Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow. (November 2022)

• Record Type: Journal Article
• Authors: Yao, Yuan; Huan, Xun; Capecelatro, Jesse
• Is Part Of: Journal of aerosol science. Volume 166(2022)
• Publication Date: 2022-11
• Subjects: Particle deposition -- Uncertainty quantification -- Multi-fidelity Monte Carlo -- Cohesion -- Sobol' indices; Aerosols -- Periodicals; Aerosols -- Periodicals; Aérosols -- Périodiques; 541.34515
• DOI: 10.1016/j.jaerosci.2022.106065
• Languages: English
• ISSNs: 0021-8502

Abstract

Abstract: Particle deposition in fully-developed turbulent pipe flow is quantified taking into account uncertainty in electric charge, van der Waals strength, and temperature effects. A framework is presented for obtaining variance-based sensitivity in multiphase flow systems via a multi-fidelity Monte Carlo approach that optimally manages model evaluations for a given computational budget. The approach combines a high-fidelity model based on direct numerical simulation and a lower-order model based on a one-dimensional Eulerian description of the two-phase flow. Significant speedup is obtained compared to classical Monte Carlo estimation. Deposition is found to be most sensitive to electrostatic interactions and exhibits largest uncertainty for mid-sized (i.e., moderate Stokes number) particles. Highlights: Uncertainty in the deposition of cohesive particles in turbulent pipe flow is efficiently quantified. A 1D Eulerian deposition model is leveraged to expedite uncertainty quantification via 3D DNS. This multi-fidelity approach achieved significant speedup than classic Monte Carlo method. Particle deposition is found to be more sensitive to electrostatics than van der Waals force. Particles with moderate Stokes number exhibit the largest uncertainty in deposition rate.