Multiscale methods have been developed to solve multiphase flow and transport problems in large-scale heterogeneous porous media accurately and efficiently. In this report, the computational efficiency of the multiscale finite-volume method (MSFV) is analyzed. The power of MSFV lies in its ability to combine local basis functions with a global coarse-scale problem to solve highly details hetero...

This PET study is concerned with the what, where, and how of implicit sequence learning. In contrast with previous studies imaging the serial reaction time (SRT) task, the sequence of successive locations was determined by a probabilistic finite-state grammar. The implicit acquisition of statistical relationships between serially ordered elements (i.e., what) was studied scan by scan, aiming to...

sedimentation is one of the most important problems in harbors that results in considerable economic costs. harbor planforms affects the flow pattern in the harbor basin and consequently, plays an important role in sediment transport and sedimentation. in the present study, a two dimensional depth-averaged hydrodynamic and sediment transport model has been developed to investigate the effect of...

The convergence of a two-pseudocomponent sequential-implicit Compositional Multiscale Finite Volume (C-MSFV) method is studied and formulation improvements are suggested. Coupling of the pressure and transport equations is found to be a major factor in the convergence of the sequential-implicit solution strategy and is determined to be the source of convergence problems for cases with large sol...

The sequential fully implicit (SFI) scheme was introduced (Jenny et al. 2006) for solving coupled flow and transport problems. Each time step SFI consists of an outer loop, in which there are inner Newton loops to implicitly sequentially solve the pressure sub-problems. In standard SFI, sub-problems usually solved with tight tolerances at every iteration. This can result wasted computations tha...

A new model, named as deep dynamic poisson factorization model, is proposed in this paper for analyzing sequential count vectors. The model based on the Poisson Factor Analysis method captures dependence among time steps by neural networks, representing the implicit distributions. Local complicated relationship is obtained from local implicit distribution, and deep latent structure is exploited...