Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks

  • Ehsan Nazemi 1.Nuclear Science and Technology Institute, Tehran, Iran 2.Shahid Beheshti University, Tehran, Iran
  • Gholam Hossein Roshani Radiation Application Department, Shahid Beheshti University, Tehran, Iran
  • Seyed Amir Hossein Feghhi Radiation Application Department, Shahid Beheshti University, Tehran, Iran
  • Reza Gholipour Peyvandi Nuclear Science and Technology Research Institute, Tehran, Iran
Keywords: Gamma ray, Artificial Neural Network, Multi Layer Perceptron, Void fraction


 Almost all the multi-phase flow meters (MPFMs) using gamma-ray attenuation, are calibrated for liquid and gas phases with constant density and pressure. When operational conditions such as temperature and pressure change in pipelines, the radiation-based multi-phase flowmeters would measure the flow rate with error. Therefore, performance of MPFMs would be improved by eliminating any dependency on the fluid properties such as density. In this work, a method based on dual modality densitometry combined with Artificial Neural Network (ANN) is proposed in order to estimate the void fraction in homogenous regime of gas-liquid two-phase flows in unstable operational conditions (changeable temperature and pressure) in oil industry. An experimental setup was implemented to generate the optimum required input data for training the network. ANNs were trained on the registered counts of the transmission and scattering detectors in various liquid phase densities and void fractions. Void fractions were predicted by ANNs with mean relative error of less than 0.78% in density variations range of 0.735 up to 0.98 g/cm3


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How to Cite
Nazemi, E., Roshani, G. H., Hossein Feghhi, S. A., & Peyvandi, R. (2015). Estimation of void fraction for homogenous regime of two-phase flows in unstable operational conditions using gamma-ray and neural networks. Boson Journal of Modern Physics, 2(1), 51-59. Retrieved from