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7eaa46a08b
Corrected form of the Rosin-Rammler distribution taking into account the
varying number of particels per parces for for fixed-mass parcels. This
distribution should be used when
\verbatim
parcelBasisType mass;
\endverbatim
See equation 10 in reference:
\verbatim
Yoon, S. S., Hewson, J. C., DesJardin, P. E., Glaze, D. J.,
Black, A. R., & Skaggs, R. R. (2004).
Numerical modeling and experimental measurements of a high speed
solid-cone water spray for use in fire suppression applications.
International Journal of Multiphase Flow, 30(11), 1369-1388.
\endverbatim
161 lines
4.1 KiB
C
161 lines
4.1 KiB
C
/*---------------------------------------------------------------------------*\
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========= |
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\\ / F ield | OpenFOAM: The Open Source CFD Toolbox
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\\ / O peration |
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\\ / A nd | Copyright (C) 2011-2016 OpenFOAM Foundation
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\\/ M anipulation |
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-------------------------------------------------------------------------------
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License
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This file is part of OpenFOAM.
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OpenFOAM is free software: you can redistribute it and/or modify it
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under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OpenFOAM is distributed in the hope that it will be useful, but WITHOUT
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
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FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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for more details.
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You should have received a copy of the GNU General Public License
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along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>.
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\*---------------------------------------------------------------------------*/
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#include "multiNormal.H"
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#include "addToRunTimeSelectionTable.H"
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// * * * * * * * * * * * * * * Static Data Members * * * * * * * * * * * * * //
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namespace Foam
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{
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namespace distributionModels
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{
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defineTypeNameAndDebug(multiNormal, 0);
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addToRunTimeSelectionTable(distributionModel, multiNormal, dictionary);
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}
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}
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// * * * * * * * * * * * * * * * * Constructors * * * * * * * * * * * * * * //
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Foam::distributionModels::multiNormal::multiNormal
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(
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const dictionary& dict,
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cachedRandom& rndGen
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)
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:
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distributionModel(typeName, dict, rndGen),
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minValue_(readScalar(distributionModelDict_.lookup("minValue"))),
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maxValue_(readScalar(distributionModelDict_.lookup("maxValue"))),
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range_(maxValue_ - minValue_),
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expectation_(distributionModelDict_.lookup("expectation")),
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variance_(distributionModelDict_.lookup("variance")),
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strength_(distributionModelDict_.lookup("strength"))
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{
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check();
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scalar sMax = 0;
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label n = strength_.size();
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for (label i=0; i<n; i++)
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{
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scalar x = expectation_[i];
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scalar s = strength_[i];
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for (label j=0; j<n; j++)
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{
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if (i!=j)
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{
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scalar x2 = (x - expectation_[j])/variance_[j];
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scalar y = exp(-0.5*x2*x2);
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s += strength_[j]*y;
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}
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}
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sMax = max(sMax, s);
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}
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for (label i=0; i<n; i++)
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{
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strength_[i] /= sMax;
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}
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}
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Foam::distributionModels::multiNormal::multiNormal(const multiNormal& p)
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:
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distributionModel(p),
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minValue_(p.minValue_),
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maxValue_(p.maxValue_),
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range_(p.range_),
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expectation_(p.expectation_),
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variance_(p.variance_),
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strength_(p.strength_)
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{}
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// * * * * * * * * * * * * * * * * Destructor * * * * * * * * * * * * * * * //
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Foam::distributionModels::multiNormal::~multiNormal()
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{}
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// * * * * * * * * * * * * * * * Member Functions * * * * * * * * * * * * * //
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Foam::scalar Foam::distributionModels::multiNormal::sample() const
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{
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scalar y = 0;
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scalar x = 0;
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label n = expectation_.size();
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bool success = false;
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while (!success)
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{
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x = minValue_ + range_*rndGen_.sample01<scalar>();
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y = rndGen_.sample01<scalar>();
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scalar p = 0.0;
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for (label i=0; i<n; i++)
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{
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scalar nu = expectation_[i];
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scalar sigma = variance_[i];
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scalar s = strength_[i];
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scalar v = (x - nu)/sigma;
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p += s*exp(-0.5*v*v);
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}
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if (y<p)
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{
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success = true;
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}
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}
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return x;
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}
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Foam::scalar Foam::distributionModels::multiNormal::minValue() const
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{
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return minValue_;
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}
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Foam::scalar Foam::distributionModels::multiNormal::maxValue() const
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{
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return maxValue_;
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}
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Foam::scalar Foam::distributionModels::multiNormal::meanValue() const
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{
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scalar mean = 0.0;
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forAll(strength_, i)
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{
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mean += strength_[i]*expectation_[i];
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}
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return mean;
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}
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// ************************************************************************* //
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