Gabriele Cosmo
157 lines
7.0 KiB
Plaintext
157 lines
7.0 KiB
Plaintext
=========================================================
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Geant4 - FastAerosol advanced example
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=========================================================
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README
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---------------------
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Authors:
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Ara Knaian : ara@nklabs.com
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Nate MacFadden: natemacfadden@gmail.com
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NK Labs, LLC (http://www.nklabs.com)
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Related Publication:
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MacFadden, N., Knaian, A., 2020, "Efficient Modeling of Particle
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Transport through Aerosols in GEANT4", Manuscript in preparation.
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Using the FastAerosol geometry classes, it is possible to efficiently
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and accurately simulate particle transport through aerosols containing
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billions of randomly-positioned droplets, using an ordinary workstation.
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This example demonstrates the use of these classes. It is based off
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exampleB1.
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1- GEOMETRY DEFINITION
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FADetectorConstruction builds a user-defined-shape aerosol of
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non-intersecting, arbitrarily-shaped droplets. The default
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bulk shape is a 500 x 500 x 5000 mm box centerred at the origin;
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the default droplet shape is a r = 1 mm sphere. Intersection checking
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for non-spherical droplets is performed with the droplet's bounding
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raidus. These droplets are randomly placed in the bulk.
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A 500 x 500 x 50 mm box-shaped aluminum detector is placed at
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(0,0,2625) mm so that particles shot along +Z from (0,0,-2612.5) mm
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travel through the aerosol before being captured in the detector.
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The background material is atmospheric air at an altitude of 14 km.
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The droplet material is liquid water.
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2- AEROSOL MODELLING
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The example can be configured to allow modeling of the aerosol using
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one of three methods, to allow for benchmarking and testing.
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The methods are (a) using the demonstrated FastAerosol class,
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(b) using a single low-density volume, and (c) using parameterized
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volumes. Alternating between these build methods can be done by
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setting one of "/geometry/fastAerosolCloud", "/geometry/smoothCloud",
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and "/geometry/parameterisedCloud" true for a, b, and c respectively.
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By default, FastAerosol dynamically populates droplets in the bulk
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as particles transport through it. This reduces memory consumption,
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especially when the number of primaries is small compared to the
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number of droplets. Pre-population is slower and uses more memory,
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but allows saving the droplet distribution over the full volume.
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Pre-population may be enabled using the "/geometery/prePopulate true"
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UI command. The example saves the droplet positions in files called
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"distribution_r???mm_n???mm-3.csv" where ??? denotes the droplet
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radius and number density respectively).
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User-specified distibution functions for droplet position and rotation
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may be specified for FastAerosol simulations. Examples of this can be
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found in lines 311-317 and 326-330 of FADetectorConstruction.cc.
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The example can be configured to model the aerosol using a
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G4PVParameterized object, instead of using FastAerosol object.
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This can be used to demonstrate that FastAerosol gives nearly
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identical transport results as parameterized geometery, but
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achieves order-of-magnitude performance gains.
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To build the aerosol as a G4PVParameterised object, a droplet
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distribution is required. The example is set up to use the droplet
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distribution automatically generated and saved by a previously-run
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pre-populated FastAerosol simulation, as this allows better comparison
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between the two classes. By default, the example looks for this
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distribution under the file name "distribution_r???mm_n???mm-3.csv",
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so the generated pre-populated FastAerosol distribution can be used
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by simply running a pre-populated FastAerosol simulation with the
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same geometry before running the parameterised simulation.
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For comparison, the example can also be run using a single volume
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containg air and water mixed together at the correct average density.
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For small-enough droplets, this works well. However, it becomes
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increasingly innacurate as the droplets appoach a critical size range,
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which is about r = 1 mm for the materials and energies used in this
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example. This demonstraties the need to model some aerosols at a
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droplet level for accurate physics results.
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2- PHYSICS LIST
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This example uses the QGSP_BIC physics list. A global step length
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limiter physics process is also included, because this can significantly
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speed up calculations using FastAerosol when particle trajectories
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curve due to the application of fields.
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3- ACTION INITALIZATION
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Nothing special.
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4- PRIMARY GENERATOR
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Particles are shot from (0,0,-2612.5) mm with a spread in X and Y of
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+/-55 mm with momentum in the +z direction. The example shoots 50 MeV
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protons by default.
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5- DETECTOR RESPONSE
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Scoring is done with a scoring grid, to allow histograms of the energy
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deposited to allow comparison of the results between geometry modeling
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methods.
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6- SAMPLE EVALUATION
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It is recommended to run the test.mac script for a test/sample
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evaluation. This will simulate the transport of 100 protons (50 MeV)
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through an aerosol. The default bulk shape is "box"; other allowed
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shapes are "ellipsoid", "cylinder", and "pipe". All shapes are aligned
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along the z axis and maximally sized to fit in the 500 x 500 x 5000 mm
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bounding box.
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By default, droplets are dynamically populated in the aerosol. One
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can populate all droplets at the beginning of the simulation by
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setting prePopulate to true. This saves the generated distribution
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of droplet centers. One can also run the same experiment, except
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modelling the aerosol as a parameterised solid, by setting
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parameterisedCloud to true and FastAerosolCloud to false. This
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requires a distribution of droplet centers; the one generated by a
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pre-opulated FastAerosol simulation automatically works. One can
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also simulate the cloud as a single average-density object by setting
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FastAerosolCloud and parameterisedCloud to false and smoothCloud to
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true.
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7 - OUTPUT
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The energy deposited into the aluminum detector by any particles is
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measured with command line scoring by a 20 x 20 x 1 scoring grid
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and saved via "/score/dumpQuantityToFile" as a csv file named
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"eDep_FastAerosol_r1p0mm_n1E-3p7mm-3_bulkbox_dropletsphere.csv"
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for the default aerosol build parameters.
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The GNU program "/usr/bin/time" may be used measure the simulation
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time and memory load. If simulating a pre-populated FastAerosol cloud,
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the time to populate the cloud is printed at population time in the
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program. This population time is also saved as
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"popTime_r???mm_n???mm-3.csv".
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The distribution of droplet centers is saved as
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"distribution_r???mm_n???mm-3.csv" where each row of this csv file
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corresponds to a droplet with the 1st, 2nd, and 3rd columns
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corresponding the the x, y, and z position of it's center respectively.
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8 - HOW TO RUN THE EXAMPLE
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Batch mode: fastAerosol test.mac
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Interactive mode: fastAerosol (init_vis.mac is executed to set-up visualisation)
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