Skip to main content
SpringerLink
Log in

Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra

  • Published:
Journal of Nondestructive Evaluation Aims and scope Submit manuscript

Abstract

Two phase flows are of particular importance in various research fields. In the current article, a novel system consists of an X-ray tube and one sodium iodide crystal detector with ability of determining type of flow regime as well as void fraction percentage of a two phase flow, is proposed. MCNP-X code was used for physical modelling of the proposed system and its performance. Radial basis function (RBF) was also implemented for analyzing and classifying the obtained data from the proposed system. Counts in each 1 keV energy bin of photon energy spectra in the detector were inserted in RBF as inputs data set and flow regime and void fraction percentage were obtained as the two outputs. After training the RBF network, the system could simultaneously recognize all the flow regimes and predict the void fraction percentage of a modelled liquid–gas two-phase flow with an acceptable error. The proposed methodology in the present paper has three main novelties and advantages over former studies. Firstly, in this system an X-ray tube is used compared to previous studies where one or more radioisotope sources served as radiation source in a radiation based multi-phase flow meter. Secondly, in former works at least two detectors were used to recognize type of flow pattern and meter volume fractions simultaneously, while in this study only one detector is utilized. Thirdly, in this study just one neural network is used, while in other studies more than one network was used.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Coker, A.K.: Fortran Programs for Chemical Process Design, Analysis, and Simulation. Elsevier, Amsterdam (1995)

    Google Scholar 

  2. Abro, E., Khoryakov, V.A., Johansen, G.A.: Determination of void fraction and flow regime using a neural network trained on simulated data based on gamma-ray densitometry. Meas. Sci. Technol. 10(7), 619–630 (1999)

    Article  Google Scholar 

  3. Faghihi, R., Nematollahi, M., Erfaninia, A., Adineh, M.: Void fraction measurement in modeled two-phase flow inside a vertical pipe by using polyethylene phantoms. Int. J. Hydrogen Energy 40(44), 15206–15212 (2015)

    Article  Google Scholar 

  4. Nazemi, E., Roshani, G.H., Feghhi, S.A.H., Setayeshi, S., Eftekhari Zadeh, E., Fatehi, A.: Optimization of a method for identifying the flow regime and measuring void fraction in a broad beam gamma-ray attenuation technique. Int. J Hydrogen Energy 41, 7438–7444 (2016)

    Article  Google Scholar 

  5. Chunguo J, Qiuguo B.: Flow regime identification of gas/liquid two-phase flow in vertical pipe using RBF neural networks. In: Proceedings of the 2009 Chinese Control and Decision Conference, IEEE, June 17–19, 2009, pp. 514–5147. https://doi.org/10.1109/CCDC.2009.5194992 (2009)

  6. Khayat, O., Afarideh, H.: Design and simulation of a multienergy gamma ray absorptiometry system for multiphase flow metering with accurate void fraction and waterliquid ratio approximation. Nukleonika 64(1), 19–29 (2019). https://doi.org/10.2478/nuka-2019-0003

    Article  Google Scholar 

  7. Hanus, R., Zych, M., Petryka, L., Jaszczur, M., Hanus, P.: Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 1: time domain. EPJ Web Conf. 114, 02035 (2016). https://doi.org/10.1051/epjconf/201611402035

    Article  Google Scholar 

  8. Hanus, R., Zych, M., Petryka, L., Jaszczur, M., Hanus, P.: Signals features extraction in liquid-gas flow measurements using gamma densitometry. Part 2: frequency domain. EPJ Web Conf. 114, 02036 (2016). https://doi.org/10.1051/epjconf/201611402036

    Article  Google Scholar 

  9. Hanus, R., Zych, M., Kusy, M., Jaszczur, M., Petryka, L.: Identification of liquid-gas flow regime in a pipeline using gamma-ray absorption technique and computational intelligence methods. Flow Meas. Instrum. 60, 17–23 (2018). https://doi.org/10.1016/j.flowmeasinst.2018.02.008

