Flow Rate Data Research Articles

The Long-Term Variations of Phytoplankton Biomass in the Lower Nakdong River: 2000~2021

Objectives : Globally, the mass proliferation of phytoplankton driven by climate change has emerged as a significant societal issue. This study analyzes over two decades of long-term hydraulic, hydrological, and water quality data collected from the lower Nakdong River, along with changes in phytoplankton community biomass. The aim is to evaluate long-term trends in water quality and the ecological changes occurring in this region.Methods : The monitoring site in the lower Nakdong River is Mulgeum, where samples were collected weekly from January 2000 to December 2021 for the analysis of physicochemical water quality characteristics, as well as phytoplankton abundance and species composition. The hydrological status was assessed using flow rate data from the Jin-dong (Haman) station of the Nakdong River, along with rainfall data from the Korea Meteorological Administration for eight regions influencing the lower Nakdong River.Results and Discussion : Analysis of the annual average concentration changes of water quality parameters at the Mulgeum intake site in the lower Nakdong River found that water quality has generally improved since the construction period (2009-2012) of the weir. This improvement is attributed to the strengthening of T-P water quality standards for sewage treatment plant discharges in 2012, which resulted in enhanced phosphorus treatment at wastewater facilities in the river's middle and upper regions. Consequently, significant reductions were observed in the annual average concentrations of BOD, NO<sub>3</sub>-N, T-N, and T-P. Evaluating long-term changes in flow rate and rainfall, it was found that both annual average rainfall and flow rate decreased after the weir was installed, particularly from May to September when temperatures rise. Rainfall decreased by approximately 8% to 38%, while flow rate decreased by 46% to 62%. Long-term temperature changes indicated that summer temperatures increased by 0.9<sup>o</sup>C to 1.4<sup>o</sup>C, and winter temperatures rose by 1.6<sup>o</sup>C to 2.0<sup>o</sup>C, resulting in an overall annual average increase of about 1.3<sup>o</sup>C. An analysis of long-term changes in phytoplankton biomass and community composition revealed an increase in average biomass from 3,639 cells/mL before the weir was installed to 4,034 cells/mL afterward, representing an increase of about 11%. In winter, the dominance period and biomass of diatoms decreased, while in summer, the biomass and dominance period of cyanobacteria increased. Notably, in August, biomass increased approximately 7.5 times, rising from 2,009 cells/mL before the dam to 15,059 cells/mL afterward. This significant increase was identified as a key factor in the overall rise in phytoplankton biomass following the weir's installation.Conclusion : Recent climate change impacts, such as rising average temperatures and shifting rainfall patterns, have become increasingly evident in South Korea. This study utilizes over 20 years of long-term data to demonstrate that, in the lower Nakdong River, there has been a gradual decline in both rainfall and flow rates, accompanied by an increase in average temperatures. Consequently, the dominance period and biomass of diatoms have decreased. In contrast, cyanobacteria, which thrive in warmer conditions, have experienced an extended dominance period into early spring and late autumn as a result of rising temperatures, leading to an overall increase in cyanobacterial biomass.

Flow characteristics of gas and liquid in pipeline revealed by machine learning on distributed acoustic sensing data

The two-phase flow rates of gas and liquid in a pipeline are crucial parameters for optimizing gas-oil production strategies and ensuring the reliability of gas-oil transportation systems. Although available measurement techniques, such as various flow meters, offer accurate flow rate data, they might face limitations in providing distributed and real-time information at multiple points. Distributed Acoustic Sensing (DAS) offers a viable alternative for long-term, multipoint dynamic monitoring of the flow. However, the data acquired through fiber optic monitoring techniques are often difficult to analyze and process in real-time, while machine learning offers automatic identification of complex patterns and relationships within the data, enabling more precise predictions and classifications. To evaluate the feasibility of DAS technology combined with machine learning methods to estimate the gas-liquid flow rate in pipelines, an experimental loop that utilized DAS was developed to measure gas-liquid two-phase flow signals in pipelines. The machine learning method was then applied to analyze the DAS signals, based on which models were established to predict flow rates and regimes. Furthermore, validation experiments were conducted to assess the predictive performance of these models. Compared to the actual flow rates measured by electronic flowmeters, the results by integration of DAS and machine learning show the predictive accuracy of two models reach 97%. In the subsequent validation experiments, both the goodness of fit for the flow rate prediction model and accuracy for the flow regime prediction model exceeded 85%. Thus, compared to current flow measurement methods, the integration of DAS and machine learning not only provides accurate flow rate estimations but also offers available prediction of flow regimes, enhancing the measurement capabilities and technology insights.

