Dawei Liu

To investigate the coupled internal–external flow and spillage characteristics of high-bypass-ratio turbofan nacelles, an improved compressor body-force model is developed. Starting from the three-dimensional Navier–Stokes equations, circumferentially averaged body-force source terms are extracted from baseline single-passage simulations. The body-force distribution is then normalized, concentrated circumferentially within blade regions using blade geometric data, and corrected radially using five-hole-probe measurements at the rotor inlet and outlet. By correlating the body force with two local aerodynamic parameters, namely the flow following angle and the axial velocity, the model responds to variations in the intake mass-flow rate and inflow angle. The model is first validated for NASA Rotor 67: the predicted characteristic curves agree with the single-passage RANS results within 0.5% over the entire operating range, and the spanwise distributions of total pressure, total temperature, and relative flow angle deviate by less than 1.5% over the 10–90% blade span, with larger deviations confined to the hub and tip regions. Coupled nacelle–fan simulations are then performed, and a powered nacelle spillage-drag test is conducted in a 2.4 m transonic wind tunnel. At Ma = 0.75, the numerical and experimental spillage-drag results show good agreement, and the predicted spillage-drag coefficient is approximately 0.026, with the nacelle frontal area as the reference area.

With the increasing demand for high-fidelity aerodynamic validation benchmarks for advanced civil aircraft design, this paper presents an open-access, high-Reynolds-number transonic wind tunnel dataset for the CHN-T1 standard large aircraft model, generated from systematic tests conducted in the European Transonic Wind Tunnel (ETW). Tests were performed on the full CHN-T1 configuration with horizontal and vertical tailplanes, under two boundary layer transition settings: fixed artificial transition and free natural transition. The test envelope covers a Mach number range of 0.20–0.90, Reynolds numbers from 3.6 × 10 ⁶ to 37.8 × 10 ⁶ , achieved via pressurization combined with cryogenic cooling down to 115 K and ambient 300 K conditions, with a total of 33 valid aerodynamic polars acquired. The comprehensive dataset includes corrected aerodynamic coefficients, freestream flow conditions and model attitude data. It addresses the scarcity of high-Reynolds-number data and the common lack of lateral-directional and full-configuration measurements in existing public benchmarks. This dataset provides a reliable benchmark for CFD solver validation, wind tunnel flow quality assessment, and the development of data-driven aerodynamic model, supporting the advancement of modern civil aircraft design.

Experimental data at flight-relevant Reynolds numbers remain scarce for publicly available transport aircraft standard models. This study analyses Reynolds number effects on the CHN-T1 large-aspect-ratio configuration using measurements from the cryogenic European Transonic Wind Tunnel, covering Mach 0.20–0.90 and unit Reynolds numbers from 3.6 × 10⁶ to 37.8 × 10⁶. A split transition strategy (fixed at low Re, free at high Re) is employed; the resulting trends are smooth and physically consistent with boundary-layer stability behaviour across the configuration change. At the cruise condition (M = 0.78), the non-induced drag coefficient C_DV follows a power law C_DV ∝ Re⁻ⁿ with n = 0.141, corresponding to about 70% of the canonical turbulent flat-plate exponent n = 1/5. At off-design Mach numbers, the exponent reduces to n = 0.126 (M = 0.60) and n = 0.125 (M = 0.85), reflecting growing pressure and wave drag contributions that scale more weakly with Reynolds number. The longitudinal static stability margin also exhibits a measurable Re dependence at the cruise condition, with the neutral point shifting aft by approximately 1% of the mean aerodynamic chord over the tested range. These findings provide quantitative benchmarks for Re scaling methodologies and CFD validation at flight-relevant conditions.

The unsteady buzz characteristics of a dorsal supersonic bump inlet for a flying-wing configuration are investigated using wind-tunnel tests and three-dimensional unsteady numerical simulations. This study focuses on off-design operation at a freestream Mach number of 1.8, with particular attention to the effects of angle of attack and downstream throttling on shock motion, pressure oscillation, and inlet stability. Wind-tunnel measurements show that the onset and development of buzz are highly sensitive to angle of attack. At high angles of attack, pressure oscillations first appear near the inlet compression surface and subsequently develop into large-amplitude fluctuations at the aerodynamic interface plane. The dominant experimental buzz frequency is approximately 60–70 Hz, and the numerical prediction of 71 Hz agrees well with the measured dominant frequency of 66 Hz. The simulations further reveal a strongly three-dimensional buzz cycle in which asymmetric separation over the bump, spanwise accumulation and discharge of low-energy flow, and alternating inlet blockage and recovery govern the large-amplitude shock excursion. The oscillatory flow field is dominated by shock–system expulsion and ingestion on the spanwise side with stronger back-pressure tolerance, accompanied by the formation of strong and weak shear layers during different stages of the cycle. These results provide insight into the buzz mechanism of dorsal bump inlets and support the assessment of starting performance and stable operating limits for supersonic inlets integrated with flying-wing configurations.

