Hello David,
thanks a lot for reading and answering my tedious questions.
Because I am a novice in this field, I try to understand more details.
As you said, r2bcorr_para_self is \alpha_s+\beta_s,
which equals to the first part on the right hand side of equ. (3.17),
and has corresponding implementation in Line 1410 in the source file
"integrate_sd_cuda_kernel.cu
<https://urldefense.proofpoint.com/v2/url?u=http-3A__integrate-5Fsd-5Fcuda-5Fkernel.cu&d=CwIDaQ&c=Ngd-ta5yRYsqeUsEDgxhcqsYYY1Xs5ogLxWPA_2Wlc4&r=vo_59UgGQLPOFUG9XRo42qkxDB-wQV2VznPwVSffS30&m=ES-lJCQeCG5SxQd-DhECTUQd_ePrcJKAPXAU22KU05g&s=PQ-rYoxdgNLIidnDknafWZbUiE8-wOEFVdJTw-MM7vA&e=
>".
How about r2bcorr_perp_self ?
If this term is \beta_s, based on equ. (3.19),
I would expect that a line of code like
"r2bcorr_perp_self = 1/( 1 - 9/16/dr2 - 3/4/dr4 - 1/4/dr6 )".
But in Line 1412 it equals to
"r2bcorr_perp_self = 1/( 1 - 25/16/dr2 )"
This is the point I get confused.
Would you please explain a little bit more ?
Best regards
Lei
On Thu, Jan 12, 2017 at 7:24 PM, David Schwörer
<address@hidden <mailto:address@hidden>> wrote:
Hi Lei,
It's been a while since I looked at this, but I think:
r2bcorr_para_self is alpha_s+beta_s
r2bcorr_para_mix is alpha_m+beta_m
r2bcorr_perp_self is beta_s
r2bcorr_perp_mis is beta_m
the confusion is that in the one basis set is in rr and one, the other
in rr and one-rr, that is why in 3.17 b_s is substracted from alpha_s,
so that after adding the one beta_s, only the first part 1/(1-alpha_s^2)
remains.
I hope that helps.
Cheers,
David
On 01/12/2017 08:50 AM, Lei Liu wrote:
> Dear all,
>
> by reading related documents, now I understand that the terms containing
> log(s) come from R^{lub}.
> The only left question is about the variables {r2bcorr_para_self,
> r2bcorr_para_mix, r2bcorr_perp_self, r2bcorr_perp_self} in function
> "sd_compute_resistance_matrix_sparse()".
>
> The first two variables corresponds to \alpha_{s} and \alpha_{m} in
> equations (3.17), (3.21) in David's thesis.
> But how about the latter two ?
> Would anyone like to do me a favour, and to explain a little bit where
> they come from ?
> I get confused because they are different from my expectation,
> \beta_{s} or \beta_{m} in equations (3.19) and (3.23).
>
> With my best wishes
> Lei
>
>
> On Wed, Jan 11, 2017 at 8:30 PM, Lei Liu <address@hidden
<mailto:address@hidden>
> <mailto:address@hidden <mailto:address@hidden>>> wrote:
>
> Dear all,
>
> I am trying to understand the lubrication correction
> in Stokesian dynamics implemented in current developing version of
> ESPResSo.
>
> According to David Schwoerer's thesis, the function
> "sd_compute_resistance_matrix_sparse()"
> computes the lubrication correction described in equation
(3.24) as
> R^{lc} = R^{lub} - R^{2b,ff}.
> In addition, there is one comment in the code
> referring R^{lub} to 'N.-Q. Nguyen and A. J. C. Ladd, PHYSICAL
> REVIEW E 66, 046708 (2002) equation (34)'.
>
> But I still do not understand this function quite well, especially
> the terms containing the variable "ls = log(s) = log(|r|/a - 2)",
> which I cannot find neither in section 3.1.2 in the thesis nor in
> Ladd's paper.
> Would anyone like to give me more references about how ESPResSo
> calculates this correction?
>
> Many thanks in advance
> Lei