真空紫外探测器关键参数校准装置

当前真空紫外空间探测技术迅猛发展,真空紫外载荷应用越来越广,其中的核心器件真空紫外探测器的特性校准变得越来越重要,针对国内真空紫外探测器关键参数校准能力缺失问题,研制了一套真空紫外探测器关键参数校准装置,实现光谱响应率、线性度等参数的校准。论述了真空紫外探测器光谱响应率、线性度参数的校准方法,介绍了校准装置的硬件组成、校准方法和软件功能,并对其进行了测量不确定度评定。测试结果证明表明,该校准装置可以对真空紫外探测器进行主要参数的校准,校准结果准确、有效。     

热线探针对数校准方法研究及改进

开展了可压缩流体中热线探针校准方法的研究,以满足其在各种速度测量场合的使用需求。研究了对数校准数学模型,发现校准系数求解过程中存在矩阵奇异性过强的问题,导致在速度小扰动条件下方程求解稳定性差。对对数校准数学模型进行了参数无量纲化及添加正向偏置的改进,建立了无量纲化对数校准数学模型。在马赫数为0.3~0.5,引射压力为150~300 k Pa范围内进行了校准实验,利用对数校准数学模型对实验数据进行拟合,拟合优度为0.997 61,拟合速度平均偏差为1.378 m/s,校准系数求解过程中系数矩阵条件数为1.595×108,矩阵奇异性过强,加入速度小扰动(1 m/s)后,拟合优度为0.379 74,拟合速度平均偏差为43.81 m/s,方程求解稳定性差。利用无量纲化对数校准数学模型对实验数据进行拟合,拟合优度为0.998 95,拟合速度平均偏差为1.203 m/s,校准系数求解过程中系数矩阵条件数为3.655×102,且无量纲化方法不受速度小扰动影响。对流体速度进行不确定度分析,速度平均不确定度为3.168 m/s,无量纲化拟合速度平均偏差明显小于速度平均不确定度。实验结果证明了无量纲化对数校准数学模型应用于可压缩流体热线探针校准的可行性。     

国内主力低速风洞大迎角实验的不确定度水平研究

笔者将AGARD-AR-304不确定度评估方法推广到大迎角领域,给出计算低速风洞大(小)迎角实验不确定度的工程方法.相应的计算软件已成功地用于国内主力低速风洞实验的不确定度评估,完成了5座风洞8个模型大迎角实验的不确定度评估,给出了我国主力低速风洞大(小)迎角实验的不确定度水平.     

基于代理模型的气动设计稳健优化方法

为了减少不确定分析的计算量,提出了一种基于代理模型的不确定性气动设计优化方法,其中代理模型主要用于简化不确定分析计算过程.运用拉丁方试验设计和Krig-ing建立了代理模型,用随机参数来表示尺寸误差和飞行条件的变化.基于代理模型,以蒙特卡洛模拟法作为不确定分析方法,求解气动性能的均值和方差.在此基础上定义了气动稳健优化问题的表达式,用遗传算法进行求解,并以某翼型的优化问题对该方法进行验证.结果表明,通过该方法得到的最优解对不确定性的敏感度大大减小,同时在不确定的情况下仍然能满足设计约束条件.     

反舰导弹基于反演的滑模控制

首先介绍了非线性系统的反演设计方法,提出了一种基于反演的滑模控制方法。它可解决非线性系统的不匹配不确定性问题。该方法通过在反演设计中引入滑模控制来改进反演的最后一步算法,并简化控制器的设计。针对新型反舰导弹参数时变、不确定、非线性的特点,采用了基于反演的滑模控制来设计其控制系统。最后,进行了仿真研究,仿真结果证明了所提出的方法的有效性。     

非线性不确定系统的鲁棒自适应控制方法研究及稳定性分析

针对一类存在外界干扰和未建模动态的非线性不定系统,特别是含线性定常的参数不确定性及状态关联的非线性不确定性系统,应用直接自适应控制方法,设计了基于李雅普诺夫稳定性理论的鲁棒自适应控制律和修正律;应用李雅普诺夫稳定判据,对系统的稳定性进行了分析,证明所设计的控制律和修正律具有较强的鲁棒稳定性。     

Compressor geometric uncertainty quantification under conditions from near choke to near stall

Geometric and working condition uncertainties are inevitable in a compressor, deviating the compressor performance from the design value. It’s necessary to explore the influence of geometric uncertainty on performance deviation under different working conditions. In this paper, the geometric uncertainty influences at near stall, peak efficiency, and near choke conditions under design speed and low speed are investigated. Firstly, manufacturing geometric uncertainties are analyzed. Next, correlation models between geometry and performance under different working conditions are constructed based on a neural network. Then the Shapley additive explanations (SHAP) method is introduced to explain the output of the neural network. Results show that under real manufacturing uncertainty, the efficiency deviation range is small under the near stall and peak efficiency conditions. However, under the near choke conditions, efficiency is highly sensitive to flow capacity changes caused by geometric uncertainty, leading to a significant increase in the efficiency deviation amplitude, up to a magnitude of ?3.6%. Moreover, the tip leading-edge radius and tip thickness are two main factors affecting efficiency deviation. Therefore, to reduce efficiency uncertainty, a compressor should be avoided working near the choke condition, and the tolerances of the tip leading-edge radius and tip thickness should be strictly controlled.     

Optimal crater landmark selection based on optical navigation performance factors for planetary landing

Planetary craters are natural navigation landmarks that widely exist and are easily observed. Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing. Due to the increase in observed crater landmarks and the limitation of onboard computation, the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical navigation. This paper designs a fast crater landmark selection method, which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters, which is different from other landmark selection methods. The factor of measurement uncertainty, which is anisotropic, correlated and nonidentically distributed, is quantified and integrated into selection based on crater pairing detection and localization error evaluation. In addition, the concept of the crater matching confidence factor is introduced, which reflects the possibility of 2D projection measurements corresponding to 3D positions. Combined with the configuration observability factor, the crater landmark selection indicator is formed. Finally, the effectiveness of the proposed method is verified by Monte Carlo simulations.     

An information-volume-based distance measure for decision-making

D-S evidence theory, as a general framework for reasoning with uncertainty, allows combining pieces of evidence from different information sources to derive a degree of belief function that is a type of fuzzy measure. However, the mass assignments given by unknown information sources are disordered. How to measure the difference between the mass assignments has aroused people’s interest. In this paper, inspired by the information volume, a novel distance-based measure is proposed to measure the difference between mass assignments. The method can refine the uncertain information given by experts and compare the refined information to obtain the difference between mass assignments. At the same time, it is verified that the measure not only meets the properties of distance, but also proves the superiority of the proposed Information Volume Distance (IVD) through simulation experiments. Meanwhile, in the process of information fusion, the reliability of each source could be quantified through IVD. Therefore, based on IVD, a new multi-source information algorithm is proposed to solve the problem of multi-source information fusion. Moreover, algorithm is applied to decision-making problem and compare with other methods to verify the effectiveness.