Biao Huang

This study systematically investigates the dynamics of particle–plate collisions across varying liquid layer depths, aiming to elucidate the influence of liquid layer thickness on impact processes, rebound behavior, and impact forces. Using a custom-built experimental setup combining high-speed imaging and multi-point dynamic pressure sensors, the effects of particle size, initial velocity, and liquid layer thickness on collision dynamics were examined. Results reveal that the liquid layer depth significantly alters liquid bridge morphology and rebound characteristics. For thin liquid layers (h/d < 0.4), collisions are dominated by lubrication and capillary effects, resulting in high restitution coefficients (e). As the liquid layer thickness increases to the shallow regime (0.4 ≤ h/d ≤ 1.2), e decreases sharply due to enhanced viscous dissipation and inertial damping. An exponential decay model fitted to the experimental data shows that e decreases exponentially with increasing h/d, with the decay rate closely related to the initial impact velocity. Additionally, measurements of impact forces in the liquid layer show that thicker liquid layers more effectively cushion the impact, resulting in significantly reduced forces. This work enhances understanding of the role of liquid layer thickness in particle–plate collisions, with implications for multiphase flow transport and engineering design.

Online monitoring is increasingly essential for the effective management and operation of urban sewer systems, yet resource limitations necessitate careful planning of sensor deployment. This study aims to address the impact of time lags on monitoring point selection in urban drainage systems using unsupervised machine learning techniques. A novel method is introduced to determine the optimal number and placement of sensors in manholes, using cluster analysis informed by simulated time-series data. The proposed methodology involves two sequential stages: the first stage clusters time-series data based on morphology similarity using the time-lagged cross-correlation (TLCC) coefficient, which measures the temporal alignment between datasets. The second stage further refines these clusters by considering magnitude similarity, employing dynamic time warping distance to quantify shape-based similarities and improve clustering accuracy. The proposed approach allows for flexible threshold adjustments to accommodate specific engineering requirements, enabling the design of monitoring strategies tailored to a predetermined number of locations. Furthermore, the study explores the impact of rainfall intensity on sensor placement, providing actionable guidance for sewer managers to improve monitoring efficiency and address urban water management challenges.

This study investigates the effects of fixed sediment deposition on the hydraulic characteristics of sewer flow to support the diagnosis of sewer blockage. Sediment beds extending over the entire pipe (i.e., continuous deposition) and localized deposits were examined under different flow rates and outlet control conditions. Continuous deposition changes the cross-sectional area of the sewer pipe, while localized deposits act similarly to short bottom obstructions. The energy losses induced by a localized deposit at various locations were found to be nearly identical, particularly in cases with backwater effects. To illustrate the relationships between the flow rate, water level, and deposit characteristics, hydraulic performance curves can be developed. The inverse problem, which involves estimating parameters characterizing sediment deposition using observed flow rates and upstream and downstream water levels, can be solved by matching hydraulic performance curves with numerous scatter points from actual monitoring data to obtain the best fit. As there is a wide range of sediment deposition patterns that result in the same overall energy loss, the concept of equivalent sediment bed height is introduced to be applied in real-world scenarios.

Falling water in dropshafts can induce a significant amount of airflow into sewer systems. In this study, the air demand in plunging-type dropshafts with medium drops of 3.38 and 1.88 m were investigated. The downward movement of air in the dropshaft is influenced by two components of falling water: the annular flow attached to the shaft wall and the bounced flow (in the forms of mainstream or water droplets). A model for predicting air pressure gradients was developed and experimentally validated. The air demand increased with the air inlet area, the drop height, the water flow rate, and the ratio of the bounced jet to the total water flow rate. A prediction model for the air demand in dropshafts was developed, thus it is possible to assess the air demand of dropshafts across a range of drop heights, diameters, and inflow conditions. When the air inlet to the shaft is restricted, a large negative pressure is generated inside the shaft, increasing the height of the water-air mixture generated by the bottom jet impact, which reduces the impact pressure at the bottom.

