Improving aircraft aerodynamic performance with bionic wing obtained by ice shape modulation

For Unmanned Aerial Vehicles (UAVs) with limited electrical power to achieve effectively anti-/de-icing at the leading edge of the wing, a strategy of ice shape modulation was proposed. Isolated simulated ice shape pieces printed by 3D printing technology are mounted on a NACA0012 finite wing model, and its lift/drag coefficients and suction-side velocity fields are measured by the six-component force balance and the Particle Imaging Velocimetry (PIV), respectively. The ratio of the spanwise length of a single ice shape piece to chord length and the spanwise length of the non-icing area between the two adjacent single ice shape pieces are defined as dimensionless ice shape length (w/c) and dimensionless modulation ratio (w/λ), respectively. The results indicate that for a fixed w/λ, the wing lift coefficient first increases and then drops with increasing w/c, and a peak value exists when w/c is between 0.1 and 0.2. The lower the w/λ is, the higher the wing lift coefficient will be. The periodical variation of the flow separation area along the spanwise direction is attributed on the one hand to the acceleration effect of the flow field in the non-icing area which reduces the separation area, and on the other hand to the cross-flow caused by the streamwise vortices from the non-icing area to the icing area which promotes the mixing of the flow field (similar to vortex generators). The obtained modulation law is verified through flight tests and provides guidance for the use of ice shape modulation scheme for UAVs that cannot be completely anti-/de-icing under severe weather conditions.     

单气泡上浮运动的层析PIV实验研究

为了揭示上浮过程中的尾流涡结构及其与气泡之间的相互作用，分别采用阴影图像法和层析PIV技术，对单个气泡在静止水中自由Z字型上浮的过程进行了实验研究，得到气泡的形状、运动和三维的尾流速度场。采用"λ" _"ci" 涡判据和有限时间李雅普诺夫指数（Finite-Time Lyapunov Exponent，FTLE），从速度场识别出三维的尾流涡结构和二维的拉格朗日拟序结构。结果表明，Z字型上浮过程中，气泡周围环绕有涡环，涡环会沿运动路径脱落交替的、方向相反的发卡涡；单个发卡涡脱落过程中，涡环一侧的FTLE脊线会闭合形成拉格朗日涡。由此可得到结论，发卡涡的周期性脱落，使涡环相对于气泡的对称性被周期性交替破坏，导致气泡形成Z字型周期运动；单个发卡涡脱落过程中，涡环两侧各自对发卡涡的流体输运存在显著差异。     

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.     

Evolution of turbulent boundary layer over a three-dimensional bump

A bump is typically used in the inlet system of an aircraft engine to compress the incoming airflow and to reduce boundary layer thickness developed over fuselage. In this work, the turbulent flow over a three-dimensional bump is experimentally studied. The bump model is mounted in a closed return wind tunnel operated at the nominal velocity 10 m/s, corresponding to a friction Reynolds number of 2300. The flow field upstream the bump, along the bump centerline and at two different spanwise plane...