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首页> 外文期刊>JSME International Journal. Series B, Fluids and Thermal Engineering >Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies
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Aerodynamics Design and Genetic Algorithms for Optimization of Airship Bodies

机译：优化飞艇机体的空气动力学设计和遗传算法

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摘要

A special and effective aerodynamics calculation method has been applied for the flow field around a body of revolution to find the drag coefficient for a wide range of Reynolds numbers. The body profile is described by a first order continuous axial singularity distribution. The solution of the direct problem then gives the radius and inviscid velocity distribution. Viscous effects are considered by means of an integral boundary layer procedure, and for determination of the transition location the forced transition criterion is applied. By avoiding those profiles, which result in the separation of the boundary layer, the drag can be calculated at the end of the body by using Young's formula. In this study, a powerful optimization procedure known as a Genetic Algorithms (GA) is used for the first time in the shape optimization of airship hulls. GA represents a particular artificial intelligence technique for large spaces, striking a remarkable balance between exploration and exploitation of search space. This method could reach to minimum objective function through a better path, and also could minimize the drag coefficient faster for different Reynolds number regimes. It was found that GA is a powerful method for such multi-dimensional, multi-modal and nonlinear objective function.
机译：一种特殊而有效的空气动力学计算方法已应用于旋转物体周围的流场，以找到大范围雷诺数的阻力系数。身体轮廓由一阶连续轴向奇异分布描述。然后，直接问题的解决方案给出了半径和无速度分布。通过积分边界层程序考虑粘性效应，并且为确定过渡位置而采用了强制过渡标准。通过避免那些导致边界层分离的轮廓，可以使用杨氏公式在车身末端计算阻力。在这项研究中，首次将强大的优化程序称为遗传算法（GA）用于飞艇船体的形状优化。遗传算法代表了一种用于大空间的特殊人工智能技术，在探索空间和探索空间之间取得了显着的平衡。该方法可以通过一条更好的路径达到最小目标函数，并且可以针对不同的雷诺数形式更快地最小化阻力系数。已经发现，遗传算法是用于多维，多模态和非线性目标函数的有力方法。

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