.. _ReferenceParticleDistributions:
.. include:: ../definitions.rst
Particle distributions
======================
MFIX-Exa can generate ``constant`` (monodisperse or single-sized) particles or
polydisperse particles that follow a ``normal``, ``log-normal``, or ``uniform``
distribution. A user-defined ``custom`` distribution can be specified by providing
discrete probabilities via a text input file.
In addition to the distribution type, the `distribution weighting`
must be specified; MFIX-Exa supports two distribution weightings:
Number-weighted diameter distribution
The number-weighted diameter density function, :math:`f_X^N(x)\,dx`, can be interpreted
as defining the *number of particles* with diameters between :math:`x` and :math:`x+dx`,
divided by the total number of particles.
Volume-weighted diameter distribution
The volume-weighted diameter density function, :math:`f_X^V(x)\,dx`, similarly can be interpreted
as defining the *volume of particles* with diameters between :math:`x` and :math:`x+dx`,
divided by the total volume of particles.
.. warning::
Number-weighted and volume-weighted distributions are not interchangeable. Failing
to specify the correct distribution weighting can result in unexpected behavior.
The code uses both distribution weightings, computing (or approximating)
whichever distribution weighting that was not provided. For example, if a
number-weighted normal distribution is provided, the corresponding volume-weighted
normal distribution is computed. The volume-weighted distribution is used to calculate
the number of particles (or parcels) to generate in an initial condition region.
The number-weighted distribution is *sampled* when DEM particles are created, while
the volume-weighted distribution is *sampled* when creating PIC parcels. Sampling the
volume-weighted distribution for PIC parcels allows for fewer parcels to represent very
small particles while using more parcels to represent large particles in the distribution.
The statistical weight of the parcels is adjusted so that the number-distribution is
represented accurately.
The following sections overview the distributions supported by MFIX-Exa and the input
parameters that control their behavior.
``constant`` distribution
^^^^^^^^^^^^^^^^^^^^^^^^^
The ``constant`` distribution defines a monodisperse (single-valued) distribution and uses the following inputs:
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| | Description | Type | Default |
+=====================+=======================================================================+=============+===========+
| constant | size of the mono-disperse distribution | Real | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
In the following example, a cuboid is filled with 1 mm diameter monodisperse particles with 2500 kg\ |cdot|\ m\ :sup:`-3`
density. The initial condition region volume is 0.001 m\ :sup:`3`, and it has a solids volume fraction of 0.05 (5% solids).
.. code-block:: bash
:caption: Snippet of inputs defining a ``constant`` distribution. **This is not a complete input file.**
# Regions for defining ICs and BCs
# -----------------------------------------------------------------------
mfix.regions = full-domain
regions.full-domain.lo = 0.0 0.0 0.0
regions.full-domain.hi = 0.1 0.1 0.1
# Initial Conditions
# -----------------------------------------------------------------------
ic.regions = full-domain
mfix.particle_init_type = Auto
# Full domain
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>
ic.full-domain.fluid.volfrac = 0.95
ic.full-domain.fluid.density = 1.0
ic.full-domain.fluid.pressure = 0.0
ic.full-domain.fluid.velocity = 0.0 0.0 0.0
ic.full-domain.solids = solid0
ic.full-domain.packing = random
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = constant
ic.full-domain.solid0.diameter.constant = 1000.e-6 # (m)
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
An MFIX-Exa DEM simulation generates approximately 95,500 particles, while a PIC simulation with
``pic.close_pack = 0.64``, ``pic.parcels_per_cell_at_pack = 64``, and an Eulerian mesh spacing
:math:`\Delta x` of 3.125 mm generates approximately 164,000 parcels.
``normal`` distribution
^^^^^^^^^^^^^^^^^^^^^^^
The ``normal`` or Gaussian distribution is given by
.. math::
:label: eq_normal_dist
f_X(x) = \frac{d}{dx}F_X(x) = \frac{1}{\sigma \sqrt{2\pi}} \exp{\left(- \frac{(x - \mu)^2 }{ 2 \sigma^2 }\right)}
where the parameter :math:`\mu` is the distribution mean, and :math:`\sigma` is the standard deviation. The inputs
used to specify the distribution are provided in the following table.
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| | Description | Type | Default |
+=====================+=======================================================================+=============+===========+
| type | distribution weighting: ``number-weighted`` or ``volume-weighted`` | string | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| mean | mean of the normal random variable, :math:`X` | Real | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| std | standard deviation of the normal random variable, :math:`X` | Real | N/A |
| | | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| min | Minimum particle diameter. Drawn samples below ``min`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| max | Maximum particle diameter. Drawn samples above ``max`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| bins | Number of bins used when discretizing the distribution to approximate | int | 64 |
| | the number of particles within an initial condition region. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
In the following example, a cuboid with volume 0.001 m\ :sup:`3` is filled with a number-weighted normal distribution
with 1 mm mean diameter and 0.25 mm standard deviation. The minimum and maximum particle diameters are 0.25 mm
and 1.75 mm, respectively.
