Prof.Denghui He

Department of Power Engineering, Institute of Water Resources and Hydro-electric Engineering,

Xi’an University of Technology, Xi’an, 710048, PRC

Bio

Dr. Denghui He graduated from the State Key Laboratory of Multiphase Flow in Power Engineering at Xi’an Jiaotong University. He currently serves as the secretary of the Power Engineering and Engineering Thermophysics at Xi’an University of Technology and was selected for the university’s “Outstanding Young Teachers” training program. His research interests mainly include fluid machinery optimization design and energy efficiency improvement, multiphase flow and fault diagnosis in aviation oil pumps, optimization design and application of multiphase pumps based on intelligent algorithms, as well as piston gravity-hydraulic energy storage technology and its applications. He has led two projects funded by the National Natural Science Foundation, one project on the development of a major national research instrument, and one project funded by the Natural Science Foundation of Shaanxi Province. Dr. He has also been involved in over 20 research projects. His research achievements include over 50 SCI/EI-indexed papers published in journals such as IJMF, POF, ASME JFE, as well as 6 granted invention patents, 6 utility model patents, and 3 patent conversion. Additionally, he holds positions as a member of the Youth Science and Technology Committee of the Shaanxi Hydropower and New Energy Engineering Society, a member of the Singapore VE Mechanical Engineering Expert Committee, a member of the Chinese Society of Engineering Thermophysics, a member of the Chinese Chemical Society, a guest editor for Energies, a youth editor of the Journal of Mechanical Engineering, and an editor for Contemporary Chemical Research.

Title

Characteristics of gas-liquid two-phase flow in centrifugal pumps

Abstract

In the field of gas-liquid two-phase transport processes in areas such as chemical engineering, nuclear power, and oil and gas extraction, research has been conducted on the flow characteristics of gas-liquid two-phase inside centrifugal pumps. A full-channel visual centrifugal pump experimental device was designed and produced to obtain the gas-liquid two-phase flow patterns inside the pump impeller. The corresponding flow pattern maps were drawn to reveal the influence of inlet parameters on the flow patterns inside the impeller. It was discovered that the inlet gas volume fraction is the most direct factor causing the transition of flow patterns. The dynamics of bubbles under four flow patterns were investigated, the process of bubble shape change was explored, and it was confirmed that the accumulation and stagnation of bubbles near the impeller inlet are the main reasons for pump performance deterioration. Ultimately, the flow characteristics of gas-liquid two-phase and the influence of inlet parameters on pump performance were revealed, and the effectiveness of existing pump surge prediction models was verified. A numerical simulation method for the flow characteristics of gas-liquid in centrifugal pumps based on a CFD-PBM coupling model was established. Compared to traditional numerical simulation methods based solely on the Eulerian-Eulerian model, this method takes into account the changes in bubble size caused by coalescence and breakup between bubbles, resulting in a simulation closer to the real flow conditions. The development and change process of bubbles inside the pump was examined, revealing the dynamics of bubble stagnation within the impeller flow passage. The characteristics of bubble fragmentation and coalescence inside the pump were explored, and the size distribution laws inside the impeller and volute were obtained. The influence of inlet gas volume fraction, liquid phase flow rate, and speed on the head and efficiency of the centrifugal pump was revealed, and a model predicting gas volume fraction and bubble size was proposed. Finally, a predictive model for flow patterns and gas-liquid two-phase pressure rise performance of centrifugal pumps based on machine learning was established.