Jia Fu

Research on hypersonic crossflow transition holds significant engineering and scientific importance. This paper investigates the impact of distributed roughness elements (DREs) on crossflow transition for a cone set at a 6° angle of attack, using experimental methods. The research was conducted in the Mach 6 wind tunnel, employing temperature-sensitive paint (TSP) as the measurement technology. Two types of nosetip were examined: a sharp nosetip with a radius of 0.1 mm and a blunt nosetip with a radius of 2.5 mm. The circumferential wavenumbers of the DREs on the nosetip included k = 35, k = 50, and k = 70. The results indicate that the nosetip with DREs at k = 50 has a more pronounced effect in promoting boundary layer transition to turbulence on the leeward side of the cone compared to the nosetips with DREs at k = 35 and k = 70. However, all three types of DREs exhibit similar effects on transition on the windward side. Additionally, the bluntness of the nosetip, at R = 2.5 mm, diminishes the effectiveness of DREs in promoting transition; however, the degree of diminished effectiveness varies with the circumferential azimuth.

Hypersonic crossflow transition typically occurs on the windward side of gliding aircraft, making the study of crossflow instability both scientifically and engineering-wise valuable. In our previous research [Niu et al., Phys. Fluids 36, 124133 (2024)], the early evolution of traveling crossflow instability on a 75° swept flat plate at Mach 6 was studied. In the current paper, we investigate the nonlinear development of crossflow transition during its later stages. The experimental studies were conducted in a Mach 6 quiet wind tunnel, employing nano-tracer-based planar laser scattering (NPLS) technique to capture boundary layer flow structures in the streamwise direction. These results were subsequently analyzed quantitatively using wavelet tools. The findings indicate that the boundary layer experiences significant nonlinear development during the later stages of crossflow transition. Throughout the transition process, the fundamental wavelength of the peak signal, denoted as λ0, is approximately 20 mm. In the zones at z = 40–60 mm, harmonics with wavelengths of 2λ0 and 3λ0 emerge. As the breakdown of the boundary layer begins (z > 60 mm), these harmonics diminish, and new harmonics with wavelengths of 12λ0 and 13λ0 increase in prominence, corresponding to characteristic frequencies of 2f0 and 3f0. This behavior aligns with observations that Mack modes dominate the transition during the later stages.

The early evolution of the traveling crossflow instability on a flat plate with a sweep angle of 75° is experimentally investigated. The experiments are conducted in a Mach 6 quiet wind tunnel through the nano-tracer-based planar laser scattering (NPLS) technique under the unit Reynolds number of 3.45 × 106 m−1. The spanwise and streamwise structures of the traveling crossflow waves are captured, and the wavelength and frequency characteristics of the traveling crossflow waves are quantitatively analyzed based on wavelet analysis. The results show that with the increase in z-coordinate and x-coordinate, the distribution range of the traveling crossflow waves in y-direction expands, the amplitude of disturbance waves increases, and saturated crossflow wave structures appear gradually. The wavelength of the crossflow waves is about 24 mm and does not change much in different positions. In addition, the propagation velocity of the crossflow waves along the x-direction at the position of x = 130 mm and z = 60 mm is quantitatively calculated, U = 303 m/s. Thus, the characteristic frequency of the crossflow waves obtained from the NPLS image is f≈12 kHz, close to 9.57 kHz measured by Kulite at the same position.

The supercritical CO ₂ Brayton cycle (S-CO ₂ BC) has attracted increasing attention because of the advantages of high efficiency, better compatibility, and safety, and to further improve the power density, turbine alternator compressor (TAC) integrated high-speed permanent magnet synchronous machine (HPMSM) sets are usually used in S-CO ₂ BC power generation devices. In terms of safe operation, it is of great significance to accurately and quickly obtain the temperature in the coupling state of the machine and magnetic bearings. Taking a 40000rpm HPMSM set used in S-CO ₂ BC power generation devices as an example, a new equivalent thermal model of winding is proposed firstly, and the temperature difference between the equivalent model and the exact model is less than 1.7%. On this basis, the lumped parameter thermal network (LPTN) model of the HPMSM set under the coupling state of the machine and magnetic bearings in totally enclosed environment is established. Finally, the steady and transient temperature results of the HPMSM set are obtained, which are verified by computational fluid dynamics (CFD) method. The range of absolute temperature error between LPTN model and CFD model is 0.2°C~2.2°C in steady state, and the relative temperature error is less than 4.2% in transient state. Furthermore, the computational efficiency of LPTN model is greatly improved compared with CFD model. The research of this paper is of great significance to temperature analysis and prediction under the coupling state of machine and magnetic bearings.

Fatigue is a feeling of physical and mental tiredness caused by long hours of work, study, or physical activity. Fatigue affects thinking, decision-making and creativity, reducing productivity and performance. Fatigue also increases the risk of errors and accidents. In order to further perceive the fatigue status of power workers and avoid serious problems caused by fatigue. We comprehensively considered the characteristics of various physiological signals, finally selected electroencephalogram signals and electrooculogram signals, and proposed a fatigue state detection method based on multiple physiological signals through data fusion and balancing processing. Experimental results show that this method can allow multiple physiological signals to work together, effectively identify fatigue states, and have higher accuracy and recall rates than previous studies in fatigue detection tasks.

Conventionalpermanent magnet (PM) machine temperature assessment approaches require physical, material, and loss distribution information. In practice, accurate structural information is rarely available, a lot of thermal parameters can only be calibrated based on extensive experiments, and the determination of various machine losses is also a demanding task. To solve these problems, an online temperature prediction method is proposed in this article. A general mathematical model that describes the relationship between machine temperature and loss variation is derived through analyzing a PM machine three-dimensional lumped parameter thermal network. With distributed sensors, this model can be determined purely from the measured data, which eliminates the need to acquire machine physical and material information. The analyses of loss distribution are also avoided by utilizing two measured temperatures. The evolution of all sensors can be concerned, so the prediction of the time and spatial distribution of temperature can be realized. The proposed method is validated by the experiments processed on a 10-kW PM machine under various working conditions. The influences of key parameters and the comparison with a neural network model are also demonstrated. The results suggest that the proposed method has the advantage of possessing closed-form expression and being computationally efficient.