Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines

A hybrid calibration approach based on support vector machines (SVM) is proposed to characterize nonlinear cross coupling of multi-dimensional transducer. It is difficult to identify these unknown nonlinearities and crosstalk just with a single conventional calibration approach. In this paper, a hybrid model comprising calibration matrix and SVM model for calibrating linearity and nonlinearity respectively is built up. The calibration matrix is determined by linear artificial neural network (ANN), and the SVM is used to compensate for the nonlinear cross coupling among each dimension. A simulation of the calibration of a multi-dimensional sensor is conducted by the SVM hybrid calibration method, which is then utilized to calibrate a six-component force/torque transducer of wind tunnel balance. From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix. Moreover, with the calibration matrix, the hybrid model can provide a basis for the design of transducers.     

Nonlinear Interactions between the Quasi 5-day Wave and Tides Based on Meteor Radar Observations at Maui

Nonlinear interactions between the quasi 5-day wave and tides based on meteor radar observation in the Mesosphere and Lower Thermosphere(MLT) at Maui are studied in this paper.Strong sum interaction between quasi 5-day wave and diurnal tide,and evident difference interaction between quasi 5-day wave and semidiurnal tide are observed during the time of attention.However,their difference and sum counterparts are clearly weaker.The secondary waves generated from those interactions beat with the tide and show intense modulation at the period of 5 days which confirms the existence of their interactions.Additionally,correlation coefficients among these waves are calculated to further explore their interactions and find that they can persist for several days although they are highly intermittent.The energy exchange among these waves can be reversible during the observational time.The periods when the significant difference interaction between the quasi 5-day wave and semidiurnal tide occur are much shorter than those when the significant sum interaction between the quasi 5-day wave and diurnal tide occur.Moreover,these two strong interactions can take place simultaneously.In generally,this study provides the proof of nonlinear interactions between quasi 5-day wave and tides which were seldom reported before.     

Reliability analysis of aero-engine blades considering nonlinear strength degeneration

To comprehensively consider the effects of strength degeneration and failure correlation, an improved stress-strength interference (SSI) model is proposed to analyze the reliability of aeroengine blades with the fatigue failure mode. Two types of TC4 alloy experiments are conducted for the study on the damage accumulation law. All the parameters in the nonlinear damage model are obtained by the tension-compression fatigue tests, and the accuracy of the nonlinear damage model is verified by the damage tests. The strength degeneration model is put forward on the basis of the Chaboche nonlinear damage theory and the Griffith fracture criterion, and determined by measuring the fatigue toughness during the tests. From the comparison of two kinds of degeneration models based on the Miner’s linear law and the nonlinear damage model respectively, the nonlinear model has a significant advantage on prediction accuracy especially in the later period of life. A time-dependent SSI reliability model is established. By computing the stress distribution using the finite element (FE) technique, the reliability of a single blade during the whole service life is obtained. Considering the failure correlation of components, a modified reliability model of aero-engine blades with common cause failure (CCF) is presented. It shows a closer and more reasonable process with the actual working condition. The improved reliability model is illustrated to be applied to aero-engine blades well, and the approach purposed in this paper is suitable for any actual machinery component of aero-engine rotor systems.     

Is the North Atlantic Oscillation modulated by solar and lunar cycles? Some evidences from Hurst autocorrelation analysis

Several studies have suggested that the Sun and Moon cycles affect the Earth climatic dynamics. Nevertheless, there is a long-standing controversy whether solar variability and tides can significantly generate climate change, and how this may occur. Spectral analysis of climatic indices has provided only indirect evidences of the effects of solar–tidal periodicities in the Earth climate. This work addresses the issue by considering the dynamics of the daily North Atlantic Oscillation index over the period from 1950 to 2009. In contrast to previous studies, this work proposes that external cycles can be detected in the autocorrelation dynamics rather than in the raw North Atlantic Oscillation index series. Here, the R/S-scaling analysis is used to quantify, via the so-called Hurst exponent, the presence of autocorrelations along the studied years. Fourier analysis scan of the autocorrelation series thus show two prominent spectral components near (±3%) the lunar tidal 4.425 and the solar 11 years cycles. Intermediate spectral components near 6.4, 7.75 and 8.9 years are proposed to be, at least partially, a result of energy capture from internal mechanisms into cycles resulting from the nonlinear resonance of the fundamental solar–tidal cycles. The dominant effect of the solar variability is clarified by showing that in about 70% of the studied period the sunspot number and the Hurst exponent phases are synchronized, indicating that a higher solar activity enhances the North Atlantic Oscillation index predictability.     

