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In nature, wind can transport objects of diverse shapes and materials over long distances spanning meters or beyond. In contrast, most noncontact manipulation methods are effective only over centimeter‐scale distances and require the manipulated objects to have specific magnetic, electrical, or other material properties and shapes. Herein, a meter‐scale distance manipulation method is presented for controlling the motion of objects of diverse shapes and materials on a plane surface using a jet‐induced airflow field. The airflow field is varied by controlling the direction of a single air jet projected onto a surface based on the positions of objects to steer the objects to follow desired trajectories up to 2.7 m away. A wide variety of objects can be automatically manipulated, from regularly shaped polystyrene hemispheres and sticks to irregularly shaped cotton wads and face masks as well as deformable tissue papers and plastic bags. The method is also robust, effectively manipulating objects under challenging conditions, e.g., external airflow disturbances, surface obstacles, and air–water interface. Finally, three application cases are demonstrated: collecting diverse objects into a target receptacle, hooking and retrieving heavy objects with a tethered mobile agent, and closing an electrical circuit with an untethered soft agent. In this article, the first demonstration of noncontact manipulation of objects using jet‐induced airflow fields up to 2.7 m away is presented. A method is developed for automatically manipulating objects of diverse shapes—regular, irregular, and deformable. The method is robust, operating under challenging conditions, including airflow disturbances, obstacles, and air–water interfaces, and is demonstrated in several practical applications.

To address the limitations of traditional computational fluid dynamics (CFD) simulations, such as high computational cost, long processing times, and limited scalability, this study identifies the inefficiencies of existing data‐driven prediction methods, which often lack spatial–temporal coordination mechanisms and fail to capture fine‐grained dynamic features of UAV airflow fields. We propose a novel deep learning model, VAN‐ConvLSTM, for rapid and accurate prediction of UAV downwash airflow. Unlike conventional ConvLSTM‐based frameworks, which struggle with modeling long‐range dependencies and detailed spatial variations, our model introduces a visual attention unit (VAN) to enhance spatiotemporal sensitivity. The model architecture combines a convolutional encoder for spatial feature extraction, a VAN module for attention‐guided temporal modeling, and a ConvLSTM decoder for sequence generation. This synergistic design improves both the accuracy and interpretability of airflow prediction. Experimental results show that the VAN‐ConvLSTM model achieves an SSIM score of 0.96, demonstrating high consistency with CFD simulations. Compared to baseline methods, our model reduces error while improving stability and spatial fidelity. Ablation studies further validate the individual contributions of VAN and ConvLSTM modules. The results, verified through three representative case studies, confirm that VAN‐ConvLSTM outperforms state‐of‐the‐art approaches across multiple evaluation metrics, while offering significantly enhanced computational efficiency. This demonstrates its strong potential as a reliable and scalable alternative to traditional CFD methods in rotor airflow prediction scenarios.

The harvesting process of super rice is associated with certain challenges involving the effectiveness of the harvesting equipment. To adapt the cleaning device to this process and keep pace with the performance improvements made to the separation device, the cleaning device must handle high impurity contents in the ejected material after initial separation. This paper proposes a structural improvement to the conventional wind-sieve cleaning device by incorporating streamlined arc plates with pneumatic characteristics onto the cleaning vibrating sieve, providing a dynamic airflow guidance structure to enhance the airflow distribution inside the cleaning chamber. First, indicators for evaluating the quality of the airflow field were established. Through the measurement and calculation of the airflow field, a multiobjective optimization orthogonal experiment was conducted on the working parameters affecting the airflow field. The parameter combination for the optimal performance of the cleaning device was determined to be as follows: a fan speed of 1250 r/min, first and second guide plate angles of 37° and 30°, respectively, and a fish-scale sieve opening of 24 mm. Field tests conducted to supplement the multiobjective orthogonal experiment helped confirm the reliability of the evaluation indicators for the airflow field. Based on an analysis of the airflow field, improvements were proposed for the conventional cleaning device, with aerodynamic arc plates designed to be installed on the cleaning vibrating sieve. With these structural modifications in place, more field tests were performed to verify the cleaning effect. The proposed airflow guidance structure was found to be effective in improving the cleaning efficiency, as evidenced by the reduction in the impurity rate from 4.78% to 1.78% and the loss rate from 2.46% to 0.78%. The establishment of evaluation indicators for the airflow field, structural improvements made to the wind-sieve cleaning device, and the significant reduction in the impurity and loss rates associated with rice cleaning provide practical guidance for the design of cutting and longitudinal-flow combine harvesters.

Multi-outlet air-assisted sprayers are increasingly used for directional and zoned airflow to match varying canopy structures. In this study, a self-developed multi-outlet orchard air-assisted sprayer was investigated. Airflow velocity and direction were tested at different inlet areas, heights, and downstream horizontal distances using a three-dimensional ultrasonic anemometer. Analysis of variance (ANOVA) and regression modeling were applied to elucidate the effects of these three factors on airflow velocity, horizontal angle (θ), and elevation angle (Φ). The results showed that a stable alternating “primary jet–interaction zone” structure was formed in the spatial airflow field under all operating conditions, indicating that the fundamental airflow pattern was mainly governed by the sprayer layout. Varying the inlet area did not alter the basic airflow structure; however, the intensity and directional stability of the primary jets were significantly modified. Larger inlet openings produced higher airflow velocities, with a maximum near-field velocity of 19.7 m s−1, whereas smaller inlet openings resulted in faster far-field attenuation and more pronounced diffusion. Increasing the inlet area caused the θ distribution peak to converge toward 0°, thereby improving axial coherence and directional stability. In contrast, decreasing the inlet area shifted Φ toward more negative values, with Φ reaching approximately −20° in the far field; moreover, far-field differences in Φ were more pronounced. Under the minimum inlet opening area condition (S1), the airflow velocity within the region 80–100 cm from the outlet can be stably maintained above 3 m/s, with a relatively uniform velocity distribution. This is beneficial for improving droplet deposition uniformity within the canopy and reducing droplet drift in non-target areas. Based on the experimental data, a regression model for mean airflow velocity was established (R2 = 0.873), demonstrating good predictive performance and indicating that inlet-opening regulation is feasible. These findings provide a basis for airflow matching and spray-parameter optimization for different canopy structures.

The accumulated methane in goaