飞机性能优化研究

本文用能量状态近似法和奇异摄动法两件方法讨论了最优飞行剖面的生成.并以某机为例,用能量状态近似法求出了给定航程下的最优飞行剖面和给定初、末状态的爬升飞行剖面;为了对两种方法比较,也给出了奇异摄动法求得的爬升剖面.     

喷管冷吹风实验的推力架设计

本文介绍了柔性推力架和刚性推力架在喷管风洞实验中的表现.讨论了: 为什么风洞实验中使用柔性推力架达不到推力测量的精度要求?为什么试车台上用柔性推力架又是可行的.通过分析,找到了问题的根源在于风洞实验中用的是气体介质,而试车台上用的介质是液体推进剂.     

智能结构应用给飞行力学带来的挑战

探讨了智能结构在航空航天中的应用可能性和应用领域，包括智能结构的发展概况。概述了智能结构技术的发展和应用给飞行力学学科带来的新课题和新挑战，如具有智能结构的飞行器的动力学建模问题，参数辩识问题，稳定性与操纵性分析及飞行试验问题，智能结构机械特性，电磁特性等相互耦合及其飞行器性能的影响等问题，智能结构设计技术将是下一世纪飞行器设计中最重要的技术这一。     

飞机纵向静稳定度补偿

讨论了利用飞行控制系统对静不稳定飞机的三种补偿方式：延迟俯仰速度反馈、迎角反印刷品几法向过载负反馈。从而改变了飞机特性，诸如减轻质量，提高升阻比，增强机动性等。最后对三种补偿方案进行了比较，采用迎角反馈补偿是最直接有效的手段，但在工程实践中，往往采用俯仰速率和迎角混合反馈补偿方式。     

从非稳态飞行中确定飞机的纵向平衡曲线

介绍了俄罗斯飞行研究院广泛应用的从非稳态飞行中确定飞机纵向平衡曲线的试飞方法。该方法具有对试飞员动作要求宽、每个动作所包含的信息量大、节省起落、缩短试飞周期的优点。同时,还就该方法所存在的不足进行了讨论。     

推力向量控制方案及计算

本文归纳推力向量控制的主要方案,着重介绍摆动喷管、流体二次喷射方案的计算思路和结果.摆动喷管中的流场计算要考虑导弹偏摆对所产生的惯性力的影响;所得壁面压力畸变图说明,它是一个三维流场.在摆动喷管设计中要将铰链力减至最小,流体二次喷射的侧向力通过“等效实体模型”,用爆炸波的解来计算.     

空载固体发动机计算与试验模态相关性分析

为了预测空载状态固体火箭发动机动态特性,对其进行了模态试验,并应用MSC.Marc进行了模态计算,然后对比两者的结果,进行了相关性分析和评估。计算得到了发动机250Hz以内的一阶弯曲模态和五阶呼吸模态。试验测得了发动机的三阶呼吸模态和一阶弯曲模态。比较试验测试模态和与之对应的计算模态:固有频率相对误差均在5%以内;振型相关图上的点大都分布在斜率为1(或 1)的直线周围;MAC(模态置信判据)值在0 9左右。说明计算与试验模态有较好的相关性,有限元计算模型比较准确的反映了实际情况。     

L8飞机从风洞到飞行的抖振相关性研究

介绍了Ｌ８飞机抖振边界修正结果，并与同类飞机Ｌ２９飞行抖振边界进行了比较；给出了Ｌ８飞机抖振边和轻度抖振边界预测值。同时将预测抖振边界和试飞得到的抖振边界作了比较。     

Modeling novel methodologies for unmanned aerial systems–Applications to the UAS-S4 Ehecatl and the UAS-S45 Bálaam

The rising demand for Unmanned Aerial Systems(UASs) to perform tasks in hostile environments has emphasized the need for their simulation models for the preliminary evaluations of their missions. The efficiency of the UAS model is directly related to its capacity to estimate its flight dynamics with minimum computational resources. The literature describes several techniques to estimate accurate aircraft flight dynamics. Most of them are based on system identification. This paper presents an alternative methodology to obtain complete model of the S4 and S45 unmanned aerial systems. The UAS-S4 and the UAS-S45 models were divided into four sub-models, each corresponding to a specific discipline: aerodynamics, propulsion, mass and inertia, and actuator. The‘‘aerodynamic" sub-model was built using the Fderivatives in-house code, which is an improvement of the classical DATCOM procedure. The ‘‘propulsion" sub-model was obtained by coupling a two-stroke engine model based on the ideal Otto cycle and a Blade Element Theory(BET) analysis of the propeller. The ‘‘mass and the inertia" sub-model was designed utilizing the Raymer and DATCOM methodologies. A sub-model of an actuator using servomotor characteristics was employed to complete the model. The total model was then checked by validation of each submodel with numerical and experimental data. The results indicate that the obtained model was accurate and could be used to design a flight simulator.     

Multi-level virtual prototyping of electromechanical actuation system for more electric aircraft

Electromechanical actuators (EMAs) are becoming increasingly attractive in the field of more electric aircraft because of their outstanding benefits, which include reduced fuel burn and maintenance cost, enhanced system flexibility, and improved management of fault detection and isolation. However, electromechanical actuation raises specific issues when being used for safety-critical aerospace applications like flight controls: huge reflected inertia to load, jamming-type failure, and increase of backlash with service due to wear and local dissipation of heat losses for thermal balance. This study proposes an incremental approach for virtual prototyping of EMAs. It is driven by a model-based system engineering process in order to enable simulation-aided design. Best practices supported by Bond graph formalism are suggested to develop a model’s structure efficiently and to make the model ready for use (or extension) by addressing the above mentioned issues. Physical effects are progressively introduced, and the realism of lumped-parameter models is increased step-by-step. In particular, multi-level component models are architected to ensure continuity between engineering activities. The models are implemented in the AMESim simulation environment, and simulation responses are given to illustrate how they can be used for preliminary sizing, control design, thermal balance verification, and faults to failure analysis. The proposed best practices intend to provide engineers with fast, reusable, and efficient means to assess performance virtually and enhance maturity, performance, and robustness.