箭上电磁环境检测接收天线研究

针对目前运载火箭舱内电磁环境愈发复杂且没有有效的测量手段的问题,研制了一种超宽带无源小型化接收天线。该天线采用多个整形单极子的方式拓展带宽并达到小型化目的。接收天线的测量频率范围覆盖500 MHz～6 GHz,尺寸约为60 mm×60 mm×100 mm。通过加入低噪声放大器,使可测量的最低电场强度不高于1 mV/m。由于多个整形极子天线存在相互干扰情况,因此接收天线在频率响应上与理论值存在差异,该差异可以通过校准进行消除。通过数值仿真和样机试验测试,该天线系统的测量能力良好,符合设计预期。将该接收天线应用装载在运载火箭舱内,可以对目前已知的箭上无线系统发射频段进行有效测量,对飞行过程中箭上真实电磁环境的确认、舱内电磁兼容设计、箭地无线链路设计具有重要作用。     

Regression model for civil aero-engine gas path parameter deviation based on deep domain-adaptation with Res-BP neural network

The variations in gas path parameter deviations can fully reflect the healthy state of aero-engine gas path components and units; therefore, airlines usually take them as key parameters for monitoring the aero-engine gas path performance state and conducting fault diagnosis. In the past, the airlines could not obtain deviations autonomously. At present, a data-driven method based on an aero-engine dataset with a large sample size can be utilized to obtain the deviations. However, it is still difficult to utilize aero-engine datasets with small sample sizes to establish regression models for deviations based on deep neural networks. To obtain monitoring autonomy of each aero-engine model, it is crucial to transfer and reuse the relevant knowledge of deviation modelling learned from different aero-engine models. This paper adopts the Residual-Back Propagation Neural Network (Res-BPNN) to deeply extract high-level features and stacks multi-layer Multi-Kernel Maximum Mean Discrepancy (MK-MMD) adaptation layers to map the extracted high-level features to the Reproduce Kernel Hilbert Space (RKHS) for discrepancy measurement. To further reduce the distribution discrepancy of each aero-engine model, the method of maximizing domain-confusion loss based on an adversarial mechanism is introduced to make the features learned from different domains as close as possible, and then the learned features can be confused. Through the above methods, domain-invariant features can be extracted, and the optimal adaptation effect can be achieved. Finally, the effectiveness of the proposed method is verified by using cruise data from different civil aero-engine models and compared with other transfer learning algorithms.     

高校青年教师思想道德建设刍议

通过对当前高校青年教师思想道德现状的分析,在肯定青年教师积极、健康、向上主流的同时,指出了存在的三个方面的问题,进而从社会大环境、学校小环境和青年教师自身三个方面深入研究了青年教师思想道德"衰微"的成因.在此基础上,探讨了加强和改进高校青年教师思想道德建设的思路和对策.     

某汽轮机弯曲叶片级的改型计算及流动分析

在四种不同弯曲静叶方案下利用CFD流场计算软件NUMECA对某汽轮机弯曲叶片级的内部三维流场进行了数值模拟。根据数值研究结果分析了不同弯静叶方案下级内流动特性、气流参数的变化,给出了四种静叶方案的级效率。研究表明本文采用的弯导叶方案没有提高级效率。本文最后分析了叶片弯曲对静叶栅内马蹄涡位置与流动结构的影响。      

阵列式高频热流信号薄膜传感器制备工艺与参数研究

阵列式高频热流信号柔性薄膜传感器在流体力学风洞试验中有着十分重要的应用前景.聚酰亚胺具有优良的力学性能、化学稳定性和耐高温特性,是微加工制备薄膜传感器的理想衬底材料之一.但该材料表面有疏水基团,制备的金属镀层极容易被破坏,降低了应用价值.针对阵列式高频热流信号柔性薄膜传感器制备中衬底材料与镀层金属结合力欠佳的情况,使用...     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.     

前缘复合改型在压气机平面叶栅中的应用初探

为了探究同时应用不同前缘改型对压气机叶栅的影响,以DMU37动叶5%叶高处叶型的平面叶栅为原型,采用数值计算方法,探究4种椭圆形前缘改型及4种椭圆鼓包复合改型对叶栅流场及性能的影响,考虑的主要影响参数分别为椭圆长短轴比和鼓包波幅。结果表明:-6°～+4°冲角时,椭圆形前缘方案可不同程度地降低前缘吸力峰及总压损失系数,特别在正冲角条件时改善效果更好;长短轴之比为4的椭圆形前缘方案综合性能最好,主流区吸力峰高度降低56.3%,+4°冲角时,总压损失系数和可比原型叶栅降低5.8%;0°冲角下,椭圆鼓包复合改型的损失相比原型叶栅均有所增大;+4°冲角时,各椭圆鼓包复合改型方案均能有效降低损失,总压损失系数最多可比原型叶栅降低7.5%,表明椭圆鼓包复合改型在+4°冲角时可以提高叶栅气动性能。     

基于振动台的TA11钛合金超高频疲劳实验和验证

提出一种利用电动振动台开展材料超高频疲劳实验的方法。采用TA11钛合金板材,在有限元计算的基础上设计出超高频疲劳试样,测试过程中的实际加载频率达到1756 Hz,同时在相同的应力水平下对该超高频试样和标准试样开展振动疲劳测试,对测试结果数据的一致性进行统计检验。结果表明:在相同应力条件下,本研究提出的超高频疲劳试样和标准振动疲劳的实验结果吻合较好。将本研究结果与同种材料的轴向高周疲劳和旋弯疲劳测试结果进行对比,一致性同样良好。     

碳纳米管薄膜电热特性及其除冰性能

采用碳纳米管薄膜(CNTF)作为电加热元件,研究碳纳米管薄膜对玻璃纤维增强树脂基复合材料结构表面的除冰性能,同时研究其电热性能。SEM14μm左右。XRD表明CNTF样品为微晶结构,结晶度差且含有少量杂质。空气环境通电,升温速率和最高恒定温度随输入电压增大而迅速提高。输入电压为5 V温度95℃。在四次电热循环后,其表面电阻略有升高,均值从2.795Ω到3.870Ω。在9 V输入电压下,CNTF被迅速烧断,CNTF样品电流承载极限在1.8 A左右。利用其焦耳热性能进行除冰,质量为20 g冰块在树脂基玻璃纤维复合材料样品的表面脱落时间为240 s。表明CNTF在飞机除冰领域具有潜在应用价值。     

大数据背景下农产品滞销的指标体系构建及风险等级评定——基于DEMATEL与灰色预测模型的研究

通过分析我国2012年至2017年发生的120个农产品滞销事件,从农产品供应链的生产、销售和运输三个关键环节和农产品滞销的主观、客观和不确定三大类九小类实质原因两个维度,建立了农产品滞销因素的供应链——实质原因(SC-RC)判别与定位矩阵,寻找农产品滞销的主要因素,并使用DEMATEL方法定位其中最关键的因素,再通过AHP-模糊综合评价法构造了预测农产品滞销风险等级的量化指标和计算公式,利用灰色预测模型预测了2019年陕西酥梨滞销风险的等级,从而验证该评价方法的科学性。