A second-order moment (SOM) model for gas-phase turbulence, combined with a Langevin-based probability density function (PDF) transport model for particle-phase turbulence, named SOM-PDF model for gas-particle turbulence, is proposed here. The SOM is solved by the well-known finite-volume method (FVM), while the PDF is solved by directly calculate the Langevin equation for both particle velocity and fluid velocities seen by particles, as proposed by Simonin (1996), using pseudo time-dependent Monte-Carlo method (MC). A time-averaged method is used to reduce the amount of stochastic-particle needed. The DSM-PDF model was applied to simulate a gas-particle turbulent flow in a pipe with expansion. The prediction results are compared with PDPA measured results and those predicted using the PDF derived ensemble-averaged stress model (ESM). The comparison shows that both models can well predict the averaged flow field of particle phase, while the DSM-PDF model got a better distribution of particle time-averaged velocity and fluctuation velocity at the area where shear is dominant. The difference may arise from several aspects: the merits of MC technology of no numerical diffusion, and most important, the capability of taken into account the effect of non-Gaussian distribution of particle velocities at shear dominant area, while Gaussian distribution is pre-assumed for ESM.