From dda11857c94df35ce07a6df29a2f5e11d4c91847 Mon Sep 17 00:00:00 2001
From: Dmitri Naumov <github@naumov.de>
Date: Wed, 29 Sep 2021 20:07:54 +0200
Subject: [PATCH] [T/TH2M] H2; Newton version of the McWorther test.
Reference files are copies of the original reference files,
because windows symbolic links do not work.
---
ProcessLib/TH2M/Tests.cmake | 1 +
.../TH2M/H2/mcWorther/mcWorther_h2_newton.xml | 6 ++
.../mcWorther_h2_newton_ts_0_t_0.000000.vtu | 55 +++++++++++++++++++
...Worther_h2_newton_ts_110_t_1000.000000.vtu | 55 +++++++++++++++++++
4 files changed, 117 insertions(+)
create mode 100644 Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton.xml
create mode 100644 Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_0_t_0.000000.vtu
create mode 100644 Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_110_t_1000.000000.vtu
diff --git a/ProcessLib/TH2M/Tests.cmake b/ProcessLib/TH2M/Tests.cmake
index 9bae571730c..0ea7c67627c 100644
--- a/ProcessLib/TH2M/Tests.cmake
+++ b/ProcessLib/TH2M/Tests.cmake
@@ -11,6 +11,7 @@ if (NOT OGS_USE_MPI)
OgsTest(PROJECTFILE TH2M/H2M/Liakopoulos/liakopoulos_TH2M.prj RUNTIME 15)
OgsTest(PROJECTFILE TH2M/H2M/Liakopoulos/liakopoulos_newton.xml RUNTIME 5)
OgsTest(PROJECTFILE TH2M/H2/mcWorther/mcWorther_h2.prj RUNTIME 55)
+ OgsTest(PROJECTFILE TH2M/H2/mcWorther/mcWorther_h2_newton.xml RUNTIME 20)
endif()
# TH2M 1d heat diffusion w/ Dirichlet-BC
diff --git a/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton.xml b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton.xml
new file mode 100644
index 00000000000..1186b7ff950
--- /dev/null
+++ b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton.xml
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="ISO-8859-1"?>
+<OpenGeoSysProjectDiff base_file="mcWorther_h2.prj">
+ <remove msel="/*/*/process/jacobian_assembler"/>
+ <replace sel="/*/time_loop/output/prefix/text()">mcWorther_h2_newton</replace>
+ <replace msel="/*/test_definition/vtkdiff/regex/text()">mcWorther_h2_newton_.*.vtu</replace>
+</OpenGeoSysProjectDiff>
diff --git a/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_0_t_0.000000.vtu b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_0_t_0.000000.vtu
new file mode 100644
index 00000000000..047d6977a2c
--- /dev/null
+++ b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_0_t_0.000000.vtu
@@ -0,0 +1,55 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="1.0" byte_order="LittleEndian" header_type="UInt64" compressor="vtkZLibDataCompressor">
+ <UnstructuredGrid>
+ <FieldData>
+ <DataArray type="Int8" Name="IntegrationPointMetaData" NumberOfTuples="238" format="appended" RangeMin="34" RangeMax="125" offset="0" />
+ <DataArray type="Int8" Name="OGS_VERSION" NumberOfTuples="21" format="appended" RangeMin="45" RangeMax="103" offset="192" />
+ <DataArray type="Float64" Name="epsilon_ip" NumberOfComponents="4" NumberOfTuples="400" format="appended" RangeMin="0" RangeMax="0" offset="276" />
+ <DataArray type="Float64" Name="saturation_ip" NumberOfTuples="400" format="appended" RangeMin="0.050000000014" RangeMax="0.050000000014" offset="368" />
