Assessment of stochastic models for predicting particle transport and deposition in turbulent pipe flows

Particle 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.

Automated summary

This study numerically investigates particle dispersion and deposition in turbulent pipe flows. The results show that the classical DRW model tends to overpredict the effect of entrance and does not yield good agreement in the deposition statistics. On the other hand, the CRW model shows promising capability in modeling particle dispersion and deposition.