Method for utilizing PIV to investigate high curvature and acceleration boundary layer flows around the compressor blade leading edge

Particle Image Velocimetry (PIV) is a well-developed and contactless technique in experimental fluid mechanics, but the strong velocity gradient and streamline curvature near the wall substantially limits its accuracy improvement. This paper presents a data processing procedure combining conventional PIV and newly developed Mirror Interchange (MI) based Interface-PIV for the measurement of the boundary layer parameter development in the blade leading edge region. The synthetic particle images are used to analyze the measurement errors in the entire procedure. Overall, three types of errors, namely the errors caused by the Window Deformation Iterative Multigrid (WIDIM) algorithm, the discrete data interpolation and integration, and the wall offset uncertainty, comprise the main measurement error. Specifically, the errors due to the discrete data interpolation and integration and the WIDIM algorithm comprise the mean bias, which can be corrected through the error analysis method proposed in the present work. Meanwhile, the errors due to the WIDIM algorithm and the wall offset uncertainty contribute to the measurement uncertainty. Computational fluid dynamics-based synthetic particle flows were generated to verify the newly developed PIV data processing procedure and the corresponding error analysis method. Results showed that the data processing method could improve the accuracy of PIV measurements for boundary layer flows with high curvature and acceleration and even with significant flow separation bubbles. Finally, the data processing method is also applied in a PIV experiment to investigate the boundary layer flows around a compressor blade leading edge, and several credible boundary flow parameters were obtained.     

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.     

A high-safety active/passive hybrid control approach for compressor surge based on nonlinear model predictive control

Surge active control can expand the stable operating range of the compressor. However, the difficulty of flow measurement, dynamic uncertainty disturbance, actuator delay characteristics, hard constraints of control variable, and system security measures have not been fully considered in the existing active control system, which significantly hinders its engineering application. Therefore, a nonlinear model predictive surge active control method is first presented based on flow estimator designed by using a continuous-time Kalman filter for dealing with the hard constraint of control variable and the impact of actuator delay of compression system with dynamic uncertainty. Then, a high-safety active/surge passive hybrid control strategy is designed, dominated by the surge active control and supplemented by the surge passive control, to ensure the compression system’s safe and stable operation. Lastly, the simulation results suggest that the flow estimator accurately estimates the compressor flow. When considering the delay impact of the actuators and sensors and measurement noise on the system, the proposed method exhibits stronger robustness than the existing methods. The active/surge passive hybrid control strategy can successfully ensure the compression system's safe and stable operation. This paper is of high practical significance for the engineering application of future compressor surge active control technologies.     

空间连续型机器人位姿与构型的能量整形控制

针对空间连续型机器人(SCR)位置和姿态机动、连续型机械臂变形控制以及系统受到外界干扰的问题，基于连接与阻尼配置-无源性控制(IDA PBC)方法设计了控制器。通过能量整形得到空间连续型机器人期望的能量函数，再对系统注入阻尼使其渐近稳定。利用非线性干扰观测器估计系统受到的干扰，并对外界干扰进行补偿。仿真结果表明：设计的控制器可以使空间连续型机器人机动到指定的位置和姿态，同时控制连续型机械臂变形为期望的构型，并且具有主动抗干扰的能力。通过选择合适的能量整形系数和阻尼注入系数，可使系统以期望的动态性能到达指定的平衡点。     

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。     

高空试车台供气压缩机组动态仿真建模方法研究

针对国内高空试车台研究中缺少对供气压缩机组动态仿真研究方法的现状，提出了一种适用于该热力系统的动态仿真建模方法。基于守恒方程，获得了压缩机、管网、换热器、调节阀的动态仿真计算模型；将Greitzer压缩机模型应用到复杂热力系统的动态建模中；在容腔模型的基础上增加流动损失计算模块，提出了“容腔-损失”管网模型。在Matlab/Simulink平台上搭建了某试车台供气压缩机组动态仿真模型。将仿真结果与实验结果对比，验证压缩机、管网等部件动态仿真模型的可靠性。其中，压力、温度的仿真结果的平均误差均小于1%；与传统的容腔模型相比，“容腔-损失”管网模型误差明显减小。证明了该建模方法的精确度与可靠性。     

用于预测轴流压气机内角区分离的RANS和SBES方法评估

为了提高压气机内角区分离的RANS建模精度，基于剪应力输运模型（SST模型），本文评估了湍流非平衡和各向异性修正对压气机角区分离预测的影响。结果表明，角区分离区上游端壁二次流以及角区分离流均呈现出很强的湍流非平衡和各向异性行为，结合非平衡和各向异性修正而提出的NSST-Helicity-QCR模型能够在各类工况下给出最为准确的角区分离预测结果。为了进一步验证提出的NSST-Helicity-QCR模型能够合理捕捉压气机端区流动物理，基于一种新型混合RANS-LES方法——应力融合涡模拟（SBES）构建的高保真时间精确湍流数据库，本文对NSST-Helicity-QCR模型进行评估反馈。结果表明，NSST-Helicity-QCR模型合理捕捉了端壁二次流以及角区分离流的湍流非平衡行为，但仍低估了角区分离区内的湍流各向异性行为。