航空发动机限寿件疲劳可靠度计算新方法

针对目前的航空发动机限寿件（ELLP）疲劳可靠性分析中的小失效概率事件以及其极限状态函数具有较强非线性的特点,提出了一种具有自更新机制的半径外自适应重要抽样（AUMCROAIS）疲劳可靠性分析方法。该方法首先利用蒙特卡罗自适应重要抽样（MCAIS）快速逼近真实设计验算点（MPP）附近,随后以近似设计验算点为中心进行极坐标抽样,并依次构造主动学习函数,对近极限状态函数和抽样半径进行最优选取,从而实现最优抽样半径的更新,通过不断的更新确定出最优抽样半径,加速失效概率计算的收敛。本方法提高了设计验算点的收敛速度同时保证了计算精度,解决了小失效概率事件以及强非线性极限状态函数可靠度计算难题,最后以某型发动机压气机轮盘为对象应用本方法,并与传统的蒙特卡罗仿真（MCS）方法、蒙特卡罗半径外自适应重要抽样法（MCROAIS）和一阶可靠性方法（FORM）进行了对比,验证了本方法的高效率、鲁棒性和仿真精度。     

Reliability and reliability sensitivity analysis of structure by combining adaptive linked importance sampling and Kriging reliability method

The application of reliability analysis and reliability sensitivity analysis methods to complicated structures faces two main challenges: small failure probability (typical less than 10⁻⁵) and time-demanding mechanical models. This paper proposes an improved active learning surrogate model method, which combines the advantages of the classical Active Kriging – Monte Carlo Simulation (AK-MCS) procedure and the Adaptive Linked Importance Sampling (ALIS) procedure. The proposed procedure can, on the one hand, adaptively produce a series of intermediate sampling density approaching the quasi-optimal Importance Sampling (IS) density, on the other hand, adaptively generate a set of intermediate surrogate models approaching the true failure surface of the rare failure event. Then, the small failure probability and the corresponding reliability sensitivity indices are efficiently estimated by their IS estimators based on the quasi-optimal IS density and the surrogate models. Compared with the classical AK-MCS and Active Kriging – Importance Sampling (AK-IS) procedure, the proposed method neither need to build very large sample pool even when the failure probability is extremely small, nor need to estimate the Most Probable Points (MPPs), thus it is computationally more efficient and more applicable especially for problems with multiple MPPs. The effectiveness and engineering applicability of the proposed method are demonstrated by one numerical test example and two engineering applications.     

计算涡轮叶片结构可靠度的自适应重要抽样法

为直接准确地计算系统结构可靠度，提出了自适应重要抽样方法，由极限状态面失效区确定的最初抽样区开始，用与随机变量联合概率密度函数成比例的抽样密度，自适应递增调整抽样，使抽样区稍大于失效区，以减少安全区的追加抽样，提高抽样效率，并将其应用于航空发动机涡轮叶片的结构可靠性分析，在获得相同的准确计算结果时，所需计算量显著减少。     

基于MCIS技术的相位差变化率单站无源定位

针对最大似然估计（ML）方法求解测相位差变化率单站无源定位问题计算量大、定位慢的问题,本文提出一种利用蒙特卡洛重要性抽样技术（MCIS）高精度、低复杂度的估计方法。根据Pincus定理推导出ML问题的近似全局解,利用重要性抽样（IS）技术构建符合高斯分布概率密度（PDF）的重要性函数,作为样本选取的依据,通过逆变换采样获得样本集,统计样本均值直接得到辐射源位置估计结果。MCIS方法简单易实现且运算量低,能够克服传统ML估计多维网格搜索耗时较长的缺陷,而且对目标位置初始估计误差有较低的敏感性。实验结果表明,MCIS算法在相同测量噪声水平下,定位精度优于EKF、NLS算法,有效减小了初始化估计误差对算法定位精度的影响,也进一步讨论分析了算法参数和不同观测条件对定位性能的影响。     

结构可靠性分析的拟蒙特卡罗方法

由于蒙特卡罗(MC)方法具有程序结构简单,收敛速度与问题维数无关等优点,故其在结构可靠性分析中得到了广泛应用。但是对于小失效概率等问题,计算效率低这一主要缺点限制了该方法的应用范围。为了解决MC方法存在的问题,通过引进单位超立方体中不同的低偏差点集代替伪随机数序列,并结合了重要抽样技术建立了结构可靠性分析的拟蒙特卡罗(QMC)方法。该方法不但可以大幅度减少抽样点数目,还能够得到确定性的估计值, 避免传统MC方法只能得到概率意义下误差的缺陷。通过数值算例可以看出本文方法具有较高的计算精度和效率,因此适用于结构可靠性分析问题。     

