In this paper, a novel slot–groove hybrid casing treatment is designed and optimized to improve the stall margin of a low-speed axial compressor. A combination of the axial slot and circumferential groove casing treatments is utilized to increase the stall margin without incurring efficiency loss. The slot meridional profile is described with 2 B-spline curves. Circumferential grooves are parametrically described with groove height and width. An in-house optimization design platform is constructed based on the nondominated sorting genetic algorithm II and Kriging surrogate model. The optimization objectives are the stall margin and the peak efficiency of the compressor at the design rotating speed. To avoid the large number of unsteady simulations that are required to predict the stall margin, a stall margin improvement indicator is proposed based on the axial momentum budget analysis at the rotor tip region. The performance of the optimal slot–groove hybrid casing treatment design is tested and simulated. The experimental data show that the optimal slot–groove hybrid casing treatment improves the stall margin by 8.42% without generating efficiency loss. The flow details are captured by unsteady simulations and analyzed in depth. The application of the optimal casing treatment enhances the blade tip axial momentum and the interface between tip leakage flow and incoming main flow is pushed downstream. Consequently, the stability of the compressor is improved.

Casing treatment has been widely adopted to enhance the operating range of compressors by extending the stall margin. In this article, a high-precision experimental investigation was conducted in a transonic compressor. Unsteady pressure is measured in the casing wall using a cluster of time-resolved transducers with axial and circumferential spatial resolution. The experimental data show that the optimized axial slot casing treatment improves the stall margin without peak efficiency penalty for all measured speedlines. A comprehensive flow field analysis indicates that the surge boundary of transonic compressor is sensitive to the position of shock wave and tip leakage flow. After the application of optimized axial slot casing treatment, the flow structure can be significantly modified, which specifically manifests as both the mitigation in shock wave detachment and redistribution of tip leakage flow trajectory toward the trailing edge of the blade. Furthermore, power spectral density analysis is conducted to examine the unsteady effect. The amplitude of characteristic frequency band induced by the unsteady fluctuation of tip leakage flow and shock wave is considerably suppressed, which can also be regard as the stability enhancement mechanism of casing treatment.

Radial inlet distortion induced by boundary layer separation can significantly affect the aerodynamic performance of the compressor. The effect of radial inlet distortion on the flow structure is numerically investigated in a high load axial flow compressor. The inlet boundary is determined by the experimental results at the downstream of the distortion generator. The results reveal that tip distortion leads to a reduction in the stall margin, whereas hub distortion extends it. Radial distortion redistributes the main flow toward undistorted region due to the obstructive effect of lattice ring. The deficit in axial velocity with tip radial distortion causes the rotor to operate at a higher incidence angle near the casing, while hub radial distortion alleviates the tip blade loading. The detailed three-dimensional flow field analysis indicates that increased blade loading with tip distortion shifts the trajectories of both primary and secondary tip leakage flow toward the leading edge, thereby expanding the blockage region. Conversely, hub radial distortion unloads the rotor tip region, thereby reducing the blockage region induced by tip leakage flow. Additionally, with hub distortion, the location of separation line on the blade suction surface moves closer to the leading edge, and the flow separation around the trailing edge is intensified.

The influences of axial-slot casing treatment (ASCT) on the performance and unsteady tip region flow are experimentally and numerically investigated in a highly-loaded mixed-flow compressor. The total pressure ratio, stall margin, and efficiency of the compressor are improved with ASCT. Static wall pressure was measured using unsteady pressure taps installed on the casing to identify the stall inception and resolve the tip region flow. The compressor stalls through spike-stall inception with the spillage of tip leakage flow (TLF). FFT analysis of dynamic pressure data shows that unsteady tip clearance flow (TCF) characterized by a frequency band of 0.4–0.6 blade passing frequency exists for both cases with and without ASCT. The addition of ASCT alleviates fluctuating amplitude of TCF in the blade passage but enhances oscillating strength of the main flow located upstream of blade leading-edge. Time-averaged solutions of unsteady simulations indicate that ASCT suppresses the spillage of TLF through suction and injection effects. However, flow separation located downstream of slots is enlarged by ASCT, which is attributed to the increased blade tip load. To reveal the underlying mechanisms, the tip flow field was further studied at various time instants. The fluids with high enthalpy are injected from slots and then divided into two parts by the rotor blade. One part of the fluids attaches on blade pressure side causing the local high static pressure spot, which leads to the increment of pressure loading at blade tip. The other part interacts with TLF periodically, thus pushing it into passage and delaying rotating stall.

Accurate and rapid prediction of compressor performance and key flow characteristics is critical for digital design, digital twin modeling, and virtual–real interaction. However, the traditional methods of obtaining flow field parameters by solving the Navier–Stokes equations are computationally intensive and time-consuming. To establish a digital twin model of the flow field in a transonic three-stage axial compressor, this study proposes a novel data-driven deep attention symmetric neural network for fast reconstruction of the flow field at different blade rows and spanwise positions. The network integrates a vision transformer (ViT) and a symmetric convolutional neural network (SCNN). The ViT extracts geometric features from the blade passages. The SCNN is used for deeper extraction of input features such as boundary conditions and flow coordinates, enabling precise flow field predictions. Results indicate that the trained model can efficiently and accurately reconstruct the internal flow field of the compressor in 0.5 s, capturing phenomena such as flow separation and wake. Compared with traditional numerical simulations, the current model offers significant advantages in computational speed, delivering a three-order magnitude speedup compared to computational fluid dynamics simulations. It shows strong potential for engineering applications and provides robust support for building digital twin models in turbomachinery flow fields.