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katyhuff avatar katyhuff commented on August 11, 2024

Adding some comparison with expected results will be necessary. Ideally, MSRE or 1-D analytical solutions. Are the fluxes the appropriate magnitude? How correct is the peak/average flux ratio? Is the temperature profile as was seen in MSRE?

from publications.

katyhuff avatar katyhuff commented on August 11, 2024

Maybe also some more performance analysis detail.

from publications.

lindsayad avatar lindsayad commented on August 11, 2024

What are you thinking about JOSS?

from publications.

katyhuff avatar katyhuff commented on August 11, 2024

from publications.

lindsayad avatar lindsayad commented on August 11, 2024

Nestor, MSRE preliminary physics report (calculations):

  • 8% fuel salt volume fraction (final design 22.5 %)
  • 1 mole% UF4
  • 93% U235 enrichment
  • average thermal flux = 2.9x10^13 nts/cm^2
  • peak thermal flux = 7.3x10^13 nts/cm^2
  • ratio = 2.5
  • average core power density = 4 W/cm^3
  • peak core power density = 10 W/cm^3

Ours:

  • 22.5% fuel salt volume fraction
  • .9 mole% UF4
  • 33% U235 enrichment
  • average thermal flux = 1.0x10^13 nts/cm^2
  • peak thermal flux = 2.3x10^13 nts/cm^2
  • ratio = 2.3
  • peak core power density = 17 W/cm^3

Really finding very little experimental data, but it must exist

from publications.

lindsayad avatar lindsayad commented on August 11, 2024

@katyhuff What would a scaling study look like?

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katyhuff avatar katyhuff commented on August 11, 2024

Typically, I see the following:

A very simple problem (specific materials, geometry, number of timesteps) is run with with a certain number of degrees of freedom (e.g. mesh resolution). Then, you investigate how quickly the problem runs when you spread that problem across an increasing number of processors. You want to see a strong decrease in runtime as the number of processors increases.

Considering the things that might impact scaling, the modeler (you) will often choose a couple of different canonical small simple problems which demonstrate the bounds of scaling behavior. For example (just an example), if you know that the scaling is 'bad' for problems with asymmetric geometries, you might run your scaling study with two different problems -- one sphere and one complex/asymmetric structure. Then, you would plot the scaling results together, to communicate that you expect most problems will fall between these scaling behavioral trends.

Here are two good examples:
http://www.sciencedirect.com/science/article/pii/S0927025611004204
http://www.sciencedirect.com/science/article/pii/S0022311512000165

from publications.

katyhuff avatar katyhuff commented on August 11, 2024

you've finished this draft! thanks @lindsayad !!!

bitmoji

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