Trajectory of a jet in crossflow in a channel bend

Schreiner, H. K.; Rennie, Colin D.; Mohammadian, Abdolmajid

doi:10.1007/s10652-018-9594-8

Cited by 15 publications

(5 citation statements)

References 30 publications

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“…The flow was measured at the 90 • V-notch weir at the outlet section, and the accuracy of the results was verified through the ADV measurement of the straight pipe section at the inlet. The three-dimensional velocity was measured using Nortek Vectrino ADV at equidistant points along the pipeline, which were located 0.083 m, 0.250 m, 0.417 m, 0.584 m, 0.751 m, and 0.918 m away from the outer bank.The measuring point was 0.012 m to 0.092 m above the river bed, and the points were taken once every 0.01 m. The method of measuring flow parameters and the data processing process has been well documented elsewhere in the literature [33][34][35].…”

Section: Laboratory Experimentsmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

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The hydrodynamic model, based on the strict conservation of momentum and continuity equations, can accurately simulate the distribution of a flow field. However, significant computing time and storage space requirements limit real-time prediction. Machine learning is well known for its fast computing speed and powerful learning ability, but its accuracy depends on an abundance of training data, hindering its wider use in locations without sufficient measurements. Application restrictions in data-deficient areas can be addressed through transfer learning, provided that two areas share common characteristics. In this study, a machine learning method based on a deep super-resolution convolutional neural network (DSRCNN) and transfer learning is proposed, validated, and applied to model two bend flows and one realistic test case. Firstly, the hydrodynamic model was established and validated against measured data. The validated model was considered to have the ability to generate real data and was used to generate a comprehensive data set for training and validating the machine learning model. Three different methods were compared and tested, with Realizable k-ε performing better than the others in predicting the outer bank flow distribution. DSRCNN was compared to a plain SRCNN (PSRCNN), as well as Bilinear, Nearest, and Bicubic methods, and the results showed that DSRCNN had the best performance. We compared Raw, RT, and TL methods, finding that the TL method performed the best overall. Therefore, the research results showed that the developed super-resolution convolutional neural network can provide more reliable predications and serve as an ideal tool for simulating flow field distribution in bends.

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Section: Laboratory Experimentsmentioning

confidence: 99%

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Yan,

Du,

Zhang

et al. 2024

Water

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“…The solution was neutrally buoyant within detectable limits. The same dye has been previously used and reported by Schreiner et al [57]. These experiments were conducted to determine the reasonable ranges for Q 0 and T (rotating period) for the current experimental conditions.…”

Section: Experiments and Considered Casesmentioning

confidence: 99%

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Yan

Mohammadian

Yu³

et al. 2023

Water

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Many high-performance fluid dynamic models do not consider fluids in a rotating environment and often require a significant amount of computational time. The current study proposes a novel parameter-based field reconstruction convolutional neural network (PFR-CNN) approach to model the solute concentration field in rotationally influenced fluids. A new three-dimensional (3D) numerical solver, TwoLiquidMixingCoriolisFoam, was implemented within the framework of OpenFOAM to simulate effluents subjected to the influence of rotation. Subsequently, the developed numerical solver was employed to conduct numerical experiments to generate numerical data. A PFR-CNN was designed to predict the concentration fields of neutrally buoyant effluents in rotating water bodies based on the Froude number (Fr) and Rossby number (Ro). The proposed PFR-CNN was trained and validated with a train-validation dataset. The predicted concentration fields for two additional tests demonstrated the good performance of the proposed approach, and the algorithm performed better than traditional approaches. This study offers a new 3D numerical solver, and a novel PFR-CNN approach can predict solute transport subjected to the effects of rotation in few seconds, and the PFR-CNN can significantly reduce the computational costs. The study can significantly advance the ability to model flow and solute transport processes, and the proposed CNN-based approach can potentially be employed to predict the spatial distribution of any physical variable in the lentic, ocean, and earth system.

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“…In terms of prototype observation, some scholars make actual measurements on the observation data of natural bends [3][4][5], and taking into account the geographical location restrictions and measurement accuracy, most scholars now use indoor test method. For physical model tests, some scholars studied macroscopic features, such as the structure, turbulence characteristics, hydraulic characteristics of bend flow by conducting different types of model tests and combining with theoretical analysis [6][7][8][9][10][11][12][13][14]; and in terms of numerical simulation, some scholars have established two-dimensional or three-dimensional bend flow mathematical models by using a variety of algorithms, such as the finite volume method, and compared with the model test to verify, supplement the model test shortcomings, and analyze the characteristics of bend flow from a microscopic point of view [5,[8][9][10][11][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Previous studies have mostly focused on hydraulic characteristics of flow in bends, and some scholars have put forward engineering auxiliary measures to improve flow conditions in bends, such as vanes [27,28], guide walls [29], spur dikes [30,31], riprap [32], etc.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Multifactor Influence Model on Energy Dissipation Rate of Rough-Strips Energy Dissipator in the Spillway Bend

Zhang

Zhen-wei

Fan

et al. 2022

Advances in Materials Science and Engineering

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e rough-strips energy dissipator (R-SED) is applied to the bottom of the spillway bend and can play the role of energy dissipation and ow stabilization. In this study, based on 18 sets of orthogonal tests and the principle of dimensional analysis, a multifactor in uence model of R-SED's energy dissipation rate was proposed. A dimensionless factor k was introduced, which can re ect the comprehensive characteristics of the geometric dimensions of R-SEDs. e multifactor in uence model of the energy dissipation rate considered nine factors, including bend radius of curvature R c , bend width B, ow velocity of the bend inlet v, R-SED's average height h L , R-SED's arrangement angle θ, R-SED's arrangement spacing ∆L, uid density ρ, dynamic viscosity coe cient μ, and gravitational acceleration g. e residual sum of squares of the model (RSS) was 6.6% and the correlation coe cient R was 83.2% (>80%), indicating the universality and feasibility of the model.e independent variables of the multifactor model of the energy dissipation rate were ranked according to the Pearson value in descending order:. is indicates that R-SEDs' layout parameters showed larger e ects on the multifactor model of the energy dissipation rate, compared with the engineering layout parameters of the spillway. e maximum relative error between the predicted value of the multifactor model and the measured value of the validation group was 6.28%, indicating good agreement. In the orthogonal tests, scenario 5 had the highest energy dissipation rate (44.83%) with k = 0.023; scenario 16 had the largest k value (0.043), with an energy dissipation rate of 40.78%. e multifactor in uence model of R-SEDs' energy dissipation rate proposed in this paper was a semi-theoretical and semi-empirical calculation formula, which can provide reference and support for similar practical engineering designs.

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Trajectory of a jet in crossflow in a channel bend

Cited by 15 publications

References 30 publications

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Predicting the Flow Fields in Meandering Rivers with a Deep Super-Resolution Convolutional Neural Network

Fast Prediction of Solute Concentration Field in Rotationally Influenced Fluids Using a Parameter-Based Field Reconstruction Convolutional Neural Network

Analysis of Multifactor Influence Model on Energy Dissipation Rate of Rough-Strips Energy Dissipator in the Spillway Bend

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