Reading the Moody chart with a linear interpolation method

Huang, Shaoqiang

doi:10.1038/s41598-022-10552-x

Cited by 3 publications

(2 citation statements)

References 9 publications

(8 reference statements)

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“…Using the Moody Diagram method carries a margin of error of approximately 4% [18], but a clear inverse relationship is established between pipe diameter and calculated total head loss.…”

Section: Discussionmentioning

confidence: 99%

A Value Engineering Analysis of Head Loss in Pumping Mains

Cobbe¹,

McDermott²

2022

ENG

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With net zero carbon emissions targets approaching over the next 20 to 30 years, the water industry must act now to develop energy efficient techniques and designs to reduce emissions and reduce the carbon footprint of water utility providers. There is also the potential for significant energy and therefore financial savings to be realised from the adoption of more energy efficient designs approaches. Water utility providers account for a significant proportion of national electricity consumption. The purpose of this research is to determine if, over the long term, opting for a larger diameter pipe at design stage can lead to significant financial and emissions savings for water utility providers when considering pumping mains. Pumping mains are widely used throughout the water and wastewater industry where a gravity solution is not possible. 72 hypothetical water main design scenarios were analysed and the long term financial and environmental impact of each hypothetical water main was assessed. It was found across all design scenarios that larger diameter water mains were capable of delivering the same rate of flow of smaller diameter pipes at a much reduced velocity and requiring reduced pumping power. It was concluded that pumped mains of larger diameters can ultimately be more energy efficient and cost effective over the long term when selected in favour of smaller diameter pumped mains in otherwise identical design scenarios.

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“…Using the Moody Diagram method carries a margin of error of approximately 4% [18], but a clear inverse relationship is established between pipe diameter and calculated total head loss.…”

Section: Discussionmentioning

confidence: 99%

A Value Engineering Analysis of Head Loss in Pumping Mains

Cobbe¹,

McDermott²

2022

ENG

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“…The FPR and TPR of the current class determine the ROC curve of the current class. The linear interpolation method [32] is used to interpolate the horizontal coordinate that does not exist relative to FPR_all in FPR, and the TPR' after interpolation is obtained. TPR_mean is obtained by arithmetic averaging TPR' after class g interpolation.…”

Section: Data Standardizationmentioning

confidence: 99%

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Zhang

et al. 2022

Data Intelligence

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Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

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Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter

Brkić,

Praks,

Praksová

et al. 2023

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This study provides novel and accurate symbolic regression-based solutions for the calculation of pipe diameter when flow rate and pressure drop (head loss) are known, together with the length of the pipe, absolute inner roughness of the pipe, and kinematic viscosity of the fluid. PySR and Eureqa, free and open-source symbolic regression tools, are used for discovering simple and accurate approximate formulas. Three approaches are used: (1) brute force of computing power, which provides results based on raw input data; (2) an improved method where input parameters are transformed through the Lambert W-function; (3) a method where the results are based on inputs and the Colebrook equation transformed through new suitable dimensionless groups. The discovered models were simplified by the WolframAlpha simplify tool and/or the equivalent Matlab Symbolic toolbox. Novel models make iterative calculus redundant; they are simple for computer coding while the relative error remains lower compared with the solution through nomograms. The symbolic-regression solutions discovered by brute force computing power discard the kinematic viscosity of the fluid as an input parameter, implying that it has the least influence.

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Reading the Moody chart with a linear interpolation method

Cited by 3 publications

References 9 publications

A Value Engineering Analysis of Head Loss in Pumping Mains

A Value Engineering Analysis of Head Loss in Pumping Mains

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Symbolic Regression Approaches for the Direct Calculation of Pipe Diameter

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