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飞行器的螺旋桨动力气动干扰及气动弹性研究
Alternative TitlePropeller-driven aerodynamic disturbance and aeroelasticity study of flight vehicles
吕召阳
Thesis Advisor聂雪媛
2022-05-15
Degree Grantor中国科学院大学
Place of Conferral北京
Subtype硕士
Degree Discipline工程力学
Keyword螺旋桨/机翼气动干扰,CFD/CSD耦合,颤振,分布式动力,神经网络
Abstract

随着21世纪高效节能理念和长航时概念的提出以及分布式动力技术的应用,大展弦比分布式螺旋桨动力飞行器逐渐成为了研究热点。对于发展大展弦比分布式螺旋桨动力飞行器来说:一方面,将会面临螺旋桨/机翼干扰问题;另一方面,将会面临机翼展弦比变化对机翼气动性能、结构动力学性能以及颤振特性的影响。同时,在对飞机进行气动优化和颤振主动控制研究中,将会面临全阶CFD仿真计算时间长、成本高等问题。因此,系统地了解和认识,螺旋桨滑流对机翼的气动干扰、机翼几何特性对机翼的气动特性和颤振特性的影响规律以及发展气动降阶模型,对大展弦比分布式螺旋桨动力飞机的研制、气动优化和实现颤振主动控制有着重要的指导意义。基于此,本文开展了一系列研究,其主要研究内容和进展如下:
1)开展了螺旋桨/机翼干扰问题的建模与仿真研究。深入了解k-omega SST湍流模型和S-A湍流模型以及滑移网格法和多参考坐标系方法在螺旋桨滑流计算仿真中的精度和适用性,了解螺旋桨滑流场与机翼绕流场相互干扰对机翼升阻特性和受力分布的影响。分别建立单桨/机翼系统和双桨/机翼系统分别研究了螺旋桨布局、旋转速度等因素对机翼升阻特性和受力分布的影响,以分析纯机翼系统、单桨/机翼系统以及双桨/机翼系统中机翼气动特性的差异。并且通过改变飞行攻角,进一步分析纯机翼系统、单桨/机翼系统升阻特性和失速特性变化规律。
2)开展了展弦比变化对机翼气动性能和结构动力学特性以及颤振特性的影响研究。深入了解机翼几何尺寸变化对机翼气动性能和结构动力学性能的影响,通过固定机翼表面面积,改变展长和弦长的方法,建立了不同展弦比的机翼气动分析模型和结构动力学分析模型,并基于CFD/CSD颤振特性分析方法,研究分析了展弦比对机翼颤振的特性的影响。
3)开展了卷积神经网络在机翼气动参数预测中的应用研究。基于卷积神经网络建立了机翼气动参数预测模型,并利用二维机翼气动弹性模型为卷积神经网络预测模型提供训练和测试数据,深入分析卷积神经网络的预测性能,计算效率和模型稳定性。

Other Abstract

With the concept of energy efficiency and long flight time in the 21st century and the application of distributed power technology, large span ratio distributed propeller-powered vehicles have gradually become a research hotspot. For the development of large span ratio distributed propeller-powered aircraft: on the one hand, it will face the problem of propeller/wing interference; on the other hand, it will face the influence of wing span ratio change on wing aerodynamic performance, structural dynamics performance and flutter characteristics. At the same time, in the research of aerodynamic optimization and active control of flutter, we will face the problems of long computation time and high cost of full-order CFD simulation. Therefore, a systematic understanding of the aerodynamic interference of propeller slipstream on the wing, the influence of wing geometry on the aerodynamic and flutter characteristics, and the development of aerodynamic downscaling models are important for the development, aerodynamic optimization and active flutter control of large span ratio distributed propeller-powered aircraft. Based on this, a series of studies are carried out in this thesis, and the main research contents and progress are as follows:
1)Modeling and simulation studies of propeller/wing interference problems are carried out. In-depth understanding of the accuracy and applicability of the k-omega SST turbulence model and S-A turbulence model, as well as the slip-grid method and the multi-reference coordinate system method in the propeller slipstream calculation and simulation, and the influence of the mutual interference between the propeller slipstream field and the wing winding field on the lift resistance characteristics and force distribution of the wing. The effects of propeller layout and rotation speed on the lift resistance characteristics and force distribution of the wing are studied separately for the single-propeller/wing system and the twin-propeller/wing system to analyze the differences of the aerodynamic characteristics of the wing in the pure wing system, the single-propeller/wing system and the twin-propeller/wing system. And by changing the angle of attack of flight, the variation of lift resistance characteristics and stall characteristics of pure wing system and single propeller/wing system are further analyzed.
2)The study on the effect of spreading chord ratio variation on the aerodynamic performance and structural dynamics of the wing as well as the flutter characteristics was carried out. By fixing the surface area of the wing and changing the span and chord lengths, we established wing aerodynamic analysis models and structural dynamics analysis models with different span ratios, and based on CFD/CSD flutter characteristics analysis method, we studied and analyzed the effect of span ratio on wing flutter characteristics.
3)A study on the application of convolutional neural network in wing aerodynamic parameter prediction was carried out. The prediction model of wing aerodynamic parameters based on convolutional neural network was established, and the two-dimensional wing aeroelastic model was used to provide training and test data for the convolutional neural network prediction model, and the prediction performance, computational efficiency and model stability of the convolutional neural network were analyzed in depth.

Language中文
Document Type学位论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/89131
Collection流固耦合系统力学重点实验室
Recommended Citation
GB/T 7714
吕召阳. 飞行器的螺旋桨动力气动干扰及气动弹性研究[D]. 北京. 中国科学院大学,2022.
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