Particle-size distribution model wherein random samples are drawn from a given arbitrary probability density function or cumulative distribution function. Input distributions are specified as pairs of (size, probability) (i.e. (point, value) ). More...

Inheritance diagram for general:

[legend]

Collaboration diagram for general:

[legend]

Public Member Functions
	TypeName ("general")

	general (const dictionary &dict, Random &rndGen)

	general (const UList< scalar > &sampleData, const scalar binWidth, Random &rndGen)

	general (const general &p)

virtual autoPtr< distributionModel >	clone () const

void	operator= (const general &)=delete

virtual	~general ()=default

virtual tmp< scalarField >	x () const

virtual tmp< scalarField >	y () const

virtual scalar	sample () const

virtual scalar	meanValue () const

virtual void	writeData (Ostream &os) const

virtual void	readData (Istream &os)

virtual dictionary	writeDict (const word &dictName) const

virtual void	readDict (const dictionary &dict)

Public Member Functions inherited from distributionModel
	TypeName ("distributionModel")

	declareRunTimeSelectionTable (autoPtr, distributionModel, dictionary,(const dictionary &dict, Random &rndGen),(dict, rndGen))

	distributionModel (const word &name, const dictionary &dict, Random &rndGen)

	distributionModel (const distributionModel &p)

virtual	~distributionModel ()=default

virtual scalar	minValue () const

virtual scalar	maxValue () const

Additional Inherited Members
Static Public Member Functions inherited from distributionModel
static autoPtr< distributionModel >	New (const dictionary &dict, Random &rndGen)

Protected Member Functions inherited from distributionModel
virtual void	check () const

Protected Attributes inherited from distributionModel
const dictionary	distributionModelDict_

Random &	rndGen_

scalar	minValue_

scalar	maxValue_

Detailed Description

Particle-size distribution model wherein random samples are drawn from a given arbitrary probability density function or cumulative distribution function. Input distributions are specified as pairs of (size, probability) (i.e. (point, value) ).

Usage

Minimal example by using constant/<CloudProperties>:

subModels
{
    injectionModels
    {
        <name>
        {
            ...

            sizeDistribution
            {
                type        general;
                generalDistribution
                {
                    cumulative      false;

                    distribution
                    (
                        (<size1>    <probability1>)
                        (<size2>    <probability2>)
                        ...
                        (<sizeN>    <probabilityN>)
                    );
                }
            }
        }
    }
}

where the entries mean:

Property	Description	Type	Reqd	Deflt
`type`	Type name: general	word	yes	-
`generalDistribution`	Distribution settings	dict	yes	-
`distribution`	<size>-<probability> pairs	dict	yes	-
`<size>`	Particle size	scalar	yes	-
`<probability>`	Volume fraction/probability	scalar	yes	-
`cumulative`	Flag to determine if input distribution is specified as cumulative or as density	bool	no	false

Note

An example for a pair within distribution subdictionary can be given as follows: "(1e-07 1.3)" means 1.3% of particles are modelled with a particle diameter of "1e-7" [m], and so on.
Variation between input pairs is assumed to be linear.
Elements in the second column (i.e. probability) are normalised.
Elements in the second column for cumulative distribution functions must start from zero and must be non-decreasing (i.e. monotonic).
Elements in the first column (i.e. size) must be specified in an ascending order.
Input pairs cannot contain any negative element.

Source files

Definition at line 169 of file general.H.