Multi-fidelity uncertainty quantification of particle deposition in turbulent pipe flow

TL;DR

This paper introduces a multi-fidelity Monte Carlo framework to efficiently quantify uncertainty in particle deposition within turbulent pipe flow, accounting for electric charge, van der Waals forces, and temperature effects.

Contribution

It develops a novel multi-fidelity approach combining high- and low-fidelity models for variance-based sensitivity analysis in multiphase flows.

Findings

Deposition sensitivity is highest to electrostatic interactions.

Uncertainty is largest for particles with moderate Stokes numbers.

Significant computational speedup over classical Monte Carlo methods.

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.