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شبیهسازی عددی دبی آبگیر با استفاده از نرمافزارFLOW-3D و ارزیابی پارامترهای هیدرولیکی با استفاده از مدل ماشین یادگیری | ||
| تحقیقات مهندسی سازه های آبیاری و زهکشی | ||
| دوره 25، پاییز - شماره پیاپی 96، آذر 1403، صفحه 46-27 اصل مقاله (1.08 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/idser.2024.366862.1591 | ||
| نویسندگان | ||
| ایمان کریمی سرمیدانی1؛ محمد حیدرنژاد* 2؛ اصلان اگدرنژاد3 | ||
| 1دانشجوی کارشناسی ارشد سواحل، بنادر و سازههای دریایی، گروه مهندسی عمران ، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 2دانشیار، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| 3استادیار، گروه علوم و مهندسی آب، واحد اهواز، دانشگاه آزاد اسلامی، اهواز، ایران. | ||
| چکیده | ||
| نقش آبشکنها در حفاظت از دیوارة رودخانه، آن ها را جزء موضوع های مهم مهندسی رودخانه قرار داده است. در این مطالعه، عملکرد دو مدل یادگیری ماشین شامل ماشین بردار پشتیبان (SVM) و برنامهریزی بیان ژن (GEP) با نرمافزار FLOW-3D در شبیهسازی میزان دبی انحرافی در مقیاس آزمایشگاهی مقایسه شدهاست. برای مدل فیزیکی در یک فلوم آزمایشگاهی با دو نوع آبشکن T-شکل و L-شکل و با زاویههای 90 و 135 درجه برای انحراف دبی به کانال آبگیری شبیهسازی شد. سه متغیر مستقل شامل عدد فرود جریان، زاویۀ آبگیری و طول نسبی آبشکنها برای مدلهای یادگیری ماشین استفاده شدند. از 96 دادۀ آزمایشگاهی، 70 درصد برای آموزش و 30 درصد برای آزمون مدلهای یادگیری ماشین اختصاص یافت. سه شاخص RMSE، MAE و R² برای ارزیابی عملکرد مدلها به کار گرفته شدند. نتایج تحقیق نشان داد که برنامهریزی بیان ژن (GEP) نسبت به ماشین بردار پشتیبان (SVM) عملکرد بهتری دارد بهطوری که در مرحلة آموزش و آزمون، مقدار ضرایب ارزیابی عملکرد برای هر دو آبشکن T-شکل و L-شکل دارای برتری بودند. در نرمافزار FLOW-3D، ضریب زبری مانینگ و نوع معادلۀ شبیهساز آشفتگی در فرآیندهای واسنجی و صحتسنجی استفاده شدند. مقایسة بین مقادیر ارزیابی عملکرد نشان دهندة برتری نسبی GEP نسبت به FLOW-3D است. | ||
| کلیدواژهها | ||
| دبی انحرافی؛ آبشکن؛ قوس رودخانه؛ مدل داده-مبنا؛ ارزیابی عملکرد | ||
| عنوان مقاله [English] | ||
| Quantitative simulation of diverged flow using machine learning techniques and FLOW3D numerical modeling | ||
| نویسندگان [English] | ||
| Iman Karimi Sarmeydani1؛ Mohammad Heidarnejad2؛ Aslan Egdernezhad3 | ||
| 1M.Sc. Student, Department of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 2Associate Professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| 3Assistant professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran. | ||
| چکیده [English] | ||
| Extend Abstract Intouduction Groynes at water intake locations significantly increase the flow diverted from rivers by optimizing incoming water control. Vaghefi and Ghodsian (2017) experimentally studied flow patterns around a T-shaped Groyne within a 90-degree arc using a moving bed. Shaker and Kashfipour (2013) compared flow velocity and shear stress distribution with and without Groynes. Behnam-Talab et al. (2018) simulated porous Groynes using FLOW-3D software. Shahinejad et al. (2022) applied a multi-objective algorithm to optimize T-shaped Groyne dimensions, achieving superior results, compared to previous designs. Zare and Honer (2016) investigated how simple Groynes reduce lateral erosion in river arches under laboratory conditions, emphasizing the influence of Groynes on erosion patterns. These studies collectively highlight the importance of Groyne design in enhancing water extraction and mitigating erosion. The review of literatures confirms that both laboratory and numerical studies have been conducted to examine the characteristics of various types of Groynes and the impact of flow patterns on them. However, there is a lack of studies addressing the simultaneous application and comparison of numerical and data-driven models in the investigation of geometric and hydraulic characteristics, particularly concerning the effect on the amount of diverted discharge from a canal into intake featuring T-shaped and L-shaped Groynes. Consequently, this research aims to evaluate the performance of two MLMs, specifically SVM and GEP in comparison with the Computational Fluid Dynamics (CFD)-based FLOW-3D model, on a laboratory scale. Materials and Methods Groynes play a crucial role in river engineering by regulating river flow. This study assesses the efficacy of two