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DOC: distributionModels: improve header file documentation

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Kutalmis Bercin
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102
src/lagrangian/distributionModels/RosinRammler/RosinRammler.H
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@ -28,13 +28,107 @@ Class
Foam::distributionModels::RosinRammler Foam::distributionModels::RosinRammler
Description Description
Rosin-Rammler distributionModel Particle-size distribution model wherein random samples are drawn
from the doubly-truncated two-parameter Rosin-Rammler (Weibull)
probability density function:
\f[ \f[
CDF(x) = f(x; \lambda, n, A, B) =
(1 - exp(-(x/d)^n + (d_0/d)^n) \frac{
/(1 - exp(-(d_1/d)^n + (d_0/d)^n) \frac{n}{\lambda}
\left( \frac{x}{\lambda} \right)^{n-1}
\exp\{ -(\frac{x}{\lambda})^n \}
}{
\exp\{- (\frac{A}{\lambda})^n \}
- \exp\{- (\frac{B}{\lambda})^n \}
}
\f] \f]
where
\vartable
f(x; \lambda, n, A, B) | Rosin-Rammler probability density function
\lambda | Scale parameter
n | Shape parameter
x | Sample
A | Minimum of the distribution
B | Maximum of the distribution
\endvartable
Constraints:
- \f$ \infty > B > A > 0\f$
- \f$ x \in [B,A] \f$
- \f$ \lambda > 0 \f$
- \f$ n > 0 \f$
Random samples are generated by the inverse transform sampling technique
by using the quantile function of the doubly-truncated two-parameter
Rosin-Rammler (Weibull) probability density function:
\f[
x = \lambda \left( q_{min} - \ln (1 - u r) \right)^{1/n}
\f]
with
\f[
r = 1 - \exp(-q_{max} + q_{min})
\f]
\f[
q_{min} = \left(\frac{A}{\lambda}\right)^n
\f]
\f[
q_{max} = \left(\frac{B}{\lambda}\right)^n
\f]
where \f$ u \f$ is sample drawn from the uniform probability
density function on the unit interval \f$ (0, 1) \f$.
Reference:
\verbatim
Doubly-truncated two-parameter Weibull distribution and its moments (tag:C):
Crénin, F. (2015).
Truncated Weibull Distribution Functions and Moments.
SSRN 2690255.
DOI:10.2139/ssrn.2690255
\endverbatim
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type RosinRammler;
RosinRammlerDistribution
{
lambda <scaleParameterValue>;
n <shapeParameterValue>;
minValue <minValue>;
maxValue <maxValue>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: RosinRammler | word | yes | -
RosinRammlerDistribution | Distribution settings | dict | yes | -
lambda | Scale parameter | scalar | yes | -
n | Shape parameter | scalar | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
\endtable
SourceFiles SourceFiles
RosinRammler.C RosinRammler.C

53
src/lagrangian/distributionModels/binned/binned.H
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@ -27,8 +27,57 @@ Class
Foam::distributionModels::binned Foam::distributionModels::binned
Description Description
A binned distribution model where the random sample is taken from a Particle-size distribution model wherein random samples are
discrete set of bins drawn from a given discrete set of (\c bin, \c probability) pairs,
where in terms of its meaning, \c bins correspond to particle sizes
and \c probabilities correspond to (relative) probability of occurrences.
The second column (i.e. \c probability) are normalised by the sum of all
its values, resulting in a normalised column in the domain of [0,1].
To generate a sample, first a sample drawn from the uniform probability
density function on the unit interval (i.e. \c u), and then, the \c bin
corresponding to the first \c probability larger than \c u is fetched
as the particle size to be further processed.
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type binned;
binnedDistribution
{
distribution
(
(<bin1> <probability1>)
(<bin2> <probability2>)
...
(<binN> <probabilityN>)
);
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: binned | word | yes | -
binnedDistribution | Distribution settings | dict | yes | -
distribution | \<bin\>-\<probability\> pairs | dict | yes | -
\<bin\> | Particle size | scalar | yes | -
\<probability\> | Probability of occurrence | scalar | yes | -
\endtable
SourceFiles SourceFiles
binned.C binned.C

