Verification and Validation: the Path to Predictive Scale-Resolving Simulations of Turbulence

TL;DR

This paper emphasizes the critical role of verification and validation in achieving reliable scale-resolving turbulence simulations, proposing a new V&V strategy suitable even without exact initial conditions or reference data.

Contribution

It introduces a simple, effective V&V approach based on grid and physical resolution refinement for turbulence simulations, applicable when reference data are lacking.

Findings

V&V is crucial for reliable turbulence simulations.

The proposed V&V method effectively separates numerical and modeling errors.

The strategy enhances understanding of flow physics and key phenomena.

Abstract

This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRS) of turbulence, i.e., computations capable of resolving a fraction of the turbulent flow scales. Toward this end, we propose a novel but simple V&V strategy based on grid and physical resolution refinement studies that can be used even when the exact initial flow conditions are unknown, or reference data are unavailable. This is particularly relevant for transient and transitional flow problems, as well as for the improvement of turbulence models. We start by presenting a literature survey of results obtained with distinct SRS models for flows past circular cylinders. It confirms the importance of V&V by illustrating a large variability of results, which is independent of the selected mathematical model and Reynolds number. The proposed V&V strategy is then used on three representative problems of practical interest. The results illustrate that it is possible to conduct reliable verification and validation exercises with SRS models, and evidence the importance of V&V to predictive SRS of turbulence. Most notably, the data also confirm the advantages and potential of the proposed V&V strategy: separate assessment of numerical and modeling errors, enhanced flow physics analysis, identification of key flow phenomena, and ability to operate when the exact flow conditions are unknown or reference data are unavailable.