Study on Application of Random Walk Method to Water Exchange in Large-Scale Bay
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摘要: 水交换矩阵是反映大型海湾不同子区域之间水交换关系的有效工具,该文对随机游动方法在水交换矩阵计算中的应用进行了研究.通过与对流扩散模型的对比,指出用随机游动方法计算水交换矩阵比用对流扩散方程求解更快更灵活.同时,为了对不同区域之间的水交换特性进行长期快速预测,采用了Thompson提出的预测矩阵方法.通过理论分析得出,影响该矩阵预测效果的因素主要有:粒子数目、分区数和预测矩阵取值时间.以一个矩形海湾为例,将数值模拟结果和预测矩阵预测结果进行对比,分析了以上各因素对预测效果的影响.结果表明,粒子数目越多,预测矩阵取值时间越长,预测矩阵的预测效果越好.在粒子数目足够多的情形下,分区数越多,预测效果越好.相反,粒子数目如果较少,过多的分区数将可能导致预测矩阵效果太差而不可用.Abstract: Water exchange matrix is an efficient tool to study the water exchange among sub-areas in large-scale bays. The application of random walk method to calculate water exchange matrix was studied. Compared against the advection-diffusion model, the random walk model is more flexible to calculate the water exchange matrix. The forecast matrix suggested by Thompson was applied to evaluate the water exchange characteristics among the sub-areas fast. According to theoretic analysis, it is found that the precision of the predicted results is mainly affected by three factors, namely the particle number, the generated time of the forecast matrix and the number of the sub-areas. The impact of the above factors was analyzed based on the results of a series of numerical tests. The results show that the precision of the forecast matrix increases with the increase of generated time of the forecast matrix and the number of the particles. If there are enough particles in each sub-area, the precision of the forecast matrix will increase with the number of the sub-areas. On the other hand, if the particles in each sub-area are not enough, excessive number of sub-areas may result in the decrease of the precision of the forecast matrix.
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Key words:
- random walk /
- markov chain /
- water exchange /
- numerical simulation /
- advection-diffusion
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