19
src/lagrangian/distributionModels/distributionModel/distributionModel.H
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@ -24,15 +24,21 @@ License
You should have received a copy of the GNU General Public License You should have received a copy of the GNU General Public License
along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>. along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>.
Namespace
Foam::distributionModels
Description
A namespace for various probability distribution model implementations.
Class Class
Foam::distributionModel Foam::distributionModel
Description Description
A library of runtime-selectable distribution models. A library of runtime-selectable doubly-truncated probability distribution
models. Returns random samples based on given distribution parameters.
Returns a sampled value given the expectation (nu) and variance (sigma^2) Available distribution models include:
- binned
Current distribution models include:
- exponential - exponential
- fixedValue - fixedValue
- general - general
@ -42,11 +48,6 @@ Description
- Mass-based Rosin-Rammler - Mass-based Rosin-Rammler
- uniform - uniform
The distributionModel is tabulated in equidistant nPoints, in an interval.
These values are integrated to obtain the cumulated distribution model,
which is then used to change the distribution from unifrom to
the actual distributionModel.
SourceFiles SourceFiles
distributionModel.C distributionModel.C
distributionModelNew.C distributionModelNew.C

82
src/lagrangian/distributionModels/exponential/exponential.H
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@ -28,7 +28,87 @@ Class
Foam::distributionModels::exponential Foam::distributionModels::exponential
Description Description
exponential distribution model Particle-size distribution model wherein random samples are drawn
from the doubly-truncated exponential probability density function:
\f[
f(x; \lambda, A, B) =
\lambda \frac{\exp(-\lambda (x - A))}{1 - \exp(-\lambda(B-A))}
\f]
where
\vartable
f(x; \lambda, A, B) | Exponential probability density function
\lambda | Rate parameter
x | Sample
A | Minimum of the distribution
B | Maximum of the distribution
\endvartable
Constraints:
- \f$ \infty > B > A > 0 \f$
- \f$ x \in [B,A] \f$
- \f$ \lambda > 0 \f$
Random samples are generated by the inverse transform sampling technique
by using the quantile function of the doubly-truncated exponential
probability density function:
\f[
x = - \frac{1}{\lambda} \ln (r)
\f]
with
\f[
r = q_{min} + u (q_{max} - q_{min})
\f]
\f[
q_{min} = \exp(-\lambda A)
\f]
\f[
q_{max} = \exp(-\lambda B)
\f]
where \f$ u \f$ is a sample drawn from the uniform probability
density function on the unit interval \f$ (0, 1) \f$.
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type exponential;
exponentialDistribution
{
lambda <lambdaValue>;
minValue <minValue>;
maxValue <maxValue>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: exponential | word | yes | -
exponentialDistribution | Distribution settings | dict | yes | -
lambda | Rate parameter | scalar | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
\endtable
SourceFiles SourceFiles
exponential.C exponential.C

35
src/lagrangian/distributionModels/fixedValue/fixedValue.H
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@ -6,6 +6,7 @@
\\/ M anipulation | \\/ M anipulation |
------------------------------------------------------------------------------- -------------------------------------------------------------------------------
Copyright (C) 2011-2016 OpenFOAM Foundation Copyright (C) 2011-2016 OpenFOAM Foundation
Copyright (C) 2021 OpenCFD Ltd.
------------------------------------------------------------------------------- -------------------------------------------------------------------------------
License License
This file is part of OpenFOAM. This file is part of OpenFOAM.
@ -27,7 +28,39 @@ Class
Foam::distributionModels::fixedValue Foam::distributionModels::fixedValue
Description Description
Returns a fixed value Particle-size distribution model wherein samples are given fixed values.
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type fixedValue;
fixedValueDistribution
{
value <value>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: fixedValue | word | yes | -
fixedValueDistribution | Distribution settings | dict | yes | -
value | Fixed value for size | scalar | yes | -
\endtable
SourceFiles SourceFiles
fixedValue.C fixedValue.C

61
src/lagrangian/distributionModels/general/general.H
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@ -28,7 +28,66 @@ Class
Foam::distributionModels::general Foam::distributionModels::general
Description Description
general distribution model 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 \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type general;
generalDistribution
{
cumulative false;
distribution
(
(<size1> <probability1>)
(<size2> <probability2>)
...
(<sizeN> <probabilityN>)
);
}
}
}
}
}
\endverbatim
where the entries mean:
\table
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
\endtable
Note
- An example for a pair within \c 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.
SourceFiles SourceFiles
general.C general.C

130
src/lagrangian/distributionModels/massRosinRammler/massRosinRammler.H
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@ -25,27 +25,141 @@ License
along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>. along with OpenFOAM. If not, see <http://www.gnu.org/licenses/>.
Class Class
Foam::massRosinRammler Foam::distributionModels::massRosinRammler
Description Description
Mass-based Rosin-Rammler distributionModel. Particle-size distribution model wherein random samples are drawn
from the two-parameter Rosin-Rammler (Weibull) probability density
function corrected to take into account varying number of particles
per parcels for fixed-mass parcels.
Corrected form of the Rosin-Rammler distribution taking into account the "There is a factor of \f$x^3\f$ difference between the size distribution
varying number of particels per parces for for fixed-mass parcels. This probability density function of individual particles and modeled parcels
distribution should be used when of particles, \f$ f_{parcels}(D) = x^3 f_{particles}(D) \f$, because the
submodel parameter, \f$ PPP \f$ (number of particles per parcel) is based
on a fixed mass per parcel which weights the droplet distribution by
a factor proportional to \f$ 1/x^3 \f$ (i.e. \f$ PPP = \dot{m}_{total}
\Delta_{t_{inj(per-parcel)}} / {m}_{particle} \f$)." (YHD:p. 1374-1375).
\c massRosinRammler should be preferred over \c RosinRammler
when \c parcelBasisType is based on \c mass:
\verbatim \verbatim
parcelBasisType mass; parcelBasisType mass;
\endverbatim \endverbatim
See equation 10 in reference: The doubly-truncated mass-corrected
Rosin-Rammler probability density function:
\f[
f(x; \lambda, n, A, B) =
x^3
\frac{n}{\lambda}
\left( \frac{x}{\lambda} \right)^{n-1}
\frac{
\exp\{ -(\frac{x}{\lambda})^n \}
}
{
\exp\{ -(\frac{A}{\lambda})^n \}
- \exp\{ -(\frac{B}{\lambda})^n \}
}
\f]
where
\vartable
f(x; \lambda, n, A, B) | Rosin-Rammler probability density function
\lambda | Scale parameter
n | Shape parameter
x | Sample
A | Minimum of the distribution
B | Maximum of the distribution
\endvartable
Constraints:
- \f$ \lambda > 0 \f$
- \f$ n > 0 \f$
Random samples are generated by the inverse transform sampling technique
by using the quantile function of the doubly-truncated two-parameter
mass-corrected Rosin-Rammler (Weibull) probability density function:
\f[
d = \lambda \, Q(a, u)^{1/n}
\f]
with
\f[
a = \frac{3}{n} + 1
\f]
where \f$ Q(z_1, z_2) \f$ is the inverse of regularised lower incomplete
gamma function, and \f$ u \f$ is a sample drawn from the uniform
probability density function on the interval \f$ (x, y) \f$:
\f[
x = \gamma \left( a, \frac{A}{\lambda}^n \right)
\f]
\f[
y = \gamma \left( a, \frac{B}{\lambda}^n \right)
\f]
where \f$ \gamma(z_1, z_2) \f$ is the lower incomplete gamma function.
Reference:
\verbatim \verbatim
Yoon, S. S., Hewson, J. C., DesJardin, P. E., Glaze, D. J., Standard model (tag:YHD):
Black, A. R., & Skaggs, R. R. (2004). 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 Numerical modeling and experimental measurements of a high speed
solid-cone water spray for use in fire suppression applications. solid-cone water spray for use in fire suppression applications.
International Journal of Multiphase Flow, 30(11), 1369-1388. International Journal of Multiphase Flow, 30(11), 1369-1388.
DOI:10.1016/j.ijmultiphaseflow.2004.07.006
\endverbatim \endverbatim
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
parcelBasisType mass;
...
sizeDistribution
{
type massRosinRammler;
massRosinRammlerDistribution
{
lambda <scaleParameterValue>;
n <shapeParameterValue>;
minValue <minValue>;
maxValue <maxValue>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: massRosinRammler | word | yes | -
massRosinRammlerDistribution | Distribution settings | dict | yes | -
lambda | Scale parameter | scalar | yes | -
n | Shape parameter | scalar | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
\endtable
Note
- In the current context, \c lambda (or \c d) is called "characteristic
droplet size", and \c n "empirical dimensionless constant to specify
the distribution width, sometimes referred to as the dispersion
coefficient." (YHD:p. 1374).
SourceFiles SourceFiles
massRosinRammler.C massRosinRammler.C

