Hydraulic optimization of corrugated stilling basin with adverse slope

T. Honar, Nafiseh Khoramshokooh, M. Nikoo
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引用次数: 5

Abstract

In this paper, perhaps for the first time, a data-driven simulation–optimization model is developed based on experimental results to find the effects of state and decision variables on the optimum characteristics of a stilling basin with adverse slope and corrugated bed. The optimal design parameters of the stilling basin are investigated to minimize the length of the hydraulic jump and ratio of the sequent depths of the jump while the relative amount of energy loss is maximized. In order to model the relationship between design variables of the bed, the experimental results are converted to a data-driven simulation model on the basis of a multilayer perceptron (MLP) neural network. Then, the validated MLP model is used in a genetic algorithm optimization model in order to determine the optimum characteristics of the bed under the hydraulic jump considering the interaction between the bed design variables and the hydraulic parameters of the flow. Results indicate that the optimum values of bed slope and the diameter of the corrugated roughness (2 r ) can be considered as −0.02 and 20 millimetres, respectively.
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不利坡度波纹型消力池水力优化
本文也许是第一次基于实验结果建立了一个数据驱动的模拟优化模型,以寻找状态变量和决策变量对具有不利坡度和波纹床的消力池优化特性的影响。以最大的相对能量损失量为目标,研究了消力池的优化设计参数,使水跃长度和水跃顺序深度比最小。为了对床层设计变量之间的关系进行建模,将实验结果转换为基于多层感知器(MLP)神经网络的数据驱动仿真模型。然后,将验证的MLP模型应用于遗传算法优化模型中,考虑床层设计变量与水流水力参数的相互作用,确定床层在水力跃变下的最优特性。结果表明,床坡的最佳值为- 0.02 mm,波纹粗糙度直径(2 r)为20 mm。
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