仿生正弦前缘对翼面动态失速的影响

动态失速导致叶片气动载荷急剧变化，造成振动载荷激增，桨叶寿命大幅衰减。针对动态失速问题，从座头鲸胸鳍在动态倾转下取得良好的流动特性获得启示，据此模化出仿生正弦前缘翼面（包含3种波峰和2种波长），旨在实现动态失速控制。借助三维非定常数值模拟方法，采用运动网格技术，基于SC1095旋翼翼型，研究了仿生前缘动态失速流动控制机理及运动参数和来流速度的影响。结果表明：正弦前缘大幅度降低俯仰力矩系数峰值和阻力系数峰值；前缘波峰越大、波长越小，阻力系数峰值与俯仰力矩系数峰值的抑制效果越明显，虽然升力系数峰值减小，但其减小量远小于前两者，例如其中一种仿生翼使俯仰力矩系数峰值减小了47.7%，阻力系数峰值减小了36.4%，升力系数峰值减小14.1%；在最大迎角附近，正弦前缘能够缓和失速特性，使载荷变化更为平缓；在高平均迎角、低俯仰频率、低马赫数下，仿生翼动态失速控制效果更强，相比较而言迎角振幅的影响较小。     

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications. Fault diagnosis of rotating machinery is vital in manufacturing system, which can prevent catastrophic failure and reduce financial losses. Recently, Deep Learning (DL)-based fault diagnosis method becomes a hot topic. Convolutional Neural Network (CNN) is an effective DL method to extract the features of raw data automatically. This paper develops a fault diagnosis method using CNN for InfRared Thermal (IRT) image. First, IRT technique is utilized to capture the IRT images of rotating machinery. Second, the CNN is applied to extract fault features from the IRT images. In the end, the obtained features are fed into the Softmax Regression (SR) classifier for fault pattern identification. The effectiveness of the proposed method is validated using two different experimental data. Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.     

Direct position determination using single moving rotating linear array: Noncoherent and coherent processing

To improve the resolution and accuracy of Direct Position Determination (DPD), this paper investigates the problem of positioning multiple emitters directly with a single moving Rotating Linear Array (RLA). Firstly, the geometry of the RLA is formulated and analysed. According to its geometry, the intercepted noncoherent signals in multiple interception intervals are modeled. Correspondingly, the MUltiple SIgnal Classification (MUSIC) based noncoherent DPD approach is proposed. Secondly, the synchronous coherent pulse signals are individually considered and formulated. And the coherent DPD approach which aims for localizing this special type of signal is presented by stacking all array responses at different interception intervals. Besides, we also derive the constrained Cramér-Rao Lower Bound (CRLB) expression for both noncoherent and coherent DPD with RLA under the constraint that the altitudes of the emitters are known. At last, computer simulations are included to examine the performance of the proposed approach. The results demonstrate that the localization accuracy and resolution of DPD with single moving linear array can be significantly improved by the array rotation. In addition, coherent DPD with RLA further improves the resolution and increases the maximum emitter number that can be localized compared with the noncoherent DPD with RLA.     

Stall flutter prediction based on multi-layer GRU neural network

The modeling of dynamic stall aerodynamics is essential to stall flutter, due to the flow separation in a large-amplitude pitching oscillation process. A newly neural network based Reduced Order Model (ROM) framework for predicting the aerodynamic forces of an airfoil undergoing large-amplitude pitching oscillation at various velocities is presented in this work. First, the dynamic stall aerodynamics is calculated by solving RANS equations and the transitional SST-γ model. Afterwards, the stall flutter bifurcation behavior is calculated by the above CFD solver coupled with structural dynamic equation. The critical flutter speed and limit-cycle oscillation amplitudes are consistent with those obtained by experiments. A newly multi-layer Gated Recurrent Unit (GRU) neural network based ROM is constructed to accelerate the calculation of aerodynamic forces. The training and validation process are carried out upon the unsteady aerodynamic data obtained by the proposed CFD method. The well-trained ROM is then coupled with the structure equation at a specific velocity, the Limit-Cycle Oscillation (LCO) of stall flutter under this flow condition is predicted precisely and more quickly. In order to predict both the critical flutter velocity and LCO amplitudes after bifurcation at different velocities, a new ROM with GRU neural network considering the variation of flow velocities is developed. The stall flutter results predicted by ROM agree well with the CFD ones at different velocities. Finally, a brief sensitivity analysis of two structural parameters of ROM is carried out. It infers the potential of the presented modeling method to depict the nonlinearity of dynamic stall and stall flutter phenomenon.     

三角翼过失速非定常洞壁干扰修正

分别以固壁条件和洞壁附近实测的压力分布,模拟各类实壁和透气壁试验段的洞壁边界条件,利用N-S方程数值求解模型在风洞中的绕流场,得出洞壁干扰对跨音速模型绕流的影响和气动力修正结果。初步研究结果表明,该方法能较有效地模拟模型在跨音速风洞中的绕流场,GBM04A模型在0.6m风洞中的试验结果经洞壁干扰修正后与无干扰参考结果吻合较好     

轴流压气机三维气动稳定性及主动控制模型

给出了考虑压气机径向扰动对失速影响的轴流压气机三维气动稳定性模型。计算结果表明压气机的失速特性与压气机基元叶栅特性密切相关。在轴流压气机三维气动稳定性模型的基础上,给出了考虑轴流压气机三维效应的主动控制模型及其传递函数。      

轴流风扇旋转失速时的流场测量与分析

针对渐近型旋转失速在一轴流风扇上进行了一些测量工作,包括失速时的激光平均流场测量和热丝动态测量两大类。激光平均流场研究表明失速团主要活动在风扇转子前缘,而转子叶片通道中的流场则主要表现为叶尖间隙流的横向堵塞流动。      

基于广义力的旋翼振动载荷计算

为描述挥舞、摆振铰和变距轴承的运动引入了三个刚体运动自由度 ,旋翼桨叶通过 5节点15自由度有限单元离散 ,计入了桨叶刚性运动与非线性弹性变形之间的动力学耦合效应。利用曲线坐标系下的本构方程 ,对经典的中等变形梁理论进行了重新的推导。另外 ,采用了Leishman Beddoes非定常和动态失速模型 ,入流由自由尾迹分析获得。导出的旋翼桨叶非线性时变常微分方程以广义力的形式给出。桨叶截面载荷和运动方程在位形空间中同时求解。由本文分析得出的桨叶振动载荷与SA349/ 2小羚羊直升机飞行测试数据吻合程度很好。     

轴向间距对压气机稳定性的影响

对低速轴流压气机五个轴向间距下的压气机特性进行了试验.通过试验分析了轴向间距对压气机失速点流量,以及对压气机工作于多团旋转失速流量范围的影响,论证了静子在轴流压气机中具有抑制扰动波发展以及增强气动稳定性的作用.实验结果表明,随着转静子之间轴向间距的减小,静子的增稳作用增强,压气机的失速流量减小.     

合成射流控制NACA0015翼型大攻角流动分离

为了研究合成射流激励器处于NACA0015翼型回流区时对其分离流动的控制,采用商用计算流体力学软件Fluent 6.1求解Reynolds平均Navier-Stokes方程,通过对翼型气动力特性、脱落漩涡结构以及射流孔口附近流动结构的分析,揭示了合成射流处于分离区时对边界层控制的机理.结果表明:当合成射流孔口处于回流区时仍可有效推迟翼面边界层分离点,缩小回流区范围,从而有效提高翼型的升力.当射流方向垂直于壁面,无量纲频率以及吹气速度比都等于1时,翼型平均升力系数提高40%左右.