Viscous Inner and Outer Pressure Forming Method of Thin-walled Tube and Its Application

Aiming at overcoming the difficulties in integral forming of thin-walled tubes with complex shapes, a novel forming method by inner and outer pressure through viscous was proposed. In this method, by dividing large deformation of the part into inner and outer pressure forming deformations, the limit deformation of tube part can be increased by several times. Meanwhile, the principle of viscous inner and outer pressure forming was provided, and key problems during the forming process such as reduction of the wall-thickness and instability wrinkling were analyzed. Thereby, the complex curved surface super-alloy GH3044 thin-walled tube with varying diameter ratio of 1.35（the ratio between the maximum and minimum diameters of the part） can be integrally formed by this method. The experimental surface of the formed part is superior in quality and the wall-thickness distribution is uniform. The results show that the viscous inner and outer pressure forming can provide a new approach for integral forming of thin-walled tubes with complex shapes.

CATASTROPHE FRACTURE OF THIN-WALL PRESSURE TUBES

Catastrophe theory was used to investigate the fracture behavior of thin-wall cylindrical tubes subjected to internal explosive pressure. Based on the energy theory and catastrophe theory, a cusp catastrophe model for the fracture vas established, and a critical condition associated with the model is given.

The approach to calculate the aerodynamic drag of maglev train in the evacuated tube

In order to study the relationships between the aerodynamic drag of maglev and other factors in the evacuated tube, the formula of aerodynamic drag was deduced based on the basic equations of aerodynamics and then the calculated result was confirmed at a low speed on an experimental system developed by Superconductivity and New Energy R＆D Center of South Jiaotong University. With regard to this system a high temperature superconducting magnetic levitation vehicle was motivated by a linear induction motor （LIM） fixed on the permanent magnetic guideway. When the vehicle reached an expected speed, the LIM was stopped. Then the damped speed was recorded and used to calculate the experimental drag. The two results show the approximately same relationship between the aerodynamic drag on the maglev and the other factors such as the pressure in the tube, the velocity of the maglev and the blockage ratio. Thus, the pressure, the velocity, and the blockage ratio are viewed as the three important factors that contribute to the energy loss in the evacuated tube transportation.

SIMUUTION OF ELECTROMAGNETIC TUBE BULGING BASED ON LOOSE COUPLING METHOD

A loose coupling method is used to solve the electromagnetic tube bulging. ANSYS/ EMAG is used to model the time varying electromagnetic field with the discharge current used as excitation, in order to obtain the radial and axial magnetic pressure acting on the tube, the magnetic pressure is then used as boundary conditions to model the high velocity deformation of tube with DYNAFORM, The radial magnetic pressure on the tube decreases from the center to the tube end, axial magnetic pressure is greater near the location equal to the coil height and slight in the other region. The radial displacement of deformed workpicces is distributed uniformly near the tube center and decreases from the center to the end; Deformation from the location equal to coil height to the tube end is little. This distribution is consistent with the distribution of radial pressure; Effect of the axial magnetic pressure on deformation can be ignored, The calculated results show well agreements with the experimental results.

Application of machine learning algorithms to predict tubing pressure in intermittent gas lift wells

Tubing pressure at gas injection depth in intermittent wells is one of the most critical parameters for production engineers to evaluate the performance of the system.However,monitoring of the tubing pressure is not usually carried out in real time.It has been realized that the generally used correlations are not effective enough due to complexity of the intermittent process which involve many parameters and assumptions to develop such equations.The focus of this study is to utilize machine learning(ML)algorithms to develop a model that can accurately predict tubing pressure in artificial intermittent gas lift wells.intelligent algorithms built on the field data provide a solution that is easy to use and universally applicable to the complex problems.Various non-linear regression ML methods are employed in this study,namely,Decision Tree-regression(DT),Random Forest-regression(RF)and K Nearest Neighbors-regression(KNN).All the tubing pressures obtained from ML models were compared with the actual values to ensure the effectiveness of the work.The developed models show that it can predict the pressure with more than 99.9%accuracy.This is an interesting result,as such outcome accuracy has not been reported usually in the open literature.