Abstract

As a piping element with excellent stress compensation performance, spherical joints are widely used in aerospace piping systems. However, in piping stress simulation, the presence of nonlinear features such as friction vice of the solid model of the spherical joints will lead to the difficulty of convergence of the calculations or the deviation of the results is too large, so in order to improve the convergence of the iterative process, “joint” joints are used to replace and simplify the original spherical joints, but this simplified calculation needs to accurately capture the characteristic parameters (such as bending stiffness and bending friction moment, etc). In order to improve the convergence of the iterative process, “joint” is usually used to replace and simplify the original spherical joint, but this simplified calculation needs to accurately capture the characteristic parameters of the spherical joint (e.g. bending stiffness and bending friction moment, etc.). This paper focuses on the bending characteristics of spherical joints by establishing a high-temperature and high-pressure test platform, and takes into account the difference between the test and the actual use of spherical joints, and further combines numerical simulation and BP neural network methods to predict the characteristics of spherical joints to meet the requirements of the actual use of spherical joints.

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