起重机主梁截面风力系数预测及结构优化设计

为解决传统计算流体力学(computational fluid dynamics, CFD)方法获取港口起重机主梁截面风力系数过程繁琐、难以实现结构快速优化设计的关键技术难题，提出了一种基于卷积神经网络的起重机主梁截面风力系数快速预测模型。本研究所提出的风力系数快速预测模型利用自由几何变形方法处理基础截面形状以获取具有丰富几何特征的起重机主梁截面图形集，并采用CFD方法计算各主梁截面图形对应的风力系数生成数据集。在此基础上，基于数据集训练预测模型并对其网络结构进行优化，建立了主梁截面与风力系数之间的非线性映射关系。此外，进一步将该预测模型与遗传算法结合建立了一种主梁截面优化设计方法，并以数据集内F₁₁截面为例将防风性能作为优化目标测试了该优化方法的准确性和效率。算例测试结果表明，所提出的风力系数快速预测模型在预测各主梁截面的风力系数时平均相对误差为1.87%,预测时间为毫秒量级，比传统CFD方法计算效率有数量级地提升；应用本研究所发展的起重机主梁截面优化设计方法优化后的F₁₁截面较优化前风力系数降低了15.89%,能够极大地提高主梁截面的防风...

Wind coefficient prediction and structural optimization design of crane girder section

To solve the key technical problems of the laborious process and difficulty in achieving rapid structural optimization design using the traditional computational fluid dynamics (CFD) method to obtain the wind coefficient of the main beam section of port crane, a rapid prediction model for wind coefficient of crane main beam section based on convolutional neural network is proposed. The wind coefficient rapid prediction model proposed in this paper uses the free geometric deformation method to process the basic section shape to obtain the cross-section pattern set of crane main beam with rich geometric characteristics, CFD method is then used to calculate the wind coefficient corresponding to the cross-section pattern of each main girder to generate the dataset. On this basis, the prediction model is trained based on the dataset and its network structure is optimized, and the nonlinear mapping relationship between the main beam section and the wind coefficient is established. In addition, this paper further combines the prediction model with the genetic algorithm to establish an optimization design method for main beam cross-section. Taking the F₁₁ section in the dataset as an example, the accuracy and efficiency of the optimization method are tested by using the windproof performance as the optimization objective. The test results show that the wind coefficient rapid prediction model proposed in this paper achieves the average relative error of 1.87% when predicting the wind coefficient of each main beam section. The prediction time is in the order of milliseconds, which is significantly faster compared to the traditional CFD method, showcasing a significant improvement in efficiency. The optimized F₁₁ section of crane main beam section developed by applying the optimization design method developed in this paper is reduced by 15.89% compared with the wind coefficient before optimization. This significant reduction greatly improves the windproof performance of the main beam section, which proves the reliability of the optimization method proposed in this paper. It can be used as a new method for the optimization design and rapid selection of crane main beam section structure.