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Viewing changes to tests/darcy_impes_p1_2phase_coreyrelperm_velBCinlet_strongpressoutlet_p1satdiag/reference_solution/darcy_impes_p1_2phase_coreyrelperm_velBCinlet_strongpressoutlet_p1satdiag_relpermupwind_1d_C_1.vtu

  • Committer: Brendan Tollit
  • Date: 2012-07-24 11:24:05 UTC
  • Revision ID: brendan.tollit05@imperial.ac.uk-20120724112405-fp4yicflsgtd2rt0
Correct some test case regression answers, mainly for 3d as some have changed slightly.

Add a consistent subcycling test case to stress this code path, although I still wouldnt 
recommend using it.

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Lines of Context:
tests/darcy_impes_p1_2phase_coreyrelperm_velBCinlet_strongpressoutlet_p1satdiag/reference_solution/darcy_impes_p1_2phase_coreyrelperm_velBCinlet_strongpressoutlet_p1satdiag_relpermupwind_1d_C_1.vtu
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vvtQiK9++D37DVPZXp6ac8feWZ/t0mKT2/bNXVYBxSy37Pv3Bcw88+eGfUqKo9wF4Rv2UflpQoedrtsz7X3udqXlmr1PUoRTzrWr9i9rNI4eNrlqf/v2a6sFc6/Y30izMFTlv2LfOv8R18b2y/aWXlIi+1gv23+QKt14sP2SvdRxu4MMPJfsE0pkyj0nXLQPic4+kCt40f7pc39r2a4L9kaLX6gxPDhvz3IkL/lN8Dn7lUdEjl4/d9o+3kfZOantkH10i9U07bmm9p5XJsUHVffYMNAJAADHRmpAAQAAAACAAABIAQAAEgAAAA==eJxjYACBD/YMozRFNACuYTCIAQAAAACAAABIAQAA+AAAAA==eJzjeZqTJSK0xd40/i177f/F9nfW8Zjyvl5srxTodSDk6mJ7+8t93jP2LbZv92s2/rdssX3Ku/0uc/sW269MKXveVLrY/sj291zbohfbt0753WvhuNi+XvjNOXa1xfbvlr1bIsO12H6XwpsLWW8X2R/e0XXh/vlF9pWrLK8kbFxkv+1uXvTxiYvso4wmNd0uWGR/rb1C28tvkf2HiSe5Jmsusl+4J/nTU8ZF9v8DJTNuXl1oXy8nenLPooX2HgKfzfYlLbRfaVnxS4ttob3KtTfeMwIX2Pef1OEtOz/d3mtilsO13BN2DLPmf/40ZY8lA50AANfUcGg=AQAAAACAAABIAQAAAAEAAA==eJzTrbDc7b99p8MT8/fGc05td9ghm3tg7f0tDp5nD97dZ7jZYdL2b9FMvzc6iJpofORm2+jwNlXExtV6g4NRn5L6M+n1DnnXFk7f8Watg+pevt5799Y4NJwRY/d+vdrh3P6I1Ns8qx16XBf1rLZf5aDBfUZvTcNKh4Mmk9liz65w0FgtvWWW8gqH6atEmMoalzuIe18p4nm6zCHn7r8JET7LHJJKPma/2rLUwenvu0Vp0ksdjslP/l1atcThgLB7jO6JxQ4zFfn7b