基于特征映射的涡轮导向叶片精铸模型自动转换研究

通过对涡轮导向叶片及其精铸模型结构特点的分析,以特征映射理论为依据,提取其结构特征、特征内部的约束关系和构成特征的几何元素,将叶片的内外零件模型添加加工余量、工艺附件、收缩率后得到精铸模具设计和制造所需要的模具型腔体模型,从而完成了精铸模具设计各模型间的自动转换,大大缩短了精铸模具的设计周期,具有重要的理论和实践应用价值.     

简单二体引力模型用于空间目标轨道面交线确定的效果分析

在空间目标碰撞预警分析中，准确地计算出空间目标的轨道面交线是进行地心距筛选、时间差筛选的前提，目前较多使用的快速确定轨道面交线的方法为简单二体引力模型。深入分析该模型，比较了其计算的空间目标轨道面交线与STK计算结果的差异，指出了简单二体引力模型在计算空间目标轨道面交线时的局限性，认为轨道摄动是影响轨道面交线计算准确性的主要原因，应该采用更能反映空间目标实际运动规律的改进的二体引力模型的方法。     

如何建立实际问题的数学模型

通过对一例实际问题的物理分析，结合高等数学知识，阐述建立相应数学模型的方法。     

高阶对象的二阶模型匹配

本文证明了对于一类高阶对象，其输出可以用一系列系数连续变化的线性定常二阶系统的输出组合完全代替。     

浅谈Java 2D图象处理技术

本文介绍了Java 2D API的图象处理思想.通过以LookupOp查找表过滤器为例,分析了图象的即时模式模型,即对内存的缓冲区图象进行处理,再绘制到屏幕上的处理技术.同时介绍了利用Java 2D中提供的过滤器对图象数据的过滤操作.     

北京航天指控中心卫星任务数字仿真软件的设计

首先给出了北京航天指控中心仿真软件的设计模型,阐述了仿真软件仿真的内容,并详细介绍了仿真软件的总体结构和关键进程的设计方法,最后显示了仿真的结果,在实际应用中该软件得到了验证。     

2021 Turkey mega forest Fires: Biodiversity measurements of the IUCN Red List wildlife mammals in Sentinel-2 based burned areas

Mega wildfires are one of the environmental disasters worldwide. This study evaluates the pre-fire species diversity and the indirect effects, including habitat loss for one of the largest wildfires in Manavgat (Antalya-Turkey) in 2021, with a two-step methodology. Here, (1) burnt areas in the Manavgat district (2021) were detected with remote sensing data from Sentinel-2A by delta Normalized Burn Ratio calculation for a selected area in Google Earth Engine, and (2) mammals' habitat vector data of International Union for Conservation of Nature (IUCN) Red List were integrated into Habitat and Biodiversity modelling of Terrset to analyze the alpha, beta, gamma diversity and Range Restriction Index for the wildfire region. In the total 4210 km² study area, 696 km² of the area was damaged by different fire severity; also, there were 56 mammal species' habitats here. These species include bats (i.e. Nyctalus leisleri), felids (i.e. Felis chaus), rodents (i.e. Rattus norvegicus) and large mammals (i.e. Ursus arctos). 88 % of these species are in IUCN's Least Concern category. The remaining classes are Near Threatened (3.7 %) and Vulnerable (7.4 %). This study also indicated that the burnt area's species richness does not totally consist of endemic species. Therefore, pre-fire species richness analyses of this study can be a base for further studies about the species' post-fire activity and occupancy.Furthermore, the 2021 mega wildfires show us the necessity of wildfire monitoring and risk studies in the entire Mediterranean ecosystem to evaluate the risks to the Sustainable Development Goals. Therefore, post-fire spatial data, fire migration monitorization, and recording of the species' activities should be performed temporally. In this way, the ability of wildlife's recovering, and the direct and indirect effects of the fire will be examined for ecosystems in the long term.     

A new modeling scheme for powered parafoil unmanned aerial vehicle platforms: Theory and experiments

A novel framework is established for accurate modeling of Powered Parafoil Unmanned Aerial Vehicle (PPUAV). The model is developed in the following three steps: obtaining a linear dynamic model, simplifying the model structure, and estimating the model mismatch due to model variance and external disturbance factors. First, a six degree-of-freedom linear model, or the structured model, is obtained through dynamic establishment and linearization. Second, the data correlation analysis is adopted to determine the criterion for proper model complexity and to simplify the structured model. Next, an active model is established, combining the simplified model with the model mismatch estimator. An adapted Kalman filter is utilized for the real-time estimation of states and model mismatch. We finally derive a linear system model while taking into account of model variance and external disturbance. Actual flight tests verify the effectiveness of our active model in different flight scenarios.     

Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.     

Predictive fault-tolerant control of an all-thruster satellite in 6-DOF motion via neural network model updating

The problem of controlling an all-thruster spacecraft in the coupled translational-rotational motion in presence of actuators fault and/or failure is investigated in this paper. The nonlinear model predictive control approach is used because of its ability to predict the future behavior of the system. The fault/failure of the thrusters changes the mapping between the commanded forces to the thrusters and actual force/torque generated by the thruster system. Thus, the basic six degree-of-freedom kinetic equations are separated from this mapping and a set of neural networks are trained off-line to learn the kinetic equations. Then, two neural networks are attached to these trained networks in order to learn the thruster commands to force/torque mappings on-line. Different off-nominal conditions are modeled so that neural networks can detect any failure and fault, including scale factor and misalignment of thrusters. A simple model of the spacecraft relative motion is used in MPC to decrease the computational burden. However, a precise model by the means of orbit propagation including different types of perturbation is utilized to evaluate the usefulness of the proposed approach in actual conditions. The numerical simulation shows that this method can successfully control the all-thruster spacecraft with ON-OFF thrusters in different combinations of thruster fault and/or failure.