斜面兜孔圆柱滚子轴承的高速防打滑特性

为解决航空发动机主轴圆柱滚子轴承的打滑问题,提出一种具有斜面兜孔结构的圆柱滚子轴承,并对其高速防打滑特性展开研究。利用Hertz接触理论及弹流润滑、流体动力润滑的经验公式,建立了滚子-内外套圈滚道、兜孔-滚子、保持架-外圈引导面的接触模型。运用多体运动学及牛顿-欧拉动力学理论,建立了轴承径向平面内的三自由度动力学模型,在此基础上,利用龙格库塔数值积分法进行了轴承的动力学数值仿真,探讨了兜孔前后壁倾角对轴承打滑的影响规律,分析了兜孔前壁倾角为5°、后壁倾角为10°下轴承的高速打滑特性,结果表明:兜孔的前后壁倾角对轴承打滑有显著影响,通过对其进行优化,可有效抑制轴承的高速打滑。     

Design and analysis of a truss deployable antenna mechanism based on a 3UU-3URU unit

Space deployable structures with large calibers, high accuracy, and large folding ratios are indispensable equipment in the aerospace field. Given that the single-DOF 3RR-3RRR deployable unit cannot be fully folded, this study proposes a 3UU-3URU deployable unit with two kinds of DOF: folding movement and orientation adjustment. First, based on the G-K formula, the DOF of the 3UU-3URU unit is analyzed. Then, the 3UU-3URU unit is used to construct a deployable truss antenna with a curved surface, and the DOF of the whole deployable antenna containing multiple 3UU-3URU units is calculated. The structural design of a deployable antenna with two loops is carried out with specific parameters and geometric relations. Next, a DOF simulation of a basic combination unit composed of three 3UU-3URU units is performed. Finally, a prototype of the basic combination unit is manufactured, and the DOF of the mechanism is experimentally verified.     

由感知到动作决策一体化的类脑导航技术研究现状与未来发展

随着脑与神经科学以及人工智能技术的持续发展,昆虫和哺乳动物大脑导航机理启发下的感知/认知/路径规划/动作决策一体化类脑导航技术得到了较大发展,可以实现由原始感知信息输入到导航动作决策的直接输出,呈现出接近动物端到端面向目标导航的智能行为,具有提高密集型无人机集群导航鲁棒性、准确性、实时响应动作、自主智能性以及计算效率的潜力。阐述了昆虫和哺乳动物大脑导航机理及其互补对称关系,以及昆虫和哺乳动物大脑导航机理启发的端到端类脑导航技术内涵;论述了类脑导航技术研究进展,包括类脑环境感知、类脑空间认知、面向目标类脑导航;分析了类脑导航向智能化、神经形态系统以及群体导航发展的新趋势;最后讨论了类脑导航技术应用于无人机密集集群系统时存在的挑战。     

A two-step weighted least-squares method for joint estimation of source and sensor locations: A general framework

It is well known that the Two-step Weighted Least-Squares(TWLS) is a widely used method for source localization and sensor position refinement. For this reason, we propose a unified framework of the TWLS method for joint estimation of multiple disjoint sources and sensor locations in this paper. Unlike some existing works, the presented method is based on more general measurement model, and therefore it can be applied to many different localization scenarios.Besides, it does not have the initialization and local convergence problem. The closed-form expression for the covariance matrix of the proposed TWLS estimator is also derived by exploiting the first-order perturbation analysis. Moreover, the estimation accuracy of the TWLS method is shown analytically to achieve the Cramér-Rao Bound(CRB) before the threshold effect takes place. The theoretical analysis is also performed in a common mathematical framework, rather than aiming at some specific signal metrics. Finally, two numerical experiments are performed to support the theoretical development in this paper.     

Multi-mode diagnosis of a gas turbine engine using an adaptive neuro-fuzzy system

Gas Turbine Engines (GTEs) are vastly used for generation of mechanical power in a wide range of applications from airplane propulsion systems to stationary power plants. The gas-path components of a GTE are exposed to harsh operating and ambient conditions, leading to several degradation mechanisms. Because GTE components are mostly inaccessible for direct measurements and their degradation levels must be inferred from the measurements of accessible parameters, it is a challenge to acquire reliable information on the degradation conditions of the parts in different fault modes. In this work, a data-driven fault detection and degradation estimation scheme is developed for GTE diagnostics based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). To verify the performance and accuracy of the developed diagnostic framework on GTE data, an ensemble of measurable gas path parameters has been generated by a high-fidelity GTE model under (a) diverse ambient conditions and control settings, (b) every possible combination of degradation symptoms, and (c) a broad range of signal to noise ratios. The results prove the competency of the developed framework in fault diagnostics and reveal the sensitivity of diagnostic results to measurement noise for different degradation symptoms.     

Global aerodynamic design optimization based on data dimensionality reduction

In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However, for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition (POD) in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.