(no subject)

From: <yyc2011_at_mail.ustc.edu.cn>
Date: Mon, 2 Sep 2013 21:01:02 +0800 (CST)

<17652047.3034261377919188455.JavaMail.coremail_at_mailweb>,<30507624.3107761378035584867.JavaMail.coremail_at_mailweb>,<30844077.3141761378087717403.JavaMail.coremail_at_mailweb>
Subject: Re: RE: RE: RE: The maximum number of binnings in USRBIN
MIME-Version: 1.0
Content-Type: multipart/mixed;
        boundary="----=_Part_768470_18561564.1378126862176"
Sender: owner-fluka-discuss_at_mi.infn.it

------=_Part_768470_18561564.1378126862176
Content-Type: text/plain; charset=iso-2022-jp
Content-Transfer-Encoding: quoted-printable

Dear Anton,

Thanks for your suggestions.

After I disscuss my probelm with you ,I feel very confused.Because before t=
his problem,I do some simulations which I think are similar to the input fi=
le I sent to you last time.

The attachments consist of the input file,the two-dimension graph of elctro=
n distribution on the detector plane,and the one-dimension graph of electro=
n distribution.In this input file,I don't consider transport cut-off and si=
ngle scattering.But the input file runs well.Meanwhile,I think the result i=
s what I want. When there are some roughness on the target surface,Is proto=
n radiography or electron radiography or X-ray radiography able to diagnose=
  the tiny roughness on the target surface? This is the problem I want to so=
lve.So,Is it really necessary to use EMFCUT card and MULSOPT card? These tw=
o cards make the problems become complex.
=20
Thanks for your time.Thanks for any suggestions.

