Francesco Gino Ciliberti, Luigi Berardi, Daniele Biagio Laucelli, Andres David Ariza, Laura Vanessa Enriquez, Orazio Giustolisi
Abstract In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.
{"title":"From digital twin paradigm to digital water services","authors":"Francesco Gino Ciliberti, Luigi Berardi, Daniele Biagio Laucelli, Andres David Ariza, Laura Vanessa Enriquez, Orazio Giustolisi","doi":"10.2166/hydro.2023.237","DOIUrl":"https://doi.org/10.2166/hydro.2023.237","url":null,"abstract":"Abstract In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136239731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonietta Simone, Alessandra Cesaro, Cristiana Di Cristo, Oreste Fecarotta, Maria Cristina Morani
Abstract Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location.
{"title":"Two different approaches for monitoring planning in sewer networks: topological vs. deterministic optimization","authors":"Antonietta Simone, Alessandra Cesaro, Cristiana Di Cristo, Oreste Fecarotta, Maria Cristina Morani","doi":"10.2166/hydro.2023.296","DOIUrl":"https://doi.org/10.2166/hydro.2023.296","url":null,"abstract":"Abstract Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136312806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope.
{"title":"Influence of the channel bed slope on Shannon, Tsallis, and Renyi entropy parameters","authors":"Gurpinder Singh, Rakesh Khosa, Manoj Kumar Jain, Tommaso Moramarco, Vijay P. Singh","doi":"10.2166/hydro.2023.008","DOIUrl":"https://doi.org/10.2166/hydro.2023.008","url":null,"abstract":"Abstract Velocity distribution plays a fundamental role in understanding the hydrodynamics of open-channel flow. Among a multitude of approaches, the entropy-based approach holds great promise in achieving a reasonable characterisation of the velocity distribution. In entropy-based methods, the distribution depends on a key parameter, known as the entropy parameter (a function of the time-averaged mean velocity and maximum velocity), that relates to channel characteristics, such as channel roughness and channel bed slopes. The entropy parameter was regarded as constant for lack of experimental evidence, which would otherwise demonstrate if it had any correlation with channel properties. A series of experiments were conducted to collect velocity data in the laboratory flume for seven different values of the channel bed slope. The experimental data analysis revealed dissimilar fluctuations in entropy parameter values with varying bed slopes, with the lowest coefficient of variation in Renyi's (∼0.5%) and the highest in Shannon's case (∼10%). Performance evaluation of the predicted results substantiated good accuracy for all three entropies with the best results of Renyi entropy and lent strong support for using a constant (overall average) value of the entropy parameter for a specific channel cross-section rather than separate values for each channel bed slope.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Veysi Kartal, Muhammet Emin Emiroglu, Okan Mert Katipoglu, Erkan Karakoyun
Abstract Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).
{"title":"Prediction of scour hole characteristics caused by water jets using metaheuristic artificial bee colony-optimized neural network and pre-processing techniques","authors":"Veysi Kartal, Muhammet Emin Emiroglu, Okan Mert Katipoglu, Erkan Karakoyun","doi":"10.2166/hydro.2023.230","DOIUrl":"https://doi.org/10.2166/hydro.2023.230","url":null,"abstract":"Abstract Preventing plunge pool scouring in hydraulic structures is crucial in hydraulic engineering. Although many studies have been conducted experimentally to determine relationship between the scour depth and water jets in several fields, available equations have deficiencies in calculating the exact scour due to complexity of scour process. This study investigated local scour depth in plunge pool using Metaheuristic Artificial Bee Colony-Optimized Feed Forward Neural Network (ABCFFNN), variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD) techniques. To set modeling, the input parameters are impact angle, densimetric Froude number, impingement length, and nozzle diameter. The models' training and testing were conducted using data available in the literature. The models' performances were compared with experiments. The results demonstrate that scour depth, length, width, and ridge height can be calculated more accurately than available equations. A rank analysis was also applied to obtain the most critical parameter in predicting scour parameters in water jet scouring. ABC-FFNN, VMD-ABCFFNN and EEMD-VMD-FFNN hybrid models were performed to obtain scour parameters. As a result, ABC-FFNN algorithms produced the best solution to predict the scour due to circular water jets, with the values for training (R2: 0.331 to 0.778) and testing (R2: 0.495 to 0.863).","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Honghong Zhang, Zhenwei Mu, Yiyun Wang, Zhen Zhou, Fan Fan, Fanqi Li, Hao Ma
