Fix the two-component k-eff estimator#4016
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nuclearkevin wants to merge 3 commits into
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After talking about this some more with @pshriwise, I'm closing the PR and shuffling it over to #3971 since its next to impossible to test in continuous-energy transport without delta tracking. |
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Description
The two-component combined k-eff estimator (derived by Urbatsch in LA-12658-MS) is used as a fallback in OpenMC for the case where the three-component k-eff estimator (also derived by Urbatsch) becomes numerically unstable. This happens in multi-group transport with survival biasing, where the collision and absorption estimators are identical.
This fallback is very rarely used in practice. I have been attempting to apply the two-component estimator to obtain a lower-variance estimation of k-eff with delta tracking (combining k-collision and k-absorption), but have been noticing some incredibly strange behaviour. Notably, the standard deviation of the two component combined estimate was consistently 2-3 orders of magnitude lower than the individual estimators (which is definitely not correct). After consulting with @pshriwise and digging through Urbatsch's original report, I found that there was an error in the two-component estimator in OpenMC where the elements of the matrix$\mathbf{\Sigma}$ were being used instead of the matrix $\mathbf{S}$ ( $\mathbf{\Sigma} = \mathbf{S} / (n - 1)$ ). After correcting this, the standard deviation estimates predicted by the two-component estimator are far more in line with the other estimators and converge to zero at approximately the same rate. The mean value of the two-component combined estimator was unaffected by the error as the (n - 1) factor cancels out in the derivation of the mean.
For more information, see Eq. 36 and Eq. 40 in: https://digital.library.unt.edu/ark:/67531/metadc665644/m2/1/high_res_d/212564.pdf
Checklist
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)