论文部分内容阅读
传统的水沙数学模型基于水沙运动守恒规律,在给定模型参数和边界条件下封闭未知变量,产生可求解的静定方程组。本文在传统水沙数学模型的基础上,引入水沙实时观测值,利用集合卡尔曼滤波等方法,构造水位、流量和含沙量等未知变量的状态空间方程,通过滤波求解获得他们的优化值,并实时更新模型方程的初始场,将传统的水沙数学模型发展为水沙实时预测数学模型。该实时校正模式应用于2011年黄河下游花园口至利津河段调水调沙实验,取得了满意的效果。
Based on the conservation laws of water and sediment, the traditional mathematical models of water and sediment closed unknown variables under given model parameters and boundary conditions, resulting in resolvable static equations. Based on the traditional mathematical models of water and sediment, the real-time observations of water and sediment are introduced, and the state space equations of unknown variables such as water level, flow rate and sediment concentration are constructed by means of ensemble Kalman filter and their optimization values , And update the initial field of the model equation in real time to develop the traditional mathematical model of water and sediment into a real-time prediction model of water and sediment. The real-time calibration model is applied to the water and sediment adjustment experiments from Huayuankou to Lijin in the lower reaches of the Yellow River in 2011 and achieved satisfactory results.