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Consecutive oscillatory eruptions of a mixture of gas and liquid in urban stormwater systems, commonly referred to as sewer geysers, are investigated using transient three-dimensional (3D) computational fluid dynamics (CFD) models. This study provides a detailed mechanistic understanding of geyser formation under partially filled dropshaft conditions, an area not previously explored in depth. The maximum geyser eruption velocities were observed to reach 14.58 m/s under fully filled initial conditions (hw/hd = 1) and reduced to 5.17 m/s and 3.02 m/s for partially filled conditions (hw/hd = 0.5 and 0.23, respectively). The pressure gradients along the horizontal pipe drove slug formation and correlated directly with the air ingress rates and dropshaft configurations. The influence of the dropshaft diameter was also assessed, showing a 116% increase in eruption velocity when the dropshaft to horizontal pipe diameter ratio (Dd/Dt) was reduced from 1.0 to 0.5. It was found that the strength of the geyser (as represented by the eruption velocity from the top of the dropshaft) increased with an increase in the initial water depth in the dropshaft and a reduction in the dropshaft diameter. Additionally, the Kelvin–Helmholtz instability criteria were satisfied during transitions from stratified to slug flow, and they were responsible for the jump and transition of the flow during the initial rise and fallback of the water in the dropshaft. The present study shows that, under an initially lower water depth in the dropshaft, immediate spillage is not guaranteed. However, the subsequent mixing of air from the horizontal pipe generated a less dense mixture, causing a change in pressure distribution along the tunnel, which drove the entire geyser mechanism. This study underscores the critical role of the initial conditions and geometric parameters in influencing geyser dynamics, offering practical guidelines for urban drainage infrastructure. 

The storm sewer geyser is a process where an air–water mixture violently erupts from a manhole. Despite the low hydrostatic pressure, violent eruptions can achieve a height of tens of meters above the ground. This current study experimentally investigates large-scale violent geysers using a large air pocket inserted from a pressurized air tank. The total length of the pipe system is approximately 88 m with a 0.1572 m diameter pipe. This large-scale experiment facilitates the investigation of spontaneous geyser eruptions. This study identifies the role of air–water volume ratio and coefficient of pressure (ratio of absolute initial static pressure to initial dynamic pressure) on the geyser intensity using eruption images and pressure plots. A total of 116 cases are tested, in which the volume ratio is parametrically increased from 0 to 1.1 under various operating conditions. A geyser score is defined to quantify the geyser eruption nature based on visual observations. The key findings are as follows: first, a sharp transition in geyser intensity is observed at the critical volume ratio of 0.5, and pre-transition and post-transition intensity exhibit a linear relationship with the volume ratio; and second, the critical volume ratio linearly varies with the coefficient of pressure. 

A combined sewer system (CSS) collects rainwater runoff, domestic sewage, and industrial wastewater in the same pipe. The volume of wastewater can sometimes exceed the system capacity during heavy rainfall events. When this occurs, untreated stormwater and wastewater discharge directly to nearby streams, rivers, and other water bodies. This would threaten public health and the environment, contributing to drinking water contamination and other concerns. Minimizing sewer overflows requires an optimization method that can provide an optimal sequence of decision variables at control gates. Conventional strategies use classical optimization algorithms, such as genetic algorithms and pattern search, to find the optimal sequence of decision variables. However, these conventional frameworks are very time-consuming, and it is almost impossible to achieve near real-time optimal control. This paper presents a faster optimization framework by using a new optimal control tool: reinforcement learning. The environment (flow modeler) used in this paper is the numerical model: Environmental Protection Agency’s Storm Water Management Model (EPA SWMM) to ensure the accuracy of environment response. The reward function is constructed based on the calculated water depth and overflow rate from SWMM. The process keeps minimizing the reward function to obtain the optimal flow release sequence at each controlled orifice gate. The combined sewer system (CSS) of the Puritan-Fenkell 7-mile facility in Detroit, MI, is chosen as the case study.