Dai Kaoshan, Du Hang, Luo Yuxiao, Han Rui, Li Ji
Department of Civil Engineering, Sichuan University, Chengdu 610207, China.
GIPSA-Lab, Grenoble INP, CNRS, Université Grenoble Alpes, 38000 Grenoble, France.
Materials (Basel). 2023 Oct 23;16(20):6815. doi: 10.3390/ma16206815.
The flexible high-neck flange is connected to the circular hollow section (CHS) tube through welding, and the placement of the weld seam and corresponding stress concentration factor (SCF) are crucial determinants of the joint's fatigue performance. In this study, three hybrid models combining ant colony optimization (ACO), a genetic algorithm (GA), and grey wolf optimization (GWO) with a random forest (RF) model were developed to predict the stress distribution on the inner and outer walls of the CHS tube under different flange parameter combinations. To achieve this, an automated finite element (FE) analysis program for flexible high-neck flange joints was initially developed based on ABAQUS 2020 software. Parameter combinations were randomly selected within a reasonable range to simulate the nonlinear mechanical behavior of the joint under uniform tension, generating a dataset comprising 5417 sets of data. The accuracy of the FE model was validated through experimental data from the literature. Based on this, feature importance analysis was conducted to reveal the influence of different variable parameters on the stress distribution in the tube of the joint. The flange parameters and tube stress distribution are considered as inputs and outputs, respectively. Three hybrid RF models, specifically ant colony optimization-based random forest (ACO-RF), genetic algorithm-based random forest (GA-RF), and grey wolf optimization-based random forest (GWO-RF), are trained for regression prediction. The results demonstrate that the three hybrid models outperform the original machine learning model in predictive accuracy. The ACO-RF model achieved the highest accuracy with average coefficients of determination (Rmean2) of 0.9983 and 0.9865 on the testing and training sets, respectively. Building upon this foundation, the study developed a corresponding open-source graphical user interface (GUI) as a tool for facilitating computations and visualizing results. Finally, a case study on fatigue damage assessment of a flexible high-neck flange joint in a wind-turbine tower is presented to demonstrate the application of the proposed model in this study.
柔性高颈法兰通过焊接与圆形空心截面（CHS）管相连，焊缝位置及相应的应力集中系数（SCF）是接头疲劳性能的关键决定因素。在本研究中，开发了三种将蚁群优化（ACO）、遗传算法（GA）和灰狼优化（GWO）与随机森林（RF）模型相结合的混合模型，以预测不同法兰参数组合下CHS管内外壁的应力分布。为此，首先基于ABAQUS 2020软件开发了一个用于柔性高颈法兰接头的自动化有限元（FE）分析程序。在合理范围内随机选择参数组合，以模拟接头在均匀拉伸下的非线性力学行为，生成了一个包含5417组数据的数据集。通过文献中的实验数据验证了有限元模型的准确性。在此基础上，进行了特征重要性分析，以揭示不同变量参数对接头管内应力分布的影响。将法兰参数和管应力分布分别视为输入和输出。对三种混合随机森林模型，即基于蚁群优化的随机森林（ACO-RF）、基于遗传算法的随机森林（GA-RF）和基于灰狼优化的随机森林（GWO-RF）进行回归预测训练。结果表明，这三种混合模型在预测精度上优于原始机器学习模型。ACO-RF模型在测试集和训练集上的平均决定系数（Rmean2）分别为0.9983和0.9865，达到了最高精度。在此基础上，该研究开发了一个相应的开源图形用户界面（GUI），作为促进计算和可视化结果的工具。最后，给出了一个风力发电机组塔筒中柔性高颈法兰接头疲劳损伤评估的案例研究，以展示本研究中所提出模型的应用。
