My model formation #564
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respected sir, thanks for providing us this wonderful neper software . question1 in this code why 0.5 is multiplied with pores distribution? |
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Replies: 3 comments 1 reply
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sir, please give suggestions. |
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please check my answer in#339 |
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0.5 is a multiplicative factor that applies to the number of cells.
The is the C/C++-style if-then-else: https://neper.info/doc/exprskeys.html?highlight=lazy#ternary-operators.
This specific value corresponds to running a (rudimental) dense sphere packing algorithm for the initial state. It helps for wide/multimodal size distributions.
True that it is buggy right now. I'll try to fix it. |
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Thanku sir for you valuable information.
…On Mon, 26 Sep, 2022, 1:11 pm Romain Quey, ***@***.***> wrote:
question1 in this code why 0.5 is multiplied with pores distribution?
0.5 is a multiplicative factor that applies to the number of cells.
lognormal(0.1,0.01)+0.5*lognormal(0.01,0.001) means that 2/3 of the
grains will follow the first distribution and 1/3 will follow the second.
question2 what is meaning of diameq>0.03?1:2 ?
The is the C/C++-style if-then-else:
https://neper.info/doc/exprskeys.html?highlight=lazy#ternary-operators.
question3 what is meaning of this weight:radeq,coo:packing?
This specific value corresponds to running a (rudimental) dense sphere
packing algorithm for the initial state. It helps for wide/multimodal size
distributions.
question4 how can remove cell in tessellation -T ? I use rmcell() but it
is not working. please write a code for this again
True that it is buggy right now. I'll try to fix it.
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0.5 is a multiplicative factor that applies to the number of cells.
lognormal(0.1,0.01)+0.5*lognormal(0.01,0.001)means that 2/3 of the grains will follow the first distribution and 1/3 will follow the second.The is the C/C++-style if-then-else: https://neper.info/doc/exprskeys.html?highlight=lazy#ternary-operators.
This specific value corresponds to running a (rudimental) dense sphere packing algorithm for the initial state. It helps for wide/multimodal size distributions.