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Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods
- Pressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measuredPressure fluctuations beneath hydraulic jumps potentially endanger the stability of stilling basins. This paper deals with the mathematical modeling of the results of laboratory-scale experiments to estimate the extreme pressures. Experiments were carried out on a smooth stilling basin underneath free hydraulic jumps downstream of an Ogee spillway. From the probability distribution of measured instantaneous pressures, pressures with different probabilities could be determined. It was verified that maximum pressure fluctuations, and the negative pressures, are located at the positions near the spillway toe. Also, minimum pressure fluctuations are located at the downstream of hydraulic jumps. It was possible to assess the cumulative curves of pressure data related to the characteristic points along the basin, and different Froude numbers. To benchmark the results, the dimensionless forms of statistical parameters include mean pressures (P*m), the standard deviations of pressure fluctuations (σ*X), pressures with different non-exceedance probabilities (P*k%), and the statistical coefficient of the probability distribution (Nk%) were assessed. It was found that an existing method can be used to interpret the present data, and pressure distribution in similar conditions, by using a new second-order fractional relationships for σ*X, and Nk%. The values of the Nk% coefficient indicated a single mean value for each probability.…
Document Type: | Article |
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Author: | Seyed Nasrollah MousaviORCiD, Renato Steinke JúniorORCiD, Eder Daniel Teixeira, Daniele BocchiolaORCiD, Narjes NabipourORCiD, Dr Amir MosaviORCiD, Shahaboddin ShamshirbandORCiD |
DOI (Cite-Link): | https://doi.org/10.3390/math8030323Cite-Link |
URN (Cite-Link): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20200402-41140Cite-Link |
URL: | https://www.mdpi.com/2227-7390/8/3/323 |
Parent Title (English): | Mathematics |
Publisher: | MDPI |
Place of publication: | Basel |
Language: | English |
Date of Publication (online): | 2020/03/02 |
Date of first Publication: | 2020/03/02 |
Release Date: | 2020/04/02 |
Publishing Institution: | Bauhaus-Universität Weimar |
Institutes and partner institutions: | Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM) |
Volume: | 2020 |
Issue: | Volume 8, Issue 3, 323 |
Pagenumber: | 16 |
Tag: | Machine learning; extreme pressure; hydraulic jump; mathematical modeling; standard deviation of pressure fluctuations; statistical coeffcient of the probability distribution; stilling basin |
GND Keyword: | Maschinelles Lernen |
Dewey Decimal Classification: | 500 Naturwissenschaften und Mathematik |
BKL-Classification: | 52 Maschinenbau, Energietechnik, Fertigungstechnik |
Licence (German): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |