一种基于RBF神经网络与布谷鸟搜索算法的Gurney襟翼优化新框架
A Novel Framework for Optimizing Gurney Flaps using RBF Neural Network and Cuckoo Search Algorithm
AI总结：
本研究提出结合RBF代理模型与布谷鸟搜索算法的Gurney襟翼优化框架，基于CFD数据优化襟翼高度与安装角，实现升阻比提升10.28%，并对比了多种元启发式算法的优化效果。
摘要：
提升气动效率对于优化飞行器性能与运行效能至关重要，它能实现更高的飞行速度并降低燃油消耗，进而减少运营成本。因此，Gurney襟翼的应用是改善翼型气动特性的一条极具前景的途径。Gurney襟翼的优化对于提升飞行器与风力涡轮叶片设计中翼型的升力和失速特性具有重要意义，而其应用效果很大程度上取决于襟翼高度和安装角这两个设计参数。本研究尝试通过一个设计优化框架对这些参数进行优化：该框架首先基于二维（2D）雷诺平均Navier-Stokes（RANS）模拟得到的CFD数据，训练径向基函数（Radial Basis Function，RBF）代理模型；随后采用Cuckoo Search（布谷鸟搜索）算法求解最优设计参数，并与其他同类优化算法进行对比。优化后的Gurney襟翼构型升阻比（Cl/Cd）显著提升10.28%，对应襟翼高度为1.9%弦长（c），安装角为-58度。本研究验证了所提设计优化框架的有效性，为Gurney襟翼的参数优化提供了有价值的参考，而元启发式算法的对比也进一步提升了本研究在Gurney襟翼设计优化领域的贡献。
英文摘要：
Enhancing aerodynamic efficiency is vital for optimizing aircraft performance and operational effectiveness. It enables greater speeds and reduced fuel consumption, leading to lower operating costs. Hence, the implementation of Gurney flaps represents a promising avenue for improving airfoil aerodynamics. The optimization of Gurney flaps holds considerable ramifications for improving the lift and stall characteristics of airfoils in aircraft and wind turbine blade designs. The efficacy of implementing Gurney flaps hinges significantly on its design parameters, namely, flap height and mounting angle. This study attempts to optimize these parameters using a design optimization framework, which incorporates training a Radial Basis Function surrogate model based on CFD data from two-dimensional (2D) Reynolds-Averaged Navier-Stokes (RANS) simulations. The Cuckoo Search algorithm is then employed to obtain the optimal design parameters and compared with other competing optimization algorithms. The optimized Gurney flap configuration shows a notable improvement of 10.28% in Cl/Cd, with a flap height of 1.9%c and a flap mounting angle of -58 degrees. The study highlights the effectiveness of the proposed design optimization framework and furnishes valuable insights into optimizing Gurney flap parameters. The comparison of metaheuristic algorithms serves to enhance the study's contribution to Gurney flap design optimization.
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