Gabriele Cosmo
112 lines
5.8 KiB
Markdown
112 lines
5.8 KiB
Markdown
\page ExampleB1Con Example B1Con
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Example of Convergence Tester
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Koi, Tatsumi \n
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SLAC National Accelerator Laboratory / PPA \n
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tkoi@slac.stanford.eedu \n
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This example shows how to use convergece tester in Geant4.
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The aim of Convergence Tester
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- After a Monte Carlo simulation, we get an answer. However how to estimate quality of the answer.
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The answer is usually given in a form of average value.
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But sometimes the value is strongly affected by single or a few events in the full calculation.
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In such case, we must concern about quality of the value.
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What we must remember is
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- Large number of history does not valid result of simulation.
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- Small Relative Error does not valid result of simulation
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Convergence tester provides statistical information
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to assist establishing valid confidence intervals for Monte Carlo results for users.
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Geometry and Physics are same to exampleB1. Please see
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[Example B1](../../html_B1/html/ExampleB1.html).
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Also run1.mac and run2.mac are like in exampleB1, with the only diffrence slightly
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increased number of events in run1.mac.
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Note that in this example, the classes with the code added for
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the purpose of demonstration of the Convergence Tester are defined in the namespace
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B1Con instead of B1 and also the executable and the test macro names are changed
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in exampleB1Con and exampleB1Con.in.
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Known problem:
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Computing time of T cannot be gotten properly in current MT migration of example of B1Con. Therefore
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FOM (=1/(R^2T) where R is relative error and T is computing time) relates numbers are unusable.
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```
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***********************************************************************************************************************
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Output example
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// Part I.A
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// Basic statistics values
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G4ConvergenceTester Output Result of DOSE_TALLY
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EFFICIENCY = 0.601
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MEAN = 4.81721e-12
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VAR = 2.15334e-23
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SD = 4.64041e-12
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R = 0.0304622
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SHIFT = 2.22459e-13
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VOV = 0.000166754
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FOM = 1238.68
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// Part I.B
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// If the largeset scored events happen at next to the last event,
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// then how much the event effects the statistics values of the calculation
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THE LARGEST SCORE = 1.07301e-11 and it happend at 487th event
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Affected Mean = 4.82311e-12 and its ratio to orignal is 1.00123
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Affected VAR = 2.15468e-23 and its ratio to orignal is 1.00062
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Affected R = 0.0304192 and its ratio to orignal is 0.998587
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Affected SHIFT = 2.1804e-13 and its ratio to orignal is 0.980133
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Affected FOM = 1238.68 and its ratio to orignal is 1
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// Part I.C
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// Convergence tests results
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MEAN distribution is RANDOM
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r follows 1/std::sqrt(N)
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r is monotonically decrease
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r is less than 0.1. r = 0.0304622
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VOV follows 1/std::sqrt(N)
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VOV is monotonically decrease
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FOM distribution is not RANDOM
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SLOPE is not large enough
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This result passes 6 / 8 Convergence Test.
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// Part II
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// Profile of statistics values in the history
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G4ConvergenceTester Output History of DOSE_TALLY
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i/16 till_ith mean var sd r vov fom shift e r2eff r2int
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1 62 4.94618e-12 2.04631e-23 4.52362e-12 0.115225 0.00313634 86.5745 -1.73435e-14 0.619048 0.00976801 0.00329797
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2 124 4.69364e-12 2.10698e-23 4.59018e-12 0.0874712 0.001597 150.228 3.11143e-13 0.6 0.00533333 0.00225666
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3 187 4.72161e-12 2.14009e-23 4.62612e-12 0.0714575 0.00101852 225.105 3.1009e-13 0.590426 0.00368986 0.00138916
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4 249 4.95617e-12 2.13982e-23 4.62582e-12 0.0590299 0.000690138 329.865 9.71971e-14 0.62 0.00245161 0.00101898
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5 312 4.8529e-12 2.13482e-23 4.62041e-12 0.0538155 0.000573301 396.887 1.95662e-13 0.607029 0.00206827 0.000818582
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6 374 5.14255e-12 2.15736e-23 4.64474e-12 0.046641 0.000432121 528.379 -6.42963e-14 0.637333 0.00151743 0.000652145
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7 437 5.03849e-12 2.13484e-23 4.62043e-12 0.0438173 0.000379317 598.673 2.54207e-14 0.636986 0.00130112 0.000614447
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8 499 4.96962e-12 2.1429e-23 4.62914e-12 0.0416574 0.000329007 662.364 9.27708e-14 0.63 0.0011746 0.000557264
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9 562 4.91513e-12 2.14709e-23 4.63367e-12 0.0397316 0.000285324 728.13 1.33544e-13 0.623446 0.0010728 0.000502991
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10 624 4.82995e-12 2.13825e-23 4.62412e-12 0.0382954 0.000272664 783.766 2.19101e-13 0.616 0.000997403 0.000466792
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11 687 4.79197e-12 2.13975e-23 4.62574e-12 0.0368022 0.000251788 848.661 2.48547e-13 0.606105 0.000944593 0.000407838
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12 749 4.77183e-12 2.15116e-23 4.63807e-12 0.0354912 0.000227501 912.513 2.6728e-13 0.601333 0.000883962 0.000373986
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13 812 4.76087e-12 2.14479e-23 4.63119e-12 0.0341162 0.000212259 987.548 2.70437e-13 0.597786 0.000827601 0.000334885
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14 874 4.81359e-12 2.13296e-23 4.6184e-12 0.0324353 0.0001976 1092.56 2.14521e-13 0.603429 0.000751082 0.000299767
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15 937 4.82018e-12 2.14558e-23 4.63204e-12 0.0313767 0.000181379 1167.52 2.18545e-13 0.601279 0.000706952 0.000276498
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16 999 4.81721e-12 2.15334e-23 4.64041e-12 0.0304622 0.000166754 1238.68 2.22459e-13 0.601 0.000663894 0.000263125
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**************************************************************************************************************************
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```
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Reference of this Convergence tests: \n
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MCNP(TM) -A General Monte Carlo N-Particle Transport Code \n
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Version 4B \n
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Judith F. Briesmeister, Editor \n
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LA-12625-M, Issued: March 1997, UC 705 and UC 700 \n
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CHAPTER 2. GEOMETRY, DATA, PHYSICS, AND MATHEMATICS \n
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VI. ESTIMATION OF THE MONTE CARLO PRECISION \n
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