Shock oscillation with low-frequency unsteadiness commonly occurs in supersonic flows and is a top priority for the control of flow separation caused by shock wave and boundary layer interaction. In this paper, the interaction of the shock caused by the compression ramp and the vortex rings generated by a micro-vortex generator (MVG) in a supersonic flow at Ma = 2.5 is simulated by the implicit large eddy simulation method. The analysis of observation and the frequency of both the vortex ring motion and the shock oscillation is carried out. The results show that the shock produced by a compression ramp flow at Ma = 2.5 has a dominant non-dimensional low frequency, which is around St = 0.002, while the vortex rings behind the MVG have a dominant high frequency which is around St = 0.038. The dominant low frequency of the shock, which is harmful, can be removed or weakened through the shock-vortex ring interaction by the vortex rings which generate high frequency fluctuations. In the shock and vortex ring interaction region, a dominant high frequency St = 0.037–0.038 has been detected rather than the low frequency St = 0.002, which indicates that the vortex ring is stiff enough to break or weaken the shock. This analysis could provide an effective tool to remove or weaken the low frequency pressure fluctuation below 500 Hz, which has a negative effect on the flight vehicle structures and the environmental protection, through the high frequency vortex generation.

Weed detection is critical for precision agriculture, enabling targeted herbicide application to reduce costs and enhance crop health. This study utilized UAV-acquired RGB imagery from cotton fields to develop and evaluate deep learning models for weed detection. As sustainable resource management gains importance in rainfed agricultural systems, precise weed identification is essential to optimize yields and minimize herbicide use. However, distinguishing weeds from crops in complex field environments remains challenging due to their visual similarity. This research employed YOLOv7, YOLOv7-w6, and YOLOv7-x models to detect and classify weeds in cotton fields, using a dataset of 9249 images collected under real field conditions. To improve model performance, we enhanced the annotation process using LabelImg and Roboflow, ensuring accurate separation of weeds and cotton plants. Additionally, we fine-tuned key hyperparameters, including batch size, epochs, and input resolution, to optimize detection performance. YOLOv7, achieving the highest estimated accuracy at 83%, demonstrated superior weed detection sensitivity, particularly in cluttered field conditions, while YOLOv7-x with accuracy at 77% offered balanced performance across both cotton and weed classes. YOLOv7-w6 with accuracy at 63% faced difficulties in distinguishing features in shaded or cluttered soil regions. These findings highlight the potential of UAV-based deep learning approaches to support site-specific weed management in cotton fields, providing an efficient, environmentally friendly approach to weed management.

In this work, a vortex identification method is developed by analyzing the physical meaning of the local rotation of fluid elements. It is shown that a point is locally rotational when the velocity gradient tensor at the point has a pair of complex eigenvalues. The local rotation can be represented by a so called vortex vector. The direction of the vortex vector is defined as that of the local fluid rotation axis, which is parallel to the eigenvector of velocity gradient tensor corresponding to the real eigenvalue. The magnitude is evaluated as the twice of the minimum angular velocity around the point among all azimuth in the plane perpendicular to the vortex vector. Based on the local fluid rotation, a vortex is identified as a connected region where the vortex vector at each point is not equal to zero. The vortex identification method is validated by applying it to Reynolds-averaged Navier-Stokes data and direct numerical simulation data. The results reveal that the method can fully describe the complex structures of vortices.

This study investigated the ability of artificial neural networks (ANNs) to resolve the nonlinear dynamics inherent in the behavior of complex fluid flows, which often exhibit multifaceted characteristics that challenge traditional analytical or numerical methods. By employing flow profile pairs that are generated through high-fidelity numerical simulations, encompassing both the one-dimensional benchmark problems and the more intricate three-dimensional boundary layer transition problem, this research convincingly demonstrates that neural networks possess a remarkable capacity to effectively capture the discontinuities and the subtle wave characteristics that occur at small scales within complex fluid flows, thereby showcasing their robustness in handling intricate fluid dynamics phenomena. Furthermore, even in the context of challenging three-dimensional problems, this study reveals that the average velocity profiles can be predicted with a high degree of accuracy, utilizing a limited number of input profiles during the training phase, which underscores the efficiency and efficacy of the model in understanding complex systems. The findings of this study significantly underscore the immense potential that artificial neural networks, along with deep learning methodologies, hold in advancing our comprehension of the fundamental physics that govern complex fluid dynamics systems, while concurrently demonstrating their applicability across a variety of flow scenarios and their capacity to yield insightful revelations regarding the nonlinear relationships that exist among diverse flow parameters, thus paving the way for future research in this critical area of study.

The dataset consists of drone images of cotton fields which were created to aid precision agriculture and machine learning-based weed detection research. The main goal is to enable the creation of object detection models for crop-weed differentiation while providing a standard for model evaluation. The dataset release serves two purposes: it supports the advancement of automated agricultural monitoring and sustainable farming practices, and it adds to the expanding research on AI solutions for agricultural productivity and environmental management.