Calibration modeling of drilling fluid rheological parameters in variable temperature environment based on support vector machine.

Traditional measurement of drilling fluid rheological parameters suffers from significant lag due to the inability of the instruments to promptly capture real-time parameters of the drilling fluid. These measurement models are typically constructed based on fixed temperature conditions and empirical formulas, rendering them inadequate for complex temperature gradient environments. Consequently, this limitation results in increased prediction errors, severely compromising the precise monitoring of drilling fluid performance. Aiming at the problems of low accuracy and poor stability of drilling fluid measurements under variable temperature conditions, a support vector machine-based calibration model for drilling fluid rheological parameters in a variable temperature environment is proposed in this paper. First, the measurement principle of the double-tube differential pressure rheology real-time measurement device is analyzed. The relationship between shear stress and shear rate is then established using differential pressure sensor and flow rate data. Utilizing the gray wolf optimization algorithm to optimize the kernel function weights and parameters, an SVM-based calibration model for predicting drilling fluid rheology correction parameters is constructed. Finally, a real-time monitoring platform for drilling fluid is developed. Experimental results show that the maximum relative errors for the predictions of apparent viscosity, plastic viscosity, and yield point are within ±5%, with coefficients of determination (R2) all greater than 0.95. These results validate the effectiveness of the proposed method in accurately monitoring the rheological performance of drilling fluids.

Adaptive sliding mode control of petrochemical flare combustion process based on radial basis function network

Steam-assisted combustion elevated flares are currently the most widely used type of petrochemical flares. Due to the complex and variable composition of the waste gas they handle, the combustion environment is severely affected by meteorological conditions. Key process parameters such as intake composition, flow rate, and real-time data of post-combustion residues are difficult to measure or exhibit lag in data availability. As a result, the control methods for these flares are limited, leading to poor control effectiveness. To address this issue, this paper proposes an adaptive sliding mode control method based on the Radial Basis Function (RBF) network. Firstly, the operational characteristics of the petrochemical flare combustion process are analyzed, and a control model for the combustion process is established based on carbon dioxide detection. Secondly, an RBF neural network-based unknown function approximator is designed to identify the nonlinear part of the actual operating system. Finally, by combining the control model of the petrochemical flare combustion and designing the RBF sliding mode controller with its adaptive control law, fast and stable control of the flare combustion state is achieved. Simulation results demonstrate that the designed control strategy can achieve tracking control of the petrochemical flare combustion state, and the adaptive law also accomplishes system identification.

Leveraging long short‐term memory networks and transfer learning for the soft‐measurement of flue gas flowrate from coal‐fired boilers

AbstractThe dynamic operation and deep peak‐shaving of power‐generating units cause significant fluctuations in flue gas flowrate, thus affecting the accuracy of CO2 emissions measured by continuous emission monitoring systems (CEMS). This study established a long short‐term memory network with an attention mechanism (LSTM‐AM) for the soft measurement of the flue gas flowrate in real‐time. First, flue gas flowrate data and continuous operation parameters over 25 days were sampled from a typical 660 MW coal‐fired boiler in China. Then, a carbon balance model was established to verify the data reliability. The LSTM‐AM model was trained and testified at the 660 MW coal‐fired boiler. Results show that the LSTM‐AM model significantly surpassed the pristine LSTM model without attention, the convolutional neural network (CNN) with LSTM, and the static support vector regression (SVR) model in the real‐time prediction of flue gas flowrate. Finally, the LSTM‐AM model was generalized to a 630 MW coal‐fired power unit via transfer learning, which was further demonstrated to outperform the model re‐trained from scratch. This work manifests the feasibility of deep learning for the soft measurement of flue gas flowrate, which is promising to solve data‐lagging issues when measuring CO2 emissions from coal‐fired power plants.

