Numerical simulation and application of soft computing in estimating vertical drop energy dissipation with horizontal serrated edge

Abstract

In the present study, FLOW-3D software was used to simulate energy dissipation by a serrated-edge drop, downstream of this structure. For this purpose, 2, 3, and 4 serrations with 2 series of relative dimensions at the edge of the vertical drop, with a relative critical depth range of 0.2–0.35 were used for simulation. Then, using artificial neural network (ANN), support vector machine (SVM), and gene expression program (GEP) methods, the accuracy of numerical models was evaluated. Results showed that increasing dimensions of the edges increased energy dissipation, and the highest and lowest energy dissipation was related to the models with 3 and 4 serrations, respectively, Compared to the edgeless state, the 4-edge model, with relative dimension of 0.1, increased energy dissipation by an average of 20%, and the 3-edge model, with relative dimension of 0.15, by an average of 69%. Results of energy dissipation prediction using ANN, SVM, and GEP methods showed that although all three models have good accuracy for estimating energy dissipation, the accuracy of ANN method with RMSE of 0.0081 and R2 of 0.9938 in the training phase and RMSE of 0.0125 and R2 of 0.9805 in the testing phase, is higher than the other two methods. HIGHLIGHT Results showed that increasing dimensions of the edges increased energy dissipation. Results of energy dissipation prediction using ANN, SVM, and GEP methods showed that all three models have good accuracy for estimating energy dissipation.;

Keywords

• artificial neural network
• flow-3d
• gene expression programming
• support vector machine
• vertical drop