Hydraulic Performance of PK Weirs Based on Experimental Study and Kernel-based Modeling

Roushangar, Kiyoumars; Asl, Mahdi Majedi; Shahnazi, Saman

doi:10.1007/s11269-021-02905-4

Cited by 21 publications

(3 citation statements)

References 32 publications

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“…The hydraulic performance of measurement structures is being improved to avoid unconventional increases in water depth in the structure upstream during high discharges and floods. The hydraulic performance of impenetrable weirs is enhanced by reducing the separation area at the upstream crest and increasing the effective length, similar to labyrinth or piano key weirs (Abhash and Pandey 2021;Mirzaei and Sheibani 2021;Roushangar et al 2021;Behroozi and Vaghefi 2022;Khowr et al 2022). In this research, attempts have been made to enhance the hydraulic performance of the PBCW by (1) reducing the effective crest length and (2) reducing the length of the flow path inside the weir and hence reducing the path loss.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests

Doustkam,

Rahmanshahi,

Fathi-Moghadam

et al. 2024

Water Resour Manage

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Due to the high technical and hydraulic performance and negligible environmental adverse impact of a porous weir, this structure is a reasonable substitute for conventional impermeable solid weirs in water transmission and distribution systems. This research focused on the hydraulic performance of porous broad-crested weirs (PBCWs) with sloping crests. To this end, 30 PBCWs models in free and submerged flow conditions were designed and examined under different operation conditions. In particular, a comparative study was performed between PBCWs with and without sloping crests, the performance of which were then compared with a solid broad-crested weir (SBCW) model. Based on the results and analysis, the PBCWs with sloping crests have a more significant discharge coefficient than common PBCWs (without sloping crests), and the upstream slope has a more significant impact on the discharge coefficient than the downstream slope. In addition, the PBCWs with sloping crests are more sensitive to tailwater than common PBCWs, and thus the discharge reduction factor of the weir with the upstream crest slope is lower than that of the downstream. Finally, based on the extensive experimental results, both nonlinear multivariable regression and gene-expression programming approaches were applied to extract empirical equations for expressing the free flow discharge coefficient and submerged flow discharge reduction factor.

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Section: Introductionmentioning

confidence: 99%

Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests

Doustkam,

Rahmanshahi,

Fathi-Moghadam

et al. 2024

Water Resour Manage

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“…High efficiency and speed of machine learning methods in modeling sophisticated hydraulic problems have made them as one of the most widely used methods in predicting the discharge coefficient of different types of weirs. A promising application of Artificial Neural Network (Bilhan et al 2011;Salmasi et al 2013;Parsaie and Haghiabi 2015;Karami et al 2018), Support Vector Machine (Azamathulla et al 2016;Roushangar et al 2021), Adaptive-Network-based Fuzzy Inference Systems (Shamshirband et al 2016;Haghiabi et al 2018) were reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid grey wolf optimization-based KELM approach for prediction of the discharge coefficient of submerged radial gates

2022

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Radial gates are widely used hydraulic structures for flow control in irrigation canals.Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.

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“…Mohammed & Sharifi (2020) proposed using the GEP to predict discharge coefficient for oblique side weir. Zhou et al (2015), Zaji et al (2016), Zaji & Bonakdari (2017), Roushangar et al (2017), Nadiri et al (2018), MajediAsl & Fuladipanah (2019, Sadeghfam et al (2019), Kumar et al (2019) and Roushangar et al (2021) proposed to evaluate discharge coefficient using data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

et al. 2021

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As a remarkable parameter, the discharge coefficient (Cd) plays an important role in determining weirs' passing capacity. In this research work, the SVM and the GEP algorithms were assessed to predict Cd of piano key weir (PKW), rectangular labyrinth weir (RLW), and trapezoidal labyrinth weir (TLW) with gathered experimental data set. Using dimensional analysis, various combinations of hydraulic and geometric non-dimensional parameters were extracted to perform simulation. The superior model for the SVM and the GEP predictor for PKW, RLW, and TLW included , and respectively. The results showed that both algorithms are potential in predicting discharge coefficient, but the coefficient of determination (RMSE, R2, Cd(DDR)max) illustrated the superiority of the GEP performance over the SVM. The results of the sensitivity analysis determined the highest effective parameters for PKW, RLW, and TLW in predicting discharge coefficients are , , and Fr respectively.

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Hydraulic Performance of PK Weirs Based on Experimental Study and Kernel-based Modeling

Cited by 21 publications

References 32 publications

Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests

Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests

An efficient hybrid grey wolf optimization-based KELM approach for prediction of the discharge coefficient of submerged radial gates

Using data mining methods to improve discharge coefficient prediction in Piano Key and Labyrinth weirs

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