Craft Group (China)

Rapid prediction of unsteady flow fields around airfoils is crucial for aerodynamic design and optimization of aircraft. However, traditional wind tunnel experiments and numerical simulation methods incur high computational costs for flow field calculations. This paper proposes an improved method based on deep neural operator networks for rapidly predicting unsteady flow fields of different symmetric airfoils under high angle-of-attack conditions. The method uses incoming flow conditions and airfoil parameters as inputs, reconstructing the flow field through spatiotemporal coordinates to achieve joint prediction of pressure and velocity. Experimental results demonstrate that this method accurately captures the unsteady flow field characteristics of different symmetrical airfoils at various high angles of attack. The inference time for a single time-step case takes only about 0.6 s, which is significantly faster than traditional numerical simulation methods. The proposed method can substantially enhance computational efficiency while maintaining flow prediction accuracy, offering a viable new approach for rapid flow field prediction and aerodynamic design optimization of aircraft.

In this study, we present a novel metamaterial beam with asymmetric wave transmission properties under reciprocal conditions, termed the distichous-opposite metamaterial beam (DOMB), realized through a distichous-opposite arrangement of resonators. Based on the DOMB's mechanical characteristics in each propagation direction, the transfer matrices for forward and backward wave propagation are derived, and corresponding computational models are developed. An experimental test rig is designed and fabricated to investigate the DOMB's bidirectional vibration characteristics. Experimental measurements and transfer matrix method calculations consistently demonstrate that elastic waves exhibit strongly asymmetric transmission in the DOMB. Within specific frequency ranges, forward-propagating waves are significantly suppressed and blocked, while reverse-propagating waves transmit unimpeded, confirming the asymmetric wave transmission characteristics of the metamaterial beam. Analysis of the asymmetric transmission mechanism reveals that the resonators' bending moments act in opposite directions during forward and backward wave propagation, inducing distinct flexural wave responses in the main beam. As a result, a localized vibration mode emerges under backward propagation, but is absent in forward propagation, leading to the observed asymmetric wave transmission in the DOMB. Structural parametric studies reveal that the asymmetric transmission in the metamaterial beams maintains a consistent asymmetric transmission peak frequency under a designated resonator natural frequency, regardless of coupling beam stiffness. The DOMBs produce pronounced asymmetric bands with surrounding broadband gaps under varying unit cell counts, demonstrating stable directional wave control.

With the rapid development of the low-altitude economy, the intelligence level of aircraft urgently needs improvement. In emergency landing scenarios, traditional guidance systems are prone to communication failures, adverse weather, or complex environmental interference, posing significant safety risks. To address the high-precision pose estimation requirements for autonomous aircraft landing, this paper proposes a monocular vision-based sparse keypoint parameterization method. Our approach employs a deep neural network to rapidly detect landing-area keypoints, establishes 2D-3D sparse correspondences, and leverages Perspective-n-Point (PnP) for real-time pose solving, overcoming the limitations of traditional systems that rely on prior calibration or cooperative markers. Experiments demonstrate that our method achieves robust pose estimation even under extreme conditions (e.g., signal interference, low visibility), providing reliable technical support for emergency landings. A computationally efficient and interference-resistant sparse keypoint representation for targets. And a cooperative-marker-free monocular vision landing guidance system, significantly enhancing landing safety in complex scenarios.

This study aims to investigate the icing behavior of unmanned aerial vehicles (UAVs) propellers under different operating conditions. Unlike previous work, a systematic analysis of droplet impingement, heat transfer characteristics, and ice accretion distribution on the blade surface is conducted, revealing the spatial distribution characteristics of propeller icing from multiple perspectives. The propeller flow field is solved using the Reynolds-averaged Navier–Stokes (RANS) equations coupled with the Multiple Reference Frame (MRF) model, the droplet motion and collection are predicted by an Eulerian multiphase approach, and the icing process is modeled using the shear-stress transport-based shallow water film method (SWIM). The results show that higher rotational speeds and larger droplet diameters significantly increase ice accumulation, with more complex ice shapes forming particularly at the blade tip. In contrast, higher temperatures reduce the overall icing amount but alter the spatial distribution of ice. The blade tip region exhibits stronger transport capacity for unfrozen water, leading to faster ice accretion. These findings provide useful guidance for designing more effective anti-icing strategies for UAV propellers.

• High-cycle and very-high-cycle fatigue of wheel steel: studied at three conditions. • Rotating bending and ultrasonic axial cycling employed for applying R = –1 and 0.3. • Multi-scale characterization reveals microstructural features near crack origins. • Elongated sulfides dominate ultrasonic fatigue in both surface and internal failure. • Failure probabilities quantified in N-S diagram for different load types and ratios. In this paper, a wheel steel, with a relatively low tensile strength and a distinct yield strength, is systematically investigated for high-cycle and very-high-cycle fatigue (failure life N f > 10 7 cycles) under rotating bending (RB, 52.5 Hz) and ultrasonic axial cycling (UAC, ∼ 20 kHz) with stress ratios R = –1 and 0.3. This work provides the first systematic evaluation of a medium-strength wheel steel commonly used in railway applications. All cracks initiated from the specimen surface and failures were limited to the HCF regime in RB tests while UAC tests revealed sulfide-induced failures, occurring both at the surface and in the interior, and extending into the VHCF regime. Microstructural analysis demonstrated nanograin formation and refinement near inclusions under R = –1, and partial cementite dissolution under R = 0.3, indicating stress-ratio-dependent initiation mechanisms. Furthermore, the fatigue limit followed a Gerber-type relation constrained by yield strength, and a probability-based failure map was established to quantify the occurrence of different failure modes. These findings extend the current understanding of VHCF beyond martensitic steels, clarify the role of elongated sulfides in wheel steels, and provide engineering insight into the fatigue reliability of railway wheels.