Commit 03d02d3d authored by Dave Moxey's avatar Dave Moxey
Browse files

Merge remote-tracking branch 'upstream/master' into feature/semtex-input

parents 7d2060da 900acb8b
......@@ -25,8 +25,11 @@ v4.4.1
- Remove the duplicate output of errorutil (!756)
- Fix BLAS CMake dependencies (!763)
- Fix interpolation issue with Lagrange basis functions (!768)
- Fix issue with average fields not working with different polynomial order
fields (!776)
**FieldConvert**:
**FieldConvert:**
- Fix issue with field ordering in the interppointdatatofld module (!754)
- Fix issue with FieldConvert when range flag used (!761)
**NekMesh**:
......@@ -36,8 +39,8 @@ v4.4.1
- Add manifold meshing option (!756)
- Fix issue with older rea input files (!765)
**FieldConvert:**
- Fix issue with field ordering in the interppointdatatofld module (!754)
**IncNavierStokesSolver**
- Fix an initialisation issue when using an additional advective field (!779)
v4.4.0
------
......
......@@ -161,15 +161,18 @@ void FilterFieldConvert::v_Initialise(
{
v_FillVariablesName(pFields);
int ncoeff = pFields[0]->GetNcoeffs();
// m_variables need to be filled by a derived class
m_outFields.resize(m_variables.size());
int nfield;
for (int n = 0; n < m_variables.size(); ++n)
{
m_outFields[n] = Array<OneD, NekDouble>(ncoeff, 0.0);
}
// if n >= pFields.num_elements() assum we have used n=0 field
nfield = (n < pFields.num_elements())? n: 0;
m_outFields[n] = Array<OneD, NekDouble>(pFields[nfield]->GetNcoeffs(), 0.0);
}
m_fieldMetaData["InitialTime"] = boost::lexical_cast<std::string>(time);
// Fill some parameters of m_f
......@@ -189,11 +192,28 @@ void FilterFieldConvert::v_Initialise(
fld->Import(m_restartFile, fieldDef, fieldData, fieldMetaData);
// Extract fields to output
int nfield,k;
for (int j = 0; j < m_variables.size(); ++j)
{
// see if m_variables is part of pFields definition and if
// so use that field for extract
for(k = 0; k < pFields.num_elements(); ++k)
{
if(pFields[k]->GetSession()->GetVariable(k)
== m_variables[j])
{
nfield = k;
break;
}
}
if(k == pFields.num_elements())
{
nfield = 0;
}
for (int i = 0; i < fieldData.size(); ++i)
{
pFields[0]->ExtractDataToCoeffs(
pFields[nfield]->ExtractDataToCoeffs(
fieldDef[i],
fieldData[i],
m_variables[j],
......@@ -211,6 +231,11 @@ void FilterFieldConvert::v_Initialise(
m_numSamples = 1;
}
if(fieldMetaData.count("InitialTime"))
{
m_fieldMetaData["InitialTime"] = fieldMetaData["InitialTime"];
}
// Divide by scale
NekDouble scale = v_GetScale();
for (int n = 0; n < m_outFields.size(); ++n)
......@@ -457,14 +482,23 @@ void FilterFieldConvert::CreateFields(
m_f->m_exp.resize(m_variables.size());
m_f->m_exp[0] = pFields[0];
int nfield;
for (int n = 0; n < m_variables.size(); ++n)
{
// if n >= pFields.num_elements() assum we have used n=0 field
nfield = (n < pFields.num_elements())? n: 0;
m_f->m_exp[n] = m_f->AppendExpList(
NumHomogeneousDir, m_variables[0]);
m_f->m_exp[n]->SetWaveSpace(false);
Vmath::Vcopy( m_outFields[n].num_elements(),
m_outFields[n], 1,
m_f->m_exp[n]->UpdateCoeffs(), 1);
ASSERTL1(pFields[nfield]->GetNcoeffs() == m_outFields[n].num_elements(),
"pFields[nfield] does not have the "
"same number of coefficients as m_outFields[n]");
m_f->m_exp[n]->ExtractCoeffsToCoeffs(pFields[nfield], m_outFields[n],
m_f->m_exp[n]->UpdateCoeffs());
m_f->m_exp[n]->BwdTrans( m_f->m_exp[n]->GetCoeffs(),
m_f->m_exp[n]->UpdatePhys());
}
......
......@@ -52,7 +52,9 @@ IF( NEKTAR_SOLVER_INCNAVIERSTOKES )
ADD_NEKTAR_TEST(ChanFlow_m8)
ADD_NEKTAR_TEST(ChanFlow_m8_BodyForce)
ADD_NEKTAR_TEST(ChanFlow_m8_singular)
ADD_NEKTAR_TEST(ChanFlow_V8P7_Avg)
ADD_NEKTAR_TEST(Channel_Flow_3modes_rad)
ADD_NEKTAR_TEST(channelTemp)
ADD_NEKTAR_TEST(Couette_3DH2D_MVM)
ADD_NEKTAR_TEST(Hex_channel_m3)
ADD_NEKTAR_TEST(Hex_channel_varP)
......
......@@ -407,14 +407,24 @@ namespace Nektar
int nvel = m_velocity.num_elements();
if(nvel == 2)
{
m_pressure->PhysDeriv(m_pressure->GetPhys(), Forcing[0], Forcing[1]);
m_pressure->PhysDeriv(m_pressure->GetPhys(),
Forcing[m_velocity[0]],
Forcing[m_velocity[1]]);
}
else
{
m_pressure->PhysDeriv(m_pressure->GetPhys(), Forcing[0],
Forcing[1], Forcing[2]);
m_pressure->PhysDeriv(m_pressure->GetPhys(),
Forcing[m_velocity[0]],
Forcing[m_velocity[1]],
Forcing[m_velocity[2]]);
}
// zero convective fields.
for(int i = nvel; i < m_nConvectiveFields; ++i)
{
Vmath::Zero(phystot,Forcing[i],1);
}
// Subtract inarray/(aii_dt) and divide by kinvis. Kinvis will
// need to be updated for the convected fields.
for(int i = 0; i < m_nConvectiveFields; ++i)
......
