Water resources can be efficiently and precisely allocated in the irrigation districts of modern agriculture, particularly in the context of the ever-increasing water scarcity. However, some operational challenges still remained on the flow measurement facilities that are currently deployed in terminal channels. Furthermore, conventional devices are frequently susceptible to severe sediment deposition, excessive head loss, and low measurement accuracy due to the complex hydraulic conditions and silt-laden water in the networks. Precise water metering and equitable water allocation are substantially limited to increase the maintenance costs and labor intensity. This study aims to optimize a portable flow measuring flume using the Myring streamline profile, and specifically engineer it for the high-precision water quantity measurement in irrigation networks. Among them, the Myring profile was originally recognized in the field of underwater vehicles, due to its exceptionally low-drag characteristics. A throatless flume structure was also constructed after optimization. Smooth flow transitions effectively minimized the flow resistance after aerodynamic design. Moreover, the formation of the vortices was prevented to reduce the sediment accumulation. The hydraulic performance of this device was enhanced to integrate the Computational Fluid Dynamics (CFD) simulations with the physical model experiments. The geometric parameters were identified as the total length of the flume l, the sharpness factor of the contraction section n, and the departure angle of the diffusion section θ. An advanced framework of optimization was established to effectively navigate the complex and multi-parameter space. A Sobol sequence sampling was also utilized to minimize the head loss ratio. A uniform and representative initial sample space was constructed for the global coverage of the parameter range. Subsequently, a coupled surrogate model was employed to integrate the Back Propagation (BP) neural network with the Particle Swarm Optimization (PSO) algorithm. While the BP network was used to map the highly non-linear relationship between geometric parameters and hydraulic efficiency. The PSO algorithm was also introduced to perform a global search, effectively overcoming the conventional BP networks to trap in local optima. The hydraulic performance of the optimal structure was then verified in a rectangular channel using FlOW-3D numerical simulation and physical tests. A contraction ratio ε and a flow rate were also covered the range of 0.50 to 0.66 and 25 to 55 L/s, respectively. The results indicate that the superior hydraulic performance was achieved in the Myring streamline flume after PSO-BP optimization, compared with the conventional ones. The exceptional energy conservation was also obtained to significantly minimize the head loss in a range of only 1.20 to 3.50 cm. The upstream backwater height was effectively controlled between 1.57 and 3.87 cm, thereby minimizing the impact on the upstream channel’s conveyance. Furthermore, the upstream Froude number remained consistently below 0.50 over all working conditions. A stable subcritical flow reduced the surface fluctuations for the reading stability. The average relative error in the flow measurement was found to be 1.8%, indicating a high degree of precision suitable for trade metering. In conclusion, the portable flume fully complied with the standard specifications for flow measurement in irrigation districts. This device can offer a simplified structure, portability, robust sediment transport capacity, minimal head loss, and high measurement accuracy, significant value for widespread application. The finding can provide a reliable technical solution for smart irrigation in precision agriculture.

