Application of data-driven models to predict the dimensions of flow separation zone

• Published in: Environmental science and pollution research international Vol. 30; no. 24; pp. 65572 - 65586
• Main Authors: Gharehbaghi, Amin, Ghasemlounia, Redvan, Latif, Sarmad Dashti, Haghiabi, Amir Hamzeh, Parsaie, Abbas
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2023; Springer Nature B.V
• Subjects: Algorithms; Angle of attack; Ant colony optimization; Aquatic Pollution; Artificial intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; butterflies; Civil engineering; computer software; data collection; Earth and Environmental Science; Ecotoxicology; Environment; Environmental Chemistry; Environmental Health; Environmental science; Flat plates; Flow separation; Gene expression; hybrids; Hydraulics; Intake channels; Laboratories; Mathematical models; Optimization; regression analysis; Research Article; Robustness (mathematics); Sediments; Software; Statistical analysis; Support vector machines; system optimization; Vanes; Waste Water Technology; Water Management; Water Pollution Control; Water quality; Data-driven models; Submerged vanes; Dimensions of flow separation zone; Lateral intake; Butterflies array
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-023-27024-y

In this research, the effect of a submerged multiple-vane system on the dimensions of flow separation zone (DFSZ) is assessed via 192 measured datasets. The vanes’ shape comprised two segments, curved and flat plates which are located in the connection of main channel to the lateral intake channel with an angle of 55°. In this direction, a butterfly’s array for the vanes’ arrangement along with different main controlling factors such as distances of vanes along the flow ( δ l ), degree of curvature ( β ), and angles of attack to the local primary flow direction ( θ ) is utilized. Through capturing photos and utilizing AutoCAD and SURFER software, maximum relative length and width are calculated. Based on the experimental measurements, maximum percentage reduction of DFSZ, in comparison with the controlled test (without submerged vanes), is obtained with θ  = 30°, β  = 34°, and δ l  = 10 cm with value of 78 and 76%, respectively. Moreover, several data-driven models, namely, gene expression programming (GEP), support vector regression (SVR), and a robust hybrid SVR with an ant colony optimization algorithm (ACO) (i.e., hybrid SVR-ACO model), are developed in order to predict DFSZ via the operative dimensionless variables realized by Spearman’s rho and Pearson’s coefficient processes. In accordance with the statistical metrics, model grading process, scatter plot, and the hybrid SVR(RBF)-ACO model are preferred as the best and most precise model to predict maximum relative length and width with a total grade ( TG ) of 6.75 and 5.8, respectively. The generated algebraic formula for DFSZ under the optimal scenario of GEP is equated with the corresponding measured ones and the results are within 0–10%.