基于马尔科夫方法的区域水交换研究
发布时间：2019-07-03 18:01
【摘要】：海洋中,在周期性潮流动力条件下,区域间的水体交换可近似认为满足马尔科夫性,利用该性质,可以快速有效地预测区域间的水交换问题。该文以渤海辽河口为研究区域,通过粒子跟踪方法得到区域间的概率传输矩阵,根据马尔科夫性,利用概率传输矩阵对辽河口水交换进行预测。研究表明,利用概率传输矩阵预测得到的水交换结果不仅与直接数值模拟吻合良好,还可极大缩减水体交换的计算模拟时间,因此对于长周期的预测,马尔科夫传输矩阵法比直接数值模拟更合理。
[Abstract]:In the ocean, under the condition of periodic tidal current dynamics, the water exchange between regions can be approximately considered to satisfy Markov property, and the problem of water exchange between regions can be predicted quickly and effectively by using this property. In this paper, the probability transfer matrix between regions is obtained by particle tracking method, and the probabilistic transfer matrix is used to predict the water exchange in Liaohe estuary according to Markov property. The results show that the water exchange results predicted by probabilistic transfer matrix are not only in good agreement with the direct numerical simulation, but also can greatly reduce the simulation time of water exchange. Therefore, Markov transfer matrix method is more reasonable than direct numerical simulation for long period prediction.
【作者单位】： 中国海洋大学海洋环境与生态教育部重点实验室;
【基金】：国家重点基础研究发展计划项目(2015CB453301) 国家水体污染控制与治理科技重大专项(2013ZX07202-007)~~
【分类号】：P731.2
，
本文编号：2509574
[Abstract]:In the ocean, under the condition of periodic tidal current dynamics, the water exchange between regions can be approximately considered to satisfy Markov property, and the problem of water exchange between regions can be predicted quickly and effectively by using this property. In this paper, the probability transfer matrix between regions is obtained by particle tracking method, and the probabilistic transfer matrix is used to predict the water exchange in Liaohe estuary according to Markov property. The results show that the water exchange results predicted by probabilistic transfer matrix are not only in good agreement with the direct numerical simulation, but also can greatly reduce the simulation time of water exchange. Therefore, Markov transfer matrix method is more reasonable than direct numerical simulation for long period prediction.
【作者单位】： 中国海洋大学海洋环境与生态教育部重点实验室;
【基金】：国家重点基础研究发展计划项目(2015CB453301) 国家水体污染控制与治理科技重大专项(2013ZX07202-007)~~
【分类号】：P731.2
，
本文编号：2509574
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