The capacity of water flow to transport sediment is one of the most fundamental indicators of river dynamics. Previous studies often use the measured data to determine the part of the coefficient, due to the complexity of the sediment-bearing flow movement. It is often required to consider the hydraulic sediment characteristics. The accuracy and universality of the model also need to be further improved in recent years. This study aims to predict the high-resolution sediment-carrying capacity of the Weihe River using incomplete self-similarity with dimensional analysis. A total of 746 sets of quasi-equilibrium observation data were gathered from three hydrological stations in the lower sections of the Weihe River from 2006 to 2023. Specifically, the flood data was measured from three hydrological stations in Xianyang, Lintong, and Huaxian City. The simple processing was carried out to obtain the characteristic parameters, such as the water depth, velocity, sediment content, median particle size, and settling velocity. A precise formula was developed to calculate the sediment transport capacity considering the specific water and sediment properties of the river. The sediment transport capacity was derived using dimensional analysis. An expression was also involved using nondimensional parameters. The relationship between the dimensionless parameter and sediment transport capacity was explained using ideal assumptions. The incomplete self-similarity was also highlighted. Additionally, several undetermined constants were determined to refine the calculation formula, according to measured data. Given the current performance of the calculation, the model was adjusted to incorporate the sediment content. The results indicate that the Velikanov parameter was more suitable for the prediction model, compared with the Froude number Fr. The sediment transport capacity was obtained using the Velikanov parameter, indicating the strong prediction under the fundamental principles of water flow dynamics; Specifically, the rougher river bed was, the smaller sediment carrying capacity of the water flow was. The multi-factor incomplete self-similar relationship was dominated by the interaction of variables. The exponential term of the incomplete self-similar model was the interaction of dimensionless parameters in the formula structure. The complex sediment transport was then simulated to enhance the performance of the model using conventional regression. The sediment content was one of the crucial influencing factors on the capacity of the water flow to transfer sediment. However, the mathematical derivation was used to define the incomplete self-similar relationship between sediment content and sediment carrying capacity; Once the sediment content approached 0, the sediment carrying capacity was converged at the same time. The prediction error substantially decreased to consider this variation. Three components were considered for the highest performance, with a correlation coefficient of 0.98 and a relative error of only 0.07. The concentration coefficient and deviation coefficient were 1.01 and 0.07, respectively. This model also surpassed the several prior formulas after river test data, due to the variations in the features of different rivers. Excellent prediction accuracy was achieved at both high and low sediment content. Thus, the model can be utilized to forecast the sediment-transport capacity of the lower Weihe River, which is characterized as a sediment-laden river. Due to the clarity of the formula structure, the coefficients of the other types of rivers can still be re-calibrated by the measured data for the feasible calculation of sediment carrying capacity. At the same time, this finding can also provide some research examples for the sediment carrying capacity on the estuary flow slope.

Water demand of irrigation areas is required for the flow-measuring flume with simple structure and excellent hydraulic performance. Among them, the morphological structure of long-eared owl wings has been proved as the outstanding noise reduction and excellent drag reduction. The impeded state of the flume in the channel flow field is also similar to the air resistance of the long-eared owl during flight. In this study, the flow-measuring facility was proposed to imitate the shape of long-eared owl. The structure of bird wing was used to design a bionic structure flume. Numerical simulations were combined with the experiments to explore the correlation among various factors. The optimal line shape was determined using internal logical connections. Firstly, Sparrow Search -based BP Neural Network (SSA-BP) was utilized to optimize the spanwise location of the owl wing. The optimal flume shape was achieved with the minimal head loss. The suitability of the optimized flume was also investigated in the irrigation channels. Furthermore, a series of flow measurement tests and numerical simulations were conducted to verify the effectiveness of the optimization. The hydraulic trials were carried out in the Laboratory of Hydraulic Engineering and Hydraulics of Northwest A＆F University in 2024. The channel was set as a flat-slope rectangular channel with the channel length L=10 m, inner width B=0.7 m, and height H=0.4 m. The flow measuring flume was 4.3 m away from the inlet of the channel. The flow measurement experiments were selected as the eight kinds of flow conditions (0.50, 0.54, 0.58, 0.60, 0.62 and 0.66), and six sets of contraction ratios (20-55 L/s, each increase of 5 L/s was a flow gradient). FLOW-3D numerical simulation was combined to analyze the hydraulic parameters, such as the head loss percentage, Froude number, backwater height, and critical submergence degree of the flow measuring flume of imitating long-eared owl wing. The experiments demonstrated that the flow measuring flume exhibited the excellent flow measurement performance after SSA-BP optimization. Within the recommended contraction ratio range of 0.60 to 0.66, the head loss percentage was ranged from 7.75% to 14.21%, the critical submergence degree was from 0.81 to 0.90, the upstream backwater height was from 0.84 to 3.16 cm, and the upstream Froude number was all less than 0.42, fully meeting the precision requirements of irrigation water measurement. Therefore, the flow measurement formula was then established. The relative error between the calculated and the actual flow was less than 5%, and the average relative error was 1.49%. The accuracy of flow measurement was higher than before. The excellent hydraulic performance was achieved in the flow-measuring flume of imitating long-eared owl wing. The finding can provide a strong reference to optimize the water resource allocation and fine-tuning water usage in irrigation areas.