Verifying an improved intermittent water distribution network analysis method based on EPA-SWMM

ABSTRACT Modelling Intermittent Water Supply (IWS) presents challenges, as traditional hydraulic methods based on EPANET are often inadequate due to their inability to simulate the network filling process. While EPA-SWMM (EPA's Storm Water Management Model)-based methods enhance IWS analysis, they remain network-specific and lack universal applicability. This study aims to calibrate and verify an improved EPA-SWMM-based model on a 6 m × 5 m laboratory-scale IWS. Experiments were conducted to capture flow rate data from demand nodes under various conditions. The EPA-SWMM model, based on uncontrolled outlets with flow rate varying by pressure, was calibrated using, an automated procedure that integrated the Genetic Algorithm (GA) into the SWMM-toolkit for optimizing minor loss and pipe roughness coefficients. Comparing model results with experimental data demonstrated the model's capability to simulate the laboratory-scale IWS system behaviour. The model was also applied to a real case study, with results closely aligning with field data, affirming its reliability. The proposed IWS modelling method offers a versatile tool for applications, such as design and scenario analysis for tackling IWS challenges and managing IWS systems. Future research should focus on a large-scale laboratory experiment with pressure and flow sensors, considering air presence in the network to mitigate errors.

A New Method for Estimating Oil Well Production Rates Based on Enthalpy Balance Utilizing Temperature Data

Abstract The fundamental objective of this study is to utilize a temperature dataset to achieve accurate rate estimation. This study introduces a graphical method, incorporating suitable assumptions, for predicting flow rates in single-phase vertical oil flow. The method takes into consideration the fouling factor and addresses the steady-state flow of heat in the wellbore, while also accounting for the transient heat conduction in the formation. To evaluate and validate the proposed model, two field cases are examined, and associated uncertainties are discussed. The model's predictions are compared with those obtained from Hassan and Kabir's approach, as well as actual separator flow rate data. The comparisons reveal that the model exhibits a high level of accuracy within its specified range of application. Statistical analyses indicate that most of the models perform comparably, although the suggested graphical model stands out for its higher accuracy and user-friendly nature. The performance of uncertainty is found to be more reliant on the accuracy of data measurement rather than the features of the model itself.

Selecting Appropriate Model Complexity: An Example of Tracer Inversion for Thermal Prediction in Enhanced Geothermal Systems

AbstractA major challenge in the inversion of subsurface parameters is the ill‐posedness issue caused by the inherent subsurface complexities and the generally spatially sparse data. Appropriate simplifications of inversion models are thus necessary to make the inversion process tractable and meanwhile preserve the predictive ability of the inversion results. In this study, we investigate the effect of model complexity on fracture aperture inversion and thermal performance prediction in a field‐scale EGS model. Principal component analysis was used to map the aperture field to a low‐dimensional latent space. The complexity of the inversion model was quantitatively represented by the percentage of total variance in the original aperture fields preserved by the latent space. Tracer, pressure and flow rate data were used to invert for fracture aperture through an ensemble‐based inversion method, and the inferred aperture field was used to predict thermal performance. With an over‐simplified aperture model, ensemble collapse occurred. The inverted aperture models failed to resolve necessary flow and transport features, leading to a biased thermal performance prediction. A complex aperture model involved excessive features and was prone to overinterpreting the inversion data. Both the tracer/pressure/flow rate data reproduction and thermal prediction showed significant uncertainties, making it difficult to properly estimate long‐term thermal performance. Fortunately, our results indicate that there exists an appropriate model complexity which can simultaneously match inversion data and predict thermal performance with an acceptable uncertainty. The quality of the fit of tracer data appears to be a useful indicator of such an appropriate model complexity.

Design of Three-Dimensional Characteristics of Perforated Plate for Liquid Nitrogen Balanced Flowmeter

To design the two-dimensional structural parameters of the core throttling component, i.e., the perforated plate for a liquid nitrogen (LN2) balanced flowmeter, this paper presented an orthogonal experiment scheme with three parameters, three factors, and three interactions, combined with the three-dimensional CFD numerical simulation. The constructed mixture multi-phase flow numerical model considers the complex cavitation effect of LN2 with a thermal effect. A DN40 balanced flowmeter and an LN2 flowrate test setup based on a standard flowmeter were constructed, and the measured flowrate data were used to evaluate the CFD model. Based on the optimized two-dimensional structure and the validated numerical model, the influence of the three-dimensional thickness of the perforated plate on the flow and pressure drop coefficient was investigated. It was found that there are two throttling forms as the thickness increases, resulting in two pressure-changing characteristics. The formation mechanism was explained by analyzing the turbulence intensity distribution. Finally, the final flow coefficient, pressure drop coefficient, and upper limit of measurement due to the cavitation were analyzed and obtained. The results provide a feasible parameter design method for the cryogenic balanced flow meters.

