Nano-porous materials have excellent thermal insulation performance,whose microstructure and physical properties,however,have great influence on the thermal conductivity.To accurately describe the stochastic phase distribution,a random internal morphology and structure generation-growth method,called the quartet structure generation set (QSGS),has been proposed in the present paper.The model was then imported into lattice Boltzmann algorithm as a fully resolved geometry and used to investigate the effects on heat transfer at the nanoscale.Furthermore,a three-dimensional Lattice Boltzmann method (LBM) D3Q15 was adopted to predict the nano-granule porous material effective thermal conductivity.This ideal method provided a significant advantage over similar porous media methods by directly controlling and adjusting of granule characteristics such as granule size,porosity and pore size distributions and studying their influence directly on thermal conductivity.To verify the accuracy of the proposed model,some experiments based on guarded hot plate meter (GHPM) were conducted.The results indicated that the simulation results agreed well with the experimental data and references values,which illustrated that this method was reliable to generate the microstructure of nano-granule.What's more,the effects of pressure,core distribution probability,cd and density were investigated.There existed an optimal density (about 120 kg?m-3) making the effective thermal conductivity being minimum and an optimal core distribution probability about cd =0.1 making the uniformity being the best.In addition,the present approach is applicable in dealing with other porous materials as well.