+ <DataArray type="Float64" Name="sigma_ip" NumberOfComponents="4" NumberOfTuples="400" format="appended" RangeMin="0" RangeMax="0" offset="464" />
+ </FieldData>
+ <Piece NumberOfPoints="202" NumberOfCells="100" >
+ <PointData>
+ <DataArray type="Float64" Name="HydraulicFlow" format="appended" RangeMin="0" RangeMax="0" offset="556" />
+ <DataArray type="Float64" Name="NodalForces" NumberOfComponents="2" format="appended" RangeMin="0" RangeMax="0" offset="628" />
+ <DataArray type="UInt64" Name="bulk_node_ids" format="appended" RangeMin="0" RangeMax="201" offset="708" />
+ <DataArray type="Float64" Name="capillary_pressure" format="appended" RangeMin="15978.0515" RangeMax="15978.0515" offset="1224" />
+ <DataArray type="Float64" Name="capillary_pressure_interpolated" format="appended" RangeMin="15978.0515" RangeMax="15978.0515" offset="1308" />
+ <DataArray type="Float64" Name="displacement" NumberOfComponents="2" format="appended" RangeMin="0" RangeMax="0" offset="1392" />
+ <DataArray type="Float64" Name="epsilon" NumberOfComponents="4" format="appended" RangeMin="0" RangeMax="0" offset="1472" />
+ <DataArray type="Float64" Name="gas_density" format="appended" RangeMin="1" RangeMax="1" offset="1556" />
+ <DataArray type="Float64" Name="gas_pressure" format="appended" RangeMin="100000" RangeMax="100000" offset="1652" />
+ <DataArray type="Float64" Name="gas_pressure_interpolated" format="appended" RangeMin="100000" RangeMax="100000" offset="1736" />
+ <DataArray type="Float64" Name="k_rel_G" format="appended" RangeMin="0.9368323293" RangeMax="0.9368323293" offset="1820" />
+ <DataArray type="Float64" Name="k_rel_L" format="appended" RangeMin="2.8177863746e-06" RangeMax="2.8177863746e-06" offset="1916" />
+ <DataArray type="Float64" Name="liquid_density" format="appended" RangeMin="1000" RangeMax="1000" offset="2012" />
+ <DataArray type="Float64" Name="liquid_pressure_interpolated" format="appended" RangeMin="84021.9485" RangeMax="84021.9485" offset="2108" />
+ <DataArray type="Float64" Name="porosity" format="appended" RangeMin="0.15" RangeMax="0.15" offset="2192" />
+ <DataArray type="Float64" Name="saturation" format="appended" RangeMin="0.050000000014" RangeMax="0.050000000014" offset="2280" />
+ <DataArray type="Float64" Name="sigma" NumberOfComponents="4" format="appended" RangeMin="0" RangeMax="0" offset="2372" />
+ <DataArray type="Float64" Name="temperature" format="appended" RangeMin="300" RangeMax="300" offset="2456" />
+ <DataArray type="Float64" Name="temperature_interpolated" format="appended" RangeMin="300" RangeMax="300" offset="2540" />
+ <DataArray type="Float64" Name="velocity_gas" NumberOfComponents="2" format="appended" RangeMin="8.2070381333e-21" RangeMax="2.5448690197e-17" offset="2624" />
+ <DataArray type="Float64" Name="velocity_liquid" NumberOfComponents="2" format="appended" RangeMin="3.8745103389e-24" RangeMax="2.1117684906e-22" offset="5260" />
+ <DataArray type="Float64" Name="xmCG" format="appended" RangeMin="1" RangeMax="1" offset="8228" />
+ <DataArray type="Float64" Name="xmWL" format="appended" RangeMin="1" RangeMax="1" offset="8324" />