<?xml version="1.0" encoding="utf-8"?>
<test>
<description>Channel Flow Vel P=8 Pre P=7 dumping average field </description>
<executable>IncNavierStokesSolver</executable>
<parameters>ChanFlow_V8P7_Avg.xml</parameters>
<files>
<file description="Session File">ChanFlow_V8P7_Avg.xml</file>
</files>
<metrics>
<metric type="L2" id="1">
<value variable="u" tolerance="1e-12">4.08607e-16</value>
<value variable="v" tolerance="1e-12">1.54276e-16</value>
<value variable="p" tolerance="1e-12">1.1993e-14</value>
</metric>
<metric type="Linf" id="2">
<value variable="u" tolerance="1e-12">6.43929e-15</value>
<value variable="v" tolerance="1e-12">6.36813e-16</value>
<value variable="p" tolerance="1e-12">4.86278e-14</value>
</metric>
</metrics>
</test>
<?xml version="1.0" encoding="utf-8"?>
<NEKTAR xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:noNamespaceSchemaLocation="http://www.nektar.info/nektar.xsd">
<EXPANSIONS>
<E COMPOSITE="C[0]" NUMMODES="8" FIELDS="u,v" TYPE="MODIFIED" />
<E COMPOSITE="C[0]" NUMMODES="7" FIELDS="p" TYPE="MODIFIEDQUADPLUS1" />
</EXPANSIONS>
<CONDITIONS>
<SOLVERINFO>
<I PROPERTY="SolverType" VALUE="VelocityCorrectionScheme" />
<I PROPERTY="EQTYPE" VALUE="UnsteadyNavierStokes" />
<I PROPERTY="AdvectionForm" VALUE="Convective" />
<I PROPERTY="Projection" VALUE="Galerkin" />
<I PROPERTY="TimeIntegrationMethod" VALUE="IMEXOrder1" />
</SOLVERINFO>
<PARAMETERS>
<P> TimeStep = 0.001 </P>
<P> NumSteps = 1000 </P>
<P> IO_CheckSteps = 1000 </P>
<P> IO_InfoSteps = 1000 </P>
<P> Kinvis = 1 </P>
</PARAMETERS>
<VARIABLES>
<V ID="0"> u </V>
<V ID="1"> v </V>
<V ID="2"> p </V>
</VARIABLES>
<BOUNDARYREGIONS>
<B ID="0"> C[1] </B>
<B ID="1"> C[2] </B>
<B ID="2"> C[3] </B>
</BOUNDARYREGIONS>
<BOUNDARYCONDITIONS>
<REGION REF="0">
<D VAR="u" VALUE="0" />
<D VAR="v" VALUE="0" />
<N VAR="p" USERDEFINEDTYPE="H" VALUE="0" />
</REGION>
<REGION REF="1">
<D VAR="u" VALUE="y*(1-y)" />
<D VAR="v" VALUE="0" />
<N VAR="p" USERDEFINEDTYPE="H" VALUE="0" />
</REGION>
<REGION REF="2">
<N VAR="u" VALUE="0" />
<N VAR="v" VALUE="0" />
<D VAR="p" VALUE="0" />
</REGION>
</BOUNDARYCONDITIONS>
<FUNCTION NAME="InitialConditions">
<E VAR="u" VALUE="0" />
<E VAR="v" VALUE="0" />
<E VAR="p" VALUE="0" />
</FUNCTION>
<FUNCTION NAME="ExactSolution">
<E VAR="u" VALUE="y*(1-y)" />
<E VAR="v" VALUE="0" />
<E VAR="p" VALUE="-2*Kinvis*(x-1)" />
</FUNCTION>
</CONDITIONS>
<GEOMETRY DIM="2" SPACE="2">
<VERTEX>
<V ID="0"> 0.0 0.0 0.0 </V>
<V ID="1"> 0.5 0.0 0.0 </V>
<V ID="2"> 1.0 0.0 0.0 </V>
<V ID="3"> 0.0 0.5 0.0 </V>
<V ID="4"> 0.5 0.5 0.0 </V>
<V ID="5"> 1.0 0.5 0.0 </V>
<V ID="6"> 0.0 1.0 0.0 </V>
<V ID="7"> 0.5 1.0 0.0 </V>
<V ID="8"> 1.0 1.0 0.0 </V>
</VERTEX>
<EDGE>
<E ID="0"> 0 1 </E>
<E ID="1"> 1 2 </E>
<E ID="2"> 0 3 </E>
<E ID="3"> 1 4 </E>
<E ID="4"> 2 5 </E>
<E ID="5"> 3 4 </E>
<E ID="6"> 4 5 </E>
<E ID="7"> 3 6 </E>
<E ID="8"> 4 7 </E>
<E ID="9"> 5 8 </E>
<E ID="10"> 6 7 </E>
<E ID="11"> 7 8 </E>
</EDGE>
<ELEMENT>
<Q ID="0"> 0 3 5 2 </Q>
<Q ID="1"> 1 4 6 3 </Q>
<Q ID="2"> 5 8 10 7 </Q>
<Q ID="3"> 6 9 11 8 </Q>
</ELEMENT>
<COMPOSITE>
<C ID="0"> Q[0-3] </C>
<C ID="1"> E[0,1,10,11] </C> <!-- Walls -->
<C ID="2"> E[2,7] </C> <!-- Inflow -->
<C ID="3"> E[4,9] </C> <!-- Outflow -->
</COMPOSITE>
<DOMAIN> C[0] </DOMAIN>
</GEOMETRY>
<FILTERS>
<FILTER TYPE="AverageFields">
<PARAM NAME="OutputFile"> AverageField </PARAM>
<PARAM NAME="OutputFrequency">1000</PARAM>
<PARAM NAME="SampleFrequency"> 10 </PARAM>
</FILTER>
</FILTERS>
</NEKTAR>
<?xml version="1.0" encoding="utf-8" ?>
<NEKTAR>
<Metadata>
<Provenance>
<GitBranch>refs/heads/fix/Averagefield_varP</GitBranch>
<GitSHA1>4e94574ce5dfafebbea392dd919ecea856d905fc</GitSHA1>
<Hostname>Spencers-MBP-2.home</Hostname>
<NektarVersion>4.5.0</NektarVersion>
<Timestamp>23-Apr-2017 23:05:22</Timestamp>
</Provenance>
<ChkFileNum>2</ChkFileNum>
<Kinvis>0.025000000000000001</Kinvis>
<SessionName0>KovaFlow_m8.xml</SessionName0>
<Time>0.10000000000000007</Time>
