叶片高变形径向流出涡轮多学科优化

André Governo, B. Saracoglu
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引用次数: 0

摘要

微型径向流出涡轮利用冲击效应为各种航空设备提供机载动力。进入的气流轴向进入涡轮壳体的进口管道,使涡轮转子旋转,涡轮转子可能具有过大的叶尖间隙和相当大的轮毂空腔,并从涡轮流道径向排出。目前正在研究的涡轮转子通常由聚合物材料制成,以降低成本进行批量生产。涡轮翼型的设计与显著悬垂部分在大轮毂腔。这种结构允许在高入口速度条件下,通过使叶片后部在离心力和流体力的作用下弯曲来调节转子的转速。然而,这种偏转可能导致材料非线性,并强烈影响通过叶片通道的流动发展,这在公开文献中尚未有记载。本文研究了大范围工况下涡轮内部的复杂流场。采用多学科设计优化程序对该类涡轮气动性能和结构性能的相互冲突影响进行了评估。该策略利用基于人工神经网络和遗传算法的内部元模型辅助优化器(CADO)来最大化功率输出,同时评估转速对叶片位移和流动行为的影响。在与优化器集成的计算流体动力学求解器中建立了一种解耦的流固耦合方法。通过涡轮初始模型的高速叶片变形实验验证了该方法的有效性。结果表明,与存在轮毂空腔时的流体载荷相比,离心力主要决定了涡轮几何形状的显著变化。叶片载荷的变化是由于转子在载荷作用下的三维几何变化,并与入射角和叶型曲率的变化有关。这样的过程会降低涡轮的效率,特别是在非常高的转速下。通过对具有相似几何形状的涡轮的初步设计选择进行广泛的优化研究,本工作的结果为径向出口涡轮的发展提供了一个全面的设计空间和指导方针。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multidisciplinary Optimization of Radial Outflow Turbines With Highly Deformable Blades
Miniature radial outflow turbines are used to generate onboard power for various aerial devices using the ram effect. The incoming air flow axially entering the inlet conduit of the turbine housing spins the turbine rotor, which may have excessive tip gap and a sizable hub cavity, and radially discharges from the turbine flow passages. The current class of turbine rotors under investigation are typically made of polymeric materials for serial production at reduced cost. The turbine airfoils are designed with significant overhanging sections over the large hub cavity. Such configuration allows regulating the rotational speed of the rotor, at high inlet velocity conditions, by enabling the rear part of the blades to bend under the effect of centrifugal and fluidic forces. However, such deflections may lead to material non-linearities and strongly affect the flow development through the blade passages, which are not yet documented in the open literature. This paper investigates the complex flow field inside such turbines for a wide range of operating conditions. A multidisciplinary design optimization routine was utilized to assess the conflicting influence of the aerodynamical and structural performance for this class of turbines. The strategy utilizes an in-house metamodel-assisted optimizer (CADO) based on artificial neural networks and genetic algorithms to maximize the power output while evaluating the impact of the rotational speed on blade displacement and flow behaviour. A decoupled fluid-structure interaction approach was established in the computational fluid dynamics solver integrated with the optimizer. The methodology validation was achieved through the experimental results on high-speed blade deformation of the initial turbine mockup. The results show striking changes to turbine geometry mainly dictated by the centrifugal force as compared to the fluidic loads play when a hub cavity exists. A change in the blade loading is identified due to the three-dimensional geometric modifications of the rotor under load solicitations and connected with a modification of the incidence angle and profile curvatures. Such a process decreases the turbine’s efficiency, especially at very high rotational speeds. The results of this work provide a comprehensive design space and guidelines on the development of radial outflow turbines through the extensive optimization study for preliminary design choices for turbines with similar geometries.
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