歼击机结构故障的模式识别与参数辨识新方法

针对自修复飞行控制系统的故障,采用基于精确线性化的方法,在原空间和特征空间提出了基于双假设/多假设（增强型）概率统计学的模式识别与参数辨识方法,给出了结构故障的可识别性和可辨识性的判别准则,并以某型歼击机为例进行了仿真研究。     

某型压气机轴流级改型静叶设计

通过对某原型轴流级静叶的分析, 并对轴流压气机设计中采用的端部处理技术进行分析的基础上, 提出了静叶改型设计以及端部处理方案, 并进行了通流计算和造型。对改型设计中的双排静叶参数进行了优化、采用端部处理技术以及任意叶片造型, 提高了轴流级效率。      

自适应结构综述

开展自适应结构研究的目的是为了衰减与控制大型柔性空间结构的振动,并保持其外形.在这种结构的承力部分集成有作动器、传感器和控制器,可对内部或外部激励自动地作出响应,并进行调整.自适应结构在未来大型空间飞行器中将具有重要的且不可代替的作用,文中对自适应结构的有关技术进行了综合论述,内容包括自适应结构的发展背景、定义与组成、基本概念与理论,以及有关关键技术.      

USME:统一SLAM度量与评测技术研究

同时定位与建图(SLAM)技术近年来得到迅速发展,但由于缺乏在统一框架下对算法的度量和比较,对SLAM的客观评估和应用造成障碍。提出了统一SLAM度量与评测(USME)框架,从指标体系、数据集及评测方法三个维度为各种SLAM方法的性能度量及比较研究提供基准。针对不同场景,建立了包括长时间运行漂移量,闭环检测能力,存在相机遮挡、光照变化和运动物体时SLAM方法的鲁棒性,以及多体协同性能等的综合性能指标体系。基于三维仿真平台,以指标体系为基准建立了合成数据序列及对应数据集,以对性能指标进行度量与评估。还建立了平均指标均值的数据处理与评测方法,以综合评价不同参数选择对方法性能的影响。通过典型SLAM方法验证了上述方法的可行性。     

航空发动机典型材料超高周疲劳试验技术研究综述

航空发动机作为飞行器最关键、最核心的部位，长期服役于高温、高载等极端环境，疲劳失效是导致发动机结构破坏的主要原因之一。随着工业的发展，发动机材料的超高周疲劳问题日益凸显。本文总结了发动机典型材料超高周疲劳关注领域的研究现状，对当前超高周疲劳试验技术的应用情况进行了阐述，包括超高周轴向振动疲劳、弯曲振动疲劳、扭转振动疲劳、复合振动疲劳等试验加载技术以及温度控制技术、损伤监测技术，并对我国航空发动机典型材料超高周疲劳试验技术的发展做出展望。     

A methodology for constructing the aircraft design schema

Aircraft collaboration design is based on a unified set of schemas containing data and model representations and interfaces across disciplines. The aircraft design schema is a central data model that specifies the paradigm for the representation of a full lifecycle model of aircraft design. The construction of the schema currently lacks theoretical guidance. This paper designs a novel methodology to construct the aircraft design schema, a fully functional, logical, and self-consistent methodology. The methodology defines a schema matrix that includes implementation strategies, principles, processes, meta-object facility, views, scenarios, and products. The aircraft conceptual design schema is constructed according to the methodology, which is a hands-on approach to help understand the concept and implementation. The features of the methodology are analyzed, and the methodology and the aircraft conceptual design schema are presented to address the pain points of multiple solution trade-offs, multiple iterations, and multidisciplinary coupling in aircraft conceptual collaboration design.     

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

Martian Buildings: Design loading

Multiplanetary life has been studied by scientists as a way to supply energy or sustain human life in the future. Mars is likely to be man’s first destination, colonization using onsite structural construction would be one of the main options. The first step to designing a reliable building is to know the applied structural loads and to have an accurate design load combination. Due to lack of complete knowledge, short span of recorded data, Martian environment, and hazardous environment that Martian structures face, constructed Martian structures should behave appropriately under the highest likely live, dead and environmental loads either simultaneously or as a worst-case scenario. The present study evaluated and calculated probable Martian structural loads, including live, internal pressure, snow, gravity (dead), dust accumulation, thermal stress, wind, marsquake, asteroid, and meteoroid impact loads and their effects. Information was gathered from previous studies and valid data from Martian landers, rovers and orbiters. Wind loads were calculated based on the over 6.5 years of data recorded by Vikings 1 and 2, temperature and winds for InSight (TWINS) sensor. A wind shear exponent and wind profile have been proposed for a Martian flat terrain construction site. Marsquake lateral loads, frequency content and seismicity were assessed using data from the seismic experiment for interior structure (SEIS) and the Viking 2 seismometer. Considering the high influx of micrometeoroids, their penetration distance, impact loads and their effects on structures were calculated. The annual probability of an asteroid impact on a settlement was assessed for a 30-year mission. A load map for Martian residential buildings that considers the worst-case scenario in which a Martian structure should be designed based on them has been proposed.