Float64" Name="v1x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="432" />
10
+ <DataArray type="Float64" Name="v1y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="1468" />
11
+ <DataArray type="Float64" Name="v2x" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="2476" />
12
+ <DataArray type="Float64" Name="v2y" format="appended" RangeMin="-0.5" RangeMax="0.5" offset="3508" />
13
+ <DataArray type="Float64" Name="f_2[1]" format="appended" RangeMin="0.00282" RangeMax="0.26974" offset="4512" />
14
+ <DataArray type="Float64" Name="f_2[2]" format="appended" RangeMin="0.053675" RangeMax="6.1083" offset="6496" />
15
+ <DataArray type="Float64" Name="f_2[3]" format="appended" RangeMin="-0.10113" RangeMax="6.1015" offset="8568" />
16
+ <DataArray type="Float64" Name="f_2[4]" format="appended" RangeMin="-2.5937" RangeMax="2.5879" offset="10660" />
17
+ <DataArray type="Float64" Name="f_2[5]" format="appended" RangeMin="-0.0003111" RangeMax="0.4657" offset="12724" />
18
+ <DataArray type="Float64" Name="f_2[6]" format="appended" RangeMin="-0.41102" RangeMax="0.42107" offset="14804" />
19
+ <DataArray type="Float64" Name="f_2[7]" format="appended" RangeMin="-0.0015885" RangeMax="0.095873" offset="16940" />
20
+ <DataArray type="Float64" Name="f_2[8]" format="appended" RangeMin="-0.034555" RangeMax="0.034369" offset="19072" />
21
+ <DataArray type="Float64" Name="f_2[9]" format="appended" RangeMin="8.2494" RangeMax="907.62" offset="21220" />
22
+ <DataArray type="Float64" Name="f_2[10]" format="appended" RangeMin="0" RangeMax="0" offset="22668" />
23
+ <DataArray type="Float64" Name="f_2[11]" format="appended" RangeMin="0" RangeMax="0" offset="22720" />
24
+ </CellData>
25
+ <Points>
26
+ <DataArray type="Float32" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0.49999998503" RangeMax="0.50000001255" offset="22772" />
27
+ </Points>
28
+ <Cells>
29
+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="24100" />
30
+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="25004" />
31
+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="25496" />
32
+ </Cells>
33
+ </Piece>
34
+ </UnstructuredGrid>
35
+ <AppendedData encoding="base64">
36
+ 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AQAAAACAAABABgAAAAYAAA==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AQAAAACAAABABgAABgYAAA==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AQAAAACAAABABgAALAYAAA==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AQAAAACAAABABgAANwYAAA==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AQAAAACAAABABgAAKgQAAA==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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
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38
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