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

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