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

Yan, Jun; Li, Wenbo; Vaz, Murilo Augusto; Lu, Hailong; Zhang, Heng-rui; Du, Hongze; Bu, Yufeng

doi:10.1007/s13344-023-0004-8

Cited by 5 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Vinicius et al [24] combined finite element analysis with deep learning and proposed the NARX-CNN network model to predict the tension and curvature of the umbilical in the first time, and the accuracy of the proposed network model is higher than that of the finite element method, which avoids the disadvantage of the high computational cost of finite elements. Yan et al [25] employed a residual neural network to predict the load-displacement curve of umbilical under radial compression, which prevented the computational errors due to the consideration of elastic deformation in the traditional theoretical calculation. The network can only predict the load-displacement curves of large-diameter umbilicals in the plastic stage with data from small-diameter umbilicals, and the accuracy of the network decreases with the reduction of input data.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network

Wang,

Shi,

Zhang

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Stress field analysis is an essential part of umbilical component layout design. The stress field analysis of an umbilical, via numerical simulation, has commonly been applied in practical engineering. The high economic and time cost associated with numerical simulation and analysis of the stress field in an umbilical has been replaced by data-driven, deep-learning-based, real-time computational methods. In this study, a novel Pyramidal Efficient U-Net (PyEf-U-Net) network is proposed to predict the stress field distribution of the umbilical. The input dataset is obtained via the Differential Evolution-Generalized Lagrange Multiplier (DE-GLM) method, which is entered into the network for training, with a detailed discussion of the effects of hyperparameters such as optimizer, learning rate, and loss function on the performance of the network. The experimental research demonstrates that the proposed PyEf-U-Net can accurately predict the stress field of the umbilical in real time with a prediction accuracy of 94.2%, which is superior to other deep learning networks. The proposed method can provide an effective way for rapid mechanical analysis and design of the umbilical in practical engineering, while the method can be extended to the mechanical analysis and design of other similar marine engineering equipment structures.

show abstract

Section: Introductionmentioning

confidence: 99%