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61.
《中国航空学报》2020,33(7):1867-1876
In order to compensate for the disturbance of wide variation in rotor demanded torque on power turbine speed and realize the fast response control of turboshaft engine during variable rotor speed, a cascade PID control method based on the acceleration estimator of gas turbine speed (Ngdot) and rotor predicted torque feedforward is proposed. Firstly, a two-speed Dual Clutch Transmission (DCT) model is applied in the integrated rotor/turboshaft engine system to achieve variable rotor speed. Then, an online estimation method of Ngdot based on the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) is proposed for power turbine speed cascade control. Finally, according to the cascade PID controller based on Ngdot estimator, a rotor demanded torque predicted method based on the Min-batch Gradient Descent-Neural Network (MGD-NN) is put forward to compromise the influence of rotor torque interference. The simulation results show that compared with cascade PID controller based on Ngdot estimator and the one combined with collective pitch feedforward control, the novel control method proposed can reduce the overshoot of power turbine speed by more than 20%, which possesses faster response, superior dynamic effect and satisfactory robustness performance. The control method proposed can realize the fast response control of turboshaft engine with variable rotor speed better. 相似文献
62.
空域复杂度评估作为衡量空域运行态势、管制员工作压力的关键手段,是运行调控的基础。由于影响因素众多,不同因素间耦合关联复杂,且标定样本很难获取,空域复杂度的准确评估被公认为航空领域的一个挑战性问题。提出了一种空域复杂度的无监督评估方法。首先通过核主成分分析挖掘原始样本各维度的非线性耦合关系,准确提取能够最大化复杂度评估信息量的主成分,进一步设计了可按需定制的主成分聚类方法,实现了无监督条件下空域复杂度的准确评估,为空域划分、流量管理提供了有效的技术支撑。 相似文献
63.
《中国航空学报》2020,33(6):1573-1588
An efficient method employing a Principal Component Analysis (PCA)-Deep Belief Network (DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study. In order to reduce the number of design variables for aerodynamic optimizations, the PCA technique is implemented to the geometric parameters obtained by parameterization method. For the purpose of predicting aerodynamic parameters, the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output, and it is trained using the k-step contrastive divergence algorithm. The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils, and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models. Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization (PSO) framework, and applied to the robust aerodynamic design optimizations of Natural Laminar Flow (NLF) airfoil and transonic wing. The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing. By employing the PCA-DBN-based surrogate model, the developed efficient method improves the optimization efficiency obviously. 相似文献
64.
65.
地磁场信息量越大的区域,导航性能越好。借用数理统计和信息论中的概念,设定了多个评价地磁场信息特点的指标,并通过主成分分析法,排除了相关指标,得到综合指标值,客观、定量地分析了适配区的导航性能,从而选择最佳导航适配区。仿真表明,本文方法是一种选择地磁导航工作区的有效方法,选择的适配区导航误差小。 相似文献
为从受谐波和随机噪声干扰的振动信号中提取出故障冲击成分,融合四大基本形态学算子提出了改进形态滤波方法--平均组合差值形态滤波(ACDIF)方法,同时与固有时间尺度分解(ITD)相结合,并将ITD-ACDIF方法应用到滚动轴承的故障诊断中。首先,对轴承振动信号进行ITD分解得到一系列旋转分量(PRC);然后,以峭度为准则筛选出含故障信息丰富的有效PRC,对每个有效分量进行ACDIF滤波提取冲击成分进行信号重构;最后,利用频谱分析提取重构信号中的故障特征。数值仿真和轴承故障振动信号的试验结果表明,本文方法可有效滤除谐波干扰,提取强背景噪声下的冲击故障特征,实现设备的故障诊断。 相似文献
67.
基于正激波位置计算的进气道/发动机系统实时仿真模型 总被引:2,自引:0,他引:2
研究了超声速条件下,基于进气道内正激波位置计算的进气道/发动机系统综合仿真问题。提出了将超声速进气道分为内、外两部分独立计算的方案,外部通过斜激波计算公式获得进气道内部的边界条件,对进气道内部采用准一维CFD模型模拟背压对正激波位置的影响。相比二维CFD计算,在保证参数精度要求的前提下有效缩短了计算时间,提高了仿真实时性。进一步将进气道模型与发动机部件级模型匹配运行,构建了进气道与发动机综合实时仿真模型。进气道/发动机综合模型的仿真表明,其能够准确模拟进气道正激波位置对涡扇发动机的动稳态影响,当来流发生扰动时,通过喷管喉道面积和压缩部件导叶角的快速调节,能够抑制正激波位置的变化,并使推力下降量减少50%。 相似文献
68.
69.
针对单参数驱动的涡扇发动机性能退化预测精度不高的问题,提出了一种基于气路参数融合的涡扇发动机性能退化预测的方法。通过监测发动机性能退化过程中多源参数,采用专家经验和核主成分分析相结合的方法,进行发动机性能参数的选择和融合,从而构建健康参数。基于非线性Wiener过程构建涡扇发动机退化模型,采用极大似然方法求得发动机退化模型的离线参数估计值;由于不同发动机性能退化的差异性,基于贝叶斯更新理念对随机参数进行实时更新,可以实现对单台发动机的性能退化实时预测。通过实例验证,采用此方法在预测末端方均根误差为0.028 3,整体预测精度提升了54.5%,可以辅助指导维修决策。 相似文献
70.
《中国航空学报》2021,34(6):162-177
In the manufacturing of thin wall components for aerospace industry, apart from the side wall contour error, the Remaining Bottom Thickness Error (RBTE) for the thin-wall pocket component (e.g. rocket shell) is of the same importance but overlooked in current research. If the RBTE reduces by 30%, the weight reduction of the entire component will reach up to tens of kilograms while improving the dynamic balance performance of the large component. Current RBTE control requires the off-process measurement of limited discrete points on the component bottom to provide the reference value for compensation. This leads to incompleteness in the remaining bottom thickness control and redundant measurement in manufacturing. In this paper, the framework of data-driven physics based model is proposed and developed for the real-time prediction of critical quality for large components, which enables accurate prediction and compensation of RBTE value for the thin wall components. The physics based model considers the primary root cause, in terms of tool deflection and clamping stiffness induced Axial Material Removal Thickness (AMRT) variation, for the RBTE formation. And to incorporate the dynamic and inherent coupling of the complicated manufacturing system, the multi-feature fusion and machine learning algorithm, i.e. kernel Principal Component Analysis (kPCA) and kernel Support Vector Regression (kSVR), are incorporated with the physics based model. Therefore, the proposed data-driven physics based model combines both process mechanism and the system disturbance to achieve better prediction accuracy. The final verification experiment is implemented to validate the effectiveness of the proposed method for dimensional accuracy prediction in pocket milling, and the prediction accuracy of AMRT achieves 0.014 mm and 0.019 mm for straight and corner milling, respectively. 相似文献