+ <DataArray type="Float64" Name="xnCG" format="appended" RangeMin="1" RangeMax="1" offset="8420" />
+ </PointData>
+ <CellData>
+ <DataArray type="UInt64" Name="bulk_element_ids" format="appended" RangeMin="0" RangeMax="99" offset="8516" />
+ <DataArray type="Float64" Name="saturation_avg" format="appended" RangeMin="0.050000000014" RangeMax="0.050000000014" offset="8796" />
+ </CellData>
+ <Points>
+ <DataArray type="Float64" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0" RangeMax="1.0198039027" offset="8872" />
+ </Points>
+ <Cells>
+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="9876" />
+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="10672" />
+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="10980" />
+ </Cells>
+ </Piece>
+ </UnstructuredGrid>
+ <AppendedData encoding="base64">
+ _AQAAAAAAAAAAgAAAAAAAAO4AAAAAAAAAbgAAAAAAAAA=eF6FzDEKhEAMheG7pLZZsZqrLBKiGyXgJENmLBaZuzuFjY2W7/3wHSBaeHUqYorJ2kJyp3+G8D1u0fzHDqHvQCkyBMiyRkJJ0J49TuxoC84WkylracBQuzeCyn61B+fz6nDKsj0jQx3rCROkV2E=AQAAAAAAAAAAgAAAAAAAABUAAAAAAAAAHQAAAAAAAAA=eF4z0zPRM9A1NDQz0k03TDRKNE9NTDECADL0BTo=AQAAAAAAAAAAgAAAAAAAAAAyAAAAAAAAIwAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAAAAAAAAAAAAAB4NjIAAAE=AQAAAAAAAAAAgAAAAAAAAIAMAAAAAAAAJwAAAAAAAAA=eF7txSEBAEAIBDBDLxwdL8I3IB4p3m1mU5vk9di2bdv25w+PesscAQAAAAAAAAAAgAAAAAAAAAAyAAAAAAAAIwAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAAAAAAAAAAAAAB4NjIAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAFQAAAAAAAAA=eF5jYBgFo2AUjIJRMAooBwAGUAABAQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAGgAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAA+DAygAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAYAEAAAAAAAA=eF4txddCCAAAAMAiIytlr1RSRkb2LE2UIquQnRFJ2TtEZtnKlhFZyd579Vc9uHu5gID/At3CLR3kVm7tNm7rYLdze3dwR3dyiDs71GHu4q7u5u7u4Z7u5d7u477u53D3d4QjHeUBjvZAxzjWgzzYQzzUcR7m4R7hkY73KI/2GI/1OI/3BE/0JE/2FE91ghM9zUlOdopTneZ0T/cMz3SGMz3LWc72bM9xjud6nud7gRc613le5MVe4nwv9TIv9wqv9CqvdoHXeK3Xeb0LvcEbXeRNLvZml7jUW7zV27zdO7zTu7zbe7zX+7zfB1zmgz7kwy73ER91hY/5uE/4pE/5tCtd5TM+63M+7wu+6Eu+7GrX+Iqv+pqv+4Zv+pZvu9Z3fNf3fN91fuCHrvcjP/YTP/UzN/i5G/3CL/3Kr/3Gb/3O7/3BH/3Jn/3FX/3N3/3DP/3Lv/3Hf/3PTW4GwEFPTg==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHgAAAAAAAAA=eF677987nc30vMP9UXqUHqVH6VF6lKaABgCJltk3AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHgAAAAAAAAA=eF677987nc30vMP9UXqUHqVH6VF6lKaABgCJltk3AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAGgAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAA+DAygAAE=AQAAAAAAAAAAgAAAAAAAAEAZAAAAAAAAHQAAAAAAAAA=eF7twTEBAAAAwqD1T+1rCKAAAAAAAAB4AxlAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCrB8ODKP0KD1Kj9Kj9ChNAQ0A1b5J5A==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCrB8ODKP0KD1Kj9Kj9ChNAQ0A1b5J5A==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJgAAAAAAAAA=eF5b4MB2uv3XW/sFo/QoPUrjpOdC6Tmj9Cg9SmPQs6E0AO0ucMY=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJgAAAAAAAAA=eF7b+1K4UG3xcbu9o/QoPUrjpPdA6V2j9Cg9SmPQO6E0ALdLILA=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJQAAAAAAAAA=eF5jZAACh34HxlF6lB6lcdL//gOB/Sg9So/S2Oi/UBoAAS/Mlg==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHQAAAAAAAAA=eF5jCePTjW/+4sAySo/So/QoPUqP0hTQAPrtGw0=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAIAAAAAAAAAA=eF4zNwaBw/Zmo/QoPUrjpI2htNEoPUqP0jhpAKMvveQ=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAIwAAAAAAAAA=eF7zZNkxc+bMlfYeo/QoPUrjpF2htMsoPUqP0jhpAPe87PM=AQAAAAAAAAAAgAAAAAAAAEAZAAAAAAAAHQAAAAAAAAA=eF7twTEBAAAAwqD1T+1rCKAAAAAAAAB4AxlAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCA0UODKP0KD1Kj9Kj9ChNAQ0A02EkBA==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCA0UODKP0KD1Kj9Kj9ChNAQ0A02EkBA==AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAlgcAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAkQgAAAAAAAA=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eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAACADAAAAAAAArwAAAAAAAAA=eF4txdciAgAAAMCQWSFbJCuzsjNDiuyMyo7y///gwd3LBQL/WtzqNgfd7g53usvd7nHIYUfc6z73O+oBD3rIwx7xqMc87pgnPOm4p5zwtGc86znPO+kFL3rJy17xqlNOO+M1r3vDm97ytnec9a73vO8DH/rIOR/7xKfO+8wFF33uC5d86Stf+8a3vvO9y37wo5/87IqrrvnFr37zuz/86S/X/e0fN9z0r/8AXBwTVw==AQAAAAAAAAAAgAAAAAAAACADAAAAAAAAGAAAAAAAAAA=eF5zY9kxc+bMlfZuo/QoPUpj0ACSQXLEAQAAAAAAAAAAgAAAAAAAAPASAAAAAAAA0AIAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAACADAAAAAAAAxgAAAAAAAAA=eF4txRF0QgEAAMB6LwiCIAiCIAiCIPgQBEEQBEEQBEEQBEEQBEEQBINBEARBEARBMBgEQRAMBoNBEARBEATdyUVCb1HHHHfCSaecdsZZ55x3wYGLLrnsiquuue6Gm2657Y677rnvgYceeeyJp575w5+ee+GlV15746133vvL3z746JPP/vGv//zvi6+++e6Hnw6F30UcdcxxJ5x0ymlnnHXOeRccuOiSy6646prrbrjpltvuuOue+x546JHHnnjqmV/7BioOAQAAAAAAAAAAgAAAAAAAAGQAAAAAAAAADAAAAAAAAAA=eF7j5KQ9AACx7gOF
+ </AppendedData>
+</VTKFile>
diff --git a/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_110_t_1000.000000.vtu b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_110_t_1000.000000.vtu
new file mode 100644
index 00000000000..043bcc18ef1
--- /dev/null
+++ b/Tests/Data/TH2M/H2/mcWorther/mcWorther_h2_newton_ts_110_t_1000.000000.vtu
@@ -0,0 +1,55 @@
+<?xml version="1.0"?>
+<VTKFile type="UnstructuredGrid" version="1.0" byte_order="LittleEndian" header_type="UInt64" compressor="vtkZLibDataCompressor">
+ <UnstructuredGrid>
+ <FieldData>
+ <DataArray type="Int8" Name="IntegrationPointMetaData" NumberOfTuples="238" format="appended" RangeMin="34" RangeMax="125" offset="0" />
+ <DataArray type="Int8" Name="OGS_VERSION" NumberOfTuples="21" format="appended" RangeMin="45" RangeMax="103" offset="192" />
+ <DataArray type="Float64" Name="epsilon_ip" NumberOfComponents="4" NumberOfTuples="400" format="appended" RangeMin="0" RangeMax="0" offset="276" />
+ <DataArray type="Float64" Name="saturation_ip" NumberOfTuples="400" format="appended" RangeMin="0.049875225101" RangeMax="0.7782278974" offset="368" />
+ <DataArray type="Float64" Name="sigma_ip" NumberOfComponents="4" NumberOfTuples="400" format="appended" RangeMin="0" RangeMax="0" offset="2424" />
+ </FieldData>
+ <Piece NumberOfPoints="202" NumberOfCells="100" >
+ <PointData>
+ <DataArray type="Float64" Name="HydraulicFlow" format="appended" RangeMin="-9.0991621946e-08" RangeMax="9.3685266636e-11" offset="2516" />
+ <DataArray type="Float64" Name="NodalForces" NumberOfComponents="2" format="appended" RangeMin="3.1622776602e+149" RangeMax="-nan" offset="4416" />