79d5CBW9X7/s2ULHa5IXPfVnD7bQawhz3FxVatD+rnt849dvWIv362cxnDstBUDjQEAXK5wXw==AQAAAACAAABIAQAA/wAAAA==eJz7UOBeqPTlvX1gSkpN47P39sH5WyOZjr+3T3fcnOC19L29nfSs1vON7+0XKnySuRvz3v5vrnTwArP39ouzdkSb8L+3/+2n2b3i2Tt7C1eWZOa97+w91IV+Ok96Z29zbEJAUto7e+sJijf9LN/Zy/9eyfGR6539L4kzp1Ruv7U/yRRqsG3lW/vuj9sto8re2uc8iG9d5PDW/pnVTp/n7G/tU/QsdsqefmN/4cT/0qaON/Zh657uumbzxv7yt58WyQ9f27d7qhU5pb62/7VwVrF23yv7pT+tzs/a8sx+9qk4n9Zt++3fm75ZzaTHZJ92xTet+cA3SwYaAwDy4HdpAQAAAACAAABIAQAA3AAAAA==eJxjYACBD/az/z1f97Hppf2jDwsPhVi9sP9wV/mwdNpzexHvh0vijz2z5zwxz1bA9Zn9lZLD/seuP7U/+9bAenXFU/szD/amGKs8tT/hmsz44/oT+xQ5hqzASU/sX9V0y/AEPbG3q67b4iT2xD4/IS7J5t5je7kjS/cVrHxs7+V3Piur/LH9r5k/K8+5P7avmfAz3VfqsX3G+j/rVr97ZP8qa1332iOP7Hu+fnmrMueR/Td+zfIXJY/sbTVr513xe2Sf1uqz5pTmI3v/Bd2zV7M+smcYYAAAkoFomQ==AQAAAACAAABIAQAA+gAAAA==eJxrWRdT31FzzD6hcFlw7bE59geO7fyvmzjJ3uFEaUrtkm77A491VyWWtwHFXy67qdpsf+DlrJPJDxrsGVj6bU801dk3FF1vY9apsWdgU35/vrrKnmFHp5iTX6V9Q8I2jsV9FfYMn7dr/TCqsG/gUGE9YAKkp9zvWzwTKH5m5k6brEp7ho/FFay7q+wbPsjO/rGqxr5h+ZmUIwX19gyhlza/XN9o77AxPrujrcWeIT2hePmxDvsDtTa9jyf02zuIH1vs9XCqvcHutQ7e+xfaXzujw5jJf9f+5t3/T7TD19h/SlpskJZ3wo5h1vzPn6bssWSgMQAAkoBv2w==AQAAAACAAADYAwAAVQEAAA==eJwzSpEulN0dbs+ABq6FFYrorMrCEP8VsFxo790aDPFtPxUS5l5qwxC/tNl3acPqfgzxlCb2rXJFUzDEjYWvPQi2mIkhLnlnft9RlXkY4lFHvp7aNmshhrjhlM9Xt5oswRBf69k0M/PBMgzx0nTeU37zVmKI2zQ+mxiTvwZDfNUpyxVSoesxxGd+8Fd6qrERQ7zixZqFoVs2YYiL37v2xtx/C4b4yjNb7Bj/bsUQb0j3evX50HYM8Q32h9bnrdyJIS62/YLb3H27McQnrNjAP19mH4b4nY8XllyqOYAhvuPlxF/R+YcwxHsMdKdKXD2KIb730LHf1wUuY4ZP9xazOa3PMMQ1CzjMN/1/jyF+y44RSH7AEP9ii11cdCYDVvE/udjFe95jN6erhwmr+FN27OqX2P75/x+L+6/hsPeGHQtW8YRw7PY2s2E3x1fzH1Z7Ad17l6k=AQAAAACAAADYAwAAzAAAAA==eJxLCPzz////9/YMaODiBkYg+QFD3OQbE1bxy3H/sJrTcw67OOMC7OY8nIBdXKX8P1ZzLCUZsKqf1YzdnAQP7P7i0sJufs5X7OoP8mI3/50ydnPEZmI3Z4I3dnHxMwxYxXU6sKsv2ohd/Xcj7Op/C2MX/6OEXTxKCLv4DlHs4pvFsYuri2EXX8ODXfyWHXbxL7bYxUVnMmAV/5OLXbznPXZzunqwx+9Tduzql9hiz0fXcNh7w44Fq3hCOHZ7m9mwm+OriT1/AQAOCnX4AQAAAACAAADYAwAACgEAAA==eJxbd9i5