In this paper, an adaptive sparse arbitrary polynomial chaos expansion (PCE) is first proposed to quantify the performance impact of realistic multi-dimensional manufacturing uncertainties. The Stieltjes algorithm is employed to generate the PCE basis functions concerning geometric variations with arbitrary distributions. The basis-adaptive Bayesian compressive sensing algorithm is introduced to retain a small number of significant PCE basis functions, requiring fewer model training samples while preserving fitting accuracy. Second, several benchmark tests are used to verify the computational efficiency and accuracy of the proposed method. Eventually, the coexistence effects of six typical machining deviations on the aerodynamic performance and flow fields of a controlled diffusion compressor cascade are investigated. The probability distributions of the machining deviations are approximated by limited measurement data using kernel density estimation. By uncertainty quantification, it can be learned that the mean performance seriously deteriorates with increasing incidences, while the performance at negative incidences is more dispersed. By global sensitivity analysis, the leading-edge profile error should be given high priority when working at negative incidences, and the inlet metal angle error would be carefully inspected first when the cascade works at high positive incidences. Furthermore, controlling the manufacturing accuracy of the suction surface profile error can play a certain role in improving the robustness of aerodynamic performance in off-design conditions. Through flow field analysis, it further proves that actual leading-edge errors are the most important ones to aerodynamics and reveals how the effects of leading-edge errors propagate in the cascade passage, thus affecting the aerodynamic loss.

Compressed air energy storage systems must promptly adapt to power network demand fluctuations, necessitating a high surge margin in the compression system to ensure safety. It is challenging to completely eliminate blade geometric variations caused by limited machining precision, the important effects of which should be considered during aerodynamic shape design and production inspection. The present paper explores the uncertainty impact of geometric deviations on the stability margin of a multi-stage axial compressor at a low rotational speed. Initially, an adaptive polynomial chaos expansion-based universal Kriging model is introduced, and its superior response performance in addressing high-dimensional uncertainty quantification problems is validated through rigorous analytical and engineering tests. Then, this model is used to statistically evaluate the stability margin improvement (SMI) of the compressor due to the Gaussian and realistic geometric variabilities separately. The results show that the mean and standard deviation of SMI are −0.11% and 0.5% under the Gaussian geometric variability, while those are 0.33% and 0.39% under the realistic variability. For both the geometric variabilities, the stagger angle and maximum thickness deviations of the first-stage rotor are the most influential parameters controlling the uncertainty variations in the stability margin. Finally, the underlying impact mechanism of the influential geometric deviations is investigated. The variation in the stability margin caused by the geometric deviations primarily results from the alteration of inlet incidences, affecting the size of the tip leakage vortex blockage and boundary-layer separation regions near the blade tip of the first-stage rotor.

Flow variations at the inlet boundary due to the compressor operational condition changes and geometric variations of the realistic compressor blades due to the manufacturing variability cannot be absolutely avoided, the global and local performance impact of which requires to be considered in the mechanism study of performance change and the aerodynamic shape design. In this paper, a method to analyze the simultaneous impact of the inflow Mach number, inlet incidence and geometric uncertainties was proposed. To make the uncertainty modeling of geometric variations faster and closer to engineering practice, a parametric mathematical model based on scanning points on the blade was introduced to describing the profile and torsion errors in the method specially. Meanwhile, a sparse grid-based Non-Intrusive Polynomial Chaos (NIPC) was used for uncertainty quantification and uncertainty sensitivity analysis to alleviate the computational burden. Then, the method was combined with a loss source calculation method to estimate the global and local aerodynamic loss changes of a controlled diffusion compressor blade in a reference flow state of high inflow Mach number and large positive incidence, and the response performance of sparse grid-based NIPC was verified. The results show that inlet incidence and torsion error have a significance uncertainty effect on the boundary-layer separation above the suction surface, which is main reason for the fluctuation of global aerodynamic loss. The uncertainty effect of profile error on the boundary-layer separation is relatively weak, but profile error could have a certain uncertainty effect on the leading edge separation. The boundary-layer separation is insensitive to Inflow Mach number. Furthermore, a stochastic aerodynamic analysis in different reference inflow states was investigated, which reveals some laws that the uncertainty of the aerodynamic losses and the sensitivity of the inflow and geometric uncertainties change with reference inflow states.

It is essential for engineering manufacture and robust design to evaluate the impact of manufacturing variability on the aerodynamics of compressor blades efficiently and accurately. In the paper, a novel quadratic curve approximation method based on the scanning points of blade design profiles was introduced and combined with Karhunen–Loève expansion, a mathematical dimensionality reduction method for modeling manufacturing variability as truncated Normal process was proposed. Subsequently, Sparse Approximation of Moment-based Arbitrary Polynomial Chaos (SAMBA PC) and computational fluid dynamics (CFD) were applied to build a computational framework for stochastic aerodynamic analysis considering manufacturing variability. Finally, the framework was adopted to evaluate the aerodynamic variations of a high subsonic compressor cascade under the design incidence. The results illustrate that the SAMBA PC method is more efficient than the traditional methods such as Monte Carlo simulation (MCS) for stochastic aerodynamic analysis. Through uncertainty quantification, the impact of manufacturing variability on the global aerodynamic performance is primarily reflected in the fluctuation of aerodynamic losses, and the fluctuation of the total losses is mainly contributed by the fluctuation of the separation loss after the suction peak (a negative pressure spike near the leading edge (LE)) and the boundary-layer loss on the suction surface (SS). With sensitivity analysis, the most important geometric modes to aerodynamics can be revealed, which provides a useful reference for manufacturing inspection process and helps reduce computational cost in robust design.

Considering the dynamic RCS characteristics of stealthy UAVs, we proposed a stealthy UAV path planning algorithm based on the Double Deep Q-Network (DDQN). By introducing the reinforcement learning model that can interact with the environment, the stealth UAV adjusts the path planning strategy through the rewards obtained from the environment to design the optimal path in real-time. Specifically, by considering the effect of RCS from different angles on the detection probability of the air defense radar, the stealth UAV realizes the iterative optimization of the path planning scheme to improve the reliability of the penetration path. Under the guidance of a goal-directed composite reward function proposed, the convergence speed of the stealth UAV path planning algorithm is improved. The simulation results show that the stealth UAV can reach the target position with the optimal path while avoiding the threat zone.