Abstract Rough-strip energy dissipators (R-SEDs) can be arranged at the bend bottom of curved spillways to dissipate energy and divert flow for bend flow. Using the entropy weight and TOPSIS methods, a multi-criteria evaluation system was established for comprehensive energy dissipation and flow diversion effects of R-SEDs. Orthogonal tests and numerical simulation were conducted to analyze factors affecting these effects (average R-SED height, R-SED angle, R-SED spacing, bend width, bend centerline radius and discharge flow rate). It was found that bend width and bend centerline radius significantly affected R-SEDs' energy dissipation effects. Average R-SED height, R-SED spacing and bend centerline radius significantly affected R-SEDs' flow diversion effects. Bend width, average R-SED height and bend centerline radius significantly affected R-SEDs' combined effects of energy dissipation and flow diversion. Their energy dissipation effects were larger than the flow diversion effects. R-SEDs can effectively alleviate adverse hydraulic phenomena in curved spillways. With the recommended parameters, R-SEDs showed the best performance, with the energy dissipation rate increasing by 18.67% and the water surface superelevation coefficient decreasing by 26.14%. The accuracy of the multi-criteria evaluation system was verified. This study can provide a reference for the R-SED design of similar curved spillways.
{"title":"Study on the influencing parameters of rough-strip energy dissipators of curved spillways based on orthogonal tests and numerical simulation","authors":"Honghong Zhang, Zhenwei Mu, Yiyun Wang, Zhen Zhou, Fan Fan, Fanqi Li, Hao Ma","doi":"10.2166/hydro.2023.201","DOIUrl":"https://doi.org/10.2166/hydro.2023.201","url":null,"abstract":"Abstract Rough-strip energy dissipators (R-SEDs) can be arranged at the bend bottom of curved spillways to dissipate energy and divert flow for bend flow. Using the entropy weight and TOPSIS methods, a multi-criteria evaluation system was established for comprehensive energy dissipation and flow diversion effects of R-SEDs. Orthogonal tests and numerical simulation were conducted to analyze factors affecting these effects (average R-SED height, R-SED angle, R-SED spacing, bend width, bend centerline radius and discharge flow rate). It was found that bend width and bend centerline radius significantly affected R-SEDs' energy dissipation effects. Average R-SED height, R-SED spacing and bend centerline radius significantly affected R-SEDs' flow diversion effects. Bend width, average R-SED height and bend centerline radius significantly affected R-SEDs' combined effects of energy dissipation and flow diversion. Their energy dissipation effects were larger than the flow diversion effects. R-SEDs can effectively alleviate adverse hydraulic phenomena in curved spillways. With the recommended parameters, R-SEDs showed the best performance, with the energy dissipation rate increasing by 18.67% and the water surface superelevation coefficient decreasing by 26.14%. The accuracy of the multi-criteria evaluation system was verified. This study can provide a reference for the R-SED design of similar curved spillways.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efficient flood risk assessment and communication are essential for responding to increasingly recurrent flash floods. However, access to high-end data center computing is limited for stakeholders. This study evaluates the accuracy-speed trade-off of a hydraulic model by (i) assessing the potential acceleration of high-performance computing in PCs versus server-CPUs and GPUs, (ii) examining computing time evaluation and prediction indicators, and (iii) identifying variables controlling the computing time and their impact on the 2D hydrodynamic models' accuracy using an actual flash flood event as a benchmark. GPU-computing is found to be 130× and 55× faster than standard and parallelized CPU-computing, respectively, saving up to 99.5% of the computing time. The model's number of elements had the most significant impact, with <150,000 cells showing the best accuracy-speed trade-off. Using a PC equipped with a GPU enables almost real-time hydrodynamic information, democratizing flood data and facilitating interactive flood risk analysis.
