ECLOUD vs. PyECLOUD at 450GeV

ECLOUD vs. PyECLOUD at 450GeV

C. O. Domínguez, G. Iadarola and F. Zimmermann

10 August 2012 - e- cloud meeting

Outline

1) Some build-up remarks

2) Benchmarking pressure with both codes


Build-up remarks


Same initial seeds

Plateau for PyEC;Slope for EC

Always bigger slope for PyEC

Build-up remarks


Build-up remarks


e- density (log scale in y)

e- density (derivative)

Bunch passage

Build-up remarks


Primary e-

Secondary e-

First two bunches

Flying time of primary e- from the center to the walls

Build-up remarks


Two bunches in saturation

Primary e- are negligibleNo more plateau

Build-up remarks


Some questions arise:

- Why can’t we see the plateau in EC?

- Why the slope is always bigger in PyEC?

Outline

1) Some build-up remarks

2) Benchmarking pressure with both codes


Pressure Benchmarking


I order to decide which code to use for benchmarking pressure measurements, some tests on the sensitivity to the number of macroparticles have been done:

- In EC I varied the parameter “npepb” to 1000 and 3000 (default: 2000).

- The mechanism in PyEC is quite different so I varied different parameters.

Default

#1 #2 #3 #4 #5 #6

N_mp_max 100k 100k 130k 100k 100k 100k 100k

N_mp_regen 50k 40k 70k 50k 50k 50k 50k

N_mp_regen_low 5k 5k 5k 2.5k 10k 5k 5k

N_mp_after_regen 10k 10k 10k 5k 20k 10k 10k

nel_mp_ref_0 (e-/m) 10 10 10 10 10 50 100






N_mp_max

N_mp_regen

N_mp_regen_low

N_mp_after_regen

nel_mp_ref_0 is the number of e- created per macroparticle during the passage of the first bunch






Default

#1 #2 #3 #4 #5 #6

N_mp_max 100k 100k 130k 100k 100k 100k 100k

N_mp_regen 50k 40k 70k 50k 50k 50k 50k

N_mp_regen_low 5k 5k 5k 2.5k 10k 5k 5k

N_mp_after_regen 10k 10k 10k 5k 20k 10k 10k

nel_mp_ref_0 (e-/m) 10 10 10 10 10 50 100



ECLOUD

Npepb=1000



ECLOUD

Npepb=2000



ECLOUD

Npepb=3000



Npepb=2000

Npepb=3000

Npepb=1000

ECLOUD



ECLOUD



PyECLOUD

N_mp_max=100000, 130000; #size of allocated vectors #Regen parametersN_mp_regen=40000, 70000;N_mp_regen_low=5000;N_mp_after_regen=10000;nel_mp_ref_0=10. #e-/m



PyECLOUD

N_mp_max=100000; #size of allocated vectors #Regen parametersN_mp_regen=50000;N_mp_regen_low=2500, 10000;N_mp_after_regen=5000, 20000;nel_mp_ref_0=10. #e-/m



PyECLOUD

N_mp_max=100000; #size of allocated vectors #Regen parametersN_mp_regen=50000;N_mp_regen_low=5000;N_mp_after_regen=10000;nel_mp_ref_0=10., 50. #e-/m



PyECLOUD

N_mp_max=100000; #size of allocated vectors #Regen parametersN_mp_regen=50000;N_mp_regen_low=5000;N_mp_after_regen=10000;nel_mp_ref_0=50., 100. #e-/m



Summary:

- In my opinion, PyEC seems more robust, at least in the parameters explored

- Other studies done on the sensitivity to grid dimensions also show that with PyEC differences are smaller.

- The shape of the different ratio lines is always more “stable” with PyEC.

Thank you for your attention!


ECLOUD vs. PyECLOUD at 450GeV

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