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@pranavkantgaur pranavkantgaur commented Dec 18, 2025

Description

This PR extends the recently added Model.keff_search method for material input parameters by leveraging derivative tallies. It adds least-squares curve fitting over a combination of K_eff evaluations and its derivatives as an alternative method to GRSecant's curve fitting. We have observed considerable reduction in the required number of MC evaluations for some cases (reproducible using test_tally_deriv_keff_search.py) which is consistent with the primary work by @smharper as reported here: https://dspace.mit.edu/bitstream/handle/1721.1/106690/969775837-MIT.pdf

It is an initial work on #980 and is related to: #3569, #2693.

Checklist

  • I have performed a self-review of my own code
  • I have run clang-format (version 15) on any C++ source files (if applicable)
  • I have followed the style guidelines for Python source files (if applicable)
  • I have made corresponding changes to the documentation (if applicable)
  • I have added tests that prove my fix is effective or that my feature works (if applicable)

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@GuySten GuySten left a comment

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This looks like a nice feature.
A better place for the examples should be in the openmc-notebooks repo though.

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The examples you added are nice but imo a better place for them should be in the openmc-notebooks repository.

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See comment above.

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See comment above.

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I suggest moving the criticality search tests into test_criticality_search.py file.

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3 participants