解结构可靠性问题的改进分层抽样法

通过引入探索性仿真 ,克服了合理确定各子层系统抽样概率预分配的困难 ;提出了几种实用有效的分层方法 ,对分层策略和仿真策略进行了深入的研究。通过求解具体问题 ,证实对求解概率积分相当有效 ,特别是对高度非线性安全边界问题 ,具有一般方法所不具备的高精度 ,该方法适于解决多破坏模式、非线性安全边界的结构体系可靠性问题     

基于自适应Kriging代理模型的交叉熵重要抽样法

对于复杂失效域和小失效概率耦合的可靠性分析问题,本文提出了一种交叉熵重要抽样（CE-IS）方法结合自适应Kriging （AK）代理模型的求解方法（CE-IS-AK）。所提方法基于交叉熵原理,用混合高斯模型逐步逼近最优重要抽样密度函数,并采用AK模型协助逼近过程中混合高斯模型的参数的更新,从而提高了CE-IS方法的计算效率。另外,本文还改进了CE-IS方法的收敛准则,避免了方法的冗余迭代,扩大了方法的适用范围。由于在CE-IS方法中引入了AK模型,因此,本文方法所构建的重要抽样函数在保证精度的基础上提高了效率。相较于AK-MCS方法,本文方法中引入了重要抽样的思想,因此在Kriging训练点数目基本相同的情况下,大幅缩减小失效概率计算时样本池规模,并且由于利用了混合高斯模型,因而对多失效域具有较好的适用性。算例分析也证明了本文所提方法的优越性。     

IMPROVED STRATIFIED SAMPLING MONTE CARLO METHOD TO ANALYZE RELIABILITY OF STRUCTURAL SYSTEMS

ＩＭＰＲＯＶＥＤＳＴＲＡＴＩＦＩＥＤＳＡＭＰＬＩＮＧＭＯＮＴＥＣＡＲＬＯＭＥＴＨＯＤＴＯＡＮＡＬＹＺＥＲＥＬＩＡＢＩＬＩＴＹＯＦＳＴＲＵＣＴＵＲＡＬＳＹＳＴＥＭＳＬｉＱｉａｎｇ；ＦｅｎｇＹｕａｎｓｈｅｎｇ（ＤｅｐａｒｔｍｅｎｔＯｆＡｉｒｃｒａｆｔＥｎ...     

压缩感知在轴流压气机周向模态识别中的应用

研究了将压缩感知应用于轴流压气机管道周向模态识别的方法。采用数值实验的方法对稀疏模态信号进行压缩感知分析。对确定性信号,讨论了信号欠采样因子和感知常数对不同稀疏比信号重构成功率的影响,当感知常数大于3.5时,感知成功率超过90%;对未知信号,随机选取测量矩阵在感知失败时信号的模态分布没有规律。在此基础上,将Nyquist-Shannon采样定理和压缩感知方法结合,提出了轴流压气机管道周向模态传感器双均布测点布置方式,发现了其感知的成功与否与信号模态数的差值和测点数相关,选择合适的测点数,可用于基于动静干涉的压气机周向模态的可靠重构。讨论了双均布测点分布的鲁棒性,发现任意减少3个测点时仍可保证研究对象中至少5个叠加模态的成功感知。最后,将压缩感知方法应用于轴流压气机管道周向模态识别,成功感知到了基于Tyler-Sofrin的轴流压气机高阶动静干涉模态。该研究为轴流压气机管道模态分析和测量提供了新的实验数据处理方法。      

有人无人直升机协同搜潜效能研究

通过分析直升机搜潜的任务过程,结合蒙特卡洛法的随机抽样特性,建立了基于蒙特卡洛法的效能 评估模型,用以评估直升机使用浮标搜潜的成功概率。基于仿真实验对有人直升机搜潜、无人直升机双机编组 搜潜、有无人直升机协同搜潜的效能进行了对比分析。     

