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

Overview

This technical paper presents a data-driven framework for detecting initial transient phases and assessing statistical convergence in CFD simulations. The approach introduces an automated method based on reverse mean standard error analysis and fractional filtering techniques to identify when simulation data transitions from transient numerical behaviour to statistically steady conditions.

The framework enables objective convergence assessment and supports more reliable interpretation of CFD time-series data while reducing unnecessary computational cost in steady and unsteady simulations.

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.

What This Paper Covers

• Automated detection of initial transient phases in CFD simulations
• Statistical convergence assessment using confidence intervals
• Reverse mean standard error (RMSE) analysis for time-series stability
• Fractional filtering techniques for noise reduction in CFD signals
• Autocorrelation-corrected statistical evaluation of simulation data