+ <DataArray type="UInt64" Name="bulk_node_ids" format="appended" RangeMin="0" RangeMax="201" offset="6456" />
+ <DataArray type="Float64" Name="capillary_pressure" format="appended" RangeMin="5393.442264" RangeMax="16006.823618" offset="6972" />
+ <DataArray type="Float64" Name="capillary_pressure_interpolated" format="appended" RangeMin="5393.442264" RangeMax="16006.823618" offset="8176" />
+ <DataArray type="Float64" Name="displacement" NumberOfComponents="2" format="appended" RangeMin="0" RangeMax="0" offset="9380" />
+ <DataArray type="Float64" Name="epsilon" NumberOfComponents="4" format="appended" RangeMin="0" RangeMax="0" offset="9460" />
+ <DataArray type="Float64" Name="gas_density" format="appended" RangeMin="1" RangeMax="1" offset="9544" />
+ <DataArray type="Float64" Name="gas_pressure" format="appended" RangeMin="100000" RangeMax="101310.82279" offset="9640" />
+ <DataArray type="Float64" Name="gas_pressure_interpolated" format="appended" RangeMin="100000" RangeMax="101310.82279" offset="10692" />
+ <DataArray type="Float64" Name="k_rel_G" format="appended" RangeMin="0.011718534433" RangeMax="0.93725903052" offset="11744" />
+ <DataArray type="Float64" Name="k_rel_L" format="appended" RangeMin="2.7176991006e-06" RangeMax="0.42668001006" offset="12912" />
+ <DataArray type="Float64" Name="liquid_density" format="appended" RangeMin="1000" RangeMax="1000" offset="14220" />
+ <DataArray type="Float64" Name="liquid_pressure_interpolated" format="appended" RangeMin="85303.999177" RangeMax="94606.557736" offset="14316" />
+ <DataArray type="Float64" Name="porosity" format="appended" RangeMin="0.15" RangeMax="0.15" offset="15428" />
+ <DataArray type="Float64" Name="saturation" format="appended" RangeMin="0.049800057924" RangeMax="0.79854103008" offset="15516" />
+ <DataArray type="Float64" Name="sigma" NumberOfComponents="4" format="appended" RangeMin="0" RangeMax="0" offset="16756" />
+ <DataArray type="Float64" Name="temperature" format="appended" RangeMin="300" RangeMax="300" offset="16840" />
+ <DataArray type="Float64" Name="temperature_interpolated" format="appended" RangeMin="300" RangeMax="300" offset="16924" />
+ <DataArray type="Float64" Name="velocity_gas" NumberOfComponents="2" format="appended" RangeMin="2.0305698608e-17" RangeMax="1.4379787065e-05" offset="17008" />
+ <DataArray type="Float64" Name="velocity_liquid" NumberOfComponents="2" format="appended" RangeMin="1.4701053829e-22" RangeMax="1.520700334e-05" offset="20308" />
+ <DataArray type="Float64" Name="xmCG" format="appended" RangeMin="1" RangeMax="1" offset="23620" />
+ <DataArray type="Float64" Name="xmWL" format="appended" RangeMin="1" RangeMax="1" offset="23716" />
+ <DataArray type="Float64" Name="xnCG" format="appended" RangeMin="1" RangeMax="1" offset="23812" />
+ </PointData>
+ <CellData>
+ <DataArray type="UInt64" Name="bulk_element_ids" format="appended" RangeMin="0" RangeMax="99" offset="23908" />
+ <DataArray type="Float64" Name="saturation_avg" format="appended" RangeMin="0.049925523775" RangeMax="0.75047964213" offset="24188" />