再入飞行器的鲁棒设计技术研究

本文将鲁棒最优设计技术应用于再入飞行器被动式滚转控制问题，分别建立了二次滚转共振和滚转过零问题的鲁棒设计定义和数学模型，通过对关键小不对称量（如ｒｃｇ，Ｃ１０，Ｃｍ０）的容许偏差的限制，确保二次滚转共振和滚转过零概率在设计者期望的范围之内。本文给出了一个实际的设计例子，并用Ｍｏｎｔｅ Ｃａｒｌｏ随机抽样方法进行验证。结果表明，本文的思路是正确的，方法是可靠的。     

Reliability Sensitivity Algorithm Based on Stratified Importance Sampling Method for Multiple Failure Modes Systems

 Combining the advantages of the stratified sampling and the importance sampling, a stratified importance sampling method (SISM) is presented to analyze the reliability sensitivity for structure with multiple failure modes. In the presented method, the variable space is divided into several disjoint subspace by n-dimensional coordinate planes at the mean point of the random vector, and the importance sampling functions in the subspaces are constructed by keeping the sampling center at the mean point and augmenting the standard deviation by a factor of l. The sample size generated from the importance sampling function in each subspace is determined by the contribution of the subspace to the reliability sensitivity, which can be estimated by it-erative simulation in the sampling process. The formulae of the reliability sensitivity estimation, the variance and the coeffi-cient of variation are derived for the presented SISM. Comparing with the Monte Carlo method, the stratified sampling method and the importance sampling method, the presented SISM has wider applicability and higher calculation efficiency, which is demonstrated by numerical examples. Finally, the reliability sensitivity analysis of flap structure is illustrated that the SISM can be applied to engineering structure.     

Decision-Directed Detector for Overlapping PCM/NRZ Signals

A decision-directed (DD) technique for the detection of overlapping PCM/NRZ signals in the presence of white Gaussian noise is investigated. The performance of the DD detector is represented by probability of error PE versus input signal-to-noise ratio (SNR). To examine how much improvement in performance can beachieved with this technique, PE's with and without DD feedback are evaluated in parallel. Further, analytical results are compared with those found by Monte Carlo simulations. The results are shown in good agreement.     

一种改进的航空发动机结构概率风险评估方法

针对航空发动机适航条款FAR33.75中关于发动机限寿件(ELLP)结构失效概率要求,提出了一种基于Kriging和蒙特卡罗半径外重要抽样(MCROIS)混合的结构概率风险评估方法。该方法针对ELLP高维、小失效概率事件以及极限状态函数为隐式、高度非线性的特点,利用Kriging元模型模拟隐式极限状态函数,然后通过主动学习迭代算法,计算最优点(MPP,最接近设计验算点的样本点),更新实验设计(DOE)并提高Kriging元模型的模拟精度。在此基础上,利用Kriging元模型确定最优抽样半径,构造半径外重要抽样密度函数,在最优抽样半径确定区域进行抽样,通过构造主动学习函数,使样本点更多落在抽样半径确定的球区域附近,加速失效概率计算的收敛,并构建了ELLP风险概率模型,解决了高维、小失效概率事件以及隐式、非线性极限状态函数的发动机结构概率风险评估难题,以某型发动机低压压气机轮盘为应用示例,与传统的蒙特卡罗仿真(MCS)方法进行了对比,验证了该方法的高效率、鲁棒性和仿真精度。     

小失效概率情况下的全局可靠性灵敏度分析的高效方法

针对目前很多算法都无法准确、高效地计算小失效概率（10^-4,甚至更小）情况下的全局可靠性灵敏度问题,本文提出了一种高效求解小失效概率情况下的全局可靠性灵敏度新算法。所提算法通过扩大标准差构造重要抽样密度函数来进行空间分割（SP）,再与无迹变换（UT）结合,利用函数在分割后的子空间内非线性程度的降低和无迹变换方法可以高效计算低非线性程度函数的前二阶矩,来高效准确地计算小失效概率情况下的全局可靠性灵敏度。所提算法的优点有：重要抽样密度函数的选择可以使得空间分割时向重要区域偏移,并且在分割区域内功能函数的复杂性被降低,从而可以利用无迹变换方法高效计算失效概率,进而高效求得全局可靠性灵敏度。与已有的算法相比,算例说明了本文所提方法的优势。     