    Article  Google Scholar 

  10. Roshani, G.H., Nazemi, E., Roshani, M.M.: Flow regime independent volume fraction estimation in three-phase flows using dual-energy broad beam technique and artificial neural network. Neural Comput. Appl. 28(1), 1265–1274 (2017)

    Article  Google Scholar 

  11. Karami, A., Roshani, G.H., Khazaei, A., Nazemi, E., Fallahi, M.: Investigation of different sources in order to optimize the nuclear metering system of gas–oil–water annular flows. Neural Comput. Appl. 32, 3619–3631 (2018). https://doi.org/10.1007/s00521-018-3673-0

    Article  Google Scholar 

  12. Roshani, G.H., Roshani, S., Nazemi, E., Roshani, S.: Online measuring density of oil products in annular regime of gas-liquid two phase flows. Measurement 129, 296–301 (2018)

    Article  Google Scholar 

  13. Roshani, M., Phan, G., Faraj, R.H., Phan, N.-H., Roshani, G.H., Nazemi, B., Corniani, E., Nazemi, E.: Proposing a gamma ra-diation based intelligent system for simultaneous analyzing and detecting type and amount of petroleum by-products. Nucl. Eng. Technol. (2020). https://doi.org/10.1016/j.net.2020.09.015

    Article  Google Scholar 

  14. Roshani, G., Nazemi, E., Roshani, M.: Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function. Flow Meas. Instrum. 54, 39–45 (2017). https://doi.org/10.1016/j.flowmeasinst.2016.10.001

    Article  Google Scholar 

  15. Mosorov, V., Rybak, G., Sankowski, D.: Plug regime flow velocity measurement problem based on correlability notion and twin plane electrical capacitance tomography: use case. Sensors 21(6), 2189 (2021)

    Article  Google Scholar 

  16. Roshani, M., Phan, G.T., Ali, P.J.M., Roshani, G.H., Hanus, R., Duong, T., Corniani, E., Nazemi, E., Kalmoun, E.M.: Evaluation of flow pattern recognition and void fraction measurement in two phase flow independent of oil pipeline’s scale layer thickness. Alex. Eng. J. 60, 1955–1966 (2021). https://doi.org/10.1016/j.aej.2020.11.043

    Article  Google Scholar 

  17. Vlasák, P., Chára, Z., Matoušek, V., Konfršt, J., Kesely, M.: Experimental investigation of fine-grained settling slurry flow behaviour in inclined pipe sections. J. Hydrol. Hydromech. 67(2), 113–120 (2019)

    Article  Google Scholar 

  18. Nazemi, E., Feghhi, S., Roshani, G., Peyvandi, R.G., Setayeshi, S.: Precise void fraction measurement in two-phase flows independent of the flow regime using gamma-ray attenuation. Nucl. Eng. Technol. 48, 64–71 (2016). https://doi.org/10.1016/j.net.2015.09.005

    Article  Google Scholar 

  19. Roshani, M., Phan, G., Roshani, G.H., Hanus, R., Nazemi, B., Corniani, E., Nazemi, E.: Combination of X-ray tube and GMDH neural network as a nondestructive and potential technique for measuring characteristics of gas-oil–water three phase flows. Measurement 168, 108427 (2021). https://doi.org/10.1016/j.measurement.2020.108427

    Article  Google Scholar 

  20. Roshani, G., Nazemi, E., Roshani, M.: Identification of flow regime and estimation of volume fraction independent of liquid phase density in gas-liquid two-phase flow. Prog. Nucl. Energy 98, 29–37 (2017). https://doi.org/10.1016/j.pnucene.2017.02.004

    Article  Google Scholar 

  21. Roshani, G., Hanus, R., Khazaei, A., Zych, M., Nazemi, E., Mosorov, V.: Density and velocity determination for single-phase flow based on radiotracer technique and neural networks. Flow Meas. Instrum. 61, 9–14 (2018). https://doi.org/10.1016/j.flowmeasinst.2018.03.006

