Prediction of Load-Displacement Curve of Flexible Pipe Carcass Under Radial Compression Based on Residual Neural Network

The carcass layer of flexible pipe comprises a large-angle spiral structure with a complex interlocked stainless steel cross-section profile, which is mainly used to resist radial load. With the complex structure of the carcass layer, an equivalent simplified model is used to study the mechanical properties of the carcass layer. However, the current equivalent carcass model only considers the elastic deformation, and this simplification leads to huge errors in the calculation results. In this study, radial compression experiments were carried out to make the carcasses to undergo plastic deformation. Subsequently, a residual neural network based on the experimental data was established to predict the load-displacement curves of carcasses with different inner diameter in plastic states under radial compression.The established neural network model’s high precision was verified by experimental data, and the influence of the number of input variables on the accuracy of the neural network was discussed. The conclusion shows that the residual neural network model established based on the experimental data of the small-diameter carcass layer can predict the load-displacement curve of the large-diameter carcass layer in the plastic stage. With the decrease of input data, the prediction accuracy of residual network model in plasticity stage will decrease.

Powell dynamic identification of displacement parameters of indeterminate thin-walled curve box based on FCSE theory

The FCSE controlling equation of pinned thinwalled curve box was derived and the indeterminate problem of continuous thin-walled curve box with diaphragm was solved based on flexibility theory. With Bayesian statistical theory,dynamic Bayesian error function of displacement parameters of indeterminate curve box was founded. The corresponding formulas of dynamic Bayesian expectation and variance were deduced. Combined with one-dimensional Fibonacci automatic search scheme of optimal step size,the Powell optimization theory was utilized to research the stochastic identification of displacement parameters of indeterminate thin-walled curve box. Then the identification steps were presented in detail and the corresponding calculation procedure was compiled. Through some classic examples,it is obtained that stochastic performances of systematic parameters and systematic responses are simultaneously deliberated in dynamic Bayesian error function. The one-dimensional optimization problem of the optimal step size is solved by adopting Fibonacci search method. And the Powell identification of displacement parameters of indeterminate thin-walled curve box has satisfied numerical stability and convergence,which demonstrates that the presented method and the compiled procedure are correct and reliable.During parameters鈥？iterative processes,the Powell theory is irrelevant with the calculation of finite curve strip element（FCSE） partial differentiation,which proves high computation effciency of the studied method.

Experimental study of evaporation of sessile water droplet on PDMS surfaces

Evaporation of sessile water droplet on polydimethylsiloxane （PDMS） surfaces with three different curing ratios （5：1, 10：1, and 20：1） was experimentally investigated in this paper. We show that the constant contact radius （CCR） evaporation on surface with high curing ratio lasts longer than that with low curing ratio. We also measured Young＇s moduli of PDMS films by using atomic force microscopy （AFM） and simulated surface deformation of PDMS films induced by sessile water droplet. With increasing curing ratio of PDMS film, Young＇s modulus of PDMS film is getting lower, and then there will be larger surface deformation and more elastic stored energy. Since such energy acts as a barrier to keep the three-phase contact line pinned, thus it will result in longer CCR evaporation on PDMS surface with higher curing ratio.

Tensile and shearing behaviors of the particle raft

The particle raft is a two-dimensional condensed soft matter,which is composed of hydrophobic solid particles floating on a liquid surface.In this study,we have realized to conduct tension and shear experiments on the particle raft by using the self-assembled loading device.The crack initiation and propagation are observed by the camera,and the curve of the non-dimensional force with respect to the displacement is given.These experimental results have been compared with the particle flow code 3D(PFC3D)simulation,and they are in excellent agreement.It indicates that the particle raft possesses mechanical properties between solid and liquid.These findings provide some inspirations on engineering new devices and new materials at the microscale.