The role of unmanned aerial systems (UAS) is becoming increasingly important in aerospace development across a range of disciplines. The integration of complete engines into aircraft fuselages, along with highly curved intake ducts, offers significant advantages due to their compact size and minimal impact on structural weight. A compact intake design can lead to substantial flow distortions and negatively affect the performance and flow stability of the engine. Today’s industry practice for flow distortion quantification in propulsion system integration relies on isolated pressure measurements at the engine front. Recent studies have shown that this low-accuracy approach is not adequate to complex future aircraft designs, as these assessment methods cannot capture pertinent features of flow distortion. Traditional distortion descriptors like the DC60 can provide a simplified total pressure distortion based on a 60-degree weighted angular section. More complex distortion patterns beyond the coverage of these sections are underrated. The phenomena arising from the instability of secondary flows cannot be captured adequately by traditional descriptors. Frequencies associated with certain flow distortion phenomena significantly affect the engine fan’s response. This study uses additional newly defined flow distortion descriptors to close these gaps, such as the area integral of the secondary kinetic energy or the resulting load of the wall pressure distribution at the aerodynamic interface plane. Using the coefficient for the secondary vorticity also makes it possible to identify the dominant frequencies of the vortex systems that occur in the coupled intake system and affect the engine. The coupling shifts the first mode of the central flow separation from 15.7 to 25.1 Hz and introduces a new 75.2 Hz mode. A dominant 150.4 Hz mode, close to the measured 153 Hz is also found. The results demonstrate that advanced, time-resolved distortion metrics enable an effective analysis of intake–compressor interaction and are therefore essential for evaluating highly integrated propulsion systems.
The windmill condition occurs after the in-flight shutdown of a turbofan, when the fan continues to spin with reduced rotational speed due to the ram pressure of the oncoming air, causing high loss and drag. This often follows damage to the fan blades, which leads to a further reduction of the rotational speed as well as out-of-balance loads and potentially harmful vibrations of the aircraft. Knowledge of the fan windmill rotational speed and drag at early stages of the engine design is crucial, as the aircraft needs to be designed to withstand the high drag and out-of-balance loads. In this paper, the windmill condition is studied for cases where sectors of the fan are damaged following a fan blade-off event. Experimental measurements in a low speed rig as well as numerical simulations are used to investigate the blade-to-blade flow features, such that differences for blades that are damaged can be observed. Cases with realistic damage are related to idealised damage cases with a similar amount of periodic damage at the blade tip. The rotor operating point for a realistic damage case is shown to depend on the rotor exit relative flow angle and axial velocity. Overall, damage towards the rotor tip is found to have a much higher impact on rotational speed than damage extending towards the hub and a case representing damage following a fan blade-off event causes a decrease in rotational speed of only 5.9%. The aerodynamic losses in the fan rotor are shown to be directly related to the flow blockage, which decreases with increasing damage area. Fan blades adjacent to damaged regions are also found to have a significant impact on the rotor work through the presence of the casing pressure side vortex.
For horizontal take-off and landing reusable hypersonic vehicles, thermal management of aerodynamic stagnation heat at high Mach numbers constitutes the most critical thermal challenge. Usually, the researcher addresses this through an integrated thermal allocation strategy employing lightweight high-efficiency precoolers, which systematically redistribute thermal loads by reducing inlet/outlet temperatures while enhancing energy input to power components, thereby achieving superior energy utilization efficiency. However, the ultra-fine characteristic dimensions (∼1 mm scale) of modern precoolers intensify fluid-thermal coupling effects, rendering conventional design methodologies inadequate with prediction deviations exceeding 30%. To resolve this, we present an inverse design framework based on a prototype sCO2 precooler. Through 15 meticulously designed operational cases encompassing air inlet temperatures around 800 K and CO2/air flow ratios of 1–1.5, we establish a data-informed methodology that synergizes: (1) Experimental characterization of thermal-hydraulic performance (including heat transfer coefficients and pressure drop correlations). (2) Reverse determination of critical design parameters through integration with conventional heat exchanger design protocols. (3) Validation under representative hypersonic flight conditions. Experimental verification demonstrates that the proposed methodology reduces temperature prediction errors to <15% compared to empirical data, while maintaining pressure drop deviations within ±10%. This advancement provides a validated design paradigm for next-generation hypersonic thermal management systems requiring strong multi-physics coupling resolution.
This study investigates the complete stall process of a counter-rotating axial compressor through multi-passage unsteady numerical simulations, integrating the Liutex vortex identification method and reduced-order variational mode decomposition (RVMD) technique. The analysis reveals that the tip secondary vortex (TSV) plays a pivotal role in stall inception. During stall initiation, two TSVs emerge within the tip passage: the upstream TSV generates backflow and undergoes periodic vortex shedding, while the downstream TSV interacts with adjacent blades, triggering leading-edge spillage that produces stall spikes. These TSVs collectively form an axially fluctuating structure with periodic and selfsustained unsteadiness, propagating circumferentially. The accumulation of fluctuation energy disrupts the stability of tip leakage vortex, ultimately causing complete blockage at the rotor 2 tip and initiating compressor stall. Furthermore, the RVMD technique proves effective in identifying non-periodic and transitional features during stall evolution, enabling efficient extraction of dominant flow structures from large-scale unsteady data. To address the high computational complexity of RVMD, singular value decomposition is employed for spatial dimensionality reduction. However, challenges persist in parameter optimization, often leading to over- or under-decomposition of flow characteristics. The findings highlight the dynamic interplay of TSV-driven mechanisms in stall development and demonstrate the potential of RVMD for complex flow diagnostics, while underscoring the need for improved adaptive parameter selection to enhance its practicality. This work provides insights into compressor stall dynamics and advances data-driven methods for turbomachinery flow analysis.
Data-driven approaches are increasingly popular, where reliable datasets being crucial for accurate flow field modeling. However, most existing experimental design methods rely on mathematical approaches that lack physical interpretability. In compressor aerodynamics, severe flow separation represents the physical limits of the dataset. Flow fields with significant separation not only lack computational accuracy but also have limited engineering applicability. Therefore, filtering such samples through flow field classification is essential. The differentiator of this paper lies in applying a data-driven method to classify flow fields during dataset construction. The dataset, containing 889 numerical samples, was labeled into three categories: two-dimensional separation, three-dimensional open corner separation, and no obvious separation. The support vector classification (SVC) algorithm was applied to analyze characteristics and classify flow field structures in compressor stator cascades. The feature analysis reveals that the axial velocity density ratio (AVDR) plays a key role in flow field classification. Validation results demonstrate that the model can rapidly detect severe separation samples, enabling quick qualification of blade profile geometries and operating conditions. This capability significantly reduces computational resource demands, confirming the model’s suitability for both dataset generation and preliminary blade design phases. Furthermore, input features with clear physical relevance, such as secondary flow intensity, enhance model accuracy.
