TY  - JOUR
A1  - Mousavi, Seyed Nasrollah
A1  - Steinke Júnior, Renato
A1  - Teixeira, Eder Daniel
A1  - Bocchiola, Daniele
A1  - Nabipour, Narjes
A1  - Mosavi, Amir
A1  - Shamshirband, Shahaboddin
T1  - Predictive Modeling the Free Hydraulic Jumps Pressure through Advanced Statistical Methods
JF  - Mathematics
N2  - 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 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.
KW  - Maschinelles Lernen
KW  - Machine learning
KW  - mathematical modeling
KW  - extreme pressure
KW  - hydraulic jump
KW  - stilling basin
KW  - standard deviation of pressure fluctuations
KW  - statistical coeffcient of the probability distribution
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20200402-41140
UR  - https://www.mdpi.com/2227-7390/8/3/323
VL  - 2020
IS  - Volume 8, Issue 3, 323
PB  - MDPI
CY  - Basel
ER  -