Yours,
Chenyuan



> -----Original E-mail-----
> From: "Anton Lechner" <Anton.Lechner_at_cern.ch>
> Sent Time: 2013-9-2 16:13:46
> To: "yyc2011_at_mail.ustc.edu.cn" <yyc2011_at_mail.ustc.edu.cn>, "fluka-discuss=
_at_fluka.org" <fluka-discuss_at_fluka.org>
> Cc:=20
> Subject: RE: RE: RE: The maximum number of binnings in USRBIN
>=20
> Dear Chenyuan,
>=20
> (1) Indeed you can have many USRBIN scorings in the same simulation, for =
different quantities and/or different spatial binnings.
>=20
> (2) Good.
>=20
> (3,4) For the scoring, it think you should really ask yourself what you w=
ant to get out of the simulation. I know now your physical problem, but I d=
on't know what you want to calculate (still, consider my comments below for=
  the y binning). Concerning the cuts I would say that you should maybe cons=
ider adapting them e.g. according to measurement conditions (if this is the=
  aim of your study). Detection of low-energy electrons can for example be l=
imited by the entrance window of a detector (or by a voltage bias applied) =
and hence, if you want to reproduce real conditions, I would maybe consider=
  such detection limits.
>=20
> (5,6) Your input file crashes. I advice you to at least test it first bef=
ore sending it to the mailing list. The problem is with the EMFCUT card: yo=
u expressed the electron cut in terms of the total electron energy (WHAT(1)=
>0), however I suppose you aimed for the kinetic energy, hence WHAT(1) shou=
ld be <0. Secondly, you misplaced the lower and upper region boundary (the =
should be on WHAT(4) and (5) and not on (5) and (6)). Finally, I would also=
  lower the cut for protons. In summary, if you want to apply a cut of 1 keV=
, your card should look as follows:
> EMFCUT -1.0E-06 1.0E-06 target0 target10
>=20
> On the other hand, the definition of your MULSOPT seems fine (now you app=
ly single scattering close to boundaries, for too short steps and for energ=
ies where Moliere theory is not applicable). As you have a very thin layer =
only, you might even want to consider applying single scattering in the ent=
ire layer (see Example 3 in the MULSOPT entry of the manual) if your CPU ti=
mes are still acceptable (you can test that).
>=20
> Cheers, Anton=20
>=20
>=20
> ________________________________________
> From: owner-fluka-discuss_at_mi.infn.it [owner-fluka-discuss_at_mi.infn.it] on =
behalf of yyc2011_at_mail.ustc.edu.cn [yyc2011_at_mail.ustc.edu.cn]
> Sent: 02 September 2013 04:08
> To: fluka-discuss_at_fluka.org
> Subject: Re: RE: RE: The maximum number of binnings in USRBIN
>=20
> Dear Anton,
>=20
> Thanks for your time.Thanks for your reply.The attachment is my new input=
  file.
>=20
> According to your reply,the followings are my questions.
>=20
> =1B$B!J=1B(B1=1B$B!K=1B(BJudging from your reply=1B$B!$=1B(Bin one input =
file,We can have many USRBIN scorings at the same time.In other words,We ca=
n get different physical quantities just from one input file.Right?
>=20
> =1B$B!J=1B(B2=1B$B!K=1B(BI understand what you said.
>=20
> =1B$B!J=1B(B3=1B$B!K=1B(BI want to get the electrons distribution on the =
detector plane.I want to study electron radiography,just like proton radiog=
raphy.For each electron energy=1B$B!J=1B(Bthe material is certain=1B$B!K=
=1B(B,there is a corresponding range in this material.If the range is too n=
ear the total target thickness,the electron beam will become sensitive to =
the target thickness.In this case, the electron beam can be used to diagnos=
e minute variations about the target thickness.This is what I want to do.
>=20
> =1B$B!J=1B(B4=1B$B!K!J=1B(B5=1B$B!K!J=1B(B6=1B$B!K=1B(BCould you see my n=
ew input file?Could you give me any advice?Perhaps EMFCUT and MULSOPT card =
are the key to the problem.I'm very rusty to the two cards.So,I wish you c=
ould give me more suggestions and some examples.
>=20
> Yours,
> Chenyuan
>=20
>=20
>=20
>=20
>=20
>=20
>=20
>=20
>=20
> > -----Original E-mail-----
> > From: "Anton Lechner" <Anton.Lechner_at_cern.ch>
> > Sent Time: 2013-9-2 0:29:48
> > To: "yyc2011_at_mail.ustc.edu.cn" <yyc2011_at_mail.ustc.edu.cn>, "fluka-discu=
ss_at_fluka.org" <fluka-discuss_at_fluka.org>
> > Cc:
> > Subject: RE: RE: The maximum number of binnings in USRBIN
> >
> > Dear Chenyuan,
> >
> > For clarity, I inserted different numbers in your Email below to refer =
to your specific questions. Here are my answers:
> >
> > (1) When saying "number of USRBINs", I mean the number of USRBIN scori=
ngs you have in your input file (always counting the USRBIN card and its co=