    Article  Google Scholar 

  22. Azizi, S., Yadav, A., Lau, Y.M., Hampel, U.: On the experimental investigation of gas-liquid flow in bubble columns using ultrafast X-ray tomography and radioactive particle trackin. Chem. Eng. Sci. 170, 320–331 (2017)

    Article  Google Scholar 

  23. Banowski, M., Hampel, U., Krepper, E., Beyer, M., Lucas, D.: Experimental investigation of two-phase pipe flow with ultrafast X-ray tomography and comparison with state-of-the-art CFD simulations. Nucl. Eng. Des. 336, 90–104 (2018)

    Article  Google Scholar 

  24. Banowski, M., Patmonoaji, A., Lucas, D., Hampel, U.: A novel fuzzy-logic based method for determination of individual bubble velocity and size from dual-plane ultrafast X-ray tomography data of two-phase flow. Int. J. Multiph. Flow 96, 144–160 (2017)

    Article  MathSciNet  Google Scholar 

  25. Behling, M., Mewes, D.: Heat and Mass Transfer. Springer, New York (2004)

    Google Scholar 

  26. Pelowitz, D.B.: MCNP-X TM User’s Manual, Version 2.5.0. LA-CP-05e0369. Los Alamos National Laboratory, Washington DC (2005)

    Google Scholar 

  27. Roshani, G.H., Nazemi, E., Feghhi, S.A.H., Setayeshi, S.: Flow regime identification and void fraction prediction in two-phase flows based on gamma ray attenuation. Measurement 62, 25–32 (2015)

    Article  Google Scholar 

  28. Roshani, G.H., Nazemi, E., Feghhi, S.A.H.: Investigation of using 60Co source and one detector for determining the flow regime and void fraction in gas-liquid two-phase flows. Flow Meas. Instrum. 50, 73–79 (2016)

    Article  Google Scholar 

  29. Hernandez, A.M., Boone, J.M.: Tungsten anode spectral model using interpolating cubic splines: unfiltered x-ray spectra from 20 kV to 640 kV. Med. Phys. 41(4), 042101 (2014)

    Article  Google Scholar 

  30. Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge University, Cambridge (2003)

    Book  Google Scholar 

  31. Roshani, G.H., Feghhi, S.A.H., Mahmoudi-Aznaveh, A., Nazemi, E., Adineh-Vand, A.: Precise volume fraction prediction in oil–water–gas multiphase flows by means of gamma-ray attenuation and artificial neural networks using one detector. Measurement 51, 34–41 (2014)

    Article  Google Scholar 

  32. Roshani, G., Nazemi, E.: Intelligent densitometry of petroleum products in stratified regime of two phase flows using gamma ray and neural network. Flow Meas. Instrum. 58, 6–11 (2017). https://doi.org/10.1016/j.flowmeasinst.2017.09.007

    Article  Google Scholar 

  33. Moradi, M.J., Roshani, M.M., Shabani, A., Kioumarsi, M.: Prediction of the load-bearing behavior of SPSW with rectangular opening by RBF network. Appl. Sci. 10(3), 1185 (2020)

    Article  Google Scholar 

  34. Roshani, G., Nazemi, E., Roshani, M.: Usage of two transmitted detectors with optimized orientation in order to three phase flow metering. Measurement 100, 122–130 (2017). https://doi.org/10.1016/j.measurement.2016.12.055

    Article  Google Scholar 

  35. Moradi, M.J., Hariri-Ardebili, M.A.: Developing a library of shear walls database and the neural network based predictive meta-model. Appl. Sci. 9(12), 2562 (2019)

    Article  Google Scholar 

  36. Karami, A., Roshani, G.H., Nazemi, E., Roshani, S.: Enhancing the performance of a dual-energy gamma ray based three-phase flow meter with the help of grey wolf optimization algorithm. Flow Meas. Instrum. 64, 164–172 (2018)