{"title":"Investigating optimal 2D hydrodynamic modeling of a recent flash flood in a steep Norwegian river using high-performance computing","authors":"A. Moraru, Nils Rüther, O. Bruland","doi":"10.2166/hydro.2023.012","DOIUrl":"https://doi.org/10.2166/hydro.2023.012","url":null,"abstract":"\u0000 \u0000 Efficient flood risk assessment and communication are essential for responding to increasingly recurrent flash floods. However, access to high-end data center computing is limited for stakeholders. This study evaluates the accuracy-speed trade-off of a hydraulic model by (i) assessing the potential acceleration of high-performance computing in PCs versus server-CPUs and GPUs, (ii) examining computing time evaluation and prediction indicators, and (iii) identifying variables controlling the computing time and their impact on the 2D hydrodynamic models' accuracy using an actual flash flood event as a benchmark. GPU-computing is found to be 130× and 55× faster than standard and parallelized CPU-computing, respectively, saving up to 99.5% of the computing time. The model's number of elements had the most significant impact, with <150,000 cells showing the best accuracy-speed trade-off. Using a PC equipped with a GPU enables almost real-time hydrodynamic information, democratizing flood data and facilitating interactive flood risk analysis.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46127874","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
River water level prediction (WLP) plays an important role in flood control, navigation, and water supply. In this study, a WaveNet-based convolutional neural network (WCNN) with a lightweight structure and good parallelism was developed to improve the prediction accuracy and time effectiveness of WLP. It was applied to predict 1/2/3 days the water levels at the Waizhou gauging station of the Ganjiang River (GR) in China, and it was compared with two recurrent neural networks (long short-term memory (LSTM) and gated recurrent unit (GRU)). The results showed that the WCNN model achieved the best prediction performance with the fewest training parameters and time. Compared with the LSTM and GRU models in the 1-day ahead prediction, the training parameters were reduced from 73,851 and 55,851 to 32,937, respectively. The root mean square error (RMSE) was reduced from 0.071 and 0.076 to 0.057, respectively. The mean absolute error (MAE) was reduced from 0.052 and 0.059 to 0.038, respectively. The Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) both increased to 0.998. This result indicated that the improved model was more efficient for WLP.
{"title":"A WaveNet-based convolutional neural network for river water level prediction","authors":"Jun Chen, Yan Huang, Teng Wu, Jing Yan","doi":"10.2166/hydro.2023.174","DOIUrl":"https://doi.org/10.2166/hydro.2023.174","url":null,"abstract":"\u0000 \u0000 River water level prediction (WLP) plays an important role in flood control, navigation, and water supply. In this study, a WaveNet-based convolutional neural network (WCNN) with a lightweight structure and good parallelism was developed to improve the prediction accuracy and time effectiveness of WLP. It was applied to predict 1/2/3 days the water levels at the Waizhou gauging station of the Ganjiang River (GR) in China, and it was compared with two recurrent neural networks (long short-term memory (LSTM) and gated recurrent unit (GRU)). The results showed that the WCNN model achieved the best prediction performance with the fewest training parameters and time. Compared with the LSTM and GRU models in the 1-day ahead prediction, the training parameters were reduced from 73,851 and 55,851 to 32,937, respectively. The root mean square error (RMSE) was reduced from 0.071 and 0.076 to 0.057, respectively. The mean absolute error (MAE) was reduced from 0.052 and 0.059 to 0.038, respectively. The Nash–Sutcliffe efficiency (NSE) and coefficient of determination (R2) both increased to 0.998. This result indicated that the improved model was more efficient for WLP.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44375371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydraulic engineering applications require a good knowledge of turbulent behaviour in non-prismatic channels. This paper aims to predict turbulent behaviour using the large-eddy simulation (LES) method. The model channel has a warped transition. We perform two-phase LES of free-surface flow and validate the results using experimental data and benchmark solution. We discuss rigorous strategies for model set-up, parameter selection and parametric value assignment, including parameters in the spectrum synthesiser (SS) and vortex method (VM) for inlet turbulence. The predicted flow displays complex structures due to eddy motions translated from upstream and locally generated by asymmetrical separation in the transition. The history of the flow dynamics may affect the flow development. The predicted velocity, energy spectrum, root-mean-square error, hit-rate and factor-of-two compare well with measurements and benchmark solution. Mapping mean-velocity distribution from experimental data, combined with SS, gives satisfactory inlet condition; alternatively, a 1/7th power-law for the mean-velocity, combined with VM, is acceptable. This paper uses the Okubo–Weiss parameter to delineate 3D instantaneous coherent structures. The LES methods are reliable, efficient and cost-effective. As compared to the simulation of prismatic channels, the flow dynamics in non-prismatic channels exhibit flow separation and turbulence interactions, which increase the flow-complexity, while offering results with crucial practical applications.
