Abstract:The overflow system of a dam safely controls the water level of a reservoir. The design of these structures should predict the damage caused by the action of the turbulent flow to which they are subject to. The combination formed by a stepped spillway followed by a stilling basin promotes a considerable portion of the energy dissipation in the actual chute of the stepped spillway but it is not sufficient to completely avoid the risk of damaging the basin. In this paper, we analyze the longitudinal distribution… Show more
“…To analyze the effect of possible errors of measurement of extreme pressures commonly adopted in projects (P 0.1% and P 99.9% ), due to the uncertainties in the position of the transducer in the steps, experimental data of pressure measured with transducers by Sanagiotto & Marques (2008) were used in this study. The standard deviation values (σ d ) and statistical probability distribution coefficients N 0.1% and N 99.9% were calculated, respectively, using Equations 11 and 12, following the same methodology used in studies of extreme pressures in hydraulic jumps (Novakoski et al, 2017b;Teixeira, 2008…”
Section: Discussionmentioning
confidence: 99%
“…The physical hydraulic structure used in this work is described in Sanagiotto (2003), Sanagiotto & Marques (2008) and Gomes (2006) and is very similar to the structure used in other studies (Conterato, 2011(Conterato, , 2014Conterato et al, 2015;Novakoski et al, 2017aNovakoski et al, , 2017bOsmar et al, 2018), with small differences in the approach channel and the channel downstream of the steps.…”
The traditional approach for the hydrodynamic characterization of the flow down stepped spillways is through physical modeling, which is susceptible to scale effects and has limitations related to experimental apparatus, laboratory space and the spatial discretization of data collection. Computational fluid dynamics (CFD) is an important tool for hydrodynamic analysis because, if used properly, it presents great potential for application in hydraulics. In this work, CFD was used to model the skimming flow down a stepped spillway to investigate the effects of possible pressure measurement errors due to uncertainties in the position of the sensors within the steps. The numerical model was validated through literature velocity profiles and pressure experimental data. The results showed that the best values of water fraction (α) to define free surface are α = 0.30 in the nonaerated region and α = 0.10 in the aerated region. Statistical parameters were calculated using experimental data to estimate extreme pressures. These parameters and the simulation results were used to determine that the extreme maximum and minimum pressures occur, respectively, in the region of 0.81 < x/l < 0.98, in the horizontal faces, and in the region of 0.93 < y/h < 0.98, in the vertical faces.
“…To analyze the effect of possible errors of measurement of extreme pressures commonly adopted in projects (P 0.1% and P 99.9% ), due to the uncertainties in the position of the transducer in the steps, experimental data of pressure measured with transducers by Sanagiotto & Marques (2008) were used in this study. The standard deviation values (σ d ) and statistical probability distribution coefficients N 0.1% and N 99.9% were calculated, respectively, using Equations 11 and 12, following the same methodology used in studies of extreme pressures in hydraulic jumps (Novakoski et al, 2017b;Teixeira, 2008…”
Section: Discussionmentioning
confidence: 99%
“…The physical hydraulic structure used in this work is described in Sanagiotto (2003), Sanagiotto & Marques (2008) and Gomes (2006) and is very similar to the structure used in other studies (Conterato, 2011(Conterato, , 2014Conterato et al, 2015;Novakoski et al, 2017aNovakoski et al, , 2017bOsmar et al, 2018), with small differences in the approach channel and the channel downstream of the steps.…”
The traditional approach for the hydrodynamic characterization of the flow down stepped spillways is through physical modeling, which is susceptible to scale effects and has limitations related to experimental apparatus, laboratory space and the spatial discretization of data collection. Computational fluid dynamics (CFD) is an important tool for hydrodynamic analysis because, if used properly, it presents great potential for application in hydraulics. In this work, CFD was used to model the skimming flow down a stepped spillway to investigate the effects of possible pressure measurement errors due to uncertainties in the position of the sensors within the steps. The numerical model was validated through literature velocity profiles and pressure experimental data. The results showed that the best values of water fraction (α) to define free surface are α = 0.30 in the nonaerated region and α = 0.10 in the aerated region. Statistical parameters were calculated using experimental data to estimate extreme pressures. These parameters and the simulation results were used to determine that the extreme maximum and minimum pressures occur, respectively, in the region of 0.81 < x/l < 0.98, in the horizontal faces, and in the region of 0.93 < y/h < 0.98, in the vertical faces.
“…The results showed that maximum pressure fluctuations were identified at the center of the vertical curve and assume values of 1% of the flow kinetic energy at the terminal tangency point of the curve. Novakoski et al [30] investigated extreme pressures with different probabilities (P* k% ) on a smooth basin downstream of a stepped spillway. The results showed that the values of P* 0.1% and P* 99.9% have lower and higher values than the values observed downstream of the smooth chute, in the region near the spillway toe, respectively.…”
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.
“…Farhoudi et al [19] studied the pressure fluctuations around some chute blocks in a St. Anthony Fall (SAF) type dissipation basin. Novakoski et al [20] showed that the negative pressures in the zone near the spillway toe represent the risk of cavitation in the dissipation basin. They concluded that the extreme pressures with the probabilities of occurrence equal to 0.1% and 1% require careful assessment.…”
Dissipation basins are usually constructed downstream of spillways to dissipate energy, causing large pressure fluctuations underneath hydraulic jumps. Little systematic experimental investigation seems available for the pressure parameters on the bed of the US Department of the Interior, Bureau of Reclamation (USBR) Type II dissipation basins in the literature. We present the results of laboratory-scale experiments, focusing on the statistical modeling of the pressure field at the centerline of the apron along the USBR Type I and II basins. The accuracy of the pressure transducers was ±0.5%. The presence of accessories within basinII reduced the maximum pressure fluctuations by about 45% compared to basinI. Accordingly, in some points, the bottom of basinII did not collide directly with the jet due to the hydraulic jump. As a result, the values of pressure and pressure fluctuations decreased mainly therein. New original best-fit relationships were proposed for the mean pressure, the statistical coefficient of the probability distribution, and the standard deviation of pressure fluctuations to estimate the pressures with different probabilities of occurrence in basinI and basinII. The results could be useful for a more accurate, safe design of the slab thickness, and reduce the operation and maintenance costs of dissipation basins.
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