.. code-block:: bash
:caption: Snippet of inputs defining a number-weighted ``normal`` distribution. **This is not a complete input file.**
# Regions for defining ICs and BCs
# -----------------------------------------------------------------------
mfix.regions = full-domain
regions.full-domain.lo = 0.0 0.0 0.0
regions.full-domain.hi = 0.1 0.1 0.1
# Initial Conditions
# -----------------------------------------------------------------------
ic.regions = full-domain
mfix.particle_init_type = Auto
# Full domain
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>
ic.full-domain.fluid.volfrac = 0.95
ic.full-domain.fluid.density = 1.0
ic.full-domain.fluid.pressure = 0.0
ic.full-domain.fluid.velocity = 0.0 0.0 0.0
ic.full-domain.solids = solid0
ic.full-domain.packing = random
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = normal
ic.full-domain.solid0.diameter.type = number-weighted
ic.full-domain.solid0.diameter.mean = 1000.e-6 # (m)
ic.full-domain.solid0.diameter.std = 250.e-6 # (m)
ic.full-domain.solid0.diameter.min = 250.e-6 # (m)
ic.full-domain.solid0.diameter.max = 1750.e-6 # (m)
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
An MFIX-Exa DEM simulation with these settings generates approximately 81,700 particles. The particle size
distribution is shown in :numref:`fig_normal_dem_FNw_MFIX_Np` (open circles) in addition to the number-weighted (orange)
and volume-weighted (blue) density functions. As noted by :cite:t:`weiner11`, the volume-weighted
probability density function (PDF) is shifted to the right of the number-weighted PDF.
.. _fig_normal_dem_FNw_MFIX_Np:
.. figure:: ./images/normal_dem_FNw.png
:width: 95%
:align: center
:alt: DEM FN and Np
MFIX-Exa DEM particle size distribution (open circles) generated from example inputs. The number-weighted
density function (orange) with 1 mm mean and 0.25 mm standard deviation, and volume-weighted
density function with ~1.17 mm mean and ~0.23 mm standard deviation are also plotted.
An MFIX-Exa PIC simulation with the same settings generates approximately 82,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 32``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm. The parcel size distribution
shown in :numref:`fig_normal_pic_FNw_MFIX_Np` (open triangles) follows the volume-weighted density function.
Small closed circles illustrate the effective particle size distribution, computed by multiplying each observation
by the parcel statistical weight. For example, if a parcel has statistical weight of 10, then 10 observations are
recorded in the bin capturing the parcel's diameter.
.. _fig_normal_pic_FNw_MFIX_Np:
.. figure:: ./images/normal_pic_FNw.png
:width: 95%
:align: center
:alt: PIC FN and Np
MFIX-Exa PIC parcel size distribution (open triangles) generated from example inputs. The number-weighted
density function (orange) with 1 mm mean and 0.25 mm standard deviation, and volume-weighted density function
(blue) with ~1.17 mm mean and ~0.23 mm standard deviation are also plotted.
The following sections outline how MFIX-Exa computes volume-weighted normal density function parameters from
number-weighted normal density function parameters, and conversely, how number-weighted normal density function
parameters are computed from volume-weighted density function parameters.
.. rubric:: Computing volume-weighted normal density function parameters, :math:`\mu_V` and :math:`\sigma_V`, from
number-weighted normal density function parameters, :math:`\mu_N` and :math:`\sigma_N`
The volume-weighted density function, :math:`f_X^V(x)`, computed from the normally distributed number-weighted density
function, :math:`f_X^N(x)`, is normally distributed with mean, :math:`\mu_V`, and variance, :math:`\sigma_V^2`. An
expression for the volume-weighted density function is obtained by multiplying :eq:`eq_normal_dist` by :math:`x^3`
and substituting in the mean and standard deviation of the number-weighted distribution.