<TimeStep>0.001</TimeStep>
</Metadata>
<ELEMENTS FIELDS="u,v,p" SHAPE="Quadrilateral" BASIS="Modified_A,Modified_A" NUMMODESPERDIR="UNIORDER:8,8" ID="0,1,2,3,4,5,6,7,8,9,10,11">eJyt93c81f8f/4+fjJbIyEhkZqVEocTjKVFWhcgOhbSQPbN3srfslZG9wv0YUWQTISMRLSHJzO/o9Tl93n/9vpfLt+/551z88bg8zjm36/VcD2+5SSVH5mVsTE9js/39KrbB38UnS16AFwp7KnA/8Qb+LV7Vb4SNDt91Ysl/l1Yp+GgJijtshUFvU9XGSdos9MvmrBnl6w8YLonj52uHt9jBUgsWGfICbENHc+Vu4g2scJBpXJqFDgvcqXTreHAJopCrtjItfoTUe4+YZefnwFNGXYl++vZaHA4nLSvmhhB/WJLyQhjiVUyVoeWzAc+q34psIoFon0h3hXiYNbKVXXCcSPFHAb5PuxjMfZH444gide1hvLRfHG2R+jSelKEn2lKtEPtFEd/hyWKMebo/99WLO4Dt4ugreCQ4hKhsNCvKGUqgqU0GEji7UMtL488KujWShPtx4brXofelxjkLfXvU8dad3SpeC+XWWjXXq7qgwgP4WupJZ5QWE5YxEPYQSR21T/io7I1m+9OuyzeHYLhZLQ3hqjhMW9mY+9z4DTyb7DfJE46MeNL7y8t3DncBqHx4/Sm8F4m8NTMzvFsDC9oC2KGsHmRE51Z+oCJo+/6Uyr5UoCw+NxryJQ7MtId2PZt+gvxetRtPXwwDnro01kNZIehLnVfajSlXYBMnnZgxdEK4Tq9pljvCeBwuTD9ERRIv1uz+9JwRGZZYaOy6f34SgdpxP/a+brjwIz2NxvADEtJ8etnDuQfY2x98cTs4+c/7ef8ffmQkmla2+eHf2rG5zc/A6gcJMwI/YXt4Ltuy0+HnDtq1RkmUQpNmiSuvQTgsKAlIwpUsdMKAIcmr2fjP+1/icEEx4ZdnvK+GotS5FVb6h3fgRczbPobCQPQqm+7GnJgjerZAGphR4Y9qVhjOel8JQML/7Y9Jy5jvI+yPnV276kjYH1/yucXblcUYr8Dwk8os7gB+z5y/nj3nEEDBksMoSwnyf/p076kDXSDjs7r/cV/cH/4KqPRgvWlu58I5GzQl8B13QE0WeVcKUX+74oTUs/f1mgc6IHRVoWkPkyvymmG4kDntiQze/tkfj9M7ok3YH4/MbNcJ+2PMlwxz7jsyYrrh3HXV7F1IqaCfm+ZxL8jw0BmPPKhBvZvqAfjUHnjT3Jc+va98+/3/ZElMBfFuIcVmpgSwuLAq1NOWiIpfjkjG/SbsL2wZWJoYjHjlhy8xdbnBSjPVx5189qj+hPf2/hjOnUPkuYoktulatvnLmAy/J3fxsNTvSfBTv2JV3dCNen5K2yze+wDLHMWdHyx6EIMkpeUs1+Q/70fkR/rR4fVtfvRyLC9t8xPF5Dd1j8DPZ6Hsu1wEfm5cV896EU3gh+o3RceTR4hqzanQMDUH0JndUWbzCXX0t13syxPd0cp4nGSUdARyD98K2nv3Aazslmx91hmE1HJn2pIs7FAIvupWbnQA2mt5pfRDqR8ivRH5n/8Lbyi2/T+moBG87f/pYE07Z4L/VVttc5wE/89sdQtlsQ2hEEanhWccJWCnbDuWsq8LjVEvKoyF6EqxlV3V+9xmBHdoxpTeLTsgD53FdF8dA9TjNkjis+SCeqOmIg7fc0VVKusaP5jcUN3kEQqZIW/0NS3jj/8M9CNG2/438ac9kSH475jcZnuJ4L/C89zOA1xdoDjs3UQZ1ovEyO5vMlvVQK3TOdv8jB6UIctYxRC+s07yk4wGQ38qfEalr7DhOOhwHx6b+/4EjVMy2veIhEH7CS1v3ZoQdIUKp5uT4grfdk1nCvs5IcMenz/+u8ssVW7776xaegwR/D982nyOjeC/5yPGp0nb/kfKMGz7X/6UJB3n0gNyBkL93v/j///b/Yj87A6I/rnNj7mKRKQcgZ/Xn1ynjQn8yGc9lvlI4Cda62aCNG8pkEu56sVnhoP8/S8yEw5ZaLZWRurYYoIUgf/8RdqHSNLoxKF3zWEogu0M9YeyW7BH7k5ZCf0j9NWaQ3ppxgmpPZRqqSHsr//hgVvM7wDk+zz8j//KarK7tv2XMA622vafXzKBz57gf6Na5Scegv/CGok7zjEOwemtr5bDvCVI9LGTRiKuC67lBn0YjNGtI9zP9BYZQtqAcKjKd1u0T3ewuYBNFV1bZH7QRu6MzB0/MSmxOaGql+cUSvNcUSbNVn65gxeq3pn6x3/3jxH3t/3n5i7/eJ7gv1/IXFgcwf+3kYX5NDxdCKZ5Im8G9UJJ3PD6I8caFMcRHVOU3APF+ju0j8STEt6/YcRyUiqU2ihKiG7GA1XAkK7ZeCJaGhnP//4hDPifnhgIfxGM5u3Yk6Tc3ODjnnGH8Tv2yD3Q8z//2WlTqgn+T+9YrRsn+K/63IeNhOB/1bnjPE8au1GV3+HjhQT/5YLS9DIse1Abc2VqJcF/YvdT3hod2P4OfCAnoG/ysBKfEx10P5fbHP/sSVH1l/nD+PM8mu+s4tvhdNGRG+3Wt2HC6di3GtZHQOwG4ezRxlw3yN0zirv5sBJTotJzKOA2xyo86W/zLBzGFmeVo9f72xELS5PY3WVr9CqO4eIZ11BE7D7h2Z260xzsAlPGfb3iEaVJIb7tIi10V4d1TVMYgo/vKp++ohWoXD64aTHtAJ6p9ey7fzoCsfvS/ucji2UX8C7LZ0RFdauxhkSJmKPnLLGAA63aJxrYMard+lz7AqvREyn1lFZNfyR0NLZ5+GYxELu//TqGnb2Q/ruLSd1uvlD/9ltdHIM37D181OGl2z3gjltYCTvtBhFadQpjJvbQWzUlZjruBsTu414NGpj1ZmBbvOlK70PN8WG1MoOkE1z4wBrHgt7gHjBW0veo7agFbiv+7zMfIxFnmZP/+ZIiIHZ/+2XsWSmHSLdLe9HOEKRFyX5ceMoF1qhqY4Km3ODJx6hfyaoOEFR5dWN+2hGujYnH+hgbA7H7OFw9ejB6AZ9W16dAoUqDpXHN+Mp/TQQ6IKH2761Gjt2kJdU6zWBbX2pNHlKMvFXouXq7Gv55P2I36O/upt7+DPMU3ReMCfx8yZXKfELgR8gmPvvL98P43nxtXWOrdshv3lubd9wYfjyzOlYvGgjE7hOeqTlz1KEnePDx6psYdPCH9GuX2PvAIdqv6lh7G+giKEw4k1xBt8PN1+OgC+y6/lqu85QHELs/XDwYTtgfkxXmiiHsj9c/8WJQ+Jwlfm8X5b7IBnZ8C82vqYJ31fCEd3ro7uFAaNHYVF8/UoyI3Sc8CxyZfoiuNXRcRQVuYHCGg1OKsHNUvBhDPY8G/Pqp5uxWZQmXLj15QfLMAnTw9Ce0i62B2P16DaYywv541jWawrlQc8zQ7BbZ9QkuTGyE+d6jyB6kR9tDFvGmFrH3dSw7ikTBmh/JTuvcIkTsPg6ntc80sAJsdOYdTHdEIXXhrI1fcfKgfujKRS22B2BrU/wZ61AEVn+64wOP7oPPwFWzp/EaiNh9HHWN6fq7C5gn2l0jLU+Dn5j0WxMrSkfSJcHdzFpV4L2eOzB5rwVNlMwczHItgozER88epzT+837EbuBwMgUM8BA+XM/et83PO6bK0XwCP+7J5oXsBH5w7C86L/e1E95v15DvjDXCSX/P3WEbiojdJ1zt/yEBoUZDhw7B1iikm+KxbKZiA/QszBzHjz4AcVsnZa1DHoBvDo4bM3QDp+eV67GUXkDsvtIXj9ht/1kKdx8UI/jvb1b18wzB/3MJxUDXyI69jTJ4qCDxHLkJMt7u+hKIMqxnf3rPFAGx+zhcqr1OjheyGiBv0+8