Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets
Vertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. This study aimed to predict the dimensionless pressure coefficient (C<sub>p</sub>) of vertical water jets by examining the relationships between expe...
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MDPI AG
2024-09-01
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| Series: | Fluids |
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| Online Access: | https://www.mdpi.com/2311-5521/9/9/205 |
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| author | Amin Salemnia Seyedehmaryam Hosseini Boldaji Vida Atashi Manoochehr Fathi-Moghadam |
| author_facet | Amin Salemnia Seyedehmaryam Hosseini Boldaji Vida Atashi Manoochehr Fathi-Moghadam |
| author_sort | Amin Salemnia |
| collection | DOAJ |
| description | Vertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. This study aimed to predict the dimensionless pressure coefficient (C<sub>p</sub>) of vertical water jets by examining the relationships between experimental parameters, such as Froude number, slope, and the ratio of waterfall height over the product of the Froude number and diameter, referred to as α, using machine learning models. Two hundred forty controlled experiments were conducted, with pressure data collected. To address the problem’s non-linearity, six machine learning models were tested: linear regression, K-nearest neighbors, decision tree, support vector regression, random forest, and XGBoost. The XGBoost model outperformed others, achieving an R-squared of 0.953 and a Root Mean Squared Error (RMSE) of 0.191. Residual analysis validated its better performance, demonstrating that it delivered the most accurate predictions with minimal bias. Feature importance analysis revealed the Froude number was the most significant predictor, followed by slope and diameter. This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems. |
| format | Article |
| id | doaj-art-fe0c5bc785c04c528399f6fcebbf7f00 |
| institution | OA Journals |
| issn | 2311-5521 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Fluids |
| spelling | doaj-art-fe0c5bc785c04c528399f6fcebbf7f002025-08-20T01:55:27ZengMDPI AGFluids2311-55212024-09-019920510.3390/fluids9090205Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water JetsAmin Salemnia0Seyedehmaryam Hosseini Boldaji1Vida Atashi2Manoochehr Fathi-Moghadam3Department of Water Engineering, Shahid Chamran University, Ahvaz 6135743136, IranDepartment of Electrical and Computer Engineering, K. N. Toosi University of Technology, Tehran 163171419, IranFaculty of Civil Engineering Department, University of North Dakota, Grand Forks, ND 58202, USAFaculty of Water and Environmental Engineering Department, Shahid Chamran University, Ahvaz 6135743136, IranVertical water jets present significant challenges for hydraulic structures due to their potential to cause erosion and structural damage. This study aimed to predict the dimensionless pressure coefficient (C<sub>p</sub>) of vertical water jets by examining the relationships between experimental parameters, such as Froude number, slope, and the ratio of waterfall height over the product of the Froude number and diameter, referred to as α, using machine learning models. Two hundred forty controlled experiments were conducted, with pressure data collected. To address the problem’s non-linearity, six machine learning models were tested: linear regression, K-nearest neighbors, decision tree, support vector regression, random forest, and XGBoost. The XGBoost model outperformed others, achieving an R-squared of 0.953 and a Root Mean Squared Error (RMSE) of 0.191. Residual analysis validated its better performance, demonstrating that it delivered the most accurate predictions with minimal bias. Feature importance analysis revealed the Froude number was the most significant predictor, followed by slope and diameter. This study emphasizes the importance of the Froude number in predicting jet behavior and shows the efficacy of advanced machine learning models in capturing complex fluid dynamics, providing valuable insights for optimizing engineering applications such as water jet cutting and cooling systems.https://www.mdpi.com/2311-5521/9/9/205vertical water jetsmachine learning modelspressure coefficientFroude number |
| spellingShingle | Amin Salemnia Seyedehmaryam Hosseini Boldaji Vida Atashi Manoochehr Fathi-Moghadam Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets Fluids vertical water jets machine learning models pressure coefficient Froude number |
| title | Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets |
| title_full | Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets |
| title_fullStr | Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets |
| title_full_unstemmed | Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets |
| title_short | Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets |
| title_sort | machine learning for dynamic pressure coefficient prediction in vertical water jets |
| topic | vertical water jets machine learning models pressure coefficient Froude number |
| url | https://www.mdpi.com/2311-5521/9/9/205 |
| work_keys_str_mv | AT aminsalemnia machinelearningfordynamicpressurecoefficientpredictioninverticalwaterjets AT seyedehmaryamhosseiniboldaji machinelearningfordynamicpressurecoefficientpredictioninverticalwaterjets AT vidaatashi machinelearningfordynamicpressurecoefficientpredictioninverticalwaterjets AT manoochehrfathimoghadam machinelearningfordynamicpressurecoefficientpredictioninverticalwaterjets |