Work

Turbulence model uncertainty estimation via Monte Carlo perturbation of the Reynolds Stress Tensor

Year: 2025

Type: article

Abstract: We propose to rely on a flat Dirichlet distribution to sample the eigenvalues of the Reynolds Stress Tensor in RANS simulations. The goal is to forward propagate the uncertainty inherent the structure of the turbulence closure to targeted QoIs. The flat Dirichlet distribution is defined over the 2-dimensional simplex delimiting the Reynolds Stress Tensor realizability conditions. This ensures the tensor positive-definiteness and serves the uncertainty forward propagation by means of diverse techniques e.g., Monte Carlo or Polynomial Chaos Expansions. Simulations are performed using a modified version of the open-source SU2 suite. Results are obtained for two reference test cases. Namely, the subsonic air flow over a backward facing step and the NACA0012 airfoil operating in subsonic conditions and with a variable angle of attack. • The Eigenspace Perturbation Method is exploited to enhance UQ in RANS simulations. • Dirichlet distribution models the uncertainty of Reynolds Stress Tensor eigenvalues. • The proposed UQ approach suits both Monte Carlo and Polynomial Chaos-based analyses.

Source: Computers & Fluids

DOI: 10.1016/j.compfluid.2025.106875

Author: Giulio Gori

Institution: Politecnico di Milano

Language: English

Topic: Probabilistic and Robust Engineering Design

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,700

Funders: European Commission, HORIZON EUROPE Marie Sklodowska-Curie Actions, European Research Executive Agency