{"title":"Large-eddy simulation of free-surface turbulent flow in a non-prismatic channel","authors":"Ruirui Zeng, S. S. Li","doi":"10.2166/hydro.2023.018","DOIUrl":"https://doi.org/10.2166/hydro.2023.018","url":null,"abstract":"\u0000 \u0000 Hydraulic engineering applications require a good knowledge of turbulent behaviour in non-prismatic channels. This paper aims to predict turbulent behaviour using the large-eddy simulation (LES) method. The model channel has a warped transition. We perform two-phase LES of free-surface flow and validate the results using experimental data and benchmark solution. We discuss rigorous strategies for model set-up, parameter selection and parametric value assignment, including parameters in the spectrum synthesiser (SS) and vortex method (VM) for inlet turbulence. The predicted flow displays complex structures due to eddy motions translated from upstream and locally generated by asymmetrical separation in the transition. The history of the flow dynamics may affect the flow development. The predicted velocity, energy spectrum, root-mean-square error, hit-rate and factor-of-two compare well with measurements and benchmark solution. Mapping mean-velocity distribution from experimental data, combined with SS, gives satisfactory inlet condition; alternatively, a 1/7th power-law for the mean-velocity, combined with VM, is acceptable. This paper uses the Okubo–Weiss parameter to delineate 3D instantaneous coherent structures. The LES methods are reliable, efficient and cost-effective. As compared to the simulation of prismatic channels, the flow dynamics in non-prismatic channels exhibit flow separation and turbulence interactions, which increase the flow-complexity, while offering results with crucial practical applications.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43510255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.
{"title":"Separation of pressure signals caused by waves traveling in opposite directions","authors":"Marco Ferrante, Aaron Zecchin","doi":"10.2166/hydro.2023.021","DOIUrl":"https://doi.org/10.2166/hydro.2023.021","url":null,"abstract":"\u0000 Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42716531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yu Shao, Kun Li, Tuqiao Zhang, Y. Jeffrey Yang, Shipeng Chu
Abstract The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.
{"title":"Modeling of truncated normal distribution for estimating hydraulic parameters in water distribution systems: taking nodal water demand as an example","authors":"Yu Shao, Kun Li, Tuqiao Zhang, Y. Jeffrey Yang, Shipeng Chu","doi":"10.2166/hydro.2023.250","DOIUrl":"https://doi.org/10.2166/hydro.2023.250","url":null,"abstract":"Abstract The normal probability density function (PDF) is widely used in parameter estimation in the modeling of dynamic systems, assuming that the random variables are distributed at infinite intervals. However, in practice, these random variables are usually distributed in a finite region confined by the physical process and engineering practice. In this study, we address this issue through the application of truncated normal PDF. This method avoids a non-differentiable problem inherited in the truncated normal PDF at the truncation points, a limitation that can limit the use of analytical methods (e.g., Gaussian approximation). A data assimilation method with the derived formula is proposed to describe the probability of parameter and measurement noise in the truncated space. In application to a water distribution system (WDS), the proposed method leads to estimating nodal water demand and hydraulic pressure key to hydraulic and water quality model simulations. Application results to a hypothetical and a large field WDS clearly show the superiority of the proposed method in parameter estimation for WDS simulations. This improvement is essential for developing real-time hydraulic and water quality simulation and process control in field applications when the parameter and measurement noise are distributed in the finite region.","PeriodicalId":54801,"journal":{"name":"Journal of Hydroinformatics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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