On-line head loss identification for monitoring of pipelines

Rojas, Jorge; Verde, Cristina; Torres, Lizeth; Pérez, Eucario Gonzalo Pérez

doi:10.1016/j.ifacol.2018.09.659

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, computer-based methods for leak location in pipelines, assume that the quasi-static friction is constant, however, this is not true (Rojas et al, 2018) as this parameter changes with time. An accurate value of the friction factor would improve the performance of leak detection methodologies.…”

Section: Introductionmentioning

confidence: 99%

Leak diagnosis in pipelines using a combined artificial neural network approach

Pérez-Pérez

López‐Estrada

Valencia‐Palomo

et al. 2021

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Leak diagnosis in pipelines using a combined artificial neural network approach

Pérez-Pérez

López‐Estrada

Valencia‐Palomo

et al. 2021

Control Engineering Practice

Self Cite

View full text Add to dashboard Cite

“…The head losses promoted by the constituents of the system, fluid characteristics, flow velocity, and external factors of the exposed environment (e.g., temperature and pressure) result in water application efficiency (Sousa & Dantas Neto, 2014; Wu et al, 2014; Chamba, Zubelzu, & Juana, 2019). Adding to the intrinsic effects of each factor, the head loss can have additional consequences such as great losses in water delivery and changes in the quality of the pipes (Rojas et al, 2018), making the irrigation project even more inefficient (Wang et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling the Darcy–Weisbach friction factor and the energy gradient of the lateral line*

Jardim

Silva

et al. 2021

Irrigation and Drainage

View full text Add to dashboard Cite

The objective was to adjust explicit mathematical models to determine the friction factor ( f ). Besides, an adaptation of the Blasius model is presented and an application of the Darcy-Weisbach equation to estimate the energy loss along the lateral line for four types of microsprinklers. f was determined by the model of Offor and Alabi and the model of Blasius for velocities between 0.5 to 3.0 m s À1 and internal pipe diameters of 0.0130, 0.0161, 0.0206, 0.0270, 0.0288, and 0.0368 m. The friction factor determined by Offor and Alabi was associated with the pipe diameters utilizing regression, and based on that an adaptation to the Blasius model was proposed. From the results of f, the energy and flow gradient along the lateral line for four NaanDanJain microsprinklers was simulated. The models of Offor and Alabi, Blasius, and Adapted Blasius showed excellent precision in determining f. The results of unit head loss, determined by the three mathematical models, showed dispersion close to zero among the pairs, showing a high correlation between the models (R 2 = 0.99).The Blasius equation is the most suitable to be inserted into the Darcy-Weisbach model to determine the maximum length of the lateral line.

show abstract

Adaptive estimation of the hydraulic gradient for the location of multiple leaks in pipelines

Rojas

Verde

2020

Control Engineering Practice

View full text Add to dashboard Cite

On-line head loss identification for monitoring of pipelines

Cited by 5 publications

References 15 publications

Leak diagnosis in pipelines using a combined artificial neural network approach

Leak diagnosis in pipelines using a combined artificial neural network approach

Modelling the Darcy–Weisbach friction factor and the energy gradient of the lateral line*

Adaptive estimation of the hydraulic gradient for the location of multiple leaks in pipelines

Contact Info

Product

Resources

About