+ </CellData>
+ <Points>
+ <DataArray type="Float64" Name="Points" NumberOfComponents="3" format="appended" RangeMin="0" RangeMax="1.0198039027" offset="25008" />
+ </Points>
+ <Cells>
+ <DataArray type="Int64" Name="connectivity" format="appended" RangeMin="" RangeMax="" offset="26012" />
+ <DataArray type="Int64" Name="offsets" format="appended" RangeMin="" RangeMax="" offset="26808" />
+ <DataArray type="UInt8" Name="types" format="appended" RangeMin="" RangeMax="" offset="27116" />
+ </Cells>
+ </Piece>
+ </UnstructuredGrid>
+ <AppendedData encoding="base64">
+ _AQAAAAAAAAAAgAAAAAAAAO4AAAAAAAAAbgAAAAAAAAA=eF6FzDEKhEAMheG7pLZZsZqrLBKiGyXgJENmLBaZuzuFjY2W7/3wHSBaeHUqYorJ2kJyp3+G8D1u0fzHDqHvQCkyBMiyRkJJ0J49TuxoC84WkylracBQuzeCyn61B+fz6nDKsj0jQx3rCROkV2E=AQAAAAAAAAAAgAAAAAAAABUAAAAAAAAAHQAAAAAAAAA=eF4z0zPRM9A1NDQz0k03TDRKNE9NTDECADL0BTo=AQAAAAAAAAAAgAAAAAAAAAAyAAAAAAAAIwAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAAAAAAAAAAAAAB4NjIAAAE=AQAAAAAAAAAAgAAAAAAAAIAMAAAAAAAA5AUAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAAAyAAAAAAAAIwAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAAAAAAAAAAAAAB4NjIAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAbgUAAAAAAAA=eF4t1Hs0FFgYAHCvVCQldbIeWx0JGU1YyWV2op3ReCTjMd4MR9Z7Vl5taL0fRWrzKEaP2ZBEG22540bjLArVbmwejYxXpVMeKWFr173uP/d8957v++495/7uzFZbTZdnCqjCWvpSYSwVVVynii2XTNFgNffK5u3myKSwRyQbY4EuVSTY/3yUhj59HRRxVehIZnPAoe5hOlpv/MfOlsoDqFEQ7X8z1ArtcPt1QESxRoaUjSVR09aIP5mSXHv3IGJIMvVM0n9ARv8mV2RyGAjFhezWMGGi3r9hqKeaDdqjpfP+1tpDqDJl7GKxPAsl1UdGlW20RWa/GawLM7BDAoOOvF6OPbKby7AZKnZAS42su6J3h9Gr58W30m46IqNJYSEj+ggqUpSTs2U6IbttIplYAzZauvXsyoK2M4po93QON3ZBwr7WxAEPV5Soqbj3Dd8NyXm/rfsi647ylM9UH8v3QPMjyhMTTC900p8yV/KdDyp5FJF4cq8fYtZF9XRE+KNGiwJJ/AAXadD6xXapgcg6UYljHhuE1hdPDNs9DEYRBSdWM/ihaCcvROZHjUgkFMcrJAt5iLMU9D4j5BgybyhovsqKQ+PedTljYfFoThL5IPblT2heMbuT2+mJpLfJHyhz0kN8N2ZjanOfZYr3bGXuqstNrbv70ry3hlkaztkUdHmZN12bDVr1VEHB0lTF0MXMpF+YER3jZZ113YLf1u13f/K4sC6kpKbJxdZCMPPC179FXdifMHSt0PAtMPoaMcWeFkLdL6NKNhvyQIfgbUP9PXd42XX1lEu9Fog+XOOnSwOA+yl/OqP9INQN7xnq5DsDhZxoepSeFeyjaFaFKznA154d5zfpOIKtRspS6be9QcjHJGv94/LAfWVWvMEczzB3gQth+bW7qnxgcl7gR0mrO0jzu3d9+I4vFDRLlyzGs4EMrmsEAsO/iezKtoLOVmc7h/0cYNDTdna9PAuarCkbHLU9Agu7NChWro5Q23C5Pw08aa25H65nDxJMpd1K3e1BmpaL/YaYPTBJPDKxMMKAOkmSh/o7jsC7ZuW/lw9QYQOT/XShjQlp/dNnRizdYJWOSI1epA8/Hyzcd06VC9bzfpH//rAPuBE8ktUwrwqrEqpHVQqsIYviOZmbpmqmZMDCTuz7pbCT0zPECV0/EDuhg+fYyZQ4ETuJo7zETsIMj2In4wMQO8mejsNOBDNF2MkURxU7ETelYyfnxnOwk2aLVOxEkhWOnah+bMJOLIp3YSdar8axE64mDzthtFCwE51h4uT0lkzspJJHnEiVECdmxcSJvEAWO9nCf4Cd/KVGnHjQiRPteOIkSaKAnYTZEScGa4iTzh7iJNuBOIkRESfr64mTJw7EyQc2cVKbS5zsvkCcHM8lTrJCiBPTF8QJY8XJtytOkBdxQltxYryOOLm6gzgx0iFOzAFx0p0jxk42NbGwE4fLumuXnUwqnuUtO6HyfSOXnZSvOHmuEyCO/9/JlMKJviGRunB0xQk3eOimcpwQqp+b3z+Ukgdc9+xdlA7hwW2SM7UbyzkwY5DZTY+TBR4x904vvmGDL1aapzIPOAGp5XGSajZmpTEpMWVBwQVQ2tVChQycZwPbFv/8h+/uBN7hd6YN2sADz4ZmU2jo1f4hQt0GlHbo9+rXOcLHegazCzxXuLNkl++jzWrg8+2xgA0FPnAW16ODUuzFCF5MLAP6+9ggbyXOl/TQVHJcoYxfjDC9Vw/kEw9Ao6yXtjo9AAaQfdB/Xu5OA9UYLDVLzbtGhMJn7FEG/XEwrB5vO69HDYI15vvFnDknWITXbaB4+yljXXlLICTnBVIZv7zmpDrBD+Qe0J78D+A/K+EfMA==AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAA2AUAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAYAEAAAAAAAA=eF4txddCCAAAAMAiIytlr1RSRkb2LE2UIquQnRFJ2TtEZtnKlhFZyd579Vc9uHu5gID/At3CLR3kVm7tNm7rYLdze3dwR3dyiDs71GHu4q7u5u7u4Z7u5d7u477u53D3d4QjHeUBjvZAxzjWgzzYQzzUcR7m4R7hkY73KI/2GI/1OI/3BE/0JE/2FE91ghM9zUlOdopTneZ0T/cMz3SGMz3LWc72bM9xjud6nud7gRc613le5MVe4nwv9TIv9wqv9CqvdoHXeK3Xeb0LvcEbXeRNLvZml7jUW7zV27zdO7zTu7zbe7zX+7zfB1zmgz7kwy73ER91hY/5uE/4pE/5tCtd5TM+63M+7wu+6Eu+7GrX+Iqv+pqv+4Zv+pZvu9Z3fNf3fN91fuCHrvcjP/YTP/UzN/i5G/3CL/3Kr/3Gb/3O7/3BH/3Jn/3FX/3N3/3DP/3Lv/3Hf/3PTW4GwEFPTg==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAZAMAAAAAAAA=eF5d02tIU3EYx/HM0MiMrHUls9RKygJdlxlSj9mFVNi6mHmhrCRR1NRlBhoZkoJWL1KTFCW7LSI0V+0+nTXzkrbSsovhukxnjqzoZisl9f87LzqvvpwD/3OePzwf4Y21ojS3e+Qqt91OclRQapxbxpZTCnKfEb+iwKygg56D84dXKulomqbIlK4kVe1Vj+W1SrI55BV8HFCSn7rBO3ixijbO2jzkGq6i2JjyVmueiiq7Syq2K1VUevKCuahPRcUh7XNmz1LTTePeoZnBanKOPt3TnKqmSC9zRGKlmlwtB5yWt6nJ/2/8gnV2NS26mldS76OhgN3OW79GaKjIvbvTnq+hmV1DsqlqDV0LWtAbadPQw/adqfM8tFT6TDo3f7eWPiSfs1kKtWReX92W3ailqJW5By476CghxSfzHukoflnF5CW5OlqyQ+wpbdGR+FPYrwCBnsLXe13xj9PTuU6JeINGT1N05R3pc+po+oAp2/dEHUUGWJuXfaqjvD/7s2QJ9VQb6PO9+kc9yaJW7aoJMVDVy8TMrTIDhR96VWMVNFCM0RDlV9ZAa9MX/vwRdJ8yblVmBHo8oC2eOddz9xhJ0JQs3bb6IRUGPJntEtFCdkGL/3N9O3V0SZISgk0kSdrXahGaaLNs8K7XGhMdO+Iochzt8R65g/Noy62iAqfRHrQHXhzrpfN7Sscauql4vJNSzoz3cNLZ8fqGsYZLWLvFrP28vkNfoDb0O/obHUSfoV3oW/QL+hn9ig6gL9E23vkO3vunqBHVowr0JipH76CP0U70CdqEPuK1HTWgSl6FcFJTy5xMgBMPODHAiR1OvOXMST2cFKuYk2gBc1IVzZxkwolTDnMSF8qc1DQyJ8IY5mTAmznJ72VOhMPMiQVOmuGkCU5GnjMn++AkG06mwckxOFkNJ7G+zElKMnPitpQ5OS9mTt7bmJMeEXPS+JQ5CdIwJx39zEmsiDmptjMnYXBSAidaOCmDk1w4SYSTKjiJhZNJcCKGEyOcfIMTKZzEw0kVnFTAiR5OsuBEASeH4SQITuRwsg1OIiX/76OFt9+v0VdoK2+P3qBmlNt/zk0Pby9NvP3knjkX3P8aefvNnee8WlE76oR7uKDDvHm4+bn/cS65ubn7cvP2oSPoBHx3IvoPQE/n4w==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAZAMAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAGgAAAAAAAAA=eF7twTEBAAAAwqD1T20ND6AAAAA+DAygAAE=AQAAAAAAAAAAgAAAAAAAAEAZAAAAAAAAHQAAAAAAAAA=eF7twTEBAAAAwqD1T+1rCKAAAAAAAAB4AxlAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAA8gIAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAA8gIAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAASQMAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAswMAAAAAAAA=eF5d031MU1cYx3GNEip1i1pSzdwYQmaMOqota8ay3WdAcWGVCoi8BFCQZTImDljoFJBA0nWuLDIUArYRQRQHNlA67BDjJog2W1DI3qK4IUxeCoJi6IrMaiY8v/0x/vrm3Pvcc88f5zOsUJkvau7QEdmfcfvGeskne/iEvecnCjy/fmris+uUPFspi9h/ldw3XC7LKx0UOfzq5vrS7ynqh1lnlOESycqVEZNP26jK7D86022jmgxxfPi9VupK/KjKIP+WpMtsBrmlhQ6uTDZqYixUna7qzN/aRO9kHPplz6Pz1C0flBkHGiilLmHH9ONz1KK7nRTzRj0ZR085vig7Q9/sSF4s9qqjqeCakTFTLRlUtdKzqho6mlU/8+bSajqQWhb97IyJNmcqVxxOP0HP9gVpRNsraSDs9sadERWkWHG5OS3jOE3KJOojtWXU6hyzVv9VSiG5uq5FLV/RqjWbil43f0nN3qYl+T/rSaMLulLlp6MCzWhqiamYRsK93tp1qJDSh4J6g8V5ZNUai/smcqkwsyL3pk8OHQ258FAalkmni0y2OueH9GBp4pNT0ank0xIrXtMTTy/HOvTtBZEUcHIqoftYKP3h3d7zXZSCmssLLpu1aylyKC+3Mswl2CL175e23hAce/WDyu7rwobt1xxas13w9nhqVpy2C1fj00pC6uyCsbHJ7fe8veQ/sP55f9/jmTbXTvednLlmiPqy5loRujZ7rr7uVfPVi7znOzMsme/4GPcgWoy2orNoFipBHQ7ue1i/NM5dibbheSiqRH3Q3agT3YvvXkQD0WSU0Ea0D/W6j3OjctQPTcLcFlSOfo3uH////zVoJyrCPhI0IJCd/CZnJ31aduJoZCf9f7OTSz/CyV12YulgJ2+b