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Automatic Shifting Method for the Identification of Generalized Radial Flow Parameters by Water Cycle Optimization

机译:基于水循环优化的广义径向流动参数辨识自动换挡方法

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摘要

The general radial flow (GRF) could successfully analyze the groundwater flow in a fractured medium which has generally a more complex mechanism due to the scale-dependent heterogeneity and dynamic processes for both individual fracture and fracture networks. A new optimization scheme, referred to as the automatic shifting method (ASM), was established in order to eradicate the subjectivity and some definite difficulties in classical graphical curve matching (GCM) for the determination flow parameters of GRF from in-situ pumping test data. The logic behind the ASM is similar to GCM but it simplifies and enhances the estimation process by optimizing newly introduced parameters (the horizontal and vertical shifts) together with the flow dimension parameter via Water Cycle Algorithm (WCA). The proposed ASM was tested with several hypothetical pumping test scenarios as well as a number of real field data. In addition, the capability of WCA was thoroughly compared with other competitive derivative-free, nature inspired population-based optimization algorithms by implementing a multi decision criteria analysis. The proposed ASM with WCA could achieve the outstanding estimation performance for the implemented analyses. In conclusion, ASM has a great potential to be modified for interpreting test data obtained from different groundwater models.
机译:一般径向流(GRF)可以成功地分析裂隙介质中的地下水流,由于单个裂缝和裂缝网络的尺度依赖性和动态过程,裂隙介质的机理通常更为复杂。为了消除原位抽水试验数据中GRF流量参数测定的主观性和经典图形曲线匹配(GCM)的局限性和一些确定的困难,建立了一种新的优化方案,即自动换挡法(ASM)。ASM 背后的逻辑类似于 GCM,但它通过水循环算法 (WCA) 优化新引入的参数(水平和垂直位移)以及流量维度参数,从而简化和增强了估算过程。所提出的ASM使用几个假设的泵送测试场景以及一些真实的现场数据进行了测试。此外,通过实施多决策准则分析,将WCA的能力与其他竞争性无导数、自然启发的基于群体的优化算法进行了彻底的比较。所提出的ASM与WCA相结合,在所实施的分析中实现了出色的估计性能。综上所述,ASM在解释从不同地下水模型获得的测试数据方面具有很大的修改潜力。

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