On the use of ensemble averaging techniques to accelerate the Uncertainty Quantification of CFD predictions in wind engineering. Issue 228 (September 2022)

• Record Type: Journal Article
• Authors: Tosi, Riccardo; Núñez, Marc; Pons-Prats, Jordi; Principe, Javier; Rossi, Riccardo
• Is Part Of: Journal of wind engineering and industrial aerodynamics. Issue 228(2022)
• Publication Date: 2022-09
• Subjects: CAARC Commonwealth Advisory Aeronautical Council -- CFD computational fluid dynamics -- CFL Courant–Friedrichs–Lewy -- CPU central processing unit -- FE finite element -- HPC high performance computing -- ILES implicit large eddy simulation -- Kratos Kratos Multiphysics -- LES large eddy simulation -- MC Monte Carlo -- NS Navier–Stokes -- OSS orthogonal subgrid scales -- QoI quantity of interest -- Re Reynolds number -- SC spatially correlated -- SE statistical error -- VMS variational multiscale; Uncertainty quantification -- Ensemble averaging -- Monte Carlo -- Statistical estimate -- CFD -- Wind engineering -- CAARC; Wind-pressure -- Periodicals; Buildings -- Aerodynamics -- Periodicals; Pression du vent -- Périodiques; Constructions -- Aérodynamique -- Périodiques; Buildings -- Aerodynamics; Wind-pressure; Periodicals
• DOI: 10.1016/j.jweia.2022.105105
• Languages: English
• ISSNs: 0167-6105

Abstract

Abstract: In this work we focus on reducing the wall clock time required to compute statistical estimators of highly chaotic incompressible flows on high performance computing systems. Our approach consists of replacing a single long-term simulation by an ensemble of multiple independent realizations, which are run in parallel. A failure probability convergence criteria must be satisfied by the statistical estimator of interest to assess convergence. The error analysis leads to the identification of two error contributions: the initialization bias and the statistical error. We propose an approach to systematically detect the burn-in time needed in order to minimize the initialization bias, as well as techniques to choose the effective time needed to keep the statistical error under control. This is accompanied by strategies to reduce simulation cost. The proposed method is assessed in application to the prediction of the drag force over high rise buildings and specifically in application to the CAARC building, a relevant benchmark for the wind engineering community. Highlights: Development of a statistical framework to analyze the computational simulation of chaotic fluid dynamics systems. Solution of chaotic systems characterized by ergodic and nonergodic uncertainties. Definition of strategies to accelerate the solution of chaotic CFD systems. Proposal of an initial condition strategy for ensuring independence of multiple realizations. Statistical analysis of engineering quantities (drag force, base moment, pressure field) using raw and central moments.