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Tikrit Journal of Engineering Sciences (2018) 25(2) 18- 26

Numerical Simulation for Estimating Energy Dissipation over Different Types of Stepped Spillways and Evaluate the Performance by Artificial Neural Network

Asmaa Abdul Jabbar Jamel
Civil Engineering Department, Tikrit University, Iraq

Abstract

In this research, Flow-3D software uses to study the energy dissipation for stepped spillways with different end sills. The study is bases on three models. The first model contains rectangular end sills in all steppes. The second model contains rectangular end sills between one step and another. The third model contains triangular end sills in all steppes. For each of these models, three different variables are adopt, slope, height of the spillway and a number of steppes, and four different discharges value, carrying the total number of experiments to (324) tests. Analytical results show that the model (3) is the highest energy dissipation for all discharges value. Empirical equations extraction to find the energy dissipation for each of these models. The artificial neural network is also adopt to prove the accuracy and efficiency of the analytical results which are at high rates of compatibility with the values of the coefficient of determination for (model 1), (model 2) and (model 3) equal to (93.47%), (88.20%) and (86.00%) respectively. Also, artificial neural network identifies the most influential factors on the energy dissipation, the friction Froude number is the highest impact on the energy dissipation for models (1) and (2), while the parameter (b/ks) for the model (1).

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Keywords: Flow-3D, Artificial Neural Network, energy dissipation, stepped spillways

How to cite

TJES: Jamel AAJ. Numerical simulation for estimating energy dissipation over different types of stepped spillways and evaluate the performance by artificial neural network. Tikrit Journal of Engineering Sciences 2018; 25 (2): 18-26.
APA: Jamel, A. A. J. (2018). Numerical simulation for estimating energy dissipation over different types of stepped spillways and evaluate the performance by artificial neural network. Tikrit Journal of Engineering Sciences, 25(2), 18-26.