Spillway and drainage tunnels have an open-channel flow pattern when operating under unpressured condition, above which air flow is driven and resisted by water flow, wall friction, and pressure difference. Unpressured tunnels present many airflow-related safety and environmental issues, including water flow fluctuation, gate vibration, shaft cover blow-off, and odor emission; therefore, it is valuable to study and predict their airflow velocity. Given the difficulty in accurate prediction of airflow velocity in unpressured tunnels and complicated influences of hydraulic, structural, and boundary pressure parameters, this study focuses on establishing high-performance prediction models and understanding the importance and independent and coupled influences of each parameter using machine learning. It is found that the water Froude number, ratio of free-surface width to unwetted perimeter, relative ventilation area, and relative tunnel length are four key parameters. By including these parameters in the input parameter combination, the machine learning models can well predict the airflow velocity in unpressured tunnels, achieving significantly higher performance than the existing empirical and theoretical models. Among these models, the models built by Random Forest and XGBoost demonstrate best performance with R2 ≥ 0.911. The interpretability analysis reveals the highest importance of the water Froude number and the ratio of free-surface width to unwetted perimeter, increases in which generally result in enhancement of the airflow velocity. The water Froude number plays a dominant role when it is ≤11.5, and a continuous increase exhibits a significantly marginal effect. The relative ventilation area and relative length of tunnels have close importances, with an increase in either generally promoting the airflow velocity. To help researchers and engineers unfamiliar with machine learning to easily and accurately predict the airflow velocity in unpressured tunnels, GPlearn algorithm is employed to establish explicit expressions, which is validated to have good performance with R2 close to 0.900.

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Rainfall-derived inflow/infiltration (RDII) modelling during heavy rainfall events is essential for sewer flow management. In this study, two machine learning algorithms, random forest (RF) and long short-term memory (LSTM), were developed for sewer flow prediction and RDII estimation based on field monitoring data. The study implemented feature engineering for extracting physically significant features in sewer flow modelling and investigated the importance of the relevant features. The results from two case studies indicated the superior capability of machine learning models in RDII estimation in the combined and separated sewer systems, and LSTM model outperformed the two models. Compared to traditional methods, machine learning models were capable of simulating the temporal variation in RDII processes and improved prediction accuracy for peak flows and RDII volumes in storm events.

This study investigated the hydraulic characteristics of stormwater sumps and their design optimization for sediment retention using physical experiments. Particle image velocimetry was utilized to measure the flow field, and the use of internal structures was investigated for improving solids retention. Results indicate that these internal structures can significantly improve the sediment removal efficiency of suspended solids with an average size of 125 μm, resulting in an efficiency improvement of 20–30%. Additionally, a modified Péclet number was proposed to more accurately evaluate the sediment removal efficiency of stormwater sumps, and recommendations were provided for further improving and optimizing sump design. This study provides insights into the hydraulic characteristics of stormwater sumps and has important implications for optimizing and designing particle removal systems for various industrial and environmental applications.

A physical model study was conducted on jet impingement in a plunging dropshaft with different drop heights of 3.38 and 1.88 m. Experimental observations show that an upward splash, annular flow, and downward bounced jet could be formed when the approaching jet hits the opposite shaft wall. The impinging pressure on the shaft wall was mainly dominated by the incoming flow velocity, and the mean pressure on the shaft wall can be reasonably well-predicted when considering the bounced flow. The pulsation of the impinging pressure on the shaft wall was significant, with the maximum pressure being approximately 1.4–3.8 times the mean value. The impinging pressure at the shaft bottom was primarily determined by the velocity at which the falling jet reached the pool. The mean value can be well estimated using the annular flow and bounced jet assumption. The pulsation intensity increased with an increase in drop height or flow rate, which could be alleviated by a plunging pool with a larger depth. However, for flows in dropshafts under study, the impinging pressure and the hydrostatic pressure of the cushion layer were within the same order of magnitude; thus, the increase of the pool depth leads to a reduction of the energy-dissipation rate.