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首页> 外文期刊>The Aeronautical Journal >Single and multi-objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm
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Single and multi-objective UAV aerofoil optimisation via hierarchical asynchronous parallel evolutionary algorithm

机译：基于分层异步并行进化算法的单目标和多目标无人机机翼优化

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Unmanned aerial vehicle (UAV) design tends to focus on sensors, payload and navigation systems, as these are the most expensive components. One area that is often overlooked in UAV design is airframe and aerodynamic shape optimisation. As for manned aircraft, optimisation is important in order to extend the operational envelope and efficiency of these vehicles. A traditional approach to optimisation is to use gradient-based techniques. These techniques are effective when applied to specific problems and within a specified range. These methods are efficient for finding optimal global solutions if the objective functions and constraints are differentiable. If a broader application of the optimiser is desired, or when the complexity of the problem arises because it is multi-modal, involves approximation, is non-differentiable, or involves multiple objectives and physics, as it is often the case in aerodynamic optimisation, more robust and alternative numerical tools are required. Emerging techniques such as evolutionary algorithms (EAs) have been shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions among many local optima, are easily executed in parallel, and can be adapted to arbitrary solver codes without major modifications. In this paper, the formulation and application of a evolutionary technique for aerofoil shape optimisation is described. Initially, the paper presents an introduction to the features of the method and a short discussion on multi-objective optimisation. The method is first illustrated on its application to mathematical test cases. Then it is applied to representative test cases related to aerofoil design. Results indicate the ability of the method for finding optimal solutions and capturing Pareto optimal fronts.
机译：无人机（UAV）设计往往集中在传感器，有效载荷和导航系统上，因为它们是最昂贵的组件。无人机设计中经常被忽视的一个领域是机身和空气动力学形状的优化。对于有人驾驶飞机，优化对于延长这些飞机的运行范围和效率很重要。传统的优化方法是使用基于梯度的技术。当将这些技术应用于特定问题并在指定范围内时，这些技术是有效的。如果目标函数和约束是可区分的，则这些方法对于找到最佳全局解是有效的。如果需要更广泛地使用优化器，或者由于它是多模式的，涉及近似的，不可微的或者涉及多个目标和物理问题而导致问题的复杂性出现的，这在空气动力学优化中通常是这样，需要更强大和替代的数值工具。新兴技术（例如进化算法（EA））表现出鲁棒性，因为它们不需要目标函数的导数或梯度，具有在许多局部最优中找到全局最优解的能力，易于并行执行，并且可以适应无需重大修改即可转换为任意求解器代码。在本文中，描述了用于翼型形状优化的进化技术的制定和应用。最初，本文介绍了该方法的功能，并简要讨论了多目标优化。首先说明该方法在数学测试用例中的应用。然后将其应用于与机翼设计有关的代表性测试用例。结果表明该方法能够找到最优解并捕获帕累托最优前沿。

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