Estimation of air-flow parameters and turbulent intensity in hydraulic jump on rough bed using Bayesian model averaging

Narges Taravatrooy, Farhad Bahmanpouri, Mohammad Reza Nikoo, Carlo Gualtieri, Azizallah Izady

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

A hydraulic jump is an abrupt transition between subcritical and supercritical flows which is associated with energy dissipation, air entrainment, spray, splashing, and surface waves. Both physical and numerical modeling were largely applied to study hydrodynamics, turbulence and air-entrainment in the hydraulic jump, while the literature about the application of classifier models is quite limited. Determining air-flow parameters and turbulent intensity has been merely performed by costly and time-consuming experimental methods, while this study is the first attempt to estimate the mentioned parameters using a computer-based methodology with desired precision. In the present study, air-flow parameters including void fraction (C) and bubble count rate (F), as well as turbulent intensity (Tu) on rough bed were estimated using Bayesian model averaging (BMA) and three multilayer perceptron (MLP), support vector regression (SVR) and generalized regression neural network (GRNN) as classifier models. To develop the stated models, the experimental data from Felder and Chanson (2016) were divided into four classes based on longitudinal distance from the jump toe. Results highlighted that the MLP and GRNN models have more accurate results compared to the SVR model. For F and Tu, the GRNN model and for C, the MLP model showed better performance than other models in four classes. The average acceptance rate between 15 and 30% of the BMA model performance for all classes proved the accuracy and efficiency of the proposed methodology. The average RMSE value of BMA results and the bests classifier models were 0.41 and 0.42, respectively, for the estimation of all three parameters. Results revealed that the BMA model by weighting individual classifier models could be able to estimate parameters with better accuracy than the best classifier model in each class. The significant outcome of this study is that the proposed model is able to render accurate results in a complex system such as hydraulic jump.

Original languageEnglish
Article number107165
Pages (from-to)107165
Number of pages1
JournalApplied Soft Computing
Volume103
DOIs
Publication statusPublished - May 1 2021

Keywords

  • Air-flow parameters
  • Bayesian model averaging
  • Classifier models
  • Hydraulic jump
  • Rough bed
  • Turbulent intensity

ASJC Scopus subject areas

  • Software

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