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Emergency response to flood plain inundations requires real‐time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high‐resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real‐time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in‐situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52 km2${\\text{km}}^{2}$flood plain inundation area were reduced by 3.13 hr with respect to results obtained without optimization from a fixed range of flow resistance parameters. Performance metrics like critical success index and probability of detection reach values above 90% across the flood plain. Plain Language Summary When a flood inundation occurs over a populated flood plain due, for instance, to a levee or dam failure, emergency response requires numerical solutions of 2D unsteady flow equations to provide real‐time forecasts of flow depth, velocity, and arrival time. These forecasts are important for supporting decisions regarding how emergency personnel resources should be allocated, and how evacuation of citizens and animals should be managed. As high‐resolution topographic data and terrain analysis methods have recently made possible detailed and efficient flood plain inundation models based on geomorphologically informed meshes, an effort can now be made to hydraulically determine flow resistance parameters from flow observations in real time. In this work, illustrated with a synthetic case study, flow observations are provided by an optimally designed network of in‐situ sensors that record flow depth over time. 2D flow model resistance parameters are determined with a Bayesian optimization technique based on observations collected by sensors with sufficient lead time for real‐time forecasting applications. With the proposed methods, flood inundations are numerically reproduced in real‐time in a hydraulically meaningful manner, and this has the potential to provide reliable flood forecasts and guidance for post‐event recovery. Key Points Flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data are determined hydraulically A sensitivity‐based flow depth sensor network design method is developed to capture in real‐time the full range of flood dynamics Bayesian optimization is found to enable reliable and rapid flow resistance parameter learning from simulated and observed flow depths

Preferential flow is usually characterized by rapid and concentrated fluid flow in fractured geological media, and preferential flow paths (PFP) dominate the fluid flux and velocity. Therefore, the identification of PFP is significant for quantitatively characterizing fluid flow in fractured media, especially in discrete fracture networks (DFN). The traditional methods of identifying PFP need to solve groundwater flow models; however, such models are limited by complex groundwater-related problems, the need for detailed hydrogeological survey data, and a high computational workload. In this study, a graph-theory-based flow resistance method is proposed for identifying the PFP in DFN. The method uses the flow resistance of fracture trace lines to identify the corresponding minimum resistance path. The flow resistance is defined as the weighted factor between the adjacent nodes in the fracture network based on the formula of the modified cubic law, and then the Dijkstra algorithm is used to determine the minimum resistance path. The flow resistance method is verified through case analysis by numerical simulation with COMSOL Multiphysics. The results show that the fluid tends to flow along the path with less flow resistance, and the minimum resistance path is essentially consistent with the preferential flow path. The method only needs to extract flow resistance values from the geometric parameters of the fractures, and then quickly analyze the fracture-network pathways to identify the preferential flow path. The method provides an effective and efficient way of identifying the preferential flow path without resorting to complex groundwater flow models to find the solution.

Flow resistance characteristic is an important parameter in the design of solenoid valve. It not only affects the determination of structural parameters of valve, but also plays a decisive role in the selection of control method of valve. In this paper, the flow resistance characteristic curve of the solenoid valve is obtained based on a 3D steady-state CFD simulation.

Predicting vegetation-induced flow resistance remains a significant challenge due to the diverse and dynamic nature of river vegetation. Although numerous empirical models are available, they often fail to generalize across different environmental conditions, leading to inaccurate predictions. This study introduces a machine learning-based framework for predicting vegetation flow resistance, incorporating nine ML methods, including SVM, XGBoost, and BP. To improve predictive performance, optimization algorithms such as PSO, WSO, and RIME were applied. A comprehensive dataset of 490 samples across multiple scales was used to evaluate model accuracy, indicated: (1) The submergence ratio and Froude number F r are the most sensitive parameters affecting C d , while missing parameters such as vegetation density and blockage ratio significantly reduce accuracy; (2) XGBoost outperforms other models, achieving the highest predictive accuracy (R 2  = 0.9552); (3) The framework remains stable across six parameter deficiency scenarios, with XGBoost maintaining R 2  > 0.85 in all cases. In conclusion, this study highlights the transformative potential of the proposed predictive framework in overcoming the long-standing challenges of estimating flow resistance in vegetated channels. It provides valuable insights for sustainable river management, bolsters restoration efforts, and enhances predictive accuracy in complex, dynamic environments.

In semiconductor manufacturing processes, numerous wet processing processes are involved in etching, development, and cleaning of wafers. The liquid waste contains tiny hazardous debris and particles and must be filtered out before being recirculated or discharged. Therefore, circulation systems, with filtration films to intercept debris, are essential for those wafer cleaning tanks to maintain their normal operation. However, the filtration films eventually become clogged and thus fail after a period of service. For semiconductor fabrication applications, these films are generally expensive and the demand amount for a large-scale fab is usually huge; hence, this represents a considerable cost based on scheduled replacement. With the rapid development of Industry 4.0, instead of traditional service period–based replacement, a service condition–based replacement would be preferred. But the latter requires a good estimate of the remaining lifetime of the filter in use, and effective strategies for cleaning the filter during periodic maintenance to prolong its lifetime for reducing the investment cost. In this work, the abovementioned goals are analyzed and demonstrated by constructing a scaled-down liquid filtration apparatus as the main carrier to monitor the filter’s flow resistance characteristics with time for seeking the optimal filter replacement moment, and for testing the effectiveness of various filtration film cleaning strategies. It is found that filtration film clogging generally is caused by “complete blocking” first, and then by “cake filtration.” Also, reverse-flow cleaning appears to be effective for reducing cake filtration, but much less so for complete blocking. Moreover, a demonstration shows that when the flow rate of the cleaning fluid is periodically varied by use of an electromagnetically actuated valve, the efficiency of filtration film cleaning could be improved by a factor of 2, and the enhancement becomes more profound as the frequency of flow rate oscillation increases. It is believed that the presented analysis and experimental demonstration would be very helpful for cost and waste management of IC foundries in the future.

