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Galerkin's formulation of the finite elements method to obtain the depth of closure

机译:Galerkin提出的有限元方法来获得闭合深度

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摘要

Coastal erosion and lack of sediment supply are a serious global problem. It is therefore necessary to determine the depth of closure (DoC) of a beach-key parameter in the calculation of the sand volume and the location of the beach protection elements-in a precise way. For this reason, this work generates a numerical model based on Galerkin's formulation of finite elements that provides sufficient precision for the determination of DoC with a minimum investment. Thus, after the generation of three models in which the difference was the dependent variables, the least complex has been chosen. It is composed of the variables: median sediment size, wave height and period associated with the mean flow, as well as the angle that the mean flow forms with respect to the studied profile in absolute value (a). The selected model has been compared with the most commonly used models currently in use, having an average absolute error of 0.36 m and an average MAPE of 7.5%, which represents an improvement of 70% over current models. In addition, it presents a high stability, since after the random disturbance of all the input variables (up to 5%), the model error remains stable, increasing the MAPE by a maximum of 7.4% and the average absolute error by 0.15 m. Therefore, it is possible to use the model to infer the DoC in other study areas where the values of the variables are similar to those studied here, although the selected method can be extrapolated to other parts of the world. (c) 2019 Elsevier B.V. All rights reserved.
机译:沿海侵蚀和缺乏沉积物供应是一个严重的全球性问题。因此,有必要以精确的方式确定沙滩关键参数的闭合深度(DoC),以计算沙量和沙滩保护元件的位置。因此,这项工作基于Galerkin的有限元公式生成了一个数值模型,该模型可以用最少的投资为DoC的确定提供足够的精度。因此,在生成三个模型(其中差异是因变量)之后,选择了最小复杂度。它由以下变量组成:中值沉积物大小,波高和与平均流量相关的周期,以及平均流量相对于研究剖面的绝对值(a)的角度。已将所选模型与当前使用的最常用模型进行比较,该模型具有0.36 m的平均绝对误差和<7.5%的平均MAPE,与当前模型相比提高了70%以上。此外,它具有很高的稳定性,因为在所有输入变量(最多5%)受到随机干扰之后,模型误差保持稳定,MAPE最大值增加了7.4%,平均绝对误差增加了0.15 m。因此,尽管所选方法可以外推到世界其他地区,但也可以使用该模型来推断其他研究领域的DoC,这些领域的变量值与此处研究的变量值相似。 (c)2019 Elsevier B.V.保留所有权利。

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