OgFeUoj2/MH+4US9mKX/BGsqWKwyGPnlC/6vMWP2LztB3yMY6nNMTiN0/wOF0Ydv/8Ju4rCGC/+3MvjO3xrnwbQxRWnOuPSAvFqpUHVQLNgNKc8zFUchqpIfkZGARELuPm/AxO/GtHDQvfXAn432MTlg2nHm82w24lpO5Sb+7g8ke9feeek5Qr8MrNJnkDGsJLIqvuW4Bsfs5n+Tc7Qj+f52OSHMj+L88n9QZEpoI+ADy215fqtHBW4tOiSrNsPrFdq9xRjEK8+C4Td3W8M/7EbufYtK7b5ufrEunk7b54RrKS48g8DO8qEHNQ+BHpVKf7ZcaoR8T0g8V9hgCNcv9jfQafyB2H4ezvJc1YIgeVW2cFx+PQA/AG1jHHoIx77zdviAb0PF19dsn7AOeJWKtFR3uMPqLg+vBTl8gdn9RIDdm239o1TMm7I+/x3twGRH8/8VsXWTayI6fFGCWCX34HDpmZJQVPB/B6rtF74zSP/796T5hf/IScne01FTuXarjBaztYxfbd7vD1bFvPDnPjWFSTbzdK9Yeuo9E6eyvtoZIdrOmXn0HIHa/ybgpYNt/Ct1TiVME/6uOu4cmjHNh0rQuu3w8exAj7zlMxKcW3ZuxGz5HEw1m2Z57Ltpv3/9f93EqqLneqQKqQ3hNFgwi0ZNcCsWSh1rw0Vo8geGxNYgKeFAfu6oFxxIXw8dbLeHr8e8yn3SUEbH70q0JJ0hHL2Bm2jGUswT/T13Yu0FpnI4OeMuJDjkS/F+0Ek3Va0H1X9ZIDsYVwZyhk/SJ6EZE7L7oRZqQzz/IpCnvDV5cIGvC91cXjVGaOODvf3xZI/qbD2+JtfyQO9UDs0IbaTdj/JB7QySjkkEGInajn0VOMrfrAz7leYLPClkTZsMxtKPPxAHLdryYV0bNj/3WmC7nOdiHutjTpM9d0Ec53NpFR+jSgNh92RPW95j2fKp1+ph2c1arGWoDq3aF5zaAHt/srXv8NZCTOIgTwcphaT404EFiOqzUrY1aWrgDsfvXrIzvBv1cx4stZfl9932BMX+XLpXRcsLWGPN1NQ8fxZg7xcUoJMdRjHWYMnPEUyTTzzhYG9yIiN13lt4h6sk5Kfk206zidk0GnDcYO2XwtBrSKW/X6kzVQOK7KpaM0/mQuuhNEb4cC2p1eRd2lhB+v/yf7l/6TGHCN1KE8QoGPz7U4ID3/fb5ZNgrIbygkPav27qT8HVrfCLhdw5i6tBIYG2MQBJe1fd/rFYhYveP9JdxkJPOSr47Qy8TKzgCHvgoqON7CdLH4l9MVRVAvxXmyS+cgIbunQg1Sk1ApZONUfR3sxGx+4aHy6yCndTwX1kFfJ8VHcQYVUvqiq1aUYXHbOAPkTwY+d3SsfG1CK28noOApQvALWm99G6+5J/3I3aDyA9fxT2KRQI/lx4+ZREj8DM71fG2H8ePVzx2RdWppAfa+enHbw96ID+p8lOWmal/u39znpvji19Crdt3scJjyU0Q+oHebPYYgMyYzKXI+1UQsfu7bEV+MbDoMauvHUuFGqPUybLUo0Ds/vX/9seONNgszvm+wNdP7t3PoeWED9EX+iF26Cg+0eHyJ9K3YzAk1biadj0b2K12YbqHG/92f3TPZv03+RRJCd7gmhy7DLghyJVpcKYWZu8WXVSuroPEUSnTKIkSaJc2tpe0TwXPvG+GX38oI2L3xf7bHx8ekPxxX4MDptfB9quyWQjb0//Wx/ryJOKs/X7I5lEeZHse38O9GAGuk+0TQ7ue/+2+takY27X0HEkyzd3FWjUj8FNQ04R/vQ0uK1hbeJ0og6mgFDNXT3MApvPKmwd8kd5NM1Et61hE7L7hVun2/pip1JZpdflB/CVp65ezDzvhxeeLW1EH4xErufMOXoE62M9wXoNnZxysWOy2yoiu/f9gv/+6QeTn9lCeyzY/e5Seq23zw9kRi68l8KPpnGH4lKUPtRk+8i7kNECWUho/h5a3f/f81/1OknTKa1l1Up8ezf4yPd4EOhdqjLlu1EKapEULq2olkMu1qH/LLoQ61diSc1lJoPMm2JeZxR4Ru18XdvOP/0a/RjDC/lh0cuv+BU0njHtaQfEt41HsNPl7cZJjY+ihjwKvSnAmctmjbHT2XAMidj8sddf7z5KudV16qQr8d9Ig9juZmBJvBQSIm5D1sFWDW1y7MslKJkR8nwy7uREGN4rnKR5fj0HE7u/+ufeP/4khzBlsBP9dCtQ79nYI4bdmNJnb0idBs0bnayM+Hc07KLe/4gxE6uFkqcinEhG7f2Om7MvtBF6pEMef9NzCI2BZx3dIIaMFjHB1YaUDubDy4fov+a8p6D6LEY0JTwoaVNl6sM7wFBG7j7tR8sd/KZE3L1HxQazBILtU8UwrumP6KbvvbT7kqdw8b3SrELlRSrZX91hBlkEmkmQt+ef9iN0n8hPEYp2zzc91D2fzawR+NK5wUZGS8uMf051N8GTohe4r5JHh55yQP9OvfU+4k/92X6ckYf5iVl1dbTGPlnt3A3Cu3jxFerUK1AR8fvYzlMOjqRj758q5sE5joqhGmgAadQ4GX4/6ImL3Ga6a/PG/0Dv0xTeC/0ErFOcHNJ3wv1JoyMTpj+IzmJ8L170bhVE2byfGlHR4vMR18Omn+r/ddzPvL9h9zlWKyf53RcZqKnQfp7nLX1wFFyvk9cdJakBi5+PHowLPwNIkUT9vfyJY0RSKSkq4IWL3F8f3/fF/ZtPBnI7gv6TSPfXXbUKYgiVTmWv8JGJ2VZg63pEJFnWsXhdGAuC75J49z3Sq/na/qrNN8S6et06aY+abdPUIvGIvG49QawN82FamVXQJdCnHFOHPmyM+S8GJswyhSO0z2V4ji0RE7D57Svkf/88c+6VwquIgfkR50XQ/Xyf0k1V51ZMlocIVLq3Y3BpINtsw1HSNhOWCMxMlgTV//v/9l8fFGUWlOh9LSfcPNH7Caw11gWueHU3j18DylNNWw+ZVtBiVYogjVYIn4zKOp2zPokvVed5Ph48AW5NZYM4PbjRS3ytgkdCOXzU6lVbIN4jX0RWYK8SlYm+lthjUejSw/ec4hM30dmIyzktHHqqWIxIucsMbJA+QxZtb92lpg5H04a1Auz672lgGBztJrtPoaZ9tczOtDXDeGmmKlriJgparN5VabOHjwbSCi1MP0OX8fepDnNZA9U7+IrenLXI0/v7ys/gjTNOqqoftbTRWq/kpcarJEH/Um+7xqTR6fA1+Kd/wy3M4hvpzMxXjQNNLRv1eWwYye7jkI56diJiquy151FwkU0i0p6WOnkP3TdSoted9YFYhTJLpig06UycSovg8DA6RhRtXVISiW7O7rlW9DoUVDa5JgyvRiD1l50Clihg+vrRLR5tGBs9wZTU5TYsCUzzvxcYSV4HKf3qd5/CpR17M3I3GuDA4ZXDknemXUhRza6NnNrgMaT3fEZQzVSM5Xghiopw30BX3VnXzUxdQrdxDr7Q4F9AN8DTv2vsA2bfcenKKzRNOR6ZM6YU9QGM3G3McTF3gX/f777xhrVmI27uh9vo6BqVIjhMXjgPngdPRbNycoJTKNqG/u1MqN9WIU3I/Hyx4upw0ydiq43ejPpiyxgtj/+2PzWqdjCfsj6nyQkcRLhU/r/NRnq9HAz9lohP54fpOfHZfIZdKexl8HrDfM6toDa/WmBvSTgVC1Wii0G3nOMmJkQLdmDMC6NNypjtrrBVM5YuT8NfcQEYZSZ3anLZQdZ1VZmHOGgkNVr65IGYN3i9/hEdlO6Dcm3/2x589LvSesD+e12ZteKbJEAug/63vnUaPWYbsy2ffeo5mPvBweiXEIUX+oTt+GRlQpap75LNcIry6KerKsRgmKe5l66nJeQ4Ziw+8j2nzAcrws21lrjZoItR+yCUvDIYMlGoP9IQi5+cuAyYQCllZ3Acv345G7s/+7I/