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Sheet-to-cloud cavitation in a Venturi pipe is investigated experimentally and numerically in this work. The multiscale vapor structures are simulated by Eulerian–Lagrangian multiscale modeling. The volume of fluid method with adaptive mesh refinement is employed to capture the macroscale cavity features, utilizing the large eddy simulation approach. The results of mesh sensitivity study demonstrate that second-order refinement is capable of capturing phase details and cavitation shedding behaviors. A comparison with experimental results reveals the mechanisms of different types of cavitation shedding induced by reentrant jet and shock wave. Taking microscale bubbles into account using the Lagrangian approach and achieving Eulerian–Lagrangian coupling, the multiscale cavitation features, including the morphological evolution of large cavities and characteristics of microbubbles, are accurately reproduced. The evolution features of microbubbles during pressure wave or condensation shock propagation are well recognized by different changing rates. The results indicate that shedding mechanisms in Venturi pipe cavitation can be categorized into three types: reentrant jet, condensation shock, and pressure wave. In the three conditions investigated in this work, shedding cycles are found to be mainly induced by the reentrant jet (σ = 1.03), reentrant jet, condensation shock, and pressure wave (σ = 0.64), and condensation shock (σ = 0.39), respectively, with a decrease in the cavitation number. [ABSTRACT FROM AUTHOR]

The magnetic anomalies generated by the ferromagnetic targets are usually buried within uncontrollable interference sources, such as the power frequency and random noises. In particular, the variability of the geomagnetic field and the low signal-to-noise ratio (SNR) of the magnetic anomalies cannot be avoided. In this paper, to improve the performance of magnetic anomaly detection (MAD) with a low SNR, we propose a novel structured low-rank (SLR) decomposition-based MAD method. In addition, a new framework based on the SLR and singular value decomposition (SVD) is constructed, dubbed SLR-SVD, and the corresponding working principle and implemented strategy are elaborated. Through comparing the SLR-SVD with two state-of-the-art methods, including principal component analysis and SVD, the results demonstrate that the proposed SLR-SVD can not only suppress the noise sufficiently, i.e., improving 55.26% approximately of the SNR, but also retain more boundary information of magnetic anomalies, i.e., decreasing approximately 68.05% of the mean squared error and improving approximately 28.47% of the structural similarity index. [ABSTRACT FROM AUTHOR]

The traditional aeromagnetic compensation model does not consider the negative effects of the gradient of the geomagnetic background field. Especially in emergency aeromagnetic surveys, this gradient effect is obvious and can reduce the performance of magnetic compensation. To solve this problem, we propose a method of reducing the gradient effect of the magnetic sensor for aeromagnetic compensation. A magnetic gradient model of the Earth's background field is established for the aeromagnetic compensation, and the gradient effect is then suppressed using altitude data. An experimental platform was built, and flight tests were conducted. Comparison results show that the adoption of the proposed method increased the improvement ratio (IR) of our compensator on two flights from 20.4 and 21.3 to 25.9 and 26.0, with the growth rates of the IR, respectively, reaching 27% and 22%. [ABSTRACT FROM AUTHOR]