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平原河网水动力模型及求解方法探讨
引用本文:卢士强,徐祖信.平原河网水动力模型及求解方法探讨[J].水资源保护,2003,19(3):5-9.
作者姓名:卢士强  徐祖信
作者单位:同济大学环境科学与工程学院,上海,200092
基金项目:上海市重点学科建设基金资助
摘    要:参考国内外有关资料 ,根据河网非恒定流水动力模型的控制方程组和汊点衔接条件 ,建立平原河网水动力节点 河道模型。介绍用特征线法 ,有限体积法和有限差分法中的直接解法、分级解法、汊点分组解法、矩阵标识法、非线性方法等求解的基本思路。对比单元划分模型、混合模型以及人工神经网络模型等平原河网水动力模型 ,分析各个模型的优缺点 ,结果表明 ,节点 河道模型原则上可以求解任何水网的水力参数 ,单元划分模型仅适用于河道流速时空变化不大的情况 ,人工神经网络模型的验证比较困难。指出改进和设计计算方法、应用向量运算和并行算法、数值模拟可视化、数值计算模型软件化是河网数值模拟的主要发展方向

关 键 词:河网  非恒定流  水动力模型  数值模拟  人工神经网络
文章编号:1004-6933(2003)03-0005-05
修稿时间:2002年9月23日

Hydrodynamic model for plain river networks and its solution
LU Shi qiang,et al.Hydrodynamic model for plain river networks and its solution[J].Water Resources Protection,2003,19(3):5-9.
Authors:LU Shi qiang  
Abstract:By reference to some related literature, a node channel hydrodynamic model for plain river networks is developed based on the governing equations and junction connection conditions for the unsteady flow hydrodynamic model, and the trains of thought of some solutions are introduced, including the characteristics method, finite volume method, and several finite difference methods, such as the direct method, stepwise method, junction grouping method, matrix identification method, and non linear method. The present model is compared with other hydrodynamic plain river network models, including the element division model, hybrid model, and artificial neural network model, and the result indicates that the node channel model can solve the hydrodynamic parameters of any river network, and that the element division model is only suitable for the rivers with little temporal and spatial variation of flow velocity, while the artificial neural network model is difficult to be verified. Finally, it is proposed that the improvement of the calculating method, application of vector arithmetic and parallel arithmetic, visualization of numerical simulation, and software development for the calculation model are the main directions for further development of numerical simulation of river networks.
Keywords:river network  unsteady flow  hydrodynamic model  numerical model  aritificial neural network
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