Discharge coefficients for ogee weirs including the effects of a sloping upstream face

2020 ◽  
Vol 20 (4) ◽  
pp. 1493-1508 ◽  
Author(s):  
Farzin Salmasi ◽  
John Abraham
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Regression Analysis ◽  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Linear Interpolation ◽  
Performance Criteria ◽  
Upstream Face ◽  
Discharge Coefficients ◽  
Regression Techniques

Abstract Discharge coefficients (C0) for ogee weirs are essential factors for predicting the discharge-head relationship. The present study investigates three influences on the C0: effect of approach depth, weir upstream face slope, and the actual head, which may differ from the design head. This study uses experimental data with multiple non-linear regression techniques and Gene Expression Programming (GEP) models that are applied to introduce practical equations that can be used for design. Results show that the GEP method is superior to the regression analysis for predicting the discharge coefficient. Performance criteria for GEP are R2 = 0.995, RMSE = 0.021 and MAE = 0.015. Design examples are presented that show that the proposed GEP equation correlates well with the data and eliminates linear interpolation using existing graphs.

scholarly journals Prediction of Discharge Capacity of Labyrinth Weir with Gene Expression Programming

2020 ◽  
Author(s):  
Hossein Bonakdari ◽  
Isa Ebtehaj ◽  
Bahram Gharabaghi ◽  
Ali Sharifi ◽  
Amir Mosavi
Keyword(s):  
Gene Expression ◽  
Relative Error ◽  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Vertex Angle ◽  
Dimensionless Parameters ◽  
Labyrinth Weir ◽  
Predicted Values ◽  
Crest Length ◽  
Labyrinth Weirs

This paper proposes a model based on gene expression programming for predicting discharge coefficient of triangular labyrinth weirs. The parameters influencing discharge coefficient prediction were first examined and presented as crest height ratio to the head over the crest of the weir (p/y), crest length of water to channel width (L/W), crest length of water to the head over the crest of the weir (L/y), Froude number (F=V/√(gy)) and vertex angle () dimensionless parameters. Different models were then presented using sensitivity analysis in order to examine each of the dimensionless parameters presented in this study. In addition, an equation was presented through the use of nonlinear regression (NLR) for the purpose of comparison with GEP. The results of the studies conducted by using different statistical indexes indicated that GEP is more capable than NLR. This is to the extent that GEP predicts the discharge coefficient with an average relative error of approximately 2.5% in such a manner that the predicted values have less than 5% relative error in the worst model.

Predicting fire resistance of SRC columns through gene expression programming

2020 ◽  
Vol 12 (2) ◽  
pp. 125-140
Author(s):  
Meisam Hassani ◽  
Mohammad Safi ◽  
Reza Rasti Ardakani ◽  
Amir Saedi Daryan
Keyword(s):  
Gene Expression ◽  
Experimental Data ◽  
Reinforced Concrete ◽  
Fire Resistance ◽  
Elevated Temperatures ◽  
Gene Expression Programming ◽  
Effective Parameters ◽  
Content Type ◽  
Geometrical Properties ◽  
Steel Reinforced Concrete

Purpose This paper aims to predict the fire resistance of steel-reinforced concrete columns by application of the genetic algorithm. Design/methodology/approach In total, 11 effective parameters are considered including mechanical and geometrical properties of columns and loading values as input parameters and the duration of concrete resistance at elevated temperatures as the output parameter. Then, experimental data of several studies – with extensive ranges – are collected and divided into two categories. Findings Using the first set of the data along with the gene expression programming (GEP), the fire resistance predictive model of steel-reinforced concrete (SRC) composite columns is presented. By application of the second category, evaluation and validation of the proposed model are investigated as well, and the correspondent time-temperature diagrams are derived. Originality/value The relative error of 10% and the R coefficient of 0.9 for the predicted model are among the highlighted results of this validation. Based on the statistical errors, a fair agreement exists between the experimental data and predicted values, indicating the appropriate performance of the proposed GEP model for fire resistance prediction of SRC columns.

A Methodology for Determining Experimental Uncertainties in Regressions

1998 ◽  
Vol 120 (3) ◽  
pp. 445-456 ◽  
Author(s):  
K. K. Brown ◽  
H. W. Coleman ◽  
W. Glenn Steele
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Discharge Coefficient ◽  
Linear Regression Analysis ◽  
Uncertainty Propagation ◽  
Experimental Information ◽  
Uncertainty Sources ◽  
Systematic Uncertainties ◽  
Proper Estimation

A methodology to determine the experimental uncertainties associated with regressions is presented. When a regression model is used to represent experimental information, the uncertainty associated with the model is affected by random, systematic, and correlated systematic uncertainties associated with the experimental data. The key to the proper estimation of the uncertainty associated with a regression is a careful, comprehensive accounting of systematic and correlated systematic uncertainties. The methodology presented in this article is developed by applying uncertainty propagation techniques to the linear regression analysis equations. The effectiveness of this approach was investigated and proven using Monte Carlo simulations. The application of that methodology to the calibration of a venturi flowmeter and its subsequent use to determine flowrate in a test is demonstrated. It is shown that the previously accepted way of accounting for the contribution of discharge coefficient uncertainty to the overall flowrate uncertainty does not correctly account for all uncertainty sources, and the appropriate approach is developed, discussed, and demonstrated.