The dynamic characteristic parameters of the synchronous dual-proportional pressure integrated valve are of great significance to its research. Among them, the outlet pressure tracking error and the flow resistance characteristics of the internal flow field have a great influence on the dynamic characteristics of the valve. The tracking error of the valve under specific working conditions is analyzed by means of digital simulation, and the flow resistance characteristics of the internal flow field are further obtained.

PurposeIn this paper, a deduced flow resistance equation for open-channel flow was tested using measurements carried out in mobile bed rills with sediment-laden flows and fixed bed rills. The main aims were to (i) assess the effect of sediment transport on rill flow resistance, and (ii) test the slope-flow velocity relationship in fixed bed rills.MethodsThe following analysis was developed: (i) a relationship between the Γ function of the velocity profile, the rill slope and the Froude number was calibrated using measurements carried out on fixed bed rills; (ii) the component of Darcy-Weisbach friction factor due to sediment transport was deduced using the corresponding measurements carried out on mobile bed rills (grain resistance and sediment transport) and the values estimated by flow resistance equation (grain resistance) for fixed bed rills in the same slope and hydraulic conditions; (iii) the Γ function relationship was calibrated using measurements carried out on mobile bed rills and the data of Jiang et al. (2018).ResultsThis analysis demonstrated that the effect of sediment transport on rill flow resistance law is appreciable only for 7.7% of the examined cases and that the theoretical approach allows for an accurate estimate of the Darcy-Weisbach friction factor. Furthermore, for both fixed and mobile beds, the mean flow velocity was independent of channel slope, as suggested by Govers (1992) for mobile bed rills.ConclusionsThe investigation highlighted that the effect of sediment transport on rill flow resistance is almost negligible for most of the cases and that the experimental procedure for fixing rills caused the unexpected slope independence of flow velocity.

The coated particle ICD water control technology has been widely used in horizontal well completion in offshore oil fields in recent years, but there is a lack of research on the simulation and prediction methods for liquid production profiles. By analyzing the dynamic inflow process of fluid, starting from the laws of momentum conservation and mass conservation, a coupled mathematical model of horizontal well reservoir wellbore pressure drop considering the mechanism of annular flow resistance was established, and solved through programming. Based on the actual well data of a certain block in an offshore oilfield, a horizontal well reservoir production model was established. The calculation results of the example show that the annular flow resistance and water control technology can effectively control the balanced liquid production of horizontal wells, thereby increasing the anhydrous oil recovery period and improving the oil recovery rate of the oilfield; The coupling model simulation and prediction method can fully reflect the mutual influence between the oil reservoir, wellbore annulus, ICD, and horizontal wellbore; The effective diameter of the ICD valve affects the changes in oil production and water content. The placement of the ICD well section has a significant effect on water control, and the compactness of the filled particles has a certain impact on pressure and water content changes.

The safety barrier is often positioned at the entrance of the inlet passage in large and medium-sized low-lift pump stations, to ensure the safety of people and animals. To elucidate the blocking effect of the safety barrier and its influence mechanism on the internal flow of the inlet passage, numerical simulation combined with the physical model test were employed to investigate the impact of the cross-section and bar spacing of the safety barrier on the head loss, and the impact mechanism was clarified. The results showed that the optimal cross-section form of the safety barrier was found to be circular from the standpoint of head loss. When the blockage ratio of the safety barrier exceeds 0.3; prompt clearance of the blockage is recommended.

Inertial microfluidics is promising for the high throughput, label-free continuous separation of multicomponent microparticles. However, conventional single spiral microchannels struggle to separate three or more particle types, while traditional cascaded systems relying on sheath fluids or multiple pumps suffer from increased operational complexity. To address this, we propose a cascaded dual spiral microfluidic chip based on passive flow resistance matching. Driven by a single syringe pump without sheath flow, it achieves continuous sorting of three particle types. An adaptive flow resistance network is incorporated: the first stage channel maintains high velocity to preferentially extract large particles via strong inertial lift forces. The fluid then enters the second stage through a predetermined geometric resistance for automatic deceleration. Experiments demonstrate that at 1.6 mL/min, the system achieves continuous separation of a 1:10:10 mixture of 15, 10, and 5 µm microparticles. The 15 µm target recovery rate reaches 92%, while the collection purities for 10 µm and 5 µm particles exceed 98% and 99%, respectively. This purely passive fluidic architecture simplifies cascaded sorting, providing a robust engineering solution for complex multicomponent sample preprocessing.