2En7BXZSmM1O7iexE4sHOykXsxNpIjvxTGInu9bBSTk72ZbPTqSD7MRfzU5077KTgDB20v8pO7n1ATtpyGEnro/ZSZGanVhXs5M2JTt5wWN83olVz056X2MnW9axk6oydjKawE6y4thJyiQ7WX6YnfR/wk5uhrITZyE7ubeYnVia2IlvDDsRTOxkWsJO/qlgJ44RdhIfzU6aUtmJSM1Ohpawk5Q4drKtgZ1YBXayCE7cT9iJ1pOdfB7MTgyz7MTmyU7a4cQX98GE+1qD/udnesE9jkZ/xfOL6EY0HO1EFZgX0JML1stRF+ZLsFYvmHuM93noLTgtRWPRDnQr5h6gKuwzseDcVtSOFqFd2MeKHkf/BZK4A0I=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJQAAAAAAAAA=eF5jZAACh34HxlF6lB6lcdL//gOB/Sg9So/S2Oi/UBoAAS/Mlg==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAIQMAAAAAAAA=eF51031MzVEcx3GR3GgVooyhJgrdmmgy07dCt6hhq2iaTYk2a3rEckVPEplKhYUmMTHpQcpDdStberKkFiXaqlV60OqecyPxq/u5//w2f712zr473z/O3vK0c4N9Jpz27t/amyc4MW+k0Fcw+LptqsqYU26brmW0oJ7Zr9W/l3K6WRlW4ifYI7Mfer2E06M6efIcwarYTs9tRpz0pfLGI4s5jW2ICw9dxGnHoMnTsIWcSgMsXx015FQ4fqZhuwGnooA+rqXPKf6OiVW+Hier94cdXBdwemw93F6jy+nhsEu8VMKp1zI+5YwOp26fjJU52sJ9/ZRuwWzh/T3NPzJncXr2OVHHb4pRurmWvdYkIw9D/iN0glG0RPK7mDEqVBRnvBtjtMsnf83tn4x21xboSIcYufV6HT/bz+jGvKzukB5GfytizA26GOVqG8mog9E+1y5jZSujwLaTo8ZNjLykknVJtYz65pbftalilPSybuPHV4xGl/vVWeczKhuy1RvMYeTotipafpNRp3urg2ECI9VZx2rXUEbuFyeGa7wZOZVvzvxjw+hL3H2rg0olrXUemJIkK8l4i3LnJm0luSRkTUY1j5PpyMS1Uy3jNN/+Q75MMPjeCcVeQR1LW9m0FofMvKfNaXP0nNY35/yMQSMRM473n57RvFbt+ka1DxrUvoBFsAS+hm9gmchyWAEroQJWidTMvxXt0ex/DnPhI/gQZsMsmAkzYCpMhNEwEkbAUBgCg2Ag9BcZIFKOTpzQyQA68UUn6ehEZaruJBadtKCTNHRSgE4mrdSdtKOTFegkBZ0koJM4dLIfnahq1J2Eo5MwdFKETgrRSSQ6qUcnCegkHJ1I0Yk/OrmNTqToxA6dEDq5jE7G0Ek2OvFAJ8fQyQF00o1OEtHJEDopQSd26OQTOhlFJ26iTjrQiQ06MUMnMnRigU6WoZOr6MQMnTijEwU6iUQnV9CJwYD6fx3RiTs6acC/fxf5DXaK/ArbYStsgU2wHlZDTVelUNNFAdT0kQefQE3H9+AtmAKT4CV4EV74zzkKnhcZJZqPFRkj8h943WiJAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAIAAAAAAAAAA=eF4zNwaBw/Zmo/QoPUrjpI2htNEoPUqP0jhpAKMvveQ=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAgQMAAAAAAAA=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eF7twTEBAAAAwqD1T+1rCKAAAAAAAAB4AxlAAAE=AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCA0UODKP0KD1Kj9Kj9ChNAQ0A02EkBA==AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAHAAAAAAAAAA=eF5jYACCA0UODKP0KD1Kj9Kj9ChNAQ0A02EkBA==AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAiQkAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAKAMAAAAAAAAkgkAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAAFAGAAAAAAAAJwAAAAAAAAA=eF5jYACBD/YMo/QoPUpj0IxQ+t9/EHg/So/SozQW+i+UBgDglEocAQAAAAAAAAAAgAAAAAAAACADAAAAAAAArwAAAAAAAAA=eF4txdciAgAAAMCQWSFbJCuzsjNDiuyMyo7y///gwd3LBQL/WtzqNgfd7g53usvd7nHIYUfc6z73O+oBD3rIwx7xqMc87pgnPOm4p5zwtGc86znPO+kFL3rJy17xqlNOO+M1r3vDm97ytnec9a73vO8DH/rIOR/7xKfO+8wFF33uC5d86Stf+8a3vvO9y37wo5/87IqrrvnFr37zuz/86S/X/e0fN9z0r/8AXBwTVw==AQAAAAAAAAAAgAAAAAAAACADAAAAAAAARgIAAAAAAAA=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AQAAAAAAAAAAgAAAAAAAACADAAAAAAAAxgAAAAAAAAA=eF4txRF0QgEAAMB6LwiCIAiCIAiCIPgQBEEQBEEQBEEQBEEQBEEQBINBEARBEARBMBgEQRAMBoNBEARBEATdyUVCb1HHHHfCSaecdsZZ55x3wYGLLrnsiquuue6Gm2657Y677rnvgYceeeyJp575w5+ee+GlV15746133vvL3z746JPP/vGv//zvi6+++e6Hnw6F30UcdcxxJ5x0ymlnnHXOeRccuOiSy6646prrbrjpltvuuOue+x546JHHnnjqmV/7BioOAQAAAAAAAAAAgAAAAAAAAGQAAAAAAAAADAAAAAAAAAA=eF7j5KQ9AACx7gOF
+ </AppendedData>
+</VTKFile>
--
GitLab