Data-Driven RANS Closures Using a Relative Importance Term Analysis Based Classifier for 2D and 3D Separated Flows

TL;DR

This paper documents elsarticle.cls, a LaTeX document class designed for Elsevier journal submissions. It explains the design goals to minimize package conflicts, ensure compatibility with standard TeX tooling, and support flexible preprint and final formats along with robust front matter handling. The work outlines dependencies (e.g., natbib, geometry, hyperref), differences from the prior elsart.cls, and practical installation steps to integrate the class into typical TeX workflows. The guidance aims to streamline manuscript preparation, improve consistency with journal styles, and facilitate reliable compilation across common TeX distributions.

Abstract

This study presents a novel approach for enhancing Reynolds-averaged Navier-Stokes (RANS) turbulence modeling through the application of a Relative Importance Term Analysis (RITA) methodology to develop a new zonally-augmented $k-ω$ SST model. Traditional Linear Eddy Viscosity Models often struggle with separated flows. Our approach introduces a physics-based binary classifier that systematically identifies separated shear layers requiring correction by analyzing the relative magnitudes of terms in the turbulence kinetic energy equation. Using symbolic regression, we develop compact correction terms for Reynolds stress anisotropy and turbulent kinetic energy production. Trained on two-dimensional configurations, our model demonstrates significant improvements in predicting separation dynamics while maintaining baseline performance and fully attached flows. Generalization tests on Ahmed body and Faith Hill three-dimensional configurations confirm robust transferability, establishing an effective methodology for targeted enhancement of RANS predictions in separated flows.