    Article  Google Scholar 

  37. Daneshvar, K., Moradi, M.J., Amooie, M., Chen, S., Mahdavi, G., Hariri-Ardebili, M.A.: Response of low-percentage FRC slabs under impact loading: experimental, numerical, and soft computing methods. In: Clough, R.W., PenzienDynamics, J. (eds.) Structures, pp. 975–988. Elsevier, Amsterdam (2020)

    Google Scholar 

  38. Sattari, M.A., Roshani, G.H., Hanus, R., Nazemi, E.: Applicability of time-domain feature extraction methods and artificial intelligence in two-phase flow meters based on gamma-ray absorption technique. Measurement 168, 108474 (2021). https://doi.org/10.1016/j.measurement.2020.108474

    Article  Google Scholar 

  39. Roshani, M., Sattari, M.A., Ali, P.J.M., Roshani, G.H., Nazemi, B., Corniani, E., Nazemi, E.: Application of GMDH neural network technique to improve measuring precision of a simplified photon attenuation based two-phase flowmeter. Flow Meas. Instrum. 75, 101804 (2020)

    Article  Google Scholar 

  40. Sharifzadeh, M., Khalafi, H., Afarideh, H., et al.: Two-phase flow component fraction measurement using gamma-ray attenuation technique. Nucl. Sci. Technol. 28, 88 (2017). https://doi.org/10.1007/s41365-017-0237-4

    Article  Google Scholar 

  41. Hanus, R., Zych, M., Petryka, L., Swisulski, D., Strzepowicz, A.: Application of ANN and PCA to two-phase flow evaluation using radioisotopes. EPJ Web Conf. 143, 02033 (2017)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Razi University, Kermanshah, Iran

    Saba Amiri

  2. Department of Software Engineering, Faculty of Engineering, Koya University, KOY45, Koya, Kurdistan Region, Iraq

    Peshawa Jammal Muhammad Ali

  3. Department of Mechanical Engineering, College of Engineering, University of Zakho, Box 12, Zakho, Kurdistan Region, Iraq

    Shivan Mohammed & Lokman Abdulkareem

  4. Faculty of Electrical and Computer Engineering, Rzeszów University of Technology, Rzeszow, Poland

    Robert Hanus

  5. Department of Chemical Engineering Technology, College of Technological Studies, The Public Authority for Applied Education and Training (PAAET), P.O.BOX 42325, 70654, Shuwaikh, Kuwait

    Adnan Alhathal Alanezi

  6. Institute of Optics and Quantum Electronics, Friedrich Schiller University Jena, Max-Wien-Platz 1, 07743, Jena, Germany

    Ehsan Eftekhari-Zadeh

  7. Electrical Engineering Department, Kermanshah University of Technology, Kermanshah, Iran

    Gholam Hossein Roshani

  8. Imec-Vision Lab, Department of Physics, University of Antwerp, 2610, Antwerp, Belgium

    Ehsan Nazemi

  9. Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Doha, Qatar

    El Mostafa Kalmoun

Authors
  1. Saba Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peshawa Jammal Muhammad Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shivan Mohammed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Robert Hanus
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lokman Abdulkareem
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Adnan Alhathal Alanezi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ehsan Eftekhari-Zadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Gholam Hossein Roshani
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ehsan Nazemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. El Mostafa Kalmoun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ehsan Nazemi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Amiri, S., Ali, P.J.M., Mohammed, S. et al. Proposing a Nondestructive and Intelligent System for Simultaneous Determining Flow Regime and Void Fraction Percentage of Gas–Liquid Two Phase Flows Using Polychromatic X-Ray Transmission Spectra. J Nondestruct Eval 40, 47 (2021). https://doi.org/10.1007/s10921-021-00782-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10921-021-00782-w

Keywords

Search

Navigation