Numerical Modeling of Bank Erosion Processes and Its Field Application

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Techniques for mesh density control

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Mesh generation is crucial in computational fluids dynamic (CFD) analysis, which solves a set of partial differential equations (PDE) based on a computational mesh. To a large extent, the success of solving these equations depends on the mesh quality. In addition to the orthogonality and the smoothness, the mesh density distribution is the key to a desirable mesh. The objective of the current research is to develop methods which make the control of mesh density simple and effective. The resulting mesh is near-orthogonal but more desirable for the numerical simulation. In this study, two new techniques for mesh density control are proposed. The first one is a three-parameter stretching function which stretches the nodes along a line in two directions and control their location of the distribution. The second method is a modified RL system (Ryskin and Leal, 1983) in which the distortion function is evaluated by the averaged scale factors and the scale factors which are formulated by weighting functions of desired mesh density distribution

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.Comment: Accepted at NeurIPS 202

Numerical simulations of channel response to riverine structures in Arkansas River

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Numerical simulation of flows, sediment transport and river channel change in complex geometries of natural environment is a challenge to computational fluid dynamics (CFD). The difficulties include not only the discretization of the physical domain with a computational mesh, but also the capabilities of simulating the short and long term channel morphologic change in response to adjustment of hydraulic structures. Therefore, a robust numerical modeling system consisting of an efficient mesh generator and fluvial process simulator is needed. In this study, the response of the Arkansas River navigation channel to riverine structure modifications was simulated by using a hydrodynamic and sediment transport computational model, CCHE2D. The feasibility of deepening the channel using modified dike fields with more, higher and longer dikes was confirmed with this model. In addition, the new design of the dike fields was further improved by multiple simulations of the computational model