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

Author:

Pereira Filipe S.1,Grinstein Fernando F.2,Israel Daniel M.2,Eça Luis3

Affiliation:

1. T - Theoretical Division, Los Alamos National Laboratory , Los Alamos, NM 87544

2. X - Computational Physics Division, Los Alamos National Laboratory , Los Alamos, NM 87544

3. Department of Mechanical Engineering, Instituto Superior Técnico, University of Lisbon , Lisbon 1049-001, Portugal

Abstract

Abstract This work investigates the importance of verification and validation (V&V) to achieve predictive scale-resolving simulations (SRSs) 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 (CCs). 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 V&V 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.

Funder

Los Alamos National Laboratory

Publisher

ASME International

Subject

Computational Theory and Mathematics,Computer Science Applications,Modeling and Simulation,Statistics and Probability

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1. Development and Verification of a Higher-Order Computational Fluid Dynamics Solver;Journal of Verification, Validation and Uncertainty Quantification;2023-12-01

2. Numerical approximations formulated as LES models;Numerical Methods in Turbulence Simulation;2023

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