Robust Subgroup Method Using DE Algorithm for Resonance Self-Shielding Calculation

TL;DR

This paper introduces a robust subgroup method combining Robust Estimation with Differential Evolution to improve resonance self-shielding calculations, effectively reducing bias and enhancing predictive accuracy in nuclear transport simulations.

Contribution

The novel integration of Robust Estimation with Differential Evolution provides a more accurate and bias-resistant subgroup method for resonance self-shielding treatment.

Findings

Removes systematic absorption bias in subgroup fits

Improves fidelity of transport simulation predictions

Addresses threshold-like conditioning failure in benchmarks

Abstract

This paper presents an enhanced version of the subgroup method for resonance self-shielding treatment, termed the robust subgroup method, which integrates Robust Estimation (RE) with a Differential Evolution (DE) algorithm. The RE approach is employed to handle model misspecification and data contamination, while the DE algorithm serves as an optimization tool within the RE framework to obtain constrained solutions. Numerical validation against experimental benchmarks shows that the proposed method removes a systematic absorption bias in conventional subgroup fits that would otherwise depress reactivity. This bias appears only in benchmarks sensitive to U-238. Mechanistically, it reflects a threshold-like conditioning failure: strong self-shielding leverage dominates the loss and is magnified by dilution-induced multicollinearity. This adverse conditioning appears to be seeded by a narrow, sparse resonance structure at low energies in fertile even-even nuclides, thereby causing rapid self-shielding response saturation and a weak Doppler broadening. By bounding influence and enforcing feasibility within an RE-DE framework, the inferred subgroup parameters track the underlying physics more faithfully, improving the predictive fidelity of subsequent transport simulations.