Thermal-hydraulic phenomena and heat removal performance of a passive containment cooling system according to exit loss coefficient

The natural circulation system has been widely studied for use in various applications because of its inherent advantage. However, it has a key weakness called flow instability that makes the system unstable. Through massive previous research, the mechanisms of flow instability were analyzed, but there was an ambiguous aspect related to the effect of experimental parameters on the phenomenon. Particularly, there has been no report on the heat transfer performance of the system when flow instability phenomena were present. In this study, thermal-hydraulic phenomena of a two-phase natural circulation system that functions as a passive containment cooling system (PCCS) was investigated according to experimental parameters, namely, the temperature boundary (120–158 °C) and exit loss coefficient (0–34.5) under atmospheric pressure conditions. The experimental results showed five different flow types in the loop. The flow modes that occurred by the interaction between flashing and boiling were classified by referring to the mass flow rate, void fraction, and visualization data. The system was more unstable when the temperature boundary conditions increased, but it was more stable when the exit loss coefficient increased. These results have only been confirmed in our research. The reason for the results is that the flow conditions are located on the boundary between Density Wave Oscillation I and the stable flow region, and that boundary does not have clear criteria. In addition, comparing the heat transfer performance of a system by heat rate can confirm the effect of flow instability on the thermal performance of the passive cooling system. As a result, the high exit loss coefficient stabilizes the system better than the low case and has similar heat removal performance.

Analysis Glycol Losses On Gas Dehydration UnitPT X Field Z

The dehydration process gas is a process of separation of gas from the water content by mixing an absorbent for example glycol, in PT X field Z process of dehydration of gas using triethylene glycol to decrease Water content Minimum below 7 lb / mmscfd to be sold to consumers. The goal of the research is to determine the water content that is absorbed in the contactor, knowing the number of glycol loss and knowing the cause of glycol loss after experiencing the process of dehydration. Every dehydrated process using glycol is always there whose name is glycol losses either in reboiler or contactor, but the losses are classified normal or abnormal loss, losses can be either liters or percentage loss glycol lost. To determine how much glycol is missing during the dehydration process then we have to look for production data such as gas flow rate data, inlet water content and outlet water content contactor then moisture content data, and the last data from glycol like SG Glycol and purity glycol, later from the data is known calculation of circulation glycol and know the amount of glycol loss whether the normal loss or abnormal loss. The water content omitted is 32.49 lbs / h while the Glycol losses found in the above-normal state are 0.35% with the lowest amount of Glycol losses of 0.02 gal / MMSCF and the largest 0.091 gal / MMSCF and the cause of Glycol loss Include the number of the tray, time, surface area of absorption, flow speed, and the temperature that is in the reboiler or the regeneration of glycol.

Applying hydrodynamic principles and flow theory to optimize the reservoir pressure survey program without shut-in the well - Case study: Biba reservoir, Algeria

The article focuses on the importance of gathering and analyzing information related to oil and gas reservoirs, particularly parameters such as permeability (k), skin factor (S), pressure (p), and temperature (T). This data is crucial for making significant decisions in adjusting production technologies, intervening in wells, and near-well bore treatment. To obtain this information, analyzing data from tests is essential. Pressure, flow rate, and time data are collected using pressure buildup testing, where wells are produced at a constant rate and then shut-in to observe pressure increase over time. Analyzing this data allows evaluation of permeability, pressure in the drainage region, skin factor, and other parameters like static pressure. The article also discusses applying principles of hydrodynamics and flow theory to optimize well pressure survey programs without disrupting production operations. This method enables the collection of necessary information while maintaining continuous field operations and providing comprehensive technological data. The results were applied on Bali reservoir in Algeria. Information gathered from pressure survey programs not only helps fine-tune and optimize daily production procedures but also significantly contributes to updating reservoir models. This serves as a basis for future development decisions within the reservoir. The method has been successfully applied at Biba field, Algeria.

