Abstract FPSO (floating, production, storage and offloading) units are widely used in the offshore oil and gas industry. Generally, FPSOs have excellent oil storage capacity owing to their huge oil cargo holds. The volume and distribution of stored oil in the cargo holds influence the strain level of hull girder, especially at critical positions of FPSO. However, strain prediction using structural analysis tools is computationally expensive and time consuming. In this study, a prediction tool based on back-propagation (BP) neural network called GAIFOA-BP is proposed to predict the strain values of concerned positions of an FPSO model under different oil storage conditions. The GAIFOA-BP combines BP model and GAIFOA which is a combination of genetic algorithm (GA) and an improved fruit fly optimization algorithm (IFOA). Results from three benchmark tests show that the GAIFOA-BP model has a remarkable performance. Subsequently, a total of 81 sets of training data and 25 sets of testing data are obtained from experiment using fiber Bragg grating (FBG) sensors installed on the surface of an FPSO model. The numerical results show that the GAIFOA-BP is capable of predicting the strain values with higher accuracy as compared with other BP models. Finally, the reserved GAIFOA-BP model is utilized to predict the strain values under the inputs of a 10-day time series of volume and distribution of stored oil. The predicted strain results are further used to calculate the fatigue consumption of measurement points.

中文翻译：

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基于GAIFOA-BP神经网络的不同储油载荷下FPSO临界位置应变预测

摘要 FPSO（浮式、生产、储存和卸载）装置广泛应用于海上石油和天然气行业。一般而言，FPSO 因其巨大的石油货舱而具有出色的储油能力。货舱中储存油的体积和分布会影响船体梁的应变水平，尤其是在 FPSO 的关键位置。然而，使用结构分析工具进行应变预测在计算上既昂贵又耗时。在这项研究中，提出了一种称为 GAIFOA-BP 的基于反向传播 (BP) 神经网络的预测工具，用于预测不同储油条件下 FPSO 模型相关位置的应变值。GAIFOA-BP结合了BP模型和GAIFOA，GAIFOA是遗传算法（GA）和改进的果蝇优化算法（IFOA）的结合。三个基准测试的结果表明 GAIFOA-BP 模型具有显着的性能。随后，使用安装在FPSO模型表面的光纤布拉格光栅（FBG）传感器，从实验中获得了总共81组训练数据和25组测试数据。数值结果表明，与其他 BP 模型相比，GAIFOA-BP 能够以更高的精度预测应变值。最后，利用保留的 GAIFOA-BP 模型预测在 10 天时间序列的储油量和分布输入下的应变值。预测的应变结果进一步用于计算测量点的疲劳消耗。使用安装在 FPSO 模型表面的光纤布拉格光栅 (FBG) 传感器进行实验，共获得 81 组训练数据和 25 组测试数据。数值结果表明，与其他 BP 模型相比，GAIFOA-BP 能够以更高的精度预测应变值。最后，利用保留的 GAIFOA-BP 模型预测在 10 天时间序列的储油量和分布输入下的应变值。预测的应变结果进一步用于计算测量点的疲劳消耗。使用安装在 FPSO 模型表面的光纤布拉格光栅 (FBG) 传感器进行实验，共获得 81 组训练数据和 25 组测试数据。数值结果表明，与其他 BP 模型相比，GAIFOA-BP 能够以更高的精度预测应变值。最后，利用保留的 GAIFOA-BP 模型预测在 10 天时间序列的储油量和分布输入下的应变值。预测的应变结果进一步用于计算测量点的疲劳消耗。保留的 GAIFOA-BP 模型用于预测在 10 天时间序列的储油量和分布的输入下的应变值。预测的应变结果进一步用于计算测量点的疲劳消耗。保留的 GAIFOA-BP 模型用于预测在 10 天时间序列的储油量和分布的输入下的应变值。预测的应变结果进一步用于计算测量点的疲劳消耗。