rresponding continuation card as *one* USRBIN) and *independently* which qu=
antity you are scoring (energy deposition, fluence, etc.). For example, if =
you have following cards in your input file, it means you have two USRBIN s=
corings:
> > USRBIN 10. ELECTRON -30. 0.4 0.4 0.0221f=
luElec
> > USRBIN -0.4 -0.4 0.022 40.0 40.0 1.&
> > USRBIN 10. POSITRON -30. 0.4 0.4 0.0221f=
luPos
> > USRBIN -0.4 -0.4 0.022 40.0 40.0 1.&
> >
> > (2) Let me illustrate this by means of the example given above: each US=
RBIN has 40*40*1=3D1600 bins, but since you have two USRBINs your total num=
ber of bins is 2*1600=3D3200. This means you need a memory allocation for 3=
200 bins. Of course, if you have further USRBINs, you need to determine the=
  number of bins for each and add them up in the same way.
> >
> > (3) This really depends what you actually want to calculate, i.e. what =
you intend to learn from the simulation. Just a hint: consider that if your=
  geometry has a granularity of 6 mm in y and at the same time you have a re=
ctangular homogeneous electron field of the same size (covering all 6 mm), =
then a 200 um bin size in y will not give you new information for most of t=
he (central) bins, but only around the region boundaries. You may want to =
consider to use a more fine-grained binning only around boundaries. But I r=
epeat myself, I really don't know what you actually want to get out of the =
simulation.
> >
> > (4) I admit that I might have expressed myself not clear enough. It is =
not necessarily the difference between incident electron energy and transpo=
rt cut-off/production cut which matters, but it is the artificial cut on e=
lectrons (in your case mostly on the secondary electrons) which you introdu=
ce by having these cuts. As you have a fluence scoring, it will of course c=
hange your result if you have lower cuts (since each electron is counted eq=
ually independent of its energy). Which cut you should choose once more dep=
ends on your physical problem.
> >
> > (5-6) You did not attach any input file (in this context, please note t=
hat the manual is always a very valuable source of information)
> >
> > Cheers, Anton
> >
> >
> > ________________________________________
> > From: owner-fluka-discuss_at_mi.infn.it [owner-fluka-discuss_at_mi.infn.it] o=
n behalf of yyc2011_at_mail.ustc.edu.cn [yyc2011_at_mail.ustc.edu.cn]
> > Sent: 01 September 2013 13:39
> > To: fluka-discuss_at_fluka.org
> > Subject: Re: RE: The maximum number of binnings in USRBIN
> >
> > Dear Anton,
> >
> > Thanks for your reply in detail.I'm very grateful to you.
> >
> > I read your mail carefully and I think about your suggestions all the d=
ay.But I still have some questions.
> >
> > First, you say "maximum number of binnings" refers to the number of USR=
BINs I can have.What is the meaning?It means we can have many
> > usrbins,one of usrbin about energy deposition,one of usrbin about fluen=
ce,one of usrbin about neutron balance,and so on.I understand
> > it,Right?
> > (1)
> >
> > Second,you say "It is not important how many bins you have per USRBIN, =
but how many you have in total for all USRBINs".I don't
> > understand what you say.Could you give me a example or a detailed expla=
in?
> > (2)
> >
> > Third,you say "your smallest geometric structure is 20 um in x and 6 mm=
  in y, while you aim for a bin size of less than 1 um in both
> > x and y".At this point,I make some changes in my input file.In the new =
input file,the smallest geometric structure is still 20 um in
> > x and 6 mm in y,but my aim for a bin size of 4 um in x and 200 um in y.=
Do you think I was right in doing this?
> > (3)
> >
> > Fourth, my primary electrons energy is 160 KeV.The transport cut-off en=
ergy is 100KeV for electrons.You ask me to consider if this
> > difference between primary energy and cut is sufficient for my problem.=
At this point,I want to know how can I estimate if this
> > difference between primary energy and cut is sufficient for my problem"=
.
> > (4)
> >
> > Meanwhile,I add the EMFCUT card in my new input file.Please
> > see my new input file.And,I will very happy if you give me some suggest=
ions.
> > (5)
> >
> > Fifth,you say "if I have such low primary electron energies and um geom=
etries, I should consider applying single scattering."In my
> > new input file,I add the MULSOPT card.This is the first time I use this=
  card.Could you see my input file?Is it wrong?Maybe You can
> > give me some examples.
> > (6)
> >
> > Thanks for your time.Thanks for any suggestions.
> >
> > Yours,
> > Chenyuan
> >
> >
> >
> >
> >
> >
> >
> >