Shock buffet is one of the critical issues affecting the aerodynamic performance, flight quality, and flight safety of large aircraft. To overcome the limitations of traditional experimental measurement methods, such as insufficient capability in capturing flow features and high cost, an integrated experimental system tailored for extreme cryogenic and high-Reynolds-number conditions is developed based on the conventional tuft technique. This system comprises “preparation of low-flow-disturbance fluorescent mini-tufts, high-efficiency large-area tuft taping, automatic generation of digital streamline, and flow topology analysis”. Furthermore, a technique for assessing the transonic shock buffet onset using dynamic flow visualization with fluorescent mini-tufts is proposed. This paper takes a typical supercritical airfoil as the research object. First, through high-precision numerical simulations, it reveals that low-energy, unstable boundary-layer separation is the core driving force for the development and maintenance of shock buffet, and that flow separation characteristics serve as an important basis for determining the shock buffet onset. Subsequently, experimental validation is conducted in a 0.3 m high-Reynolds-number transonic wind tunnel. Using a dual-excitation-band composite light source, simultaneous measurements of pressure-sensitive paint (PSP) and fluorescent mini-tuft patterns are realized. The experimental results show that under extreme conditions, characterized by a wide total temperature range of 110 K to 280 K and strong scouring at Mach numbers from 0.6 to 0.9, the fluorescent mini-tufts (approximately 0.05 mm in diameter) exhibit excellent flow-following capability without any detachment. The digitized flow patterns of the fluorescent mini-tufts, obtained via computer image recognition algorithms, clearly reveal the location and area of boundary-layer separation. The trends show good agreement with the cryogenic PSP results, providing an important reference for determining the shock buffet onset.

The S-shaped inlet is increasingly used in modern aviation for its compact layout and stealth benefits, but its complex geometry induces strong pressure gradients and secondary flows that impact performance. Existing studies on S-shaped inlets are mostly based on the rigid-wall assumption, neglecting deformation of lightweight structures under aerodynamic loads and their feedback effects on the flow field. This study investigates fluid–structure interaction (FSI) effects using a scale-adaptive simulation (SAS) with the Spalart–Allmaras turbulence model, coupled with a finite element structural solver via a bidirectional tightly coupled approach. Numerical simulations compare rigid and elastic S-shaped inlets, analyzing the influence of Mach number (0.2–0.8), angle of attack (−4° to 8°), and sideslip angle (0–10°). Results show that wall elasticity alters the internal flow field, delaying secondary flows and inhibiting vortex development. At higher Mach numbers (Ma ≥ 0.6), local supersonic regions and shock waves form in the bend, intensifying separation and increasing total pressure loss and distortion. Angle of attack has limited impact within 0–8°, while sideslip angle induces asymmetric streamwise vortices, redistributing outlet pressure with minimal effect on average performance. These findings offer theoretical guidance for designing S-shaped inlets that account for aeroelastic effects.

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component.

3D bird pose estimation plays a pivotal role in ecological conservation research. However, it remains a formidable challenge due to extensive joint deformation, severe self-occlusion, and the scarcity of 3D ground truth data. Therefore, practical solutions typically rely on accurate 2D keypoint detection from monocular images and subsequent 3D lifting. Although the High-Resolution Network (HRNet) has established a benchmark in 2D pose estimation by preserving high-resolution feature representations, its architecture, which relies on small convolution kernels, faces difficulties in capturing the global long-range dependencies necessary to resolve severe occlusions. To address these deficiencies, the core contributions of this work are summarized as follows: (1) We design a Gated LS-Block with a partial channel gating strategy to decouple channel mixing from spatial mixing, and extract global long-range dependencies via the proposed Large–Small Convolution (LSConv) to minimize feature redundancy. (2) We embed this block into Stage 2 of HRNet, enhancing multi-scale feature learning while slightly reducing model parameters and computational overhead; (3) To alleviate the ill-posed nature of monocular 3D lifting without paired supervision, we develop an unsupervised 3D reconstruction algorithm. Experimental results on the Animal Kingdom dataset demonstrate that our method achieves a 0.9% improvement in PCK@0.05 while reducing GFLOPs by 3.3%. These results verify that the proposed architecture enhances the model’s representation capability for bird poses while ensuring efficient inference. Meanwhile, we validate the applicability of the proposed 3D reconstruction algorithm via qualitative experiments, and further demonstrate that our unsupervised 3D lifting algorithm successfully preserves low symmetry error and robust bone length consistency with proxy metrics.