5DtroXaORwXqZqK+f0aLADyzHblQ6V4CIjMvTGc96WFQccXXIDkOZWiXI9H0psLqGuNZfK4MJEV2Dm3HnJE0OXvyVw3gDhe7oPpe28yKyaJeqRysuEPIi5jYl9wN0yQk3QuvjCQn2Xalbhg+Qufn1lztiXeFf9zvq+WGGfclIKsWxgV29baJuLZzkPFahCgP9fG386ZeRfT3VG9pVWZDc+HZPjFMUXRtzpdIX44BnsmqyGfXsCHeu74//7/u2krb9v7W6u6+Y4L9fsAMbG8H/yWEqlyCjndgvZaos2u9l6MWP7OzedBsk/dzvAGlTEKJxfK/ZaHhVytj6W3nZ+1OoNYeWotHeFmr37dffq2qMGkguHVXbbw8sK+YBQgNWCPXvPG3NbwOHQidNVDTtEHPG/B//ReR/1W37v5h2S2Xb/3EuhYi1VHr8o/0scW8+PwfTO5VR7Pxx8EoitzPnVQZyN90nQVaSiOY44oN/qOyqe6XMcPN+7nn0QTDF/qScLyRZrgmRtVqjH2ekBF90hYFfc26GV2IoalryWfTsCAVZkUvGp7FoVC+x+4//9tdtnHUI/h+hcxJ+Q/DfyqDpddaTChQ2Wrawz68e3RyeH7VZDoW1/bITqt9KEQeTWdLC4zKEw6p6PgUXS2obvKdrVLqBhsgzdx1Wl0Vkz8/K/hxyAfEpw1X3GUs086silemsJ5gvkWw9sn+AyOJKtMzDXP59v///D/f/p79rpf/zfxCFxm77fySUQb+E4D+uUGt2q1sDH211xpXt5k68ufjdW3J+ZRBo7sE6dcAOflyTLuJzDYBfGn1ysoZX65iSC3Lqh/jQUarIhf2/reFSSLGLaMpNJChHai2jaQdLaFyOzNoGdY158yfQ2cCSVq78MRJH1Mo198d/mfWM7m3/qZ9Xam7771yqaqBI8L/IJClmaeM5CqbNEZFwikO3gvw6nbIJ/qtr3EfXEuHaQvvNZ9G7pIoZqpQC75xH6g7yAtOHfcFMzpU+aocNImNfStvfEAY2Cr3drytD0e2rR4/cawgFZbpjD6O0o1HKIfI//i8shkZoEvzfx7an3pzg/5464/w49wqoFlpHn7zr4fK45T2ZhDDEJc/Dd/VDKXxeiKM30yoDHKiynFtJrFVr88wzlrmBOBO2JKdY5dAlT47mC6KuwFYt9PIh7gGaeHEyeSLRE4RbSxVrFR6gpTK7zK9Vrv/cf2I3Hq3cGXXy3o/mw/eYHePiRu4GTyscQw+iWG7dNb9pWvS29Tg5ox4bmnq0V1mrjgad1kwl7UckQOx+TJ5mfih+Ev/9eaSSs18eNine36+QY4Q9WmM+5cZFi5l8sYt/FV6LZpRtRyKHFFEw9dLxk2WuQOz+4ejw+goDeyQ7/Nsw77wDcGWWnHE8bIhkHqsyup9RQDPrlz+aXr2O8EFfqhovaKGVIVxD2KYqInY//Nry8vWHaViP7pWzsrx38RvWbf3XbDnwpzAVReekHqj+xEnFkBoPTV8qzuuli8LhtFPWYoXRQOz+a5uzzdPhjiCP007PUzeAN2ldly7WuKLeRTUyDWVJEA1uZaaYtEbiIRe4nGoN0FWdxtG9UkqI2H1pqatVnFKX8MdSn7boh9Bj/Biy7VtuRvIdO+50vw5G9UWd7fS/UyGvV4bd1O0GvP4Y2yHflAnE7ts1ipCNDFqgrOdfpovdb8Os4rH3p5l00U8PWgpB+huQ0JascclIFq30fjjyvtUIzjf+0KLt04V/3Y/Yjb3lnbf0Blhg5qU25nJbFJk8VX/W4YLQ7m8dTL7kJ9FqiGF81rAM+uQhojxxQRTRc4p8+2RwERG7f9dF/Rlhf+wFD9lzwv6EDlcs6OYY4X0vWqxscNPiEyX62u0v1sGai4S9naAJ3ImWy7pBaY2I3e/c37oy1WCHRKr27pG3cgQrjRNn4sfU0VX/j5V25sfRSFcGu16mLLpB+cr3S64sen+F1Sy3/RoQuy/jcuEtYX/8cdNSdsR7F7MQrBt2tuHA5DYHrGODepDdeU4JpBKLmN1NaHYJXYG7IelrEdSRiNj98dxl2QLkCKWVcBvjM4ID1GyB4gIuiHGKoxclK8JRGT3Wk3wPkKuspZFIuiYaeaaTXyzEhYjdN3RXzRWWuoR90bfwtA+lxzcWBF1zF2yBHMFJZPszBAa+2mr/aElBjVKDtAc/K6GMCOEXekXpiNj9M3rCFrZclqgxGB75f7sNuK86G3J++uj46pOcFApDIF38MtiaqI74910atSzSAc2uD5qaXcL/vB+xG5IDNI+l9EkQdVqqbsHMYZQyFkoy/ZERrbjV+JoV0aDYwiWJNSVWxFdsydChToPmNeFlpTEpELsfObScu+3/UVaxVieC/zG+chzSBP+/BvAy1XPSYssfq1oen65FBiplNY81jqJDfkejqn54ALH7A1Hf92rU26DnlKaZn4psoP0Iv901x1tI/ndNrIeQOqJsu56SqGmKPl0IE+Qr1kN1CQ4FxV0GiNj9U/VuYgYE/9HjQ7JKBP+vDRzcaCT43+NJ112S3gM0ftXOx84kwIvN3taARQno5C73vMgYA8TuGzKT3BJIdYL4+9iZG2oawGRqRnLvijs6MZrN12rOA3fUrWfut9migCurQopahuidpW91+JIqInbf3ZdK6DDB//IaRdGtx/SYVRLDTpXZZlTPvbF0xjgY6ejpNR/gToO834qkzXtuQvmPhIj4gUwgdv+4dTplHa8FIs+QftPSfwtO1IuYy00YoJNMuNHKJkO42VuyK8RFGVFXB9ZujBtCnbV8h2mqFvzrfv/af2L3jT/wF2z7r5rQd3bbf3oqKxtVgv/jX9hZhQj+31ogTb83XQv66ExbWup1OODh0d+55oiI3T9xP97x7iMbdIelxEf0hB1ki65Z1ncZoblvscZK+ReRrsgNQYsqbfS78LbPRxYV9CRsbl7D6iwQu8+lJHSAsD9+UPvaKOH7H6MWv+b+leB/3d3DsRVhPcj4/Vn6WvI4FB+xB1hvKkAN/vbjn9mRiNj9rzFHks8rOYGydWfT3VAtuCg7EVG7/hD5Ml/eEJs+B/QSoq6pbNboMO1Okc11LSSgylu9EiOFiN1PmfgVIkjwv2s46wgNwX8vwarRBZYWmG2+s/LGJwQehzQFzC+koAZJi6zs2MvoTans02dt6YjY/Un2309Ygi3Q+0rHbF0RM3hnL8VNymaE0uUNxuRy9QGnbUzy46wWyrwjoLapoA08u+1SJA6w/3P/id2oJx+gO+MfJrmPxVzs6NPT6NGqKIvgVX7EbqtKApepkXllj90xHDswvHhQ8FpNHJYTU09UBtMiYvf9f7FurOrM4Sv3qrIoK1Rg17b4Dl2LvoeluFMM39NgxTrYqJyeXW5DZazSGo+kY1GWvJHeFePYv90/sfakXVXKsHbE5EjMpVcRIHqqjebI7WQgTTeP543XANaZxU87B8NQB4HnB79D0Z6NCcN+Ni9E7L5MO3+F4UIBphXddTdu3RY/uPbIN4VeEI8JvHodFTUIokms0S5SlWj64yO1/TLFyMw8u6IusOhv9+ezW5ZfPE2pTX2fbVFamY9uzhnsMb5YjUafWWkziFajErmUPaG8RYivn/Rn+/oTFErx5VtJigMidh+HMhf7CjTx9Hdf9u9I48BYjhxq5heZQKx0O0z1hOpR9jmuMLm85+g1KS7nyZNclO/VxsvkVfC3+xfOVLZamJdJ5smZ+NSfjgVSq/6tm6xFQL8TxihGSsE+OsP7rGoJKPFFvPwxmwP7bCvKd5fEw7/uR+zGf+c9aqNybu98F6qOgupZHuudv4w6KxupMvm00ZXwJ4KbidJwaThVoVP2BORnaI3dSTcDYvf9/tsfs4heuHRJoQIvSquXcCf6Hj5dbSGpR4sVH/DuwrGAF61QTPur6Jl/BAwlJ8lPbAYCsfufd15I3pGRUsub