基于特征模型的高超声速飞行器的自适应姿态控制

研究了基于特征模型的自适应控制方法在高超声速飞行器姿态控制中的应用。对一类非线性强耦合以及大范围参数不确定性的类X-20高超声速飞行器无动力滑翔段六自由度动力学模型,建立含耦合的多输入多输出(MIMO)特征模型,并设计基于特征模型的黄金分割控制器。控制律设计中通过对非对角元控制量强制一步滞后,使得各通道控制量可以计算简单。最后通过飞行高度和速度大范围变化的长时间飞行过程中姿态角的跟踪控制仿真,验证了控制器的鲁棒性和适应性。     

Rescheduling of observing spacecraft using fuzzy neural network and ant colony algorithm

This paper aims at rescheduling of observing spacecraft imaging plans under uncertainties. Firstly, uncertainties in spacecraft observation scheduling are analyzed. Then, considering the uncertainties with fuzzy features, this paper proposes a fuzzy neural network and a hybrid rescheduling policy to deal with them. It then establishes a mathematical model and manages to solve the rescheduling problem by proposing an ant colony algorithm, which introduces an adaptive control mechanism and takes advantage of the information in an existing schedule. Finally, the above method is applied to solve the rescheduling problem of a certain type of earth-observing satellite. The computation of the example shows that the approach is feasible and effective in dealing with uncertainties in spacecraft observation scheduling. The approach designed here can be useful in solving the problem that the original schedule is contaminated by disturbances.     

弹射加速度激光多普勒校准装置的改造

介绍了弹射加速度激光多普勒校准装置改造的内容,分析了改造后的新校准装置的测量不确定度。     

直流增益直流偏移的不确定度评价

介绍了最小二乘法评价直流增益和直流偏移两项指标以及不确定度分析和评价过程,同时给出了一个分析评价实例,该过程及结论可应用在对于测量标准进行相应指标的不确定度分析,也可用于估计指标本身的不确定度。     

误差与不确定度

测量误差与测量不确定度,是计量学中的两个重要基本概念。随着现代科技,工业和商贸的发展,而用测量不确定度来表示测量结果的可靠程度越来越被认可和接受。讨论误差测量与测量不确定度,阐明以不确定度表示测量结果的必然性。     

Quantification of parametric uncertainty in γ-Reθ model for typical flat plate and airfoil transitional flows

Owing to the lack of physical knowledge of boundary layer transition, the γ-Re_θ transition model introduces closure parameters, which increase the uncertainty of transition prediction. The objective of this work is to quantify the uncertainties of closure parameters in the quantities of interests and identify the key parameters. The six closure parameters in the uncertainty intervals are used as input variables, and the uncertainties of the output results are propagated by a stochastic expansion based on the point-collocation nonintrusive polynomial chaos method. The relative contribution of each parameter to uncertainty is evaluated by the Sobol index. The computational cases include natural and bypass transitional flows on zero-pressure-gradient flat plates, and subsonic and transonic flows around airfoils. For most cases, c_e2, c_a2, and c_a1 dominate the uncertainty, and the influence of σ_θt is also significant when the history effects of flow are evident. The contribution of parameters in airfoils is more complex than that in flat plates. The transonic airfoil case shows that flow separation dramatically changes the distribution of Sobol indices, which poses a challenge to the accurate prediction of transition. Generally, c_e2 and c_a2 are the key parameters of the γ-Re_θ model.     

A gradient-based method assisted by surrogate model for robust optimization of turbomachinery blades

The design optimization taking into account the impact of uncertainties favors improving the robustness of the design. A Surrogate-Assisted Gradient-Based (SAGB) method for the robust aerodynamic design optimization of turbomachinery blades considering large-scale uncertainty is introduced, verified and validated in the study. The gradient-based method is employed due to its high optimization efficiency and any one surrogate model with sufficient response accuracy can be employed to quantify the nonlinear performance changes. The gradients of objective performance function to the design parameters are calculated first for all the training samples, from which the gradients of cost function can be fast determined. To reveal the high efficiency and high accuracy of SAGB on gradient calculation, the number of flow computations needed is evaluated and compared with three other methods. Through the aerodynamic design optimization of a transonic turbine cascade minimizing total pressure loss at the outlet, the SAGB-based gradients of the base and optimized blades are compared with those obtained by the Monte Carlo-assisted finite difference method. Moreover, the results of both the robust and deterministic aerodynamic design optimizations are presented and compared to demonstrate the practicability of SAGB on improving the aerodynamic robustness of turbomachinery blades.     

Robust spline-line energy management guidance algorithm with multiple constraints and uncertainties for solid rocket ascending

For the solid rocket with depletion shutdown system, effective energy management is significant to meet terminal constraints by exhausting excess energy. Several traditional energy management algorithms cannot satisfy the altitude constraint and path constraints are not sufficiently considered. The velocity adjustment capability of these algorithms is limited and the uncertainties are not considered. Based on the on-line programming of velocity capability curve, Spline-Line Energy Management(SLE...     

Uncertainty analysis of turbulence model in capturing flows involving laminarization and retransition

Flows experiencing laminarization and retransition are universal and crucial in many engineering applications. The objective of this study is to conduct an uncertainty quantification and sensitivity analysis of turbulence model closure coefficients in capturing laminarization and retransition for a rapidly contracting channel flow. Specifically, two commonly used turbulence models are considered: the Spalart-Allmaras (SA) one-equation model and the Menter Shear Stress Transport (SST) two-equation model. Thereby, a series of steady Reynolds Averaged Navier-Stokes (RANS) predictions of aero-engine intake acceleration scenarios are carried out with the purposely designed turbulence model closure coefficients. As a result, both SA and SST models fail to capture the retransition phenomenon though they achieve pretty good performance in laminarization. Using the non-intrusive polynomial chaos method, solution uncertainties in velocity, pressure, and surface friction are quantified and analyzed, which reveals that the SST model possesses much great uncertainty in the non-laminar regime, especially for the logarithmic law prediction. Besides, a sensitivity analysis is performed to identify the critical contributors to the solution uncertainty, and then the correlations between the closure coefficients and the deviations of the outputs of interest are obtained via the linear regression method. The results indicate that the diffusion-related constants are the dominant uncertainty contributors for both SA and SST models. Furthermore, the remarkably strong correlation between the critical closure coefficients and the outputs might be a good guide to recalibrate and even optimize the commonly used turbulence models.     

Bayesian uncertainty analysis of SA turbulence model for supersonic jet interaction simulations

The Reynolds Averaged Navier-Stokes(RANS) models are still the workhorse in current engineering applications due to its high efficiency and robustness. However, the closure coefficients of RANS turbulence models are determined by model builders according to some simple fundamental flows, and the suggested values may not be applicable to complex flows, especially supersonic jet interaction flow. In this work, the Bayesian method is employed to recalibrate the closure coefficients of Spalart-Allma...