A Framework for Initial Transient Detection and Statistical Assessment of Convergence in CFD Simulations

TL;DR

This paper introduces a robust, automated framework for detecting initial transients and assessing convergence in CFD simulations using statistical metrics and filtering techniques, ensuring reliable steady-state analysis.

Contribution

It presents a novel combination of RMSE, fractional filtering, and autocorrelation-corrected confidence intervals for transient detection and convergence assessment in time series data.

Findings

Method is independent of numerical parameters.

Framework reduces false positives in autocorrelated data.

Validated through simulations with varying time steps.

Abstract

Time series data often contain initial transient periods before reaching a stable state, posing challenges in analysis and interpretation. In this paper, we propose a novel approach to detect and estimate the end of the initial transient in time series data. Our method leverages the reversal mean standard error (RMSE) as a metric for assessing the stability of the data. Additionally, we employ fractional filtering techniques to enhance the detection accuracy by filtering out noise and capturing essential features of the underlying dynamics. Combining with autocorrelation-corrected confidence intervals we provide a robust framework to automate transient detection and convergence assessment. The method ensures statistical rigor by accounting for autocorrelation effects, validated through simulations with varying time steps. Results demonstrate independence from numerical parameters (e.g., time step size, under-relaxation factors), offering a reliable tool for steady-state analysis. The framework is lightweight, generalizable, and mitigates inflated false positives in autocorrelated datasets.