normal.C

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License
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#include "normal.H"
#include "addToRunTimeSelectionTable.H"
#include "mathematicalConstants.H"

// * * * * * * * * * * * * * * Static Data Members * * * * * * * * * * * * * //

namespace Foam
{
namespace distributionModels
{
    defineTypeNameAndDebug(normal, 0);
    addToRunTimeSelectionTable(distributionModel, normal, dictionary);
}
}

// * * * * * * * * * * * * * * * * Constructors  * * * * * * * * * * * * * * //

Foam::distributionModels::normal::normal
(
    const dictionary& dict,
    Random& rndGen
)
:
    distributionModel(typeName, dict, rndGen),
    minValue_(distributionModelDict_.template lookup<scalar>("minValue")),
    maxValue_(distributionModelDict_.template lookup<scalar>("maxValue")),
    expectation_(distributionModelDict_.template lookup<scalar>("expectation")),
    variance_(distributionModelDict_.template lookup<scalar>("variance")),
    a_(0.147)
{
    check();
    info();
}


Foam::distributionModels::normal::normal(const normal& p)
:
    distributionModel(p),
    minValue_(p.minValue_),
    maxValue_(p.maxValue_),
    expectation_(p.expectation_),
    variance_(p.variance_),
    a_(p.a_)
{}


// * * * * * * * * * * * * * * * * Destructor  * * * * * * * * * * * * * * * //

Foam::distributionModels::normal::~normal()
{}


// * * * * * * * * * * * * * * * Member Functions  * * * * * * * * * * * * * //

Foam::scalar Foam::distributionModels::normal::sample() const
{

    scalar a = erf((minValue_ - expectation_)/variance_);
    scalar b = erf((maxValue_ - expectation_)/variance_);

    scalar y = rndGen_.sample01<scalar>();
    scalar x = erfInv(y*(b - a) + a)*variance_ + expectation_;

    // Note: numerical approximation of the inverse function yields slight
    //       inaccuracies

    x = min(max(x, minValue_), maxValue_);

    return x;
}


Foam::scalar Foam::distributionModels::normal::minValue() const
{
    return minValue_;
}


Foam::scalar Foam::distributionModels::normal::maxValue() const
{
    return maxValue_;
}


Foam::scalar Foam::distributionModels::normal::meanValue() const
{
    return expectation_;
}


Foam::scalar Foam::distributionModels::normal::erfInv(const scalar y) const
{
    scalar k = 2.0/(constant::mathematical::pi*a_) +  0.5*log(1.0 - y*y);
    scalar h = log(1.0 - y*y)/a_;
    scalar x = sqrt(-k + sqrt(k*k - h));
    if (y < 0.0)
    {
        x *= -1.0;
    }
    return x;
}


// ************************************************************************* //