.. math::
\tilde{f}_X^V(x) = \frac{x^3}{\sigma_N \sqrt{2\pi}} \exp{\left(- \frac{(x - \mu_N)^2 }{ 2 \sigma_N^2 }\right)}
The mean of this distribution is computed as
.. math::
:label: eq_normal_dist_FN_to_FV_mean
\begin{align}
\mu_V =& \frac{ \int_{-\infty}^{\infty} x \tilde{f}_X^V(x) dx }{\int_{-\infty}^{\infty} \tilde{f}_X^V(x) dx } \\[10pt]
=& \frac{3\sigma_N^4 + 6\sigma_N^2\mu_N^2 + \mu_N^4}{3\sigma_N^2 \mu_N + \mu_N^3}
\end{align}
and the variance is given by
.. math::
:label: eq_normal_dist_FN_to_FV_variance
\begin{align}
\sigma^2_V =& \frac{ \int_{-\infty}^{\infty} (x - \mu_V)^2 \tilde{f}_X^V(x) dx }{\int_{-\infty}^{\infty} \tilde{f}_X^V(x) dx } \\[10pt]
=& \frac{3\sigma_N^4 (3\mu_N + 2b) + \sigma^2(\mu_N^3 + 6\mu_N^2 b + 3\mu_Nb^2)+\mu_N^3 b^2}{3\sigma_N^2 \mu_N + \mu_N^3}
\end{align}
where :math:`b = (\mu_N - \mu_V)`. Because the volume-weighted distribution is normally distributed, the computed
mean and variance can be substituted directly into :eq:`eq_normal_dist`.
.. math::
f_X^V(x) = \frac{1}{\sigma_V \sqrt{2\pi}} \exp{\left(- \frac{(x - \mu_V)^2 }{ 2 \sigma_V^2 }\right)}
.. rubric:: Computing number-weighted normal density function parameters, :math:`\mu_N` and :math:`\sigma_N`, from
volume-weighted normal density function parameters, :math:`\mu_V` and :math:`\sigma_V`
The number-weighted density function mean and standard deviation, :math:`\mu_N` and :math:`\sigma_N`, are computed from the volume-weighted
mean and standard deviation, :math:`\mu_V` and :math:`\sigma_V`, by solving the nonlinear system of equations constructed from
:eq:`eq_normal_dist_FN_to_FV_mean` and :eq:`eq_normal_dist_FN_to_FV_variance` for :math:`\mu_N` and :math:`\sigma_N`.
.. math::
\begin{align}
f_1(\mu_N,\sigma_N) &= \left( 3\sigma_N^4 + 6\sigma_N^2\mu_N^2 + \mu_N^4 \right) - \mu_V \left( 3\sigma_N^2 \mu_N + \mu_N^3 \right) = 0 \\
f_2(\mu_N,\sigma_N) &= \left(3\sigma_N^4 (3\mu_N + 2b) + \sigma^2(\mu_N^3 + 6\mu_N^2 b + 3\mu_Nb^2)+\mu_N^3 b^2\right) - \sigma^2_V \left( 3\sigma_N^2 \mu_N + \mu_N^3 \right) = 0
\end{align}
This system is solved using the homotopy method outlined in :cite:t:`Burden10`. To ensure rapid convergence, initial guesses for
:math:`\mu_N` and :math:`\sigma_N` are calculated from the density function created by dividing :eq:`eq_normal_dist` by :math:`x^3`
and substituting in the mean and standard deviation of the volume-weighted distribution, :math:`\mu_V` and :math:`\sigma_V`.
.. math::
:label: eq_normal_dist_FV_to_FN_initial_expression
\tilde{f}_X^N(x) = \frac{1}{x^3\sigma_V \sqrt{2\pi}} \exp{\left(- \frac{(x - \mu_V)^2 }{ 2 \sigma_V^2 }\right)}
:math:`\tilde{f}_X^N(x)`, shown in :numref:`fig_normal-dist-pdf-approx_FN`, is not the number-weighted density function,
but an approximation to it. The mean and variance are calculated by numerically integrating :math:`\tilde{f}_X^N(x)`
over the interval :math:`x \in \left[ x_\mathrm{min}, x_\mathrm{max} \right]` where :math:`x_{min}` and :math:`x_{mid}`
are found by setting the derivative of :eq:`eq_normal_dist_FV_to_FN_initial_expression` to zero and computing the roots.
:math:`x_\mathrm{max}` is chosen to mirror :math:`x_\mathrm{min}` across the midpoint.
.. math::
:label: eq_normal_dist_FV_to_FN_discrete_values
\begin{align}
x_{\mathrm{min}} \; =& \; \left( \mu_V - \sqrt{\mu_V^2 -12\sigma_V^2}\right)/2 \\
x_{\mathrm{mid}} \; =& \; \left(\mu_V + \sqrt{\mu_V^2 -12\sigma_V^2}\right)/2 \\
x_{\mathrm{max}} \; =& \; 2x_{\mathrm{mid}} - x_{\mathrm{min}}
\end{align}
.. _fig_normal-dist-pdf-approx_FN:
.. figure:: ./images/normal-dist-pdf-approx_FN.png
:width: 95%
:align: center
:alt: approximate number-weighted pdf
The number-weighted normal distribution function approximated by dividing the volume-weighted
distribution function by x\ :sup:`3`. The approximate mean and variance are computed by numerically
integrating this function from x\ :sub:`min` to x\ :sub:`max`.
.. rubric:: Sampling a normal distribution
MFIX-Exa samples the normal distribution with mean ``m_mean`` and standard deviation ``m_stddev`` to return an
distribution observation.
.. code::
amrex::Real observation;
do { observation = amrex::RandomNormal(m_mean, m_stddev, a_engine); }
while (!(m_min <= observation && observation <= m_max));
return observation;
The function ``amrex::RandomNormal`` returns one pseudo-random real number (double). The sample is discarded and the distribution
is sampled again until the value satisfies :math:`\mathrm{xmin} \le x` and :math:`x \le \mathrm{xmax}`.
``log-normal`` distribution
^^^^^^^^^^^^^^^^^^^^^^^^^^^
The ``log-normal`` distribution is given by
.. math::
f_X(x) = \frac{d}{dx}F_X(x) = \frac{1}{x \sigma \sqrt{2\pi}} \exp{\left(- \frac{(\mathrm{ln}(x) - \mu)^2 }{ 2 \sigma^2 }\right)}
where :math:`\mu` and :math:`\sigma` are parameters defining the distribution.
.. note::
:math:`\mu` and :math:`\sigma` **are not** the mean and standard deviation of :math:`x`. They are the mean and
standard deviation of :math:`\mathrm{ln}(x)`. The mean (expectation), :math:`E(x)`, and variance, :math:`V(x)`,
of the log-normal random variable are given by
.. math::
E(x) = \exp{\left(\mu + \sigma^s/2\right)}
and
.. math::
V(x) = \exp{\left(2\mu + 2\sigma^2\right)} - \exp{\left(2\mu + \sigma^2\right)}
respectively. See :cite:t:`william2014` for an overview of log-normal distributions.
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| | Description | Type | Default |
+=====================+=======================================================================+=============+===========+
| type | distribution weighting: ``number-weighted`` or ``volume-weighted`` | string | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| mean | mean of of the normal random variable, | Real | N/A |
| | :math:`\mathrm{ln}(X)` | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| stddev | standard deviation of the normal random variable, | Real | N/A |
| | :math:`\mathrm{ln}(X)` | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| min | Minimum particle diameter. Drawn samples below ``min`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| max | Maximum particle diameter. Drawn samples above ``max`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| bins | Number of bins used when discretizing the distribution to approximate | int | 64 |
| | the number of particles within an initial condition region. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
In the following example, a cuboid with volume 0.001 m\ :sup:`3` is filled with a volume-weighted log-normal distribution
with the listed parameters. The minimum and maximum particle diameters are 0.23 mm
and 3.00 mm, respectively.
.. code-block:: bash
:caption: Snippet of inputs defining a number-weighted ``log-normal`` distribution. **This is not a complete input file.**
# Regions for defining ICs and BCs
# -----------------------------------------------------------------------
mfix.regions = full-domain
regions.full-domain.lo = 0.0 0.0 0.0
regions.full-domain.hi = 0.1 0.1 0.1
# Initial Conditions
# -----------------------------------------------------------------------
ic.regions = full-domain
mfix.particle_init_type = Auto
# Full domain
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>
ic.full-domain.fluid.volfrac = 0.75
ic.full-domain.fluid.density = 1.0
ic.full-domain.fluid.pressure = 0.0
ic.full-domain.fluid.velocity = 0.0 0.0 0.0
ic.full-domain.solids = solid0
ic.full-domain.packing = random
ic.full-domain.solid0.volfrac = 0.25
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = log-normal
ic.full-domain.solid0.diameter.type = volume-weighted
ic.full-domain.solid0.diameter.mean = -3.662955279
ic.full-domain.solid0.diameter.std = 1.04
ic.full-domain.solid0.diameter.min = 30.e-6 # (m)
ic.full-domain.solid0.diameter.max = 3000.e-6 # (m)
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
An MFIX-Exa DEM simulation with these settings generates approximately 162,000 particles. The particle size
distribution (open circles), and number-weighted (orange), and volume-weighted (blue) density functions are
shown in :numref:`fig_log_normal_dem_FVw_MFIX_Np`.
.. _fig_log_normal_dem_FVw_MFIX_Np:
.. figure:: ./images/log-normal_dem_FVw.png
:width: 95%
:align: center
:alt: DEM FN and Np
MFIX-Exa DEM particle size distribution (open circles) and number-weighted (orange) and volume-weighted (blue)
density functions.