141
src/lagrangian/distributionModels/multiNormal/multiNormal.H
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@ -28,12 +28,149 @@ Class
Foam::distributionModels::multiNormal Foam::distributionModels::multiNormal
Description Description
A multiNormal distribution model Particle-size distribution model wherein random samples are drawn
from a mixture of a finite set of doubly-truncated univariate normal
probability density functions:
\f[
g (\mathbf{x}; \mathbf{\mu}, \mathbf{\sigma}, A, B) =
\sum_i w_i f(x_i; \mu_i, \sigma_i, A, B)
\f]
with for any distribution:
\f[
f(x; \mu, \sigma, A, B) =
\frac{1}{\sigma}
\frac{
\phi \left( \frac{x - \mu}{\sigma} \right)
}{
\Phi \left( \frac{B - \mu}{\sigma} \right)
- \Phi \left( \frac{A - \mu}{\sigma} \right)
}
\f]
where
\vartable
f(x; \mu, \sigma, A, B) | Doubly-truncated univariate normal distribution
\mu | Mean of the parent general normal distribution
\sigma | Standard deviation of the parent general normal distribution
\phi(\cdot) | General normal probability density function
\Phi(\cdot) | General normal cumulative distribution function
x | Sample
A | Minimum of the distribution (the same for each distribution)
B | Maximum of the distribution (the same for each distribution)
w_i | Weighting factor
\endvartable
Constraints:
- \f$ \infty > B > A > 0 \f$
- \f$ x \in [B,A] \f$
- \f$ \sigma^2 > 0 \f$
- \f$ w_i >= 0 \f$
Random samples are generated by a combination of the inverse transform
sampling technique and categorical sampling in three steps:
- Draw a sample from the uniform probability density function
on the unit interval \f$u = (0, 1)\f$
- Find the interval among normalised cumulative weight intervals
wherein \f$ u \f$ resides
- Draw a sample from the distribution corresponding to the interval by
using the quantile function of the doubly-truncated univariate normal
probability density function by the following expressions (similar to
\c distributionModels::normal):
\f[
x = \mu + \sigma \sqrt{2} \, {erf}^{-1} \left( 2 p - 1 \right)
\f]
with
\f[
p = u \,
\left(
\Phi\left(
\frac{B - \mu}{\sigma}
\right)
- \Phi\left(
\frac{A - \mu}{\sigma}
\right)
\right)
+ \Phi\left( \frac{A - \mu}{\sigma} \right)
\f]
\f[
\Phi(\xi) =
\frac{1}{2}
\left(
1 + {erf}\left(\frac{\xi - \mu}{\sigma \sqrt{2} }\right)
\right)
\f]
where \f$ u \f$ is another sample drawn from the uniform probability
density function on the unit interval \f$ (0, 1) \f$.
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim \verbatim
model = sum_i strength_i * exp(-0.5*((x - expectation_i)/variance_i)^2 ) subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type multiNormal;
multiNormalDistribution
{
minValue <min>;
maxValue <max>;
mu
(
<mean1>
<mean2>
...
);
sigma
(
<standard deviation1>
<standard deviation2>
...
);
weight
(
<weight1>
<weight2>
...
);
}
}
}
}
}
\endverbatim \endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: multiNormal | word | yes | -
multiNormalDistribution | Distribution settings | dict | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
mu | List of means of parent general normal <!--
--> distributions | scalarList | yes | -
sigma | List of standard deviations of parent <!--
--> general normal distributions | scalarList | yes | -
weight | List of weights of a given distribution in <!--
--> the distribution mixture | scalarList | yes | -
\endtable
Notes
- The sum of normal distributions (i.e. a mixture distribution) should not
be confused with the sum of normally-distributed random variables.
- \c minValue and \c maxValue are the same for all distributions
in the distribution mixture.
- \c weight should always be input in a non-decreasing (i.e. monotonic) order.
SourceFiles SourceFiles
multiNormal.C multiNormal.C