涡轮盘概率风险的评估方法

针对CCAR33.70条款对涡轮盘开展概率风险评估的要求,研究了响应面法在定义重点采样区间以及计算裂纹扩展寿命中的应用,给出了在蒙特卡洛模拟过程中应用响应面法定义失效域,缩短样本方差、提高抽样效率的方法.分析结果表明应用重点采样法可以极大地提高抽样效率.采用重点采样法获得的失效样本数与总样本数的比例高达94%,远大于传统的直接抽样法获得的失效样本数与总样本数的比例（仅为33%）.该方法可以在保证分析精度的条件下减少样本的数量.另外,应用响应面法拟合寿命近似方程,可以极大地缩减裂纹扩展寿命的计算时间:利用寿命近似方程开展104次随机抽样所花费的时间仅有2min,远低于应用传统寿命计算方法所花费的时间（288 min）.通过对寿命近似方程的寿命计算误差分析可以看出:最大计算误差仅有1.0%,计算结果与传统裂纹扩展寿命模型的计算结果吻合度高.该研究成果可以提高涡轮盘概率风险评估的效率.      

Detection Performance of the Cell Averaging LOG/CFAR Receiver

The cell averaging LOG/CFAR receiver is a special implementation of a constant-false-alarm-rate (CFAR) receiver in which the noise level estimate is derived from a set of contiguous time samples of the output of a logarithmic (LOG) detector as obtained from a tapped delay line. This CFAR receiver is capable of operating over a larger dynamic range of noise levels than a conventional cell averaging CFAR receiver, but with somewhat poorer detectability. The performance in stationary Gaussian noise of the cell averaging LOG/CFAR receiver with no post-detection integration is determined in this paper. For a small number of reference noise samples, results were obtained by a Monte Carlo simulation using the technique of importance sampling. For a large number of reference noise samples, a second moment analysis gave the desired results. Both these results can be summarized in the following simple formula, NLOG = 1.65NLIN - 0.65, which relates the number of reference samples required by each of the two receivers for equivalent performance. Thus, for the cell averaging LOG/CFAR receiver to give the same detection performance as the conventional cell averaging CFAR receiver, the number of reference noise samples has to be increased by up to 65 percent.     

Nonlinear hαMissile Longitudinal Autopilot Design with θ- D Method

In this paper, a new nonlinear H_infin control technique, called thetas-D H_infin method, is employed to design a missile longitudinal autopilot. The thetas-D H_infin design has the same structure as that of linear H_infin, except that the two Riccati equations that are part of the solution process are state dependent. The thetas-D technique yields suboptimal solutions to nonlinear optimal control problems in the sense that it provides an approximate solution to the Hamilton-Jacobi-Bellman (HJB) equation. It is also shown that this method can be used to provide an approximate closed-form solution to the state dependent Riccati equation (SDRE) and consequently reduce the on-line computations associated with the nonlinear H_infin implementation. A missile longitudinal autopilot design demonstrates the capabilities of thetas-D method. This new nonlinear H_infin design also shows favorable results as compared with the linear H design based on the linearized model.     

A noncoherent adaptive detection technique

An adaptive detection technique suitable for both stationary and nonstationary noise environments based upon a generalized likelihood ratio test (GLRT) formulation is presented. The detector, which is statistically equivalent to a special form of the Wilks's lambda test, noncoherently combines the information contained in a pulse train of arbitrary length for decision-making purposes. The probability density function of the test under the noise only hypothesis is shown to be central χ². Under the signal plus noise hypothesis, an exact statistical characterization of the test cannot be obtained, and, therefore, a Chernoff bound is derived. Results in terms of the probability of detection versus signal-to-noise ratio (SNR) obtained from Monte Carlo simulation, the Chernoff bound, and the optimal matched filter case are examined. The performance of the noncoherent detector is shown to be a function of the covariance matrix estimate and the number of data samples     

Multitarget tracking using the joint multitarget probability density

This work addresses the problem of tracking multiple moving targets by recursively estimating the joint multitarget probability density (JMPD). Estimation of the JMPD is done in a Bayesian framework and provides a method for tracking multiple targets which allows nonlinear target motion and measurement to state coupling as well as nonGaussian target state densities. The JMPD technique simultaneously estimates both the target states and the number of targets in the surveillance region based on the set of measurements made. We give an implementation of the JMPD method based on particle filtering techniques and provide an adaptive sampling scheme which explicitly models the multitarget nature of the problem. We show that this implementation of the JMPD technique provides a natural way to track a collection of targets, is computationally tractable, and performs well under difficult conditions such as target crossing, convoy movement, and low measurement signal-to-noise ratio (SNR).