Enhancing Traffic Sustainability: An Analysis of Isolation Intersection Effectiveness through Fixed Time and Logic Control Design Using VisVAP Algorithm

This paper addresses the limitations of the fixed-time approach in traffic signal control, which can lead to bottlenecks and inefficiencies. Proposing an alternative algorithm based on design logic control, the study integrates data from inductive detectors and non-linear traffic flow rates to optimize signaling plans. Analytical models are developed for both fixed and semi-actuated traffic signal control approaches, with PTV Vissim software (version 8, 64 bit) used for simulation. The design logic control dynamically adjusts signaling plans, determining the duration of the green interval for the secondary road based on arrival traffic flow. In the absence of traffic, it eliminates the green interval, advancing to the next phase, thereby reducing cycle time. This dynamic adjustment follows a conditional “if-then” statement, optimizing traffic signal operation. The design logic control algorithm was tested in a real isolation intersection with four scenarios, using non-linear traffic flow rate data for one peak hour. Results demonstrated that the proposed design logic control, based on the Semi-Actuated Traffic Signal Control (SATSC) approach, outperformed the commonly used Fixed-Time Signal Control (FTSC) with overall reduction of queue lengths by 39.6% and reduction of vehicle delays by 51.3%. The findings suggest its viability as a solution for many cities, contributing to a more sustainable traffic system.

Расчет радиуса влияния пластовой дегазационной скважины

In order to increase productivity and increase the load on the clearing face of coal mines, artificial reduction of methane content of the developed layers, which is carried out by wells drilled through the formation. The purpose of the study is to develop a methodology for calculating the radius of influence of degassing formation wells, using which you can manage the effectiveness of these wells, changing the period of degassing by comparing the calculated and actual data. The main parameter for degassing by parallel formation wells is the radius, relative to the axis of the well, from which the methane is extracted. For each particular layer of the Karaganda basin, the distance between the wells is chosen experimentally, taking into account its thickness, permeability and gas content. Determination of the last two factors is complicated, and often in practice there is a deviation of the actual radius of influence from the calculated one, and this factor determines the efficiency of degassing. The developed methodology allows to make effective decisions on the timing of degassing by formation wells on the basis of a comparative analysis of the actual well flow rate with the calculated one, to achieve the standard methane concentrations in the planned production areas. The peculiarity of this methodology is that the decisions made are based on actual flow rate data, which are difficult to determine reliably.

Numerical Study of Rarefied Gas Flow in Diverging Channels of Finite Length at Various Pressure Ratios

In the present work, the gas flows through diverging channels driven by small, moderate, and large pressure drops are studied, considering a wide range of the gas rarefaction from free molecular limit through transition flow regime up to early slip regime. The analysis is performed using the Shakhov kinetic model, and applying the deterministic DVM method. The complete 4D flow problem is considered by including the upstream and downstream reservoirs. A strong effect of the channel geometry on the flow pattern is shown, with the distributions of the macroscopic quantities differing qualitatively and quantitatively from the straight channel flows. The mass flow rate data set from the complete solution is compared with the corresponding set obtained from the approximate kinetic methodology, which is based on the fully developed mass flow rate data available in the literature. In addition, the use of the end-effect approach significantly improves the applicability range of the approximate kinetic methodology. The influence of the wall temperature on the flow characteristics is also studied and is found to be strong in less-rarefied cases, with the mass flow rate in these cases being a decreasing function of the temperature wall. Overall, the present analysis is expected to be useful in the development and optimization of technological devices in vacuum and aerospace technologies.

Determination of Effective Parameters for Hydropower Plants’ Energy Generation: A Case Study

The current study aims to conduct a comprehensive analysis of the key parameters that affect the energy output of the Bagisli Hydropower Plant, located on the Zap River in the Dicle (Tigris) basin of Turkey. It considers the daily data for the time interval of 2016–2019 related to flow rate, precipitation, and temperature in relation to the generation of electricity. The relationship between energy output and flow rate is evident; however, the energy production is limited by the design flow rate. The largest flow rates were seen during the spring season after the occurrence of peak precipitation, which exhibited a shifting pattern. Similarly, energy generation also reaches its highest level during this time period. Another outcome of this study is that there is no apparent association between daily precipitation and daily energy generation. Moreover, a novel correlation with an R2 value of 0.89 has been proposed, in the deviation band of ±20%, to estimate energy generation when only flow rate data are available.

An Investigation of Thrust Bearing Temperature Rise in Unit 2 Koto Panjang Hydropower Plant

Turbines and generators are the main components of hydropower plants that are supported by several bearings, one of them is the thrust bearing. Thrust bearing unit 2 experienced a temperature rise that exceeded the alarm limit (77℃). This study aims to investigate the temperature rise and provide a solution. The investigation was conducted by visual observation; collating temperature and oil flow rate data; and reviewing the hydropower design. The results showed that the temperature rise was attributed to air (oil vapor) trapped in the lubricant circulation system. Therefore, the lubricant circulation system was modified by adding a pipe to the stayring as an outlet for trapped lubricant vapor. This measure effectively normalized the thrust-bearing temperature and flow rate.

