摘要
为提高翼型的非定常状态下的抗空化性能,采用前缘改进的类型函数转换方法表征翼型几何,利用最优拉丁超立方抽样方法在样本空间中取样,通过CFD计算得到各翼型的非定常下的空化性能参数,借助BP神经网络构建翼型几何外形到空化性能参数的映射关系,并以时均无量纲空泡面积作为优化目标,结合遗传算法(GA)针对σ=0.83空化条件下二维NACA66(MOD)进行空化性能优化,并对原始翼型和采用不同神经网络结构优化得到的两种翼型进行非定常空化流场计算及分析,最后开展了三维优化水翼的多工况适应研究,优化结果表明:双层BP(back propagation)相较于单层BP神经网络具有更深的数据挖掘能力;在σ=0.83条件下,两种优化翼型的时均无量纲空泡面积分别降低了12.8%和19.2%、升阻比分别提高了1.4%和5.0%,空泡脱落周期分别降低了3.7%和7.4%,抗空化和能量性能均得到改善.翼型几何的改变,影响了流场中高压区域以及压力梯度的分布,从而抑制了空泡的生长发展,同时加强了前缘回射流的强度,提高了空泡的脱落频率.利用双层BP神经网络训练结合遗传算法优化得到的水翼优化效果更佳,其在σ=1.29,1.44下无量纲空泡体积分别降低了14.7%和55.0%,升阻比分别提升16.5%和34.2%,能够较好地适应多空化条件.
In order to improve the anti-cavitation performance of the airfoil under unsteady conditions,a modified leading-edge type function transformation method is employed to characterize the airfoil geometry.The optimal Latin hypercube sampling method was used to sample in the optimized space,and the cavitation performance parameters of each airfoil under unsteady state were calculated by CFD.The mapping relationship between airfoil geometry and cavitation performance parameters was constructed by back propagation(BP)neural network,and the time-averaged non-dimensionless cavitation area was used as the optimization objective.The cavitation performance of two-dimensional NACA66(MOD)under the condition ofσ=0.83 was optimized by using genetic algorithm(GA).The unsteady cavitation flow field of the original airfoil and the two airfoils optimized by different neural network structures are calculated and analyzed.Finally,the multi-condition adaptation study of the 3D optimized hydrofoil is carried out,and the optimization results show that:Double-layer BP neural network has deeper data mining ability than single-layer BP neural network.Under the condition ofσ=0.83,the time-averaged non-dimensionless cavitation area of the two optimized airfoils is reduced by 12.8%and 19.2%,the lift-drag ratio is increased by 1.4%and 5.0%,and the caving cycle is reduced by 3.7%and 7.4%,respectively.The anti-cavitation and energy performance are improved.The modification of airfoil geometry affects the distribution of high-pressure regions and pressure gradients within the flow field,which not only suppresses the growth and development of cavitation but also enhances the intensity of the leading-edge backflow,leading to an increased frequency of cavitation shedding.The utilization of a two-layer BP neural network combined with genetic algorithm optimization results in a more effective hydrofoil design.Under the conditions ofσ=1.29 andσ=1.44,the non-dimensional cavitation volume is reduced by 14.7%and 55.0%,respectively,while the lift-to-drag ratio is increased by 16.5%and 34.2%,respectively.This optimized design can better adapt to various cavitation conditions.
作者
纪祥
王巍
李智健
吴向阳
王晓放
Ji Xiang;Wang Wei;Li Zhijian;Wu Xiangyang;Wang Xiaofang(School of Energy and Power Engineering,Dalian University of Technology,Dalian 116024,Liaoning,China;Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education,Dalian University of Technology,Dalian 116024,Liaoning,China)
出处
《力学学报》
北大核心
2025年第5期1054-1065,共12页
Chinese Journal of Theoretical and Applied Mechanics
基金
国家自然科学基金资助项目(51876022)。