Shear flow energy redistribution stipulated by the internal-gravity wavy structures in the dissipative ionosphere

The linear mechanism of generation, intensification and further nonlinear dynamics of internal gravity waves (IGW) in stably stratified dissipative ionosphere with non-uniform zonal wind (shear flow) is studied. In case of the shear flows the operators of linear problem are non-selfadjoint, and the corresponding Eigen functions – nonorthogonal. Thus, canonical – modal approach is of less use studying such motions. Non-modal mathematical analysis becomes more adequate for such problems. On the basis of non-modal approach, the equations of dynamics and the energy transfer of IGW disturbances in the ionosphere with a shear flow is obtained. Exact analytical solutions of the linear as well as the nonlinear dynamic equations of the problem are built. The increment of shear instability of IGW is defined. It is revealed that the transient amplification of IGW disturbances due time does not flow exponentially, but in algebraic – power law manner. The effectiveness of the linear amplification mechanism of IGW at interaction with non-uniform zonal wind is analyzed. It is shown that at initial linear stage of evolution IGW effectively temporarily draws energy from the shear flow significantly increasing (by an order of magnitude) own amplitude and energy. With amplitude growth the nonlinear mechanism turns on and the process ends with self-organization of nonlinear solitary, strongly localized IGW vortex structures (the monopole vortex, the transverse vortex chain or the longitudinal vortex street). Accumulation of these vortices in the ionospheric medium can create the strongly turbulent state.     

Prediction of nonlinear pilot-induced oscillation using an intelligent human pilot model

This paper proposes a method to predict nonlinear Pilot-Induced Oscillation (PIO) using an intelligent human pilot model. This method is based on a scalogram-based PIO metric, which uses wavelet transforms to analyze the nonlinear characteristics of a time-varying system. The intelligent human pilot model includes three modules: perception module, decision and adaptive module, and execution module. Intelligent and adaptive features, including a neural network receptor, fuzzy decision and adaptation, are also introduced into the human pilot model to describe the behavior of the human pilot accommodating the nonlinear events. Furthermore, an algorithm is proposed to describe the procedure of the PIO prediction method with nonlinear evaluation cases. The prediction results obtained by numerical simulation are compared with the assessments of flight test data to validate the utility of the method. The flight test data were generated in the evaluation of the Smart-Cue/Smart-Gain, which is capable of reducing the PIO tendencies considerably. The results show that the method can be applied to predict the nonlinear PIO events by human pilot model simulation.     

Accuracy analysis of GNSS-IR snow depth inversion algorithms

In recent years, with the continuous development of Global Navigation Satellite System (GNSS), it has been applied not only to navigation and positioning, but also to Earth surface environment monitoring. At present, when performing GNSS-IR (GNSS Interferometric Reflectometry) snow depth inversion, Lomb-Scargle Periodogram (LSP) spectrum analysis is mainly used to calculate the vertical height from the antenna phase center to the reflection surface. However, it has the problem of low identification of power spectrum analysis, which may lead to frequency leakage. Therefore, Fast Fourier Transform (FFT) spectrum analysis and Nonlinear Least Square Fitting (NLSF) are introduced to calculate the vertical height in this paper. The GNSS-IR snow depth inversion experiment is carried out by using the observation data of P351 station in PBO (Plate Boundary Observatory) network of the United States from 2013 to 2016. Three algorithms are used to invert the snow depth and compared with the actual snow depth provided by the station 490 in the SNOTEL network. The observations data of L1 and L2 bands are respectively used to find the optimal combination between different algorithms further to improve the accuracy of GNSS-IR snow depth inversion. For L1 band, different snow depths correspond to different optimal algorithms. When the snow depth is less than 0.8 m, the inversion accuracy of NLSF algorithm is the highest. When the snow depth is greater than 0.8 m, the inversion accuracy of FFT algorithm is higher. Therefore, according to the different snow depth, a combined algorithm of NLSF + FFT is proposed for GNSS-IR snow depth inversion. Compared with the traditional LSP algorithm, the inversion accuracy of the combined algorithm is improved by 10%. For L2 band data, the results show that the accuracy of snow depth inversion of various algorithms do not change with the variations of snow depth. Among the three single algorithms, the inversion accuracy of FFT algorithm is better than that of LSP and NLSF algorithms.     

模糊控制方法及其在飞控中的应用

对于较大的飞行包线，飞机对象的参数变化剧烈，采用常规控制方法往往不能保证在较大飞行包线范围内均取得良好的控制效果，基于此，提出了一种新型的自组织模糊控制方法，该对系统内部参数的剧烈变化具有很强的适应性，然后对飞机飞行控制系统的控制律设计进行了研究。用所提出的模糊控制方法设计了部分控制律，最后以某型国产飞机对象，采用飞机六自由度非线性仿真模型，在一个较大的飞行包线范围内，进行了飞行仿真，取得了良好的     

一种非线性规划的求解方法与燃气发生器的设计

阐述了一种新的非线性规划的求解方法。即对每个参变量在其可能变化区间内，按照特定的步长依次代入约束方程来观察是否满足约束条件，从而求得若干组可行解。变量变化的步长通过非线性方程组各个方程在近似结点的全微分的分析获得。步长的确定是本方法的关键。文中并示范性列举了本方法在燃气发生器设计中的作用。