17n+cyMaRtZH7j84mQoOzQUO8cM2MDI4/Xz0XQTK5apmvewSjpjw/snTEIWI3Vf5b3+8gp3Xca91W+xpAOnDWTpBzPDkfNkNqUF0Wuco3niiAspC2NjvChaDkdZaOY96PhC7XyWpeZv/QlYtFx9jHKdsPsonD2irLa1Gs3KdxxNOPEd3T3pIvJ4pRkp7DgvMiKehpf7CXrXfAYjYfWm5P/tjY40WTEpZHPhVbTe7C54T4BX1+XDVQAPYxvs4ZVrXgFDKYo84Vz4oxX+6x/8+E4jdL28jZekca5Lsu9FmfWQ4Ei5HX82yon4GE1E33h2YKAH3oRDGt4dLYOtR30OBiRzwM9p/I1orBf51P2I3TniEpOIoveqGXltG3Lp9Gj0jj3+j6y6AtMKGFSNfkyM/8WWe41Qc0J/gmujMfxpcXNoKchEDInY/wuI//6+wUg0pEvzHNzBmnSX4fy7MgPaoCitWp+BtIK3bhr5QXhe4VxSDbu6IELp0O/lv98XoGXA2ITZ1tluqeDXLUPCjY2WYvpIIKSIS78WYzdFONylbnV/hKCzwqkHSt3BEVXgrKk/FARG7b+TA98d/c9/zu5MJ/s/KF677MAjiw4slro0ND0IX8xfnrY4KxCPwjZInpQiB9DGHzqGiv92/Ok/vREbKLzUfsPcRH10Bqr4aG67sVI203y5ppJE9R9nCClyWMUWotXVr1Zo7BdXyM8uYHXZAxO5PZKT/8X8s/SRtTyoHRkYzYJMrPIGc8DqZEe/x6FiZRUC9ynNEtq/6w8Czp+jijoYDm4YFf7vvfkGkywdqa4NXwrOjXsTAPi4plwauIhiuUTplXlIKaXljjA3HS2Aw9Zkam20O1Fu11pZeif/n/f61/8TuO7kd/uO/7ssNZsL3P77lQo60KsF/chDojrrKin8h7ErD9bYV+ijydcXORsAOrqfXlFijgNj9olOG1ZdCbKRSxFNNWRsjgMo3ZYdMaDKM6C/a0Q1LQ9vcC3N/10h05hi6vjwSgcr85G8JxRH+n/4/3WdnP/rH/7enX2oGEvznZxOrnTogiPlwiSh+9RxEnZcCcuu9K2D3w5JBkuQioMPRdBqpFQCx+9ep7czq2vjqdtq3B/Dk5SNDrYS27s/VqDTaQdR15TnymF2SuXixBJVO1rXckkhH/dwm1g/2BiBi91O+Zf3xn6nbQ2IPwX+RUjfmM+4TUPDo8KeYkAa4fKfHw+JADdzy4CiJFsgD6aIHzHzDmUDsPnteWOuZ5G7JdhKOfDqPSDBVDzKrFHwGAjYtl5/XlMDNWBKtsrVikH32OvjzzRwYlFd5Y7A/BVJQqNWi5gjWnytR1tPWhwn5XMOqsvOxC/wcvEnXrbCO2HtknVvCWEn2ubNUc2sINr/dMensh/lHE/XnAt8h4nlqW+bn8q/7MByn98CegnwM1z1Zz3bGCsO5j+6LcDyJ4XDS043lnQReUjTZ+Ye3n7Eg4dH/+/uDbGPB2ckW3VewmM4qj0K8te8E2vYGwg8244P8Z2NQ90CL2GzIet3AnHELk8wxWPSKrmjZF0Xkzx2mXYIvldISfrdfl/gYwYwP3jNfZTfAiMmN2xYNPKHEZ2X6WyVFUGJ0XnWZunab8DF6ObWggAT7e7/1r2Ofy+3QNE0WbfBsEDJxCbrurOANS3FXGe/W+yCK3qWXFrk2aO/XO9EpyA4oRgSydSYj/t7PVNL+QP04Db5EritKdicT/stIKNOFEXqsLvXN1jfbvfgzXGG6i5wUWMaccDqLyBrEY72iJld3/N/76ajZ2R65I0m+GrrEWV8wFJAJ/UDjgpQOFZ/G+SeDgD7HXtydVOQ0x57N9zYN3Pd/j//yLePv/XWG801c1LT44i+3/UlkGPFxBUPf1cwZsRccgnrl5HvwnhunSLTdqbAnsex368tXwGV06HatMDn2r/sRz39MfBe/zY+moELINj/TIdylVQR+0jZCvPAEfh6iUxlhZOvo4AsPDbfufth5/lle6tt3f78/3pv2jY3dc0Au34IpmP3D0Ik+EQrKRm8ovRmpEroajhIMT4u7XjNFneG6XWKnbkL0TcvTpK9i/r7/N5t+VyJLaTH3Wb6IneHMWOvHzMW0AUb81mrgee5ESkzuvnvh1whKfKjg3elSm020+KpIRKGABE+8fxhPQWLXb4mCH7d89FkOQ1s2Cx3MFE7A9z1xgvlVIDpkIBgUFuOOdkrsNw1xtYQTlLt67ppH/70/YWk9885xGqz0iTovy04mTMzk8l3TEXp80uUV9i7bvdhHBnL3JU4K/NnB9m8xImtox0eucJ2rO/7er9gQOb9T3xE9klz+6pBoDzM+B6jOJvuhTT/GmmqWRHh225t/3DwdXX9erKf2LhUUlZ8mdNlk/b2/xovGYmMfLSajKNWwU4kRK51n3qdiyojvxlxKxqn3YPfd7q2nWFHhraa80r43rqC5Va+3dLzk+H/dj3heMlyoYpufqZSXVtv8NOykeMJJ4OfSERpxXwI/YkWbmkYtnchzseXtEO8w2s9ffP0a/+j//v/RplrtihzOjJIMJIWii76eXwo+hENPkpaxnEIkilRMyqIdswKRXcOjV1n1QayGy5xTIuzv+8fh6t6zl9Hilb6zXogKZcb/1m6kTx5kxPovk2JOcZR4/Ez+FfIoSmwa755S9mATTjZcoaV59j/+43Au+985oP7WHCZjOX/kuc/zOWOBL8wdf32aKtcLHao/HlP0yhJxt5d3jXg6gJyYOe2M4v/1Hyd91CSW4P97R1GwJ2fC237f21lH8D+Lzfxkq81ePAvPxQgtLgqs7eiD6h3CazAFgQ9P/K//uPqp093uiOnYs09cBX6Q9GpvXcOcE2ryN6ro6EkGX63UgBfqqaiFV3tI8lsacDyr51r9/H/9xxk2KwkQ/A9t7qz/eI4RX6ZuYCFJ8H8ryPGCHcF/m+XTn7+5UWHdvyPa4wj+L6q9D6T5H///3+5HPC+tMue3zc/6BFv/Nj8UhfrvXhP44c8qHqwm8OPps+fKqwPr6PpPv+v2Pf3Qbaft20o9+r+/H4p2hbshT3uP4a+cQWg9qcbjjWwoWNydYCIjCUPRT8538u41BKfet1RzA3ehKJtJtaz3fz5/nIqTURktVq+YqUMdwowVVMUu9g4y4g3aVPYdiKXE3jSrUWpGUeKXSjZa71huIuo8DZaX/+P/9qPstDVS/TAb+HJvKDpBy5PneMcNZN5vaX8K9EcNBX1zFztc0A/PA7/nvlrBrG9GFV111P/cv7u8heB/hu83mt3kTNiuEwOvDr2jx5dQ2e/Qs9mLXTq7sfaSiwKfRFXJxSG8hpJqTnq4/Y//hIfZpoATivp013/quyM0YfFOOyp9kWZX2AdV70S43DPw88jFdGQQ0GEJq6lAGs6o9/tG1v/cz+l3gJIWq9EI8q9SZMRm5Nx/mxD8jzJqlzAi+C/+/WyKJ8H/DwLDua4E/wWL6eMzeMjxxO6zkB36dPzZLwgoZazybG3ELrZccpZZ98NEluxtpJ7IY6+kNuIY2MkwighLW7YPzcidck0MHB4BsRtL01t+4ld/IXclhZzThPO4erWNLMJ5XH3rtVkveQLnZe5LdmTbvAsPmz7dft0/FnpSELH7obXX9M3YU9H9mdZYR400COQNad9zIBVSvMb3RThFQiCS+7mYGQ1dSkwPXXt8oWSdXbJILgKI3R+1OllKqcSKP7jAUH3QaBn1YwpCM2+7QFPw2c4zQ6NIY6TBafF1G1zOORq036IXWb7t4piMbwNi9w39fp3V7gxCAv4kg2/W7yPlI4/DPZnd0HcT+tnlZkt0wyxQpJ/UC63PvY4UumuMxmiUJyKLXRGx+6ny+DCTp8x4zgPBWoVNP5BR0MG3ZoYD4Ngu5KvrPoRYWYJWsQe98PBdoyrH4y5E/6QoKvteNxC7nwJNB8mPWYPxkfMp7JKZKAaatSUznqJvXHqmuUdTkG4ktenthAwkvzw7tXgtGr07YFFYfO0JInZ/j6mC0mv6Q3iyKE/6wyM/0X5u+1fNRz7Bq7iWH8cKR1F434IxY/AU0L2PuvwZ34M0bXYlij+f+Of9iN14kJzWNv/kFxw3smzzIJyvCYm8xU84/8N+SasoUR7rzznQ85WCDMOX2F0Ji2tG43suVu7yiwZi9/f237BSF01EAgvnFSS1o0BZy1pE9WAECJmMCpUn+EHvPH/CK2lf8HT+5eZr6wK7uYWvr8v4ALH7i6/iHh1UYMW8wnLwO64sw7PZc98rUTf6fax3grViFI6qvv3ue/Y1ygsISB0y6YXl1ts7/fraELH7u/RbXVzzI1FDUv2xUVs/yE/naezg8gdrMqFVyof20Gg5FyE76AwNh0hqXATuwUjwxtVLJ+yB2H1e4GqvzWPGmjYl3VQmfsByxeyRpF0D6Lhz7aXdfUOQ59nhX8jbi57Tmm8qkHfDyoSMxSGxbkTsftEL4ZO3Ux2RgnYjb6JOCFJgFnqjtBWBxCl2TgydeoyUjuKYr3OEoocxYWIUU37o1Vg5b3K1DyJ2f+nkoR3feQ9h3p+XqT0DliFK0PRHYuAMWqmlO/LWYRze5XxilUUf0OU+d3nroV547jMp/NBh/J/3I3YDHWG11iPww3A7t2ybH5/YrFfb/DSyiCx9JfCjZb5EG0zgR1DWkcb4zlPEPta5ryUzBRG77/myUnAzJRZ9d8j29bB8DF8M+b+lzjvB0tRR3VVZN/jNVK7eflYJ6i8WhfmlWEHf6XWPkXJ9IHZ/4SG34rgsK56Lkv4m6ZFl9KZmWDRwvht88S7RvUqjSOxoU2anWjsMKu/bM8DTi754nzJaTX4NxO4z/eAqPnzWG61eiDbuWwtALzfJmqLGwxAlLdXUh0wvJPTrRnMsSxg66m+llH/LDv1OvyZiX+ePiN1fizCrGc5ixjM5fJbWe/oDzVBrOIc0D8DDweGgPOkhdNpbS5O/vBfePTByElDpQp1TTEZ3fQn+/5/ui8x0rt9Qc4BjPo9Xg75lIVnZA8nNSnlojwfn2zmZVOQvL7Wa3JyJPFiC+0r9YpCCx42Uu7eTELH7Vy+xVnw7cAh/fv5W/s2en6jly+O6jlOfoHtZc/li7CiKVT5zdi5yCq7lcc7jK3rQU20N6dLKiX/ej9h97kkbJ7ewXzCHrtNs8/NLFGfJQDhfembA7TyBH647PbdbNkgxioVg6c9GhH68dNbRyk8EYvep3zad8b0cifD6cu5XT1jC4C2eQ+pp95EgXYVK2DttkH1A/23tjTMSvrKs0OevC5TmWgq7Bc0RsfvaSpOW18+zYjTNKqlxjMuAAnfwM5zoQVLlhd3PTo4CSXKe40hmO5KdzF6I5eqFNFGvNrLm14jYfVxu+Kv+ilC0sjupda+0ESgl+XcOk2tC9aSHlH8pGeB6yG3DfquizIrwSjtFMaBckGzxaRIDYvf7o5otnuUwY4vLPDfFG35A/IWR39NeAyjj9/T7+35DsDjuV1n5sBel8Wun8UEXUFoa0D0z6EbE7rMrH1HsmrRCHwrYRi7siEQnNk4mSJnFIgF7XbUXo2GoQj24kFs7AmEJIXw+PwORlH7hpxjZAETsfoTmnEUmzyGM9NOWfaHzMmBY9XX69BmUW6Lx9bjmOOymddbvuvQB1fi8Hw5r7QXzqzRGnG7jiNj97LGfyu8WPuGvXrcsqU1+i/nu02QkvZ+MCQ9sXubZb4gl1rQWx/xiws7KVxZ7idUgdrvvOeegB4jduHojiVabcL5Xou/eFuH8bXVBhckHyZjB8eSAg7yGmOF42l3sPAuWyDCwLsQ9ger3PVcMcu7+2312hYuW6Su96CujNG+0wywo1Hs3Z2yMQdc38mzq1hEIStRlaMW1wtI6V1kb9wvwH0dSXseKEbH79Rq7Z6h6uPEmw3L1h1RJ8Gb8GXIc4uT4Od2dO1Qa18Hl9AdoPLIB1pPkdFRa3+BJkBqfyuo8ELuvUnppZEWqHzEGSzfULH2CHGFVpr38UxCnw/BokXYMjJxLpveefwMsWqY+vPovYcV+ejjsQwUQu08tp8RJo8KFP5YPDhY7NuG7jd95j6ktaCjNUy4y+AmrMWxkLvQ/APIjJh5JTcMirXfADYNpIHaf5Vi8COf8e+QWIoDbLb4M+ZYaXAtu88CtKvaA0nwKTmUbl1OujMOa1VFTTvMOaA4b3inf3QbE7k8bv+IL0+PG9/8MyP9K4C/79aUxiohFsNySSsq7+Q3K6eiU4lcmgHEtkcTEcBjSDfftMj/c8c/7EbuRWTvctc3PaN+7eSCcr8bpXhcl8MN5GSf2hsCPo3yt6D32g5ib5PiI0j5AVQ1GLB7KXUDs/nTVPLWkdz9a5/nIwXfyM5j2OmTt+PIeBnTPPRD7NArMnPrJRxy7we0OqrOWb4EBRqog16NhiNj9ji9Xk5V7uLGoA1hAgSYJ5v04WSaJlxzLVzjkj3uzjn6w8swMHthA92TaK7vCviEliW53ke/zf7t/RvlX0KJhDzqwpv7sddxH6N996DVt7AQcFu/eWaI5DIrnPnTjfnfB0OlAxKLYBKXJZ0OZniQCsfvMzr1CcuJcmPJLry3+vVuI63RW8bNOHEbp+HjvyahlNEZL2ep5dQlt2kFfadlHdHdg11wq+czf7l8J48h8PT+GaHRahhskfkDJkZPfGpI/w88GvcPRfRPQfChJNFhiAE6QRnXupm0GiivSDmNL1UDsvnJsi24qDTc2d1FcQsZmFfl2bH4WkV1BN7xj93R9XUCVucmy4gqf0Gv6d7vKX00g/RqFqGWT4X/ej9iN5qIh3DY/wVzVW78J599feFL5jsAP+6OIzBUeQ+xd8XtyemkWTP0CY1Ek/QRyd2Bx0r7X/bf7YcvBTTrmb1Dlm8UbAaxf4PrHwzLk4h/ARWWL593qGOxsXZ2pftILP0P8iq75v4Tk+GOnZk4EALH7BvwB1Ng0N/7haKaFZ8AOPNeb/LAQHTL8Y6WVp9Jv1mDzZGOD6fs1+H1dMbv93leoY4y/E8k6D8Tusxb+YD5Q9gZdZb2pX13yBWyWM9p1D30E01evBU9QET7/uV29xnxvYaNoIJ3XuhVU13Sdj3cBELt/RzznbpU+F359ucjg8dA6CAw1/37S/huehmq46RUtwVvu8Rx3vUXI1BvuoYieAtbqyNzjaVNA7P4Jhv7+FLVJxFzFfj6hchl+h/RfVtqch5QCb7qBXdNAccbtVXDWBAgyjmqvpHWCzWhxVZlMOxC7f6qH6e2Xm9z4M4wpPyMZfwD/dY6ZZ5sLUDAomlCc8BW8+3tFSy9MAN1BsctTxUPgv2OVI4Ki/Z/3I3ZfSfyEyCjh/JhIUWo94fzvKYsIXQI/LUajG1rUhhhNgsXpa9hBzKFw1I+Cph7VO6+8IeVrB2L3S6Ql7lpbDiD7y2pHS89+Bb+Ie+FU41Mg0PCzmVloAkL9948Pa7+Ba4d4d0iNvoJT1P29jb+zgdh9mbcvfQ5OcWOOmGfFfOIOzPplXDRemwybZHIxXlleQz/PKuwmn1lDGRajW9oVX9HukZSlOa75v93XzBNJeTDWi2iahIff6c9Ck6p3eWrMJHDe8dn7/vY74JUo5mHf6gVLOv2tjvvN8MPZR/QkQyEQu9/PrK2RdYEL0/DDlGkbN1FUW+zKEWEcVqGK1Z/p+4kuno+XW3z4A8H5498f/JpGx5x/UHmyfPzb/dnPvsm9E+PI8Nzl5Zf9P0C4W0vYo+cLlFNEM1cHvgf5w98vi0wPwvXdxY0DgS2w+6NWacxkHRC7z0N6yGCImRsDu4d0tpkrqIgiREoh6BfaavA3MLdZQGPLlKNhN2cRCY/R98c6E2g4qe3Jw/Ah9P8DocFTJgAA</ELEMENTS>
</NEKTAR>
......@@ -6,17 +6,18 @@