117
src/lagrangian/distributionModels/normal/normal.H
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@ -28,13 +28,124 @@ Class
Foam::distributionModels::normal Foam::distributionModels::normal
Description Description
A normal distribution model Particle-size distribution model wherein random samples are drawn
from the doubly-truncated univariate normal probability density function:
\f[
f(x; \mu, \sigma, A, B) =
\frac{1}{\sigma}
\frac{
\phi \left( \frac{x - \mu}{\sigma} \right)
}{
\Phi \left( \frac{B - \mu}{\sigma} \right)
- \Phi \left( \frac{A - \mu}{\sigma} \right)
}
\f]
where
\vartable
f(x; \mu, \sigma, A, B) | Doubly-truncated univariate normal distribution
\mu | Mean of the parent general normal distribution
\sigma | Standard deviation of the parent general normal distribution
\phi(\cdot) | General normal probability density function
\Phi(\cdot) | General normal cumulative distribution function
x | Sample
A | Minimum of the distribution
B | Maximum of the distribution
\endvartable
Constraints:
- \f$ \infty > B > A > 0 \f$
- \f$ x \in [B,A] \f$
- \f$ \sigma^2 > 0 \f$
Random samples are generated by the inverse transform sampling technique
by using the quantile function of the doubly-truncated univariate normal
probability density function:
\f[
x = \mu + \sigma \sqrt{2} \, {erf}^{-1} \left( 2 p - 1 \right)
\f]
with
\f[
p = u \,
\left(
\Phi\left(
\frac{B - \mu}{\sigma}
\right)
- \Phi\left(
\frac{A - \mu}{\sigma}
\right)
\right)
+ \Phi\left( \frac{A - \mu}{\sigma} \right)
\f]
\f[
\Phi(\xi) =
\frac{1}{2}
\left(
1 + {erf}\left(\frac{\xi - \mu}{\sigma \sqrt{2} }\right)
\right)
\f]
where \f$ u \f$ is sample drawn from the uniform probability
density function on the unit interval \f$ (0, 1) \f$.
Reference:
\verbatim \verbatim
model = strength * exp(-0.5*((x - expectation)/variance)^2 ) Governing expressions (tag:B):
Burkardt, J. (2014).
The truncated normal distribution.
Department of Scientific Computing Website,
Florida State University, 1-35.
URL:people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
(Retrieved on: 19 Feb 2021)
\endverbatim \endverbatim
strength only has meaning if there's more than one distribution model Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type normal;
normalDistribution
{
mu <mean>;
sigma <stardard deviation>;
minValue <min>;
maxValue <max>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: normal | word | yes | -
normalDistribution | Distribution settings | dict | yes | -
mu | Mean of the parent general normal distribution <!--
--> | scalar | yes | -
sigma | Standard deviation of the parent general normal <!--
--> distribution | scalar | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
\endtable
Note
- The mean and standard deviation of the parent general normal probability
distribution function (i.e. input) are not the same with those of the
truncated probability distribution function.
SourceFiles SourceFiles
normal.C normal.C

64
src/lagrangian/distributionModels/uniform/uniform.H
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@ -28,7 +28,69 @@ Class
Foam::distributionModels::uniform Foam::distributionModels::uniform
Description Description
Uniform/equally-weighted distribution model Particle-size distribution model wherein random samples are drawn
from the doubly-truncated uniform probability density function:
\f[
f(x; A, B) = \frac{1}{B - A}
\f]
where
\vartable
f(x; A, B) | Doubly-truncated uniform distribution
x | Sample
A | Minimum of the distribution
B | Maximum of the distribution
\endvartable
Constraints:
- \f$ \infty > B > A > 0 \f$
- \f$ x \in [B,A] \f$
- \f$ \sigma^2 > 0 \f$
Random samples are generated by the inverse transform sampling technique
by using the quantile function of the uniform probability density function:
\f[
x = u \, (B - A) + A
\f]
where \f$ u \f$ is sample drawn from the uniform probability
density function on the unit interval \f$ (0, 1) \f$.
Usage
Minimal example by using \c constant/\<CloudProperties\>:
\verbatim
subModels
{
injectionModels
{
<name>
{
...
sizeDistribution
{
type uniform;
uniformDistribution
{
minValue <min>;
maxValue <max>;
}
}
}
}
}
\endverbatim
where the entries mean:
\table
Property | Description | Type | Reqd | Deflt
type | Type name: uniform | word | yes | -
uniformDistribution | Distribution settings | dict | yes | -
minValue | Minimum of the distribution | scalar | yes | -
maxValue | Maximum of the distribution | scalar | yes | -
\endtable
SourceFiles SourceFiles
uniform.C uniform.C