论文标题
集成的智能Jaya Runge-Kutta方法，用于求解各种楔形角度的Falkner-Skan方程
Integrated intelligent Jaya Runge-Kutta method for solving Falkner-Skan equations for Various Wedge Angles
论文作者
论文摘要
在这项工作中，将有效的Jaya算法与经典的Runge-Kutta方法相结合的混合智能计算方法应用于用各种楔形角度求解Falkner-SKAN方程，这是各种计算流体机械问题的基本方程。通过一些坐标转换，Falkner-Skan边界层问题然后将其转换为有限间隔定义的自由边界问题。然后，使用更高阶的减少策略，可以将整个问题归结为解决具有规定初始条件和边界条件的耦合微分方程的求解。发现混合Jaya runge-kutta方法可产生稳定而准确的结果，并能够提取这些未知参数。经典拍摄方法对猜测初始值的敏感性可以通过集成的强大优化方法可以轻松克服。此外，事实证明，JAYA算法无需调整算法特异性参数，可有效且稳定，可最大程度地减少应用中的适应性功能。通过使用JAYA方法与PSO（粒子群优化），遗传算法（GA），超频带和经典分析方法进行比较，混合Jaya runge-kutta方法可以产生更稳定和准确的结果，这显示出更大的复杂多种局部和多层流量问题的巨大潜力。
In this work, the hybrid intelligent computing method, which combines efficient Jaya algorithm with classical Runge-Kutta method is applied to solve the Falkner-Skan equations with various wedge angles, which is the fundamental equation for a variety of computational fluid mechanical problems. With some coordinate transformation, the Falkner-Skan boundary layer problem is then converted into a free boundary problem defined on a finite interval. Then using higher order reduction strategies, the whole problem can be boiled down to a solving of coupled differential equations with prescribed initial and boundary conditions. The hybrid Jaya Runge-Kutta method is found to yield stable and accurate results and able to extract those unknown parameters. The sensitivity of classical shooting method to the guess of initial values can be easily overcome by an integrated robust optimization method. In addition, the Jaya algorithm, without the need for tuning the algorithm-specific parameters, is proved to be effective and stable for minimizing the fitness function in application. By comparing the solutions using the Jaya method with PSO (particle swarm optimization), Genetic algorithm (GA), Hyperband, and the classical analytical methods, the hybrid Jaya Runge-Kutta method yields more stable and accurate results, which shows great potential for solving more complicated multi-field and multiphase flow problems.
