Gas liquid cylindrical cyclone flow regime identification using machine learning combined with experimental mechanism explanation

The flow regimes of GLCC with horizon inlet and a vertical pipe are investigated in experiments,and the velocities and pressure drops data labeled by the corresponding flow regimes are collected.Combined with the flow regimes data of other GLCC positions from other literatures in existence,the gas and liquid superficial velocities and pressure drops are used as the input of the machine learning algorithms respectively which are applied to identify the flow regimes.The choosing of input data types takes the availability of data for practical industry fields into consideration,and the twelve machine learning algorithms are chosen from the classical and popular algorithms in the area of classification,including the typical ensemble models,SVM,KNN,Bayesian Model and MLP.The results of flow regimes identification show that gas and liquid superficial velocities are the ideal type of input data for the flow regimes identification by machine learning.Most of the ensemble models can identify the flow regimes of GLCC by gas and liquid velocities with the accuracy of 0.99 and more.For the pressure drops as the input of each algorithm,it is not the suitable as gas and liquid velocities,and only XGBoost and Bagging Tree can identify the GLCC flow regimes accurately.The success and confusion of each algorithm are analyzed and explained based on the experimental phenomena of flow regimes evolution processes,the flow regimes map,and the principles of algorithms.The applicability and feasibility of each algorithm according to different types of data for GLCC flow regimes identification are proposed.

Characteristics and Mechanisms of Co-seismic Groundwater Responses in Tayuan Well

Co-seismic responses of the groundwater level and temperature in the Tayuan well of 68 earthquakes （Ms ≥ 7.0） from January 2004 to September 2007 were analyzed. Results show that the Tayuan well has a strong ability to record large earthquakes worldwide, and the coseismic response shows a pattern of water level oscillation →temperature decrease→ oscillation stop → temperature resumption. Further analyses indicate that the amplitude of the water level and temperature change is not ouly concerned with the epicenter distance and magnitude, but is also related to the temporal state of aquifer while the seismic wave arrives. Mechanisms of water level oscillation, temperature decrease, water level oscillation stop and temperature resumption are discussed, with the results from previous research on the co-seismic response mechanisms analyzed. These include gas escape, heat diffusion and cold water seepage. Results show that a single mechanism could not explain the co-seismic response of the Tayuan well water level to multiple earthquakes; the results were garnered from a variety of jointly acting mechanisms.