UOnLe3sGNOD94d3l0KeY4sfmPmh1OYYp/uZWbdfWJZjie6dn/g1sxBR/zxzLlR+DKa5R+C/B1gxT3Lmi6/JNPkxxnWsBk0OevcMQ/3B26aItezDFVXOy0n5OxBSfHBwnLJ2GKX6k6akZlyWm+I26htB1XJjit1W3Rj++9RZDXG7qWe/SlZjicbvTmSTLMMVn/tO6Ee+AKZ55eGfTQnZM8XdSnFqXTr3BEOfx1wjX6cAUfyLEI5djgyneyHku7PSD1xjibqtiGTVTMMX97m+7tbPrFYY4xzk5pinlzzDEsx1nTzr+5RKGuN5d7yy7ImYM8bSiprTmA98s0cVHAX4AAL1SdtY=AQAAAACAAABAAQAAFQAAAA==eJzTdZZ5/chMyl53lCaLBgDoYHRBAQAAAACAAABAAQAAEgAAAA==eJxjYACBB/YMozRZNACmTSzZAQAAAACAAABAAQAAFQAAAA==eJzbvff6zfs1t213j9Jk0QDqnOA5AQAAAACAAABAAQAAFQAAAA==eJzTdZZ5/chMyl53lCaLBgDoYHRBAQAAAACAAADYAwAAmQAAAA==eJx1zk0KggAUxHHvpPtuk2fSG7Ru5S4QgqDAMERJpG9LTTtC6FuO/1kOj/k9x5lPGExZzPcr6T13zBruI+ktG9iJpfeXY7awv5P+sJ8CbgI7R/gnhf0T/JmBm4NbgHsGtwS3AvcCO9rbP1fYv8Gfd3Af4Gpv7hPcF7g1uG9wtTf3A24DbgtuB6725n7B7cEdwP1J/wduI4uJAQAAAACAAACAAgAAWAAAAA==eJxdxUkCgQAAAMAsoYUUZSkRLf7/QoecZi4TBLMFL3nFaw55w1veccQxJ5zyng+c8ZFzLvjEZy654gtf+cZ3rrnhB7f85Bd3/OYP9zzwyBN///8AUOsGQQ==AQAAAACAAABAAQAAWgAAAA==eJwtxdEGwgAAAMAkk8lMkklmZmaSZGaSyczMJNn/f00P3b3cevW3ceCtQ+8cOfbeBx+d+OSzU2fOXbh05Yuvvvnu2o1bP/x055d7Dx49efbbH3+9+AfO3gZpAQAAAACAAAAoAAAADAAAAA==eJxjZiYOAAAJxAB5
 
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aXkn7Aw/zv/8acoeSwYaAwA3HXnaAQAAAACAAABIAQAA9wAAAA==eJyz2sJWfEjoqf2/dOOfF14/tF9yQTkhzfuBfeXBEF/98Hv2HlPZXp6ac8d+sx7bpcUmt+17u6wCillu2dvvC5h55s8N+y/JjnIXhG/Yz89LEzrsdN3+3Z7nbldartlLJUU45Vy7ar+iRuPoYZOr9ttuv7ZaMPeK/d40C0NV/iv2CfMfcW1sv2z/3FNKZB/rZfubUqUbD7Zfsv9+zO4gA88l+5ASmXLPCRftQ6KzD+QKXrS/+dzfWrbrgr3+4hdqDA/O27McyUt+E3zO/v0RkaPXz522z/RRdk5qO2R/8YDVNO25pvaeVybFB1X32DDQCQAAVD5thA==AQAAAACAAABIAQAAEgAAAA==eJxjYACBD/YMozRFNACuYTCIAQAAAACAAABIAQAA+AAAAA==eJxzVM7NEhHaYu8x9y177f/F9olveEx5Xy+2n9LndSDk6mL7YIF+7xn7FttXdzcb/1u22H6X1gGXuX2L7fsWlD1vKl1sH/D5Pde26MX2E87/7rVwXGxv6PfmHLvaYvvSh++WyHAttv8V9eZC1ttF9nmfui7cP7/IfsljyysJGxfZTxLLjz4+cZH9nKxJTbcLFtmf2l+h7eW3yD735EmuyZqL7Os+J396yrjIvr5DMuPm1YX2gSGiJ/csWmhv7/rZbF/SQvs1uRW/tNgW2r9gf+s9I3CBfTGfLm/Z+en2y3dkOVzLPWFn