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.     

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...     

低速巡飞弹非匹配干扰观测控制器设计

针对低速巡飞弹倾斜转弯控制系统的非线性、强耦合、不确定的特征,研究了一种基于干扰观测器的自适应反演控制方法。将参数摄动、外界干扰和执行机构的响应偏差分别等效进成系统的匹配干扰和非匹配干扰,并设计在有限时间内估计误差收敛的非线性干扰观测器进行估计。利用反演控制的思想,在虚拟控制量中抵消非匹配干扰,在实际控制量中抵消匹配干扰。利用李雅普诺夫理论设计自适应调节律补偿干扰观测器的估计误差,改进控制系统的瞬态性能。仿真结果表明,干扰观测器估计误差在有限时间内收敛到一定区间,系统能够有效地克服干扰的影响,快速准确地跟踪滚转角、攻角、侧滑角参考指令。     

叶顶间隙偏差对叶片气动性能影响的不确定性研究

在压气机叶片加工过程中，实际加工得到的叶片外形与设计叶型不可避免会存在一些偏差。为研究叶片叶顶间隙尺寸波动对性能的影响，以Rotor37为研究对象，采用三维定常数值模拟方法，基于非嵌入式混沌多项式中二维正态分布变量产生方法，分别对叶片前缘叶顶间隙和尾缘叶顶间隙进行尺寸波动干扰，研究了叶顶间隙不确定性对气动性能和流场的不确定性影响，评估了公差带内叶顶间隙偏差与气动性能变化的相关性，并探究了叶顶间隙变化对叶片稳定裕度的影响机理。研究发现，叶顶间隙偏差对等背压条件下叶片质量流量、等熵效率和总压比的平均水平几乎不会造成影响；叶顶间隙偏差所造成的气动性能分布标准差随工况点向不稳定边界的逼近而逐渐增大。同时，叶顶间隙不确定性会导致样本稳定裕度均值有所降低，不确定性分析中稳定裕度均值相对原型叶片下降了2.42%。流场结构方面，叶顶间隙偏差主要影响80%叶高以上部分流场；叶顶间隙偏差对叶顶区域泄漏流产生的影响，导致了叶片稳定裕度的变化。     

大气层内固体火箭多约束鲁棒三维能量管理制导

针对固体运载火箭大范围精确调节终端约束的要求，提出一种新型的大气层内鲁棒三维能量管理制导方法，通过在线规划侧向速度能力曲线消耗剩余发动机能量。将终端约束表示为关于攻角和速度能力曲线参数的方程组，将闭环制导问题转化为方程组的求解。针对飞行过程中的动压、过载，以及控制变化率等过程约束，构造了攻角和速度能力曲线的可行边界。针对气动系数和发动机参数的不确定性，采用容积卡尔曼滤波器对不确定性进行辨识。仿真结果表明，与模型预测静态规划算法和改进粒子群算法相比，本算法的终端速度调节范围、鲁棒性以及计算效率大幅度提高。     

Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance

Inevitable geometric variations significantly affect the performance of turbines or even that of entire engines; thus, it is necessary to determine their actual characteristics and accurately estimate their impact on performance. In this study, based on 1781 measured profiles of a typical turbine blade, the statistical characteristics of the geometric variations and the uncertainty impact are analyzed, and some commonly used uncertainty modelling methods based on PrincipalComponent Analysis(PCA)...     

Adjoint-based robust optimization design of laminar flow wing under flight condition uncertainties

It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions. In order to seek a more robust natural laminar flow control effect, it is necessary to develop an effective optimization design method. Meanwhile, attention must be given to the impact of crossflow(CF) instability brought on by the sweep angle. This paper constructs a robust optimization design framework based on discrete adjoint methods ...     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).