<files>
<file description="Session File">KovaFlow_m8.xml</file>
<file description="Session File">KovaFlow_m8.rst</file>
<file description="Session File">KovaFlow_m8_avg.rst</file>
</files>
<metrics>
<metric type="L2" id="1">
<value variable="u" tolerance="1e-12">4.70499e-05</value>
<value variable="v" tolerance="1e-12">0.000157969</value>
<value variable="p" tolerance="1e-12">0.00158632</value>
<metric type="L2" id="1">
<value variable="u" tolerance="1e-12">4.58337e-05</value>
<value variable="v" tolerance="1e-12">0.000172894</value>
<value variable="p" tolerance="1e-12">4.4124e-05</value>
</metric>
<metric type="Linf" id="2">
<value variable="u" tolerance="1e-12">6.85934e-05</value>
<value variable="v" tolerance="1e-12">0.000191491</value>
<value variable="p" tolerance="1e-12">0.00500792</value>
<value variable="u" tolerance="1e-12">7.43491e-05</value>
<value variable="v" tolerance="1e-12">0.000224175</value>
<value variable="p" tolerance="1e-12">0.00062356</value>
</metric>
</metrics>
</test>
\ No newline at end of file
</test>
......@@ -4,7 +4,8 @@
xsi:noNamespaceSchemaLocation="http://www.nektar.info/schema/nektar.xsd">
<EXPANSIONS>
<E COMPOSITE="C[0]" NUMMODES="6" FIELDS="u,v,p" TYPE="MODIFIED" />
<E COMPOSITE="C[0]" NUMMODES="6" FIELDS="u,v" TYPE="MODIFIED" />
<E COMPOSITE="C[0]" NUMMODES="5" FIELDS="p" TYPE="MODIFIEDQUADPLUS1" />
</EXPANSIONS>
<CONDITIONS>
......@@ -81,4 +82,13 @@
</COMPOSITE>
<DOMAIN> C[0] </DOMAIN>
</GEOMETRY>
<FILTERS>
<FILTER TYPE="AverageFields">
<PARAM NAME="OutputFile"> KovaFlow_m8 </PARAM>
<PARAM NAME="RestartFile"> KovaFlow_m8_avg.rst </PARAM>
<PARAM NAME="OutputFrequency">100</PARAM>
<PARAM NAME="SampleFrequency"> 10 </PARAM>
</FILTER>
</FILTERS>
</NEKTAR>
<?xml version="1.0" encoding="utf-8" ?>
<NEKTAR>
<Metadata>
<Provenance>
<GitBranch>refs/heads/fix/Averagefield_varP</GitBranch>
<GitSHA1>ded15b6c0f464a373d4b4809ec2cbfb24bf48cd4</GitSHA1>
<Hostname>Spencers-MBP-2.home</Hostname>
<NektarVersion>4.5.0</NektarVersion>
<Timestamp>23-Apr-2017 22:39:14</Timestamp>
</Provenance>
<FinalTime>0.10000000000000007</FinalTime>
<InitialTime>0</InitialTime>
<NumberOfFieldDumps>10</NumberOfFieldDumps>
</Metadata>
<ELEMENTS FIELDS="u,v,p" SHAPE="Quadrilateral" BASIS="Modified_A,Modified_A" NUMMODESPERDIR="UNIORDER:6,6" ID="0-11" COMPRESSED="B64Z-LittleEndian" BITSIZE="64">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</ELEMENTS>
</NEKTAR>
<?xml version="1.0" encoding="utf-8"?>
<test>
<description>Channel Flow P=4 with temperature field</description>
<executable>IncNavierStokesSolver</executable>
<parameters>channelTemp.xml</parameters>
<files>
<file description="Session File">channelTemp.xml</file>
</files>
<metrics>
<metric type="L2" id="1">
<value variable="u" tolerance="1e-10">8.82097e-07</value>
<value variable="v" tolerance="1e-10">1.56407e-07</value>
<value variable="c1"tolerance="1e-4"> 1.02325</value>
<value variable="p" tolerance="1e-3"> 0.048 </value>
</metric>
<metric type="Linf" id="2">
<value variable="u" tolerance="1e-8">1.12525e-05</value>
<value variable="v" tolerance="1e-8">1.31727e-06</value>
<value variable="c1"tolerance="1e-4">0.947434</value>
<value variable="p" tolerance="1e-4">0.0240083</value>
</metric>
</metrics>
</test>
This diff is collapsed.
......@@ -26,7 +26,6 @@ ADD_NEKTAR_TEST(chan3D_probe)
ADD_NEKTAR_TEST(cube_prismhex)
# ADD_NEKTAR_TEST(outflow_pointdatatofld) # need to redefine outflow.pts since GetOffsetElmtId
ADD_NEKTAR_TEST(cube_prismhex_range)
ADD_NEKTAR_TEST(outflow_pointdatatofld)
ADD_NEKTAR_TEST(chan3D_equispacedoutput)
ADD_NEKTAR_TEST(chan3D_isocontour)
ADD_NEKTAR_TEST(interpfield)
......
......@@ -12,7 +12,7 @@
<value variable="p" tolerance="10">385033</value>
</metric>
<metric type="Linf" id="2">
<value variable="p" tolerance="10">175442</value>
<value variable="p" tolerance="20">175442</value>
</metric>
</metrics>
</test>
......
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