machine learning algorithms—support vector machine (SVM) and gene expression programming (GEP)—in comparison with FLOW-3D software for simulating diverted flow in a laboratory setting. The experimental model was tested in a laboratory flume with T-shaped and L-shaped Groynes positioned at 90 and 135-degree angles to channel the discharge into the intake system. The machine learning models incorporated three independent variables: the flow Froude number, the angle of water intake, and the relative length of the Groynes. Out of 96 laboratory data points, 70% were allocated for model training and 30% for model testing. Model performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²) indices. Results and Discussion The results indicated that the GEP model surpassed the SVM model. For the L-shaped Groyne, the values for (R², MAE, RMSE) during both the training and testing phases were (0.9325, 0.9878, 1.2536) and (0.9836, 0.4102, 0.6325), respectively. For the T-shaped Groyne, the corresponding values were (0.9025, 1.2534, 1.8502) during training and (0.9873, 0.3337, 0.4972) during testing. In the FLOW-3D model, after calibration and validation, a Manning's roughness coefficient of 0.035 and the Prandtl's mixing length model were chosen for turbulence simulation. The performance indices during the testing phase for the L-shaped and T-shaped Groynes were (0.9607, 0.9363, 1.2070) and (0.9513, 1.1256, 1.3759), respectively. The GEP model showed a relative advantage over the FLOW-3D model. Concutions This study compares the performance of MLMs (SVM, GEP) with FLOW-3D in simulating diverted flow using T-shaped and L-shaped Groynes. Results from laboratory flume tests showed GEP outperformed SVM and FLOW-3D, particularly in simulating flow diversion, evaluated by RMSE, MAE, and R² performance indices. | ||
| کلیدواژهها [English] | ||
| Diverted Flow, Computational Fluid Dynamics, Data-Driven model, Performance Assessment | ||
| مراجع | ||
|
Acharya, A., & Duan, J. G. (2011). Three dimensional simulation of flow field around series of spur dikes. International Refereed Journal of Engineering and Science, 2(7), 36-57. Aezzi, S., Khanjani, M. J., & Zounemat-Kermani, M. (2018). Two Dimensional Simulation of Flow pattern and Bed Changes in Straight and Meandering Channels under the Effect of Spur Dike. Iranian Journal of Irrigation & Drainage, 12(4), 970-981. (in Persian) Al-Zubaidy, R. A., & Hilo, A. N. (2022). Numerical investigation of flow behavior at the lateral intake using Computational Fluid Dynamics (CFD). Materials Today: Proceedings, 56, 1914-1926. Alauddin, M., & Tsujimoto, T. (2011). Optimum Design of Groynes for Stabilization of Lowland Rivers. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering), 67(4), 145-150. Ali-Nagizadeh-Behbahani, H. (2013). Investigating the hydraulic conditions of the flow in reservoirs with a deviation angle of 90 and 75 degrees using a physical model Shahid Chamran University of Ahvaz]. Ahvaz. (in Persian) Aminvash, E., Kalateh, F., Daneshfaraz, R., & Abraham, J. (2024). Investigation of the Performance of Soft Computing Methods in the Hydraulic Evaluation of the Slot Fishway on the Inclined Drop. Journal of Hydraulic Structures, 10(1), 46-65. Behnamtalab, E., Cheshomi, A., & Khamchin-Moghaddam, F. (2019). Numerical simulation of porous spur dyke using Flow 3D software. Journal of Hydraulics, 14(1), 35-48. https://doi.org/10.30482/jhyd.2019.139136.1306. (in Persian) Chenari, S. A., Nadian, H. A., Ahadiyan, J., Valipour, M., Oliveto, G., & Sajjadi, S. M. (2024). Enhancing Hydraulic Efficiency of Side Intakes Using Spur Dikes: A Case Study of Hemmat Water Intake, Iran. Water and Soil Science, 16(16), 2254. Daneshfaraz, R., Aminvash, E., Ghaderi, A., Abraham, J., & Bagherzadeh, M. (2021). SVM performance for predicting the effect of horizontal screen diameters on the hydraulic parameters of a vertical drop. Applied sciences, 11(9), 4238. Daneshfaraz, R., Aminvash, E., Mirzaee, R., & Abraham, J. (2021). Predicting the energy dissipation of a rough sudden expansion rectangular stilling basins using the SVM algorithm. Journal of Applied Research in Water and Wastewater, 8(2), 98-106. Esmaeli, P., Bodaghpour, S., Rostami, M., & Mirzaee, M. (2020). Experimental