An MFIX-Exa PIC simulation with the same settings generates approximately 410,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 32``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm.
:numref:`fig_log_normal_pic_FVw_MFIX_Np` shows the parcel size distribution (open triangles) and number-weighted (orange)
and volume-weighted (blue) density functions. Again, small closed circles indicate the effective particle distribution
captured by the PIC parcels.
.. _fig_log_normal_pic_FVw_MFIX_Np:
.. figure:: ./images/log-normal_pic_FVw.png
:width: 95%
:align: center
:alt: PIC FN and Np
MFIX-Exa PIC parcel size distribution (open triangles), number-weighted density function (orange),
and volume-weighted density function (blue). Small closed circles indicated the particle size
distribution modeled by the parcels.
.. rubric:: Converting between number-weighted and volume-weighted parameters for log-normal distributions
As reported by :cite:t:`elhilo12,elhilo12a`, number-weighted and volume-weighted log-normal distributions
use the same :math:`\sigma` parameter
.. math::
\sigma = \sigma_{N} = \sigma_{V}
while the parameter :math:`\mu` is computed directly from the other distribution.
.. math::
\begin{align}
\mu_V =& \mu_N + 3.0\sigma^2 \\[10pt]
\mu_N =& \mu_V - 3.0\sigma^2
\end{align}
.. rubric:: Sampling a log-normal distribution
Rather than sample the log-normal distribution, MFIX-Exa samples the normal distribution with
mean ``m_mean`` and standard deviation ``m_stddev``.
.. code::
amrex::Real observation;
do { observation = amrex::RandomNormal(m_mean, m_stddev, a_engine); }
while (!(m_log_min <= observation && observation <= m_log_max));
return std::exp(observation);
The function ``amrex::RandomNormal`` returns one pseudo-random real number (double). The sample is discarded and the distribution
is sampled again until the value satisfies :math:`\log \mathrm{xmin} \le \log x` and :math:`\log x \le \log \mathrm{xmax}`.
The log-normal variable is returned by applying the exponential function, :math:`x = \exp ( \log x )`.
``uniform`` distribution
^^^^^^^^^^^^^^^^^^^^^^^^
The ``uniform`` distribution is given by
.. math::
:label: eq_uniform_dist
f_X(x) = \frac{d}{dx}F_X(x) =
\begin{cases}
\left(x_\mathrm{max} - x_\mathrm{min}\right)^{-1}, & \text{for} \; x \in \left[ x_\mathrm{min}, x_\mathrm{max} \right] \\
0, & \text{otherwise}
\end{cases}
where :math:`x_\mathrm{min}` and :math:`x_\mathrm{max}` are the minimum and maximum particle diameters. The inputs
used to specify the distribution are provided in the following table.
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| | Description | Type | Default |
+=====================+=======================================================================+=============+===========+
| type | distribution weighting: ``number-weighted`` or ``volume-weighted`` | string | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| min | Minimum particle diameter. Drawn samples below ``min`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| max | Maximum particle diameter. Drawn samples above ``max`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| bins | Number of bins used when discretizing the distribution to approximate | int | 64 |
| | the number of particles within an initial condition region. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
In the following example, a cuboid with volume 0.001 m\ :sup:`3` is filled with a number-weighted uniform distribution.
The minimum and maximum particle diameters are 0.25 mm and 1.75 mm, respectively.
.. code-block:: bash
:caption: Snippet of inputs defining a number-weighted ``uniform`` distribution. **This is not a complete input file.**
# Regions for defining ICs and BCs
# -----------------------------------------------------------------------
mfix.regions = full-domain
regions.full-domain.lo = 0.0 0.0 0.0
regions.full-domain.hi = 0.1 0.1 0.1
# Initial Conditions
# -----------------------------------------------------------------------
ic.regions = full-domain
mfix.particle_init_type = Auto
# Full domain
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>
ic.full-domain.fluid.volfrac = 0.95
ic.full-domain.fluid.density = 1.0
ic.full-domain.fluid.pressure = 0.0
ic.full-domain.fluid.velocity = 0.0 0.0 0.0
ic.full-domain.solids = solid0
ic.full-domain.packing = random
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = uniform
ic.full-domain.solid0.diameter.type = number-weighted
ic.full-domain.solid0.diameter.min = 250.e-6 # (m)
ic.full-domain.solid0.diameter.max = 1750.e-6 # (m)
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
An MFIX-Exa DEM simulation with these settings generates approximately 110,000 particles. The particle size
distribution (open circles), and number-weighted (orange), and volume-weighted (blue) density functions are
shown in :numref:`fig_uniform_dem_FNw_MFIX_Np`.
