动态负调约束下固体冲压发动机燃气流量控制算法研究

变流量固体冲压发动机工作过程中，常采用控制燃气发生器压强的方式调节燃气生成量，在跟踪变化压强指令过程中，流量会产生明显的负调现象，严重影响发动机系统的安全性和可靠性。为 解决这一问题，本文以具有高度非线性、强时变特性的燃气发生器系统为对象，构建了基于粒子群算法的流量调节自适应PI控制器，并给出一种利用自适应微分跟踪器对期望控制指令进行“整形”的负调改善算法；首先，结合负调发生、终止判据，综合考量系统流量安全裕度，负调发生方向，构建了适配于动态负调约束的自适应跟踪微分器（TD），克服了传统跟踪微分器在负调抑制过程中控制精度不高，指向性不强等缺陷；进一步，开展了多工况下的负调抑制算法仿真校验，结果表明：所设计自适应控制算法具备良好的燃气流量控制能力，可实现在基本不改变响应时间情况下，将最大负调量削减56.6%的控制效果，展现出良好的工程应用前景。     

Adaptive sliding mode control of spacecraft attitude-orbit dynamics on SE(3)

In this paper, to solve the problem of parameters uncertainty in spacecraft tracking control, an adaptive controller based on sliding mode is proposed for the relative spacecraft attitude-orbit dynamics on the Lie group SE(3). The dynamic equations of relative attitude orbit error for two spacecraft are established in the framework of Lie group SE(3). Considering the uncertainty of spacecraft parameters, a formal decomposition of known and unknown parameters, the state variables and control variables is firstly made in the original system. An online estimator is designed to evaluate the unknown parameters. A sliding mode controller is developed to actuate the spacecraft to track the target spacecraft. Then a Lyapunov function of tracking error and parameters estimated error is designed to prove the stability of the closed-loop system. Finally, the simulation results and analysis are presented to verify the effectiveness and feasibility of the proposed method.     

Pressure distribution feature-oriented sampling for statistical analysis of supercritical airfoil aerodynamics

In the field of supercritical wing design, various principles and rules have been summarized through theoretical and experimental analyses. Compared with black-box relationships between geometry parameters and performances, quantitative physical laws about pressure distributions and performances are clearer and more beneficial to designers. With the advancement of computational fluid dynamics and computational intelligence, discovering new rules through statistical analysis on computers has beco...     

Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper,first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter(CKF) under multiplicative noise is analyzed. An i...     

气液比对气液同轴离心式喷嘴缩进室内流动过程的影响

为了研究气液同轴离心式喷嘴缩进室内部非定常流动过程,采用Level Set和VOF相耦合的方法结合网格自适应技术对缩进长度为8 mm的液体中心式气液同轴离心式喷嘴流动过程进行了数值仿真研究,计算得到了较为精细的液膜一次破碎过程、流场结构和压力振荡特性。结果表明：液膜的破碎模式受气液比的影响较大,随气液比的增加,液膜破碎模式由曲张表面波主导的破碎变为穿孔破碎。此外,清晰获得了自激振荡过程,分析了缩进室内部压力场及速度场分布特征,研究发现随着气体压降增加,气体环缝出口会出现膨胀波和激波,形成一个“扇环形”的超声速流场区域；激波后气流分离,出现旋涡,形成局部高压区,旋涡中心随激波面周期性地上下移动,致使局部压力出现周期性振荡。     

A multi-fidelity simulation method research on front variable area bypass injector of an adaptive cycle engine

Front Variable Area Bypass Injector(Front-VABI) is a component of the Adaptive Cycle Engine(ACE) with important variable-cycle features. The performance of Front-VABI has a direct impact on the performance and stability of ACE, but the current ACE performance model uses approximate models for Front-VABI performance calculation. In this work, a multi-fidelity simulation based on a de-coupled method is developed which delivers a more accurate calculation of the Front-VABI performance based on Comp...     

超临界二氧化碳多槽型干气密封泄漏流动与动力特性研究

针对超临界二氧化碳（SCO₂）旋转机械面临的严重泄漏、气流激振引发转子失稳等问题，以美国通用电气公司10MWe SCO₂循环中高压涡轮的轴端密封为研究对象，设计了螺旋角15°和30°的螺旋槽、T型槽和ST型槽的四种槽型结构的干气密封。采用基于动网格技术和非定常CFD数值方法的微尺度摄动模型，研究了在实验边界条件下干气密封的稳态性能及在轴向简谐微扰动下SCO₂涡轮轴端干气密封的非稳态动力学特性。对比分析了3种动压槽深度、2种动压槽角度下的干气密封泄漏量、静态气膜刚度、动态气膜刚度和阻尼系数。研究表明：四种槽型均满足泄漏流量的设计要求。随着动压槽深度的增加，干气密封开启力与泄漏流量均增大。ST型槽干气密封有着较大的气膜刚度和刚漏比，同时其在面对轴向微尺度摄动时最为稳定，是综合性能最优秀的干气密封结构。     

一种提高GNSS测速精度的自适应Kalman滤波算法

在卫星导航系统动态定位中,采用基于瞬时多普勒观测量的最小二乘法确定速度,当载体高机动时,多普勒误差迅速增大,从而导致测速精度大幅度降低。针对该问题,提出一种同时实现动态模型自适应修正和观测模型自适应更新的Kalman滤波算法。算法采用滑动窗方式来建立实时更新的动态模型参数,使当前统计模型自适应地跟踪载体的动态特性。此外,算法提出观测模型的自适应更新方法,通过设置载体状态判决门限,高、中机动时仅进行受动态应力影响小的伪距更新,低机动下添加精度较高的伪距率更新。通过Sprient GSS8000模拟器产生的动态场景验证表明,相对于最小二乘法和常规Kalman滤波算法,提出的自适应Kalman滤波算法能够全面提高载体在多种运动状态下的测速精度。     

鲁棒Kalman滤波及其在水下组合导航中的应用

在水下捷联惯导(SINS)/多普勒计程仪(DVL)组合导航系统中,当外部辅助信息受到野值等非高斯噪声污染时,选取调节因子γ为固定值将会降低基于Huber方法的鲁棒Kalman滤波(HRKF)算法的精度和鲁棒性。针对此问题,提出了一种基于马氏距离(MD)算法的调节因子自适应的鲁棒Kalman滤波(HRAKF)算法。首先利用MD算法对正常/异常的观测量进行辨识;进而建立γk递推关系式,并根据量测噪声特性对γ值进行实时调整;最后利用γk求取Huber权函数,并对量测噪声阵进行修正。选取8000s船载实测数据,分别利用Kalman滤波(KF)、HRKF及HRAKF算法进行水下组合导航半物理仿真试验。试验结果初步表明:在观测量受到野值或混合高斯分布噪声污染时,相较于KF和HRKF,HRAKF可实现更高精度、更加稳定的组合导航。