首页|Assessment of stochastic models for predicting particle transport and deposition in turbulent pipe flows
Assessment of stochastic models for predicting particle transport and deposition in turbulent pipe flows
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
? 2022 Elsevier LtdParticle dispersion and deposition in turbulent pipe flows were studied numerically. The mean flow solution was calculated via a Reynolds Stress Model. Three different stochastic models were used to evaluate the effect of instantaneous fluctuations on entrained particles including the standard Discrete Random Walk (DRW) model available in Ansys-Fluent and two Continuous Random Walk (CRW) schemes that solve a normalized Langevin equation to better account for the effects of inhomogeneous turbulence on particle entrainment. Special attention is paid to the inconsistent directional vector resulting from solving the Langevin equation in non-rectangular geometries in location-specific coordinate systems. Concentration, axial distributions of deposition velocity, and deposition velocity versus particle relaxation time were compared with existing experiments and direct numerical simulation (DNS) using two different geometries. The validity of DNS data for near wall corrections were discussed. A third geometry representative of the hot section of a gas turbine was employed to study the capability of DRW and CRW on modeling spatially-developing flows. Comparisons were drawn between different stochastic models with an all-stick condition and experimental results. It was shown that the classical DRW model tends to overpredict the effect of entrance and does not yield good agreement in the deposition statistics. The CRW model shows a promising capability of modeling particle dispersion and deposition. However, the accuracy of CRW relies heavily upon the fidelity of the underlying mean flow solution and root-mean-square turbulence statistics.
CFDCRWDepositionDRWParticle tracking
Lo C.、Bons J.、Yao Y.、Capecelatro J.
展开 >
Department of Mechanical Engineering The Ohio State University
Department of Aerospace Engineering The Ohio State University
Engineering and Process Science Core R&D The Dow Chemical Company
Department of Mechanical Engineering University of Michigan
NSTL
Elsevier