> > > -----Original E-mail-----
> > > From: "Anton Lechner" <Anton.Lechner_at_cern.ch>
> > > Sent Time: 2013-8-31 19:30:33
> > > To: "yyc2011_at_mail.ustc.edu.cn" <yyc2011_at_mail.ustc.edu.cn>, "fluka-dis=
cuss_at_fluka.org" <fluka-discuss_at_fluka.org>
> > > Cc:
> > > Subject: RE: The maximum number of binnings in USRBIN
> > >
> > > Dear Chenyuan,
> > >
> > > You misunderstood the comment in the manual: "maximum number of binni=
ngs" refers to the number of USRBINs you can have and not to the number of =
bins (see Manual: "A 'binning' is a regular spatial mesh"). The number of b=
ins you can have is limited by the memory available for scorings:
> > > *) See following answer on the FLUKA discuss list: http://www.fluka.o=
rg/web_archive/earchive/new-fluka-discuss/5260.html: the memory allocation =
is hard-wired in the library and is 360 MB
> > > *) It is not important how many bins you have per USRBIN, but how man=
y you have in total for all USRBINs
> > > *) Your mesh of 10000*10000*1 occupies 800 MB and is hence far beyond=
  the limit of allocated memory
> > >
> > > Apart from this, I have some further comments:
> > > *) Even if such a large number of bins would be possible, you should =
ask yourself if it would make sense. First, your smallest geometric structu=
re is 20 um in x and 6 mm in y, while you aim for a bin size of less than =
1 um in both x and y. Secondly, such a large number of bins would probably =
be challenging in terms of statistical error.
> > > *) Your primary electrons have an energy of 160 keV, but the transpor=
t cut-off and production threshold set by PRECISIO is 100 keV. You should a=
sk yourself if this difference between primary energy and cut is sufficien=
t for your problem. You can set lower cut values with the EMFCUT card.
> > > *) Note that if you have such low primary electron energies and um ge=
ometries, you should consider applying single scattering. I also quote the =
FLUKA manual: "The minimum recommended energy for PRIMARY electrons is abou=
t 50 to 100 keV for low-Z materials and 100-200 keV for heavy materials, un=
less the single scattering algorithm is used. Single scattering transport a=
llows to overcome most of the limitations at low energy for the heaviest ma=
terials at the price of some increase in CPU time."
> > >
> > > Cheers, Anton
> > >
> > >
> > >
> > > ________________________________________
> > > From: owner-fluka-discuss_at_mi.infn.it [owner-fluka-discuss_at_mi.infn.it]=
  on behalf of yyc2011_at_mail.ustc.edu.cn [yyc2011_at_mail.ustc.edu.cn]
> > > Sent: 31 August 2013 05:19
> > > To: fluka-discuss_at_fluka.org
> > > Subject: The maximum number of binnings in USRBIN
> > >
> > > Dear Fluka experts,
> > >
> > > I have a question about the maximum number of binnings that I can def=
ine in USRBIN.It seems to go wrong when I set the number of
> > > binnings=3D10000 or 8000 in my input file.But when I set the number o=
f binnings=3D1000,it runs normal. Meanwhile,I note in the Fluka
> > > manual"The maximum number of binnings that the user can define is 400=
. This value can be changed by modifying the parameter MXUSBN
> > > in member USRBIN of the flukapro library or directory and then re-com=
piling and linking Fluka." In the manual,It says the maximum
> > > number of binnings that the user can define is 400.But when I set the=
  number of binnings=3D1000,I do not modify the parameter
> > > MXUSBN.But the fluka runs normal.The attachments are my input file an=
d .out file.Could you help me?
> > >
> > > Thanks for any suggestions.
> > >
> > > Yours
> > > Chenyuan
> > >
> >
> >

------=_Part_768470_18561564.1378126862176
Content-Type: application/octet-stream; name="bjuc.inp"
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="bjuc.inp"
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------=_Part_768470_18561564.1378126862176
Content-Type: image/jpeg; name="one-dimension graph of electrons
  distribution.jpg"
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="one-dimension graph of electrons
  distribution.jpg"
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------=_Part_768470_18561564.1378126862176
Content-Type: image/jpeg; name="two-dimension graph of electrons
  distribution.jpg"
Content-Transfer-Encoding: base64
Content-Disposition: attachment; filename="two-dimension graph of electrons
  distribution.jpg"
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------=_Part_768470_18561564.1378126862176--
Received on Mon Sep 02 2013 - 16:50:54 CEST

This archive was generated by hypermail 2.3.0 : Mon Sep 02 2013 - 16:50:55 CEST