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Application of 3D Numerical Model and Intelligent Systems in Discharge Coefficient Estimation of Combined Weir-Gate
Water Resources ( IF 0.9 ) Pub Date : 2020-07-30 , DOI: 10.1134/s0097807820040028
Nima Aein , Mohsen Najarchi , Seyyed Mohammad Mirhosseini Hezaveh , Mohammad Mehdi NajafiZadeh , Ehsanollah Zeighami

Abstract

Combined weir-gate structure is one of the important structures which are control the water level, measure discharge and avoid sediment deposition behind the weir. In this study, first try to simulate four combined triangular weir-rectangular gates with different geometric conditions via 3D numerical software (Flow-3D) by using experimental data. Then, dimensionless analyses were done to find the non-dimension parameters affected the discharge coefficient of this structure. At the end, four different intelligent system models were used to estimate the discharge coefficient, evaluated and compared the results with each other. Results show that the Flow-3D software has a high capability to simulate the flow over the combined structure. Besides, the values of goodness of fit criteria show that the numerical solver, estimate the water head and discharge coefficient very well. Moreover, in all models, the results show that the discharge coefficient has an inverse relation with dimensionless parameters (h/b, h/d and h/y) and discharge coefficients in this study are between 0.3–0.9. On the other hand, results of accuracy analyses of four intelligent system models of MLP, RBF, GRNN and M5P show that the MLP model is the superior model in this study, and after that, the rest of models’ sort as M5P, RBF and GRNN in this study.


中文翻译:

3D数值模型和智能系统在组合堰门流量系数估算中的应用

摘要

组合式堰门结构是控制水位,测量流量和避免泥沙沉积在堰后的重要结构之一。在这项研究中,首先尝试通过使用实验数据通过3D数值软件(Flow-3D)模拟具有不同几何条件的四个组合的三角堰-矩形闸门。然后,进行无量纲分析,以发现影响该结构放电系数的非尺寸参数。最后,使用了四种不同的智能系统模型来估计排放系数,评估并相互比较结果。结果表明,Flow-3D软件具有很高的模拟组合结构上的流动的能力。此外,拟合优度标准的值表明,数值求解器 很好地估计水头和流量系数。此外,在所有模型中,结果表明,放电系数与无量纲参数成反比(h / bh / dh / y)以及流量系数在0.3-0.9之间。另一方面,对MLP,RBF,GRNN和M5P四个智能系统模型的准确性分析结果表明,MLP模型是本研究中的上乘模型,其后,其余模型分别为M5P,RBF和M5P。 GRNN在这项研究中。
更新日期:2020-07-30
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