基于亲和传播算法的车辆自组织网络分簇组网方法

随着5G通信和自动驾驶汽车的发展，车辆自组织网络VANET（Vehicular Ad Hoc Network）作为一种新型的移动自组织网络，因其在改善道路安全、为驾驶员和乘客提供便利方面的潜力，而引起学术界和工业界的广泛关注。它可以通过提供交通流量、事故通知、危险警告、可能存在的定位偏差、天气等信息来增强道路安全，从而提高交通效率。由于车辆的高速移动会经常引起网络拓扑中断，因此一个设计良好的路由协议至关重要。提出了一种适用于车辆自组织网络环境的基于分簇的路由协议，设计并实现了一种基于亲和传播算法的分簇组网模型V-APC（VANET-Affinity Propagation Clustering）。通过重新定义亲和传播算法的相似函数，设计了簇头的选择过程、簇的形成过程还有簇的维护过程。结果表明：采用上述方法形成的簇，在簇内的通信性能和簇稳定性方面具有显著优势，使得该协议在路由延迟和数据包转发成功率方面表现优异。     

客机隔热隔声棉点火特性与产烟毒性研究

为研究客机隔热隔声棉点火特性与产烟毒性,使用相关装置进行了试验研究.结果表明:点火持续时间和辐射板温度均对隔热隔声棉点火具有明显促进作用.点火持续时间越长,火焰沿Y轴蔓延距离越大,点火15s时,明火附近区域的隔热隔声棉出现被烧穿现象,但烧穿面积不大.火焰沿Y轴蔓延距离增长速率却随点火持续时间的增大而减小,验证了隔热隔声...     

Macro-phenomenological high-strain-rate elastic fracture model for ice-impact simulations

The ice impact can cause a severe damage to an aircraft’s exposed structure, thus, requiring its prevention. The numerical simulation represents an effective method to overcome this challenge. The establishment of the ice material model is critical. However, ice is not a common structural material and exhibits an extremely complex material behavior. The material models of ice reported so far are not able to accurately simulate the ice behavior at high strain rates. This study proposes a novel high-precision macro-phenomenological elastic fracture model based on the brittle behavior of ice at high strain rates. The developed model has been compared with five reported models by using the smoothed particle hydrodynamics method so as to simulate the ice-impact process with respect to the impact speeds and ice shapes. The important metrics and phenomena (impact force history, deformation and fragmentation of the ice projectile and deflection of the target) were compared with the experimental data reported in the literature. The findings obtained from the developed model are observed to be most consistent with the experimental data, which demonstrates that the model represents the basic physics and phenomena governing the ice impact at high strain rates. The developed model includes a relatively fewer number of material parameters. Further, the used parameters have a clear physical meaning and can be directly obtained through experiments. Moreover, no adjustment of any material parameter is needed, and the consumption duration is also acceptable. These advantages indicate that the developed model is suitable for simulating the ice-impact process and can be applied for the anti-ice impact design in aviation.     

Adjoint-based robust optimization design of laminar flow wing under flight condition uncertainties

It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions. In order to seek a more robust natural laminar flow control effect, it is necessary to develop an effective optimization design method. Meanwhile, attention must be given to the impact of crossflow(CF) instability brought on by the sweep angle. This paper constructs a robust optimization design framework based on discrete adjoint methods ...     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).