## 5 Conclusions  
A detailed investigation was performed on the dynamic pressure behavior of two-phase flow in a horizontal pipe to identify parameters that can be used for flow regime identification. The method is based on using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probability Density Function (PDF), to select useful parameters for flow regime identification. A simulation model was established and validated against experimental results and empirical correlations were made to simulate different flow regimes successfully and obtain dynamic pressure signals. Post processing of these signals show different characteristics in both the time and frequency domain for all three flow regimes. It was shown that different characteristics, like dominant frequency, FFT amplitude, PDF location, and PDF maximum, obtained from the FFT and PDF of dynamic pressure signals can be used to identify the flow regime successfully. Each flow regime has a specific dominant frequency range and an identifiable location and value of the PDF maximum. The use of dynamic pressure compared to other measurable properties for the identification of the flow regime offers many advantages, e.g., ease of deployment and implementation of the algorithm to existing instrumentation devices, relatively inexpensive, requires no optical access to flow, and provides no radiation or other safety concerns. The proposed method is highly recommended for industrial applications where no visual access to flow is possible and characterization is only allowed using indirect measurements like pressure signals. This work forms a steppingstone, providing the numerical foundation and pilot case for further future work in the identification of different flow patterns using simple indirect measurements. Flow pattern and pressure drop are primary functions of pipe diameter [42; 43]. The next step to extend this work is investigating the effect due to different pipe diameters, covering an extended range of superficial velocities and pipe orientations, and using an artificial neural network to train the flow regime markers. At the same time, the idealization of the CFD model and its limitations are fully understood as the  
Figure 11: Probability Density Function for pressure signals of different flow regimes: (**a**) stratified, (**b**) slug, and (**c**) annular. current research does not consider the fluid-structure interaction where the fluid pressure is transmitted to its conduit and the interaction thereof. Conceptualization, U.K. and W.P. ; Writing-original draft preparation, U.K.; Data collection U.K.; Supervision, W.P. and N.S. ; Visualization U.K.; Data Analysis, U.K. and W.P. ; Project administration, U.K., W.P. and N.S. ; Methodology, U.K. and W.P. ; Validation, U.K.; Writing-review and editing, W.P., N.S. ; Funding acquisition, W.P. All authors have read and agreed to the published version of the manuscript. The first and second author wish to specially acknowledge the funding for this study from the Malaysia Ministry of Higher Education through a Fundamental Research Grant Scheme FRGS/1/2019/TK03/UTP/02/10. All the authors wish to thank Yayasan Universiti Teknologi PETRONAS under YUTP-15 LC0-456 and YUTP 015 LC0-252 for funding. Not applicable. The authors declare that all data can be obtained from the corresponding author upon request. The authors  
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