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Optimization of submerged floating tunnel cross section based on parametric Bézier curves and hybrid backpropagation - genetic algorithm
Marine Structures ( IF 4.0 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.marstruc.2020.102807
Pengxu Zou , Jeremy Bricker , Wim Uijttewaal

Abstract The cross-section geometry of a submerged floating tunnel (SFT) has a large effect on hydrodynamic characteristics, structural behavior and service level, making the tunnel cross section the primary factor in optimizing efficiency. Minimizing the mean drag and the dynamic variability in the lift of the SFT cross section under bi-directional (i.e., tidal) flow has a dramatic impact on the reduction of structural displacements and mooring loads. Based on a parametric Bezier curve dynamically comprising the leading-edge radius, tunnel height and width to define the SFT geometry, a sensitivity analysis of the Bezier curve parameters for a fixed aspect ratio with prototype dimensions under uniform flow conditions was conducted by applying Computational Fluid Dynamics (CFD), and the pressure distribution around the SFT cross-section surface was analyzed. A theoretical method comprising the Karman vortex street parameters was employed to verify the CFD simulation results. In order to determine the SFT cross section with optimal hydrodynamic properties, the mean drag and Root Mean Square (RMS) lift coefficients were selected as optimization objectives, and four Bezier curve parameters were the input variables, in a neural network and genetic algorithm optimization process (a hybrid BP-GA structure), which is less likely to become trapped in local minima. The results show the optimal tunnel cross section has a mean drag and a RMS lift coefficient reduced by 0.9% and 6.3%, respectively, compared to the original CFD dataset.

中文翻译:

基于参数贝塞尔曲线和混合反向传播-遗传算法的水下浮式隧道断面优化

摘要 水下浮动隧道(SFT)的横截面几何形状对水动力特性、结构行为和服务水平有很大影响,使隧道横截面成为优化效率的主要因素。在双向(即潮汐)流下,最小化 SFT 横截面升力的平均阻力和动态变化对减少结构位移和系泊载荷具有显着影响。基于动态包括前沿半径、隧道高度和宽度的参数贝塞尔曲线来定义 SFT 几何形状,通过应用计算流体对固定纵横比和原型尺寸在均匀流动条件下的贝塞尔曲线参数进行敏感性分析动力学(CFD),并分析了 SFT 截面表面周围的压力分布。采用包含卡门涡街参数的理论方法来验证 CFD 模拟结果。为了确定具有最佳水动力特性的 SFT 截面,选择平均阻力和均方根 (RMS) 升力系数作为优化目标,四个 Bezier 曲线参数作为输入变量,在神经网络和遗传算法优化过程中(混合 BP-GA 结构),不太可能陷入局部最小值。结果表明,与原始 CFD 数据集相比,最佳隧道横截面的平均阻力和 RMS 升力系数分别降低了 0.9% 和 6.3%。采用包含卡门涡街参数的理论方法来验证 CFD 模拟结果。为了确定具有最佳水动力特性的 SFT 截面,选择平均阻力和均方根 (RMS) 升力系数作为优化目标,四个 Bezier 曲线参数作为输入变量,在神经网络和遗传算法优化过程中(混合 BP-GA 结构),不太可能陷入局部最小值。结果表明,与原始 CFD 数据集相比,最佳隧道横截面的平均阻力和 RMS 升力系数分别降低了 0.9% 和 6.3%。采用包含卡门涡街参数的理论方法来验证 CFD 模拟结果。为了确定具有最佳水动力特性的 SFT 截面,选择平均阻力和均方根 (RMS) 升力系数作为优化目标,四个 Bezier 曲线参数作为输入变量,在神经网络和遗传算法优化过程中(混合 BP-GA 结构),不太可能陷入局部最小值。结果表明,与原始 CFD 数据集相比,最佳隧道横截面的平均阻力和 RMS 升力系数分别降低了 0.9% 和 6.3%。选择平均阻力和均方根(RMS)升力系数作为优化目标,四个贝塞尔曲线参数作为输入变量,在神经网络和遗传算法优化过程(混合BP-GA结构)中,这种可能性较小陷入局部最小值。结果表明,与原始 CFD 数据集相比,最佳隧道横截面的平均阻力和 RMS 升力系数分别降低了 0.9% 和 6.3%。选择平均阻力和均方根(RMS)升力系数作为优化目标,四个贝塞尔曲线参数作为输入变量,在神经网络和遗传算法优化过程(混合BP-GA结构)中,这种可能性较小陷入局部最小值。结果表明,与原始 CFD 数据集相比,最佳隧道横截面的平均阻力和 RMS 升力系数分别降低了 0.9% 和 6.3%。
更新日期:2020-11-01
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