+HH+509T9lgy0AkAAETLcCk=AQAAAACAAABIAQAAAQEAAA==eJz7XmG523/7TgdLi/fGc05tdyiSzT2w9v4Wh31nDt7dZ7jZQXL7t2im3xsdjhhrfORm2+iwK1XExtV6g8ObXiX1Z9LrHZ5cXTh9x5u1DqF7+Hrv3Vvj8Oq0GLv369UOM/ZHpN7mWe1g47qoZ7X9KocrXGf01jSsdKgxmcwWe3aFw6tV0ltmKa9wMFklwlTWuNxhn9eVIp6nyxzM7v6bEOGzzEGn5GP2qy1LHVT+vluUJr3UYYX85N+lVUscpgm7x+ieWOxQoMjff/vtIofnle/3P1u20GG92HVfzemzHYxq8hwXV7U6hG3cPv/Y1Sv28t3KaQzHTlsx0BgAAO24cvE=AQAAAACAAABIAQAA/wAAAA==eJz7WuBeqPTlvX1QSkpN47P39n75WyOZjr+3D3fcnOC19L29nvSs1vON7+0nK3ySuRvz3v5zrnTwArP39rOydkSb8L+3f+Gn2b3i2Tt7EVeWZOa97+x11IV+Ok96Z29wbEJAUto7e80Jijf9LN/Zc/1eyfGR6539c4kzp1Ruv7XfzhRqsG3lW/usj9sto8re2vs+iG9d5PDW/oLVTp/n7G/t/fQsdsqefmN/+MT/0qaON/Zu657uumbzxn7Pt58WyQ9f2+d5qhU5pb62v7FwVrF23yt7jR9W52dteWZfcDjOp3XbfnsemTermfSY7NOu+KY1H/hmyUBjAAC2a3P3AQAAAACAAABIAQAA3AAAAA==eJxjYACBD/az/z1f97Hppf2jDwsPhVi9sP9wV/mwdNpzexHvh0vijz2z5zwxz1bA9Zn9lZLD/seuP7U/+9bAenXFU/szD/amGKs8tT/hmsz44/oT+xQ5hqzASU/sX9V0y/AEPbG3q67b4iT2xD4/IS7J5t5je7kjS/cVrHxs7+V3Piur/LH9r5k/K8+5P7avmfAz3VfqsX3G+j/rVr97ZP8qa1332iOP7Hu+fnmrMueR/Td+zfIXJY/sbTVr513xe2Sf1uqz5pTmI3v/Bd2zV7M+smcYYAAAkoFomQ==AQAAAACAAABIAQAA+gAAAA==eJz7sSamvqPmmD1D8bLg2mNz7B3O7PyvmzjJvuFuaUrtkm57hje6qxLL2+wbLr1cdlO12Z7h5ayTyQ8a7BsE+21PNNXZM7Rdb2PWqbFnUFd+f766yr5hd6eYk1+lfUPmNo7FfRX2DP+3a/0wqrBv4FdhPWACpGfe71s8Eyh+eeZOm6xKewbGkgrW3VX2DF9lZ/9YVWPfsO5MypGCenuHsEubX65vtHfYGJ/d0dZi75CRULz8WIc9Q51N7+MJ/fYO4scWez2cam+wa62D9/6F9rfP6DBm8t+1n/Dj/xPt8DX2Pl2LDdLyTtgZfpz/+dOUPZYMNAYARvNtTA==AQAAAACAAADYAwAAUgEAAA==eJzzi5UulN0dbs+ABo6FFororMrCEFcJXi60924NhjjjX4WEuZfaMMQDtvsubVjdjyH+tZl9q1zRFAxxHpFrD4ItZmKIl9yZ33dUZR6GuMTRr6e2zVqIIb5x6uerW02WYIgXeDXNzHywDEPcLJ33lN+8lRjiLY3PJsbkr8EQf3vKcoVU6HoM8fwP/kpPNTZiiBu8WLMwdMsmDHGle9femPtvwRCvO7PFjvHvVgzxD2lerz4f2o4h/s3u0Pq8lTsxxbdecJu7bzeGeM7yDfzzZfZhiLt9vLDkUs0BDHGplxN/RecfwnSPvu5UiatHMcRfHDn2+7rAZUxzeraYzWl9hiHen8dhvun/ewzxx7aMIFswxD/bYBfnm8GAVfxFDnbxvHfYzcnpZsIqvocNu3pD6z///2Nxfw8Oe5tsWbCKs4Rht1eTFbs589T+YbUXAD50lN8=AQAAAACAAADYAwAAyQAAAA==eJzjCvzz////9/YMaGDDBkYg+QFDnOEbE1bx2Lh/WM3RPYdd/MZ87OYsmYBd3KPsP1Zz9kswYFUv0IzdnD3u2P11QQO7+a+/YFdvyIvd/Dol7OaEzcBuzkkv7OLOpxmwise0Y1fftwG7egEj7OrlhbGLCylhF68QxC7+WAS7+BUx7OKJotjF73PjMN8Wu/hnG+zifDMYsIq/yMEunvcOuzk53TjSDxuO9GCNPR/14LC3yZYFqzhLGHZ7NVmxmzNPDXv+AgBCOHLcAQAAAACAAADYAwAAQwEAAA==eJzTXMA/UTvylT0DGlgtdaUsue8FhvhK3yUr1nY/xxBndDjRePL8MwxxebvKA0stMcUnWfEW5O9+iiGum7Xd+ZUvpvjbBLX9x948wRCfVfkmzWIqprjFnm2f/F0xxVMvvjq79PdjDPH1LldeaW7HFFd7otnNVo4pnv79mn2vFaa4KENaiyYjprhqxlOLsFOPMMSD4ro/T56KKb6p6Y9OXBKm+D3JQDcZA0zxomP5TJz/HmKIl/KLnmU+jSkeIceqrD0VU3x1k/yuRxGY4pUv9lks58UUn3eNe+ufmQ8wxMuqJp8+d/4ehnjKpOhZx85gxu/HyT3Sa/6/xxB/bMsIJD9giH+2wS7ON4MBq/iLHOziee+wm5PTzYRVfA8bdvWG1n/+/8fi/h4c9jbZsmAVZwnDbq8mK3Zz5qn9w2ovAMljlOA=AQAAAACAAADYAwAACQEAAA==eJyLPuxcqPTlvT0DGuD68O5y6FNM8ai5D1pdjmGKZ96q7dq6BFM8YHrm38BGTPEJzLFc+TGY4ssL/iXYmmGKp5R3Xb7JhymuczVgcsizdxjiR84sXbRlD6b4haystJ8TMcVjg+KEpdMwxfc1PjXjssQU31LbELqOC1O8QGVr9ONbbzH9Nfmsd+lKTPFzO9OZJMswxb/90boR74ApfuvgzqaF7JjidpKcWpdOvcEQT/LVCNfpwBQPEOSRy7HBFL/Efi7s9IPXGOKrVsQyaqZgip+6u+3Wzq5XGOImp+WYppQ/wxAPtJ096fiXSxji3Ae8s+yKmDHE+Qub0poPfLNEFx8F+AEAJx52PA==AQAAAACAAABAAQAAFQAAAA==eJzTdZZ5/chMyl53lCaLBgDoYHRBAQAAAACAAABAAQAAEgAAAA==eJxjYACBB/YMozRZNACmTSzZAQAAAACAAABAAQAAFQAAAA==eJzbvff6zfs1t213j9Jk0QDqnOA5AQAAAACAAABAAQAAFQAAAA==eJzTdZZ5/chMyl53lCaLBgDoYHRBAQAAAACAAADYAwAAmQAAAA==eJx1zk0KggAUxHHvpPtuk2fSG7Ru5S4QgqDAMERJpG9LTTtC6FuO/1kOj/k9x5lPGExZzPcr6T13zBruI+ktG9iJpfeXY7awv5P+sJ8CbgI7R/gnhf0T/JmBm4NbgHsGtwS3AvcCO9rbP1fYv8Gfd3Af4Gpv7hPcF7g1uG9wtTf3A24DbgtuB6725n7B7cEdwP1J/wduI4uJAQAAAACAAACAAgAAWAAAAA==eJxdxUkCgQAAAMAsoYUUZSkRLf7/QoecZi4TBLMFL3nFaw55w1veccQxJ5zyng+c8ZFzLvjEZy654gtf+cZ3rrnhB7f85Bd3/OYP9zzwyBN///8AUOsGQQ==AQAAAACAAABAAQAAWgAAAA==eJwtxdEGwgAAAMAkk8lMkklmZmaSZGaSyczMJNn/f00P3b3cevW3ceCtQ+8cOfbeBx+d+OSzU2fOXbh05Yuvvvnu2o1bP/x055d7Dx49efbbH3+9+AfO3gZpAQAAAACAAAAoAAAADAAAAA==eJxjZiYOAAAJxAB5
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