New Discharge Coefficient of Throat Tap Nozzle Based on ASME Performance Test Code 6 for Reynolds Number From 2.4 × 105 to 1.4 × 107

2013 ◽  
Vol 136 (1) ◽  
Author(s):  
Noriyuki Furuichi ◽  
Kar-Hooi Cheong ◽  
Yoshiya Terao ◽  
Shinichi Nakao ◽  
Keiji Fujita ◽  
...  
Keyword(s):  
Experimental Data ◽  
Reynolds Number ◽  
Discharge Coefficient ◽  
Performance Test ◽  
Reynolds Numbers ◽  
Experimental Results ◽  
National Metrology Institute ◽  
Reference Curve ◽  
Number Range ◽  
Discharge Coefficients

The throat tap nozzle of the American Society of Mechanical Engineers performance test code (ASME PTC) 6 is widely used in engineering fields, and its discharge coefficient is normally estimated by an extrapolation in Reynolds number range higher than the order of 107. The purpose of this paper is to propose a new relation between the discharge coefficient of the throat tap nozzle and Reynolds number by a detailed analysis of the experimental data and the theoretical models, which can be applied to Reynolds numbers up to 1.5 × 107. The discharge coefficients are measured for several tap diameters in Reynolds numbers ranging from 2.4 × 105 to 1.4 × 107 using the high Reynolds number calibration rig of the National Metrology Institute of Japan (NMIJ). Experimental results show that the discharge coefficients depend on the tap diameter and the deviation between the experimental results and the reference curve of PTC 6 is 0.75% at maximum. New equations to estimate the discharge coefficient are developed based on the experimental results and the theoretical equations including the tap effects. The developed equations estimate the discharge coefficient of the present experimental data within 0.21%, and they are expected to estimate more accurately the discharge coefficient of the throat tap nozzle of PTC 6 than the reference curve of PTC 6.

Flow Nozzles With Zero Beta Ratio

1964 ◽  
Vol 86 (3) ◽  
pp. 538-540 ◽  
Author(s):  
Hans J. Leutheusser
Keyword(s):  
Experimental Data ◽  
Satisfactory Agreement ◽  
Discharge Coefficient ◽  
Theoretical Equation ◽  
Analytical Expression ◽  
Discharge Coefficients ◽  
Analytical Work

An analytical expression for the discharge coefficient of ASME long-radius flow nozzles with zero beta ratio is presented. The latter condition corresponds to the installation of a metering nozzle at the outlet from a very large supply reservoir. The prediction of the theoretical equation is compared with experimentally determined discharge coefficients for nozzles of this type. Reference is made to analytical work in this field by other investigators and conclusions are drawn as to the degree of analytical sophistication required in order to obtain satisfactory agreement between analytical and experimental data.

Gene expression programming to predict the discharge coefficient in rectangular side weirs

2015 ◽  
Vol 35 ◽  
pp. 618-628 ◽  
Author(s):  
Isa Ebtehaj ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji ◽  
Hamed Azimi ◽  
Ali Sharifi
Keyword(s):  
Gene Expression ◽  
Discharge Coefficient ◽  
Gene Expression Programming ◽  
Side Weirs

Discharge Coefficients for Circular Side Outlets

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Laszlo Czetany ◽  
Peter Lang
Keyword(s):  
Experimental Data ◽  
Genetic Algorithm ◽  
Least Squares ◽  
Discharge Coefficient ◽  
Nonlinear Least Squares ◽  
Fluid Distribution ◽  
Vast Amount ◽  
Discharge Coefficients ◽  
New Equation

Fluid distributors are widely used in various industrial and ventilation applications. For the appropriate design of such distributors, the discharge coefficient has to be known to predict the energy and fluid distribution performance. Despite the vast amount of experimental data published, no generally applicable equations are available. Therefore, a new equation is presented for sharp-edged circular side outlets, which can be widely used for calculating the discharge coefficient. The equation is developed by regression with nonlinear least squares combined with genetic algorithm on experimental data available in the literature. The equation covers a wider range than the others presented in the literature.

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