.. _fig_uniform_dem_FNw_MFIX_Np:
.. figure:: ./images/uniform_dem_FNw.png
:width: 95%
:align: center
:alt: DEM FN and Np
MFIX-Exa DEM uniform particle size distribution (open circles) and number-weighted (orange)
and volume-weighted (blue) uniform density functions.
An MFIX-Exa PIC simulation with the same settings generates approximately 410,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 32``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm.
:numref:`fig_uniform_pic_FNw_MFIX_Np` shows the parcel size distribution (open triangles) and number-weighted (orange)
and volume-weighted (blue) density functions. Again, small closed circles indicate the effective particle distribution
captured by the PIC parcels.
.. _fig_uniform_pic_FNw_MFIX_Np:
.. figure:: ./images/uniform_pic_FNw.png
:width: 95%
:align: center
:alt: PIC FN and Np
MFIX-Exa PIC uniform parcel size distribution (open triangles), number-weighted (orange) and
volume-weighted (blue) uniform density functions. Small closed circles indicated the particle size
distribution modeled by the parcels.
.. rubric:: Sampling a uniform distribution
DEM simulations where a number-weighted distribution is specified and
PIC simulations where a volume-weighted distribution is specified compute a uniformly
distributed variable :math:`rand \in \left( 0, 1 \right)`, then scale and translate the
result such that :math:`x \in \left[ x_\mathrm{min}, x_\mathrm{max} \right)`.
.. code::
amrex::Real rand = amrex::Random(a_engine);
return m_min + (m_max-m_min)*rand;
The function ``amrex::Random`` returns one pseudo-random real number (double) from a
uniform distribution between 0.0 and 1.0 (0.0 included, 1.0 excluded).
Inverse sampling is used when a volume-weighted distribution is specified for a DEM simulation and
when a number-weighted distribution is specified for a PIC simulation. This approach generates
random variates from the continuous distribution function :math:`F` using the inverse function :math:`F^{-1}`.
If :math:`\mathcal{U}` is a uniform random variable on the interval :math:`[0,1]` and
:math:`F` is a continuous cumulative distribution function on :math:`[a,b]` with inverse
.. math::
F^{-1}(u) = \inf \left\{ x: F(x) = u \right\}
then :math:`F^{-1}(\mathcal{U})` has cumulative distribution function :math:`F` (adapted from :cite:t:`devroye86`).
The number-weighted density function, :math:`f_X^N(x)`, is computed from a specified volume-weighted density function
by dividing :eq:`eq_uniform_dist` by :math:`x^3`. The brace notation indicating that the density
function is zero for :math:`x \notin \left[ x_\mathrm{min}, x_\mathrm{max} \right]` is dropped for conciseness.
.. math::
f_X^N(x) = \frac{1}{x^3 (x_{\mathrm{min}} - x_{\mathrm{max}})}
The cumulative distribution function and its inverse are
.. math::
\mathrm{Pr}\left(X \le x\right) = F_X^N(x)
= \frac{ \int_{a}^{x} f_X^N(y) dy }{\int_{a}^{b} f_X^N(y) dy }
= \frac{ b^2(x^2-a^2)}{x^2(b^2 - a^2)}
and
.. math::
{F_X^N}^{-1}(u) = \frac{ab}{\sqrt{b^2 - u(b^2 -a^2)}}
where :math:`a = x_{\mathrm{min}}` and :math:`b = x_{\mathrm{max}}`. To sample :math:`f_X^V(x)`, a uniformly distributed
variable :math:`u \in \mathcal{U}(0,1)` is generated and substituted into the inverse cumulative distribution function.
.. code::
amrex::Real rand = amrex::Random(a_engine);
amrex::Real const b(m_max);
amrex::Real const a(m_min);
amrex::Real const a2(a*a), b2(b*b);
return (a*b)/std::sqrt(b2 - rand*(b2-a2));
Similarly, a volume-weighted density function, :math:`f_X^V(x)`, is computed from a number-weighted distribution
by multiplying :eq:`eq_uniform_dist` by :math:`x^3`. Again, the brace notation indicating that the density
function is zero for :math:`x \notin \left[ x_\mathrm{min}, x_\mathrm{max} \right]` is dropped for conciseness.
.. math::
f_X^V(x) = \frac{x^3}{(x_{\mathrm{min}} - x_{\mathrm{max}})}
The cumulative distribution function and its inverse are
.. math::
\mathrm{Pr}\left(X \le x\right) = F_X^V(x)
= \frac{ \int_{a}^{x} f_X^V(y) dy }{\int_{a}^{b} f_X^V(y) dy }
= \frac{ x^4 - a^4 }{ b^4 - a^4 }
and
.. math::
{F_X^V}^{-1}(u) = \left(a^4 + u(b^4 - a^4)\right)^{1/4}
where :math:`a = x_{\mathrm{min}}` and :math:`b = x_{\mathrm{max}}`. To sample :math:`f_X^V(x)`, a uniformly distributed
variable :math:`u \in \mathcal{U}(0,1)` is generated and substituted into the inverse cumulative distribution function.
.. code::
amrex::Real rand = amrex::Random(a_engine);
amrex::Real const b(m_max);
amrex::Real const a(m_min);
amrex::Real const a4(a*a*a*a), b4(b*b*b*b);
return std::pow(a4 + rand*(b4-a4),0.25);
``custom`` distribution
^^^^^^^^^^^^^^^^^^^^^^^
A user-defined ``custom`` distribution can be specified by providing discrete probabilities via a text file.
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| | Description | Type | Default |
+=====================+=======================================================================+=============+===========+
| custom | File name of user-defined distribution | string | N/A |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| min | Minimum particle diameter. Drawn samples below ``min`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| max | Maximum particle diameter. Drawn samples above ``max`` are discarded | Real | N/A |
| | and a new sample is drawn. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
| interpolate | Enable linear interpolation between discrete bins. This option is | bool | false |
| | only available when the initial distribution probability is zero. | | |
+---------------------+-----------------------------------------------------------------------+-------------+-----------+
The distribution input file contains four components:
+ line 1: number of distribution entries (integer)
+ line 2: specifies the distribution as ``CDF`` or ``PDF`` (string)
+ line 3: comment or blank line (unused by solver)
+ remaining lines define the bin and probability (Real Real)
The following sections provide a few examples of custom distribution configurations.
.. rubric:: Bidisperse mixutre without interpoaltion
In the following example, a cuboid with volume 0.001 m\ :sup:`3` is filled with a number-weighted distribution. The
distribution is a 50/50 mixture of two particle sizes.
.. code-block:: bash
:caption: Snippet of inputs defining a number-weighted ``custom`` distribution. **This is not a complete input file.**
# Regions for defining ICs and BCs
# -----------------------------------------------------------------------
mfix.regions = full-domain
regions.full-domain.lo = 0.0 0.0 0.0
regions.full-domain.hi = 0.1 0.1 0.1
# Initial Conditions
# -----------------------------------------------------------------------
ic.regions = full-domain
mfix.particle_init_type = Auto
# Full domain
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~>
ic.full-domain.fluid.volfrac = 0.95
ic.full-domain.fluid.density = 1.0
ic.full-domain.fluid.pressure = 0.0
ic.full-domain.fluid.velocity = 0.0 0.0 0.0
ic.full-domain.solids = solid0
ic.full-domain.packing = random
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = custom
ic.full-domain.solid0.diameter.custom = bidisperse-pdf.dist
ic.full-domain.solid0.diameter.type = number-weighted
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
ic.full-domain.solid0.density = constant
ic.full-domain.solid0.density.constant = 2500.0 # (kg/m^3)
.. code-block:: bash
:caption: Custom distribution defining a 50/50 mixture of two particle sizes.
:linenos:
2
PDF
# bidisperse mixture
500.e-6 0.5
1000.e-6 0.5
An MFIX-Exa DEM simulation with these settings generates approximately 180,000 particles with half
of the particles having diameter 0.5 mm and half have diameter 1.0 mm.
An MFIX-Exa PIC simulation with the same settings generates approximately 82,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 32``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm. Approximately
10% of the parcels have diameter 0.5 mm and 90% have diameter 1.0 mm.
.. rubric:: Bimodal mixutre
The following example uses the same layout as the previous example, however the custom distribution
is of a CDF describing a bimodal distribution created by combining two normal distributions.
.. code-block:: bash
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = custom
ic.full-domain.solid0.diameter.custom = bimodal-cdf.dist
ic.full-domain.solid0.diameter.type = number-weighted
.. code-block:: bash
:caption: Custom bimodal distribution created by combining two normal distributions
:linenos:
25
CDF
# bimodal distribution created from two normal distributions
0.000144 0.0000
0.000240 0.0063
0.000336 0.0360
0.000384 0.0818
0.000416 0.1356
0.000432 0.1919
0.000456 0.2495
0.000480 0.3053
0.000496 0.3587
0.000528 0.4055
0.000568 0.4447
0.000584 0.4822
0.000600 0.5191
0.000616 0.5566
0.000632 0.5958
0.000664 0.6408
0.000704 0.6942
0.000720 0.7501
0.000752 0.8075
0.000768 0.8638
0.000784 0.9177
0.000816 0.9635
0.000864 0.9932
0.000960 0.9995
0.001056 1.0000
An MFIX-Exa DEM simulation with these settings generates approximately 2,285,000 particles. The particle size
distribution (open circles), and number-weighted (orange), and volume-weighted (blue) density functions are
shown in :numref:`fig_custom_bimodal_dem_MFIX`.
.. _fig_custom_bimodal_dem_MFIX:
.. figure:: ./images/custom_bimodal_dem_FNw.png
:width: 95%
:align: center
:alt: DEM custom bimodal distribution
MFIX-Exa DEM custom bimodal particle size distribution (open circles) and number-weighted (orange)
and volume-weighted (blue) density functions.
An MFIX-Exa PIC simulation with the same settings generates approximately 82,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 32``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm. The parcel size distribution
shown in :numref:`fig_custom_bimodal_pic_MFIX` (open triangles) follows the volume-weighted density function.
Small closed circles illustrate the effective particle size distribution, computed by multiplying each observation
by the parcel statistical weight.
.. _fig_custom_bimodal_pic_MFIX:
.. figure:: ./images/custom_bimodal_pic_FNw.png
:width: 95%
:align: center
:alt: PIC custom bimodal distribution
MFIX-Exa PIC custom bimodal parcel size distribution (open triangles), number-weighted (orange) and
volume-weighted (blue) density functions. Small closed circles indicated the particle size
distribution modeled by the parcels.
.. rubric:: Measured size distribution
The final example uses the same layout as the previous examples, however the custom distribution
is a CDF describing an experimentally measured size distribution and the distribution type is ``volume-weighted``.
.. code-block:: bash
ic.full-domain.solid0.volfrac = 0.05
ic.full-domain.solid0.velocity = 0.00 0.00 0.00
ic.full-domain.solid0.diameter = custom
ic.full-domain.solid0.diameter.custom = qicpic-cdf.dist
ic.full-domain.solid0.diameter.type = volume-weighted
.. code-block:: bash
:caption: Measured custom distribution
:linenos:
32
CDF
0.00025 0.0013499
0.000298387 0.00250452
0.000346774 0.00448884
0.000395161 0.00777403
0.000443548 0.0130136
0.000491935 0.0210638
0.000540323 0.0329789
0.00058871 0.0499683
0.000637097 0.0733046
0.000685484 0.104184
0.000733871 0.143547
0.000782258 0.191886
0.000830645 0.24907
0.000879032 0.314239
0.000927419 0.385785
0.000975806 0.461453
0.00102419 0.538547
0.00107258 0.614215
0.00112097 0.685761
0.00116935 0.75093
0.00121774 0.808114
0.00126613 0.856453
0.00131452 0.895816
0.0013629 0.926695
0.00141129 0.950032
0.00145968 0.967021
0.00150806 0.978936
0.00155645 0.986986
0.00160484 0.992226
0.00165323 0.995511
0.00170161 0.997495
0.00175 0.99865
An MFIX-Exa DEM simulation with these settings generates approximately 963,000 particles. The particle size
distribution (open circles), and number-weighted (orange plus), and volume-weighted (blue cross) probabilities
are shown in :numref:`fig_custom_qicpic_dem_MFIX`.
.. _fig_custom_qicpic_dem_MFIX:
.. figure:: ./images/custom_qicpic_dem.png
:width: 95%
:align: center
:alt: DEM custom qicpic distribution
MFIX-Exa DEM custom bimodal particle size distribution (open circles) and number-weighted (orange plus)
and volume-weighted (blue cross ) probabilities.
An MFIX-Exa PIC simulation with the same settings generates approximately 164,000 parcels when ``pic.close_pack = 0.64``,
``pic.parcels_per_cell_at_pack = 64``, and the Eulerian mesh spacing :math:`\Delta x` is 3.125 mm. The parcel size distribution
shown in :numref:`fig_custom_qicpic_pic_MFIX` (open triangles) follows the volume-weighted probabilities.
Small closed circles illustrate the effective particle size distribution, computed by multiplying each observation
by the parcel statistical weight.
.. _fig_custom_qicpic_pic_MFIX:
.. figure:: ./images/custom_qicpic_pic.png
:width: 95%
:align: center
:alt: PIC custom bimodal distribution
MFIX-Exa PIC custom bimodal parcel size distribution (open triangles), number-weighted (orange plus) and
volume-weighted (blue cross) probabilities. Small closed circles indicated the particle size
distribution modeled by the parcels.