Assessing the salinization mechanisms of coastal brackish springs

Brackish springs have been extensively reported in Mediterranean coastal carbonate formations. The phenomenon is puzzling because these springs may discharge high flow rates with significant salinities at elevations of several meters above sea level. Although these springs have been studied for millennia (since the ancient Greeks), controversy persists. In essence, they are typically assumed to consist of a karstic conduit bringing fresh groundwater that meets a saltwater conduit at a branching point, where the brackish mixture flows up to the spring mouth. Here, we analyze the hydraulics of the system for two simple cases, depending on whether seawater flow occurs through an open conduit or a porous aquifer. To this end, we derive the equations governing variable-density turbulent flow through the conduits. These turn out to be similar to the traditional ones, except that (1) Bernouilli’s head (energy per unit weight) is substituted by the energy per unit volume, and (2) this energy is not a potential, as a flow path dependent term needs to be added. We solve these equations to obtain the characteristic functions relating fresh to seawater discharge depending on spring concentration. These functions are specific to every brackish spring and representative of the karst system and salinization mechanism. They allow us to show that the spring salinity is mostly controlled by the weight of the water column flowing towards the spring mouth (for low flow rates) and by energy dissipation (for high flow rates). When concentration and flow rate data are available, it is possible to characterize: (1) the resistance of the upwards conduit from the response at high flow rate; (2) the depth of the branching point from the concentration at low discharges; and (3) the hydraulic resistance of the seawater conduit from the slope of concentration vs. flow rate at low spring discharges. These patterns are compared to field data from three different springs, which yields insights on the conceptual model governing every particular spring.

Development and Testing of a System Thermal-Hydraulics Model for a 50-MWel-Class Pressurized Water Reactor–Small Modular Reactor

Abstract This paper describes the development, analysis, testing of a RELAP5-3D system thermal-hydraulics model for a 50-MWel-class pressurized water reactor–small modular reactor (PWR–SMR), similar to that by NuScale Power. This study focuses on a series of sensitivity tests to investigate the impacts of model changes. Parameters considered in the sensitivity study included the surge line junction resistance (SLJR), steam generator (SG) heat transfer area (SGHTA), SG primary flow area (SGPFA), SG secondary pressure (SGSP), and SG secondary flowrate (SGSF). Results for the reference and sensitivity simulations are compared with available design data. The flow in the primary circuit of the PWR–SMR is driven by natural circulation and can be sensitive to changes in hydraulic resistance and pressure drop in system components. Initial analysis results demonstrated significant flow oscillations. As a result of sensitivity studies, it was found that the surge line junction resistance needed to be increased by increasing the form loss coefficient from 4.9 to 30.0, to reduce mass flow oscillation amplitude to less than ±2%. Modifications to the steam generator heat transfer area, primary flow area, or secondary pressure have very little impact in reducing flow oscillations. However, it was found that the steam generator secondary flowrate will affect primary circuit flow oscillations, and when the SGSF was artificially increased from 68 kg/s (design data) to 91 kg/s (a 36% increase), the oscillations were eliminated, along with better matching with design data for core flowrate and inlet/outlet temperatures. Further improvements to the model for the steam generator will be required.

A Multi-Parameter Flexible Smart Water Gauge for the Accurate Monitoring of Urban Water Levels and Flow Rates

Urban drainage and waterlogging prevention are critical components of urban water management systems, as they help to mitigate the risks of flooding and water damage in cities. The accurate collection of liquid level and flow rate data at the end of these systems is crucial for their effective monitoring and management. However, existing water equipment for this purpose has several shortcomings, including limited accuracy, inflexibility, and difficulty in operation under specific working conditions. A new type of multi-parameter flexible smart water gauge was developed to address these issues. This technology uses underwater simulation robot technology and is designed to overcome the deficiencies of existing water equipment. The flexibility of the gauge allows it to be adapted to different working conditions, ensuring accurate data collection even in challenging environments. The accuracy of the new water gauge was tested through a series of experiments, and the results showed that it was highly accurate in measuring both liquid level and flow rate. This new technology has the potential to be a key tool in smart water conservancy, enabling the more efficient and accurate monitoring of water levels and flow rates. By providing a new solution to the problem of collecting terminal equipment for urban drainage and waterlogging prevention, this technology can help to improve the resilience and sustainability of urban water management systems.