study of Longitudinal Components of Velocity Around Simple Series Spur Dikes. Irrigation and Water Engineering, 11(2). https://doi.org/10.22125/iwe.2020.120721. (in Persian) Ferreira, C. (2001). Algorithm for solving gene expression programming: a new adaptive problem. Complex Systems, 13(2), 87-129. Fuladipanah, M., & Majedi-Asl, M. (2022). Soft Computing Application to Amplify Discharge Coefficient Prediction in Side Rectangular Weirs. Irrigation and Water Engineering, 12(4), 213-233. https://doi.org/10.22125/iwe.2022.150692. (in Persian) Fuladipanah, M., Majedi-Asl, M., & Haghgooyi, A. (2020). Application of intelligent algorithm to model head-discharge relationship for submerged labyrinth and linear weirs. Journal of Hydraulics, 15(2), 149-164. https://doi.org/10.30482/jhyd.2020.232388,1461. (in Persian) Gupta, L.K., Pandey, M. & Anand Raj, P. (2024). Numerical simulation of local scour around the pier with and without airfoil collar (AFC) using FLOW-3D. Environ Fluid Mech 24, 631–649. https://doi.org/10.1007/s10652-023-09932-2. Hosseini-Asli, N., Parvaresh-Rizi, A., Rostami, M., & Fahoudi, J. (2015). 3D simulation of flow pattern in the vicinity of the inlet canal of lateral intake diversion dam. Iranian Water Researches Journal, 9(3), 89-98. (in Persian) Kalita, H. M., Sarma, A. K., & Bhattacharjya, R. K. (2014). Evaluation of Optimal River Training Work using GA Based Linked Simulation- Optimization Approach. Water resources management, 28(8), 2077-2092. Koncar, N. (1997). Optimisation methodologies for direct inverse neurocontrol (Doctoral dissertation). University of London. Mandal, A., Gautam, H., & Ahmad, Z. (2024). Sediment control and flow redistribution with submerged vanes: a review. Water Practice & Technology, 19(5), 2197-2212. Maroofi-nia, E., Asna-Ashari, A., Hassan-Zadeh, Y., Khosh-tinat, S., & Amin-nejad, B. (2016). Investigation of the Effects of Submerged Vanes and Spur Dikes on Sediment Control at Lateral Intakes by SSIIM2 Software. Irrigation Sciences and Engineering, 39(4), 169-180. https://doi.org/10.22055/jise.2016.12505. (in Persian) Remesan, R., & Mathew, J. (2016). Hydrological data driven modelling. Springer International Pu. Sedaghat-Shayegan, D., & Roosta, M. R. (2024). Numerical Investigation of Block Form Effect in Amount of Dissipated Energy of Baffled Apron Spillway Drop by FLOW-3D. Journal of Civil and Environmental Engineering, 54.1(114), 56-62. https://doi.org/10.22034/jcee.2020.13438.1349. (in Persian) Shahinejad, B., Yonesi, H. A., & Mirbeyksabzevari, M. (2023). Optimum Design of spur dikes dimension using multi-objective of harmony search algorithm, Case study: Zanjanrood River. Hydrogeomorphology, 10(34), 165-141. https://doi.org/10.22034/hyd.2023.54639.1670. (in Persian) Shaker, E., & Kashefipour, S. (2014). Experimental Investigation on Velocity and Shear Stress Distributions in a 90-Degree Bend With and Without Series of Groynes. Water and Soil Science, 24(3), 55-68. (in Persian) Talaat, A., Attia, K., Elsaeed, G., & Ibraheem, M. (2009). Implementation of Spur Dike to Reduce Bank Erosion of Temporary Diversion Channels during Barrages Construction. Australian Journal of Basic and Applied Sciences, 3(4), 3190-3205. Vaghefi, M., & Ghodsian, M. (2010). Experimental study on flow field and scour pattern around t shaped spur dike in a 90 degree bend. Civil engineering infrastructures journal (ceij) (journal of faculty of engineering). Sid, 44(2), 265-276. https://doi.org/Https://sid.Ir/paper/166592/en. (in Persian) Vaghefi, M., Ghodsian, M., & Akbari, M. (2017). Experimental investigation on 3D flow around a single T-shaped spur dike in a bend. Periodica Polytechnica Civil Engineering, 61(3), 462-470. Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer-Verlag. Yang, J., Shen, Z., Zhang, J., Teng, X., Zhang, W., & Dai, J. (2022). Experimental and numerical investigation of flow over a spillway bend with different combinations of permeable spur dikes. Water Supply, 22(2), 1557-1574. Zare, M., & Honar, T. (2016). The Effect of Groyne on Reduction of the Scour Depth around Bridge Piers in River Bends. Journal of Water and Soil Science, 19(74), 167-192.
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