Previsão de séries temporais de esforços em risers flexíveis durante a fase de instalação por meio de redes neurais convolucionais

Abstract

Offshore projects for flexible pipe installation play a relevant role in connecting wells to platforms. The development of these projects involves numerical simulations to analyze the behavior of equipments, with a particular focus on flexible pipes. Often, environmental loadings generated by ocean waves are modeled deterministically, known as regular wave modeling, due to its computational efficiency. However, this approach is a substantial simplification compared to the actual motion of waves, which is better represented by modeling with irregular waves. This work proposes a methodology to make the use of irregular wave modeling feasible in installation analyses, employing artificial neural networks. Specifically, convolutional neural networks are trained with data generated from short length finite element-based numerical simulations. The developed model focuses on modeling the tension at the top of the pipe and the touchdown zone for various configurations that occur during the installation of a laze wave flexible pipe. Additionally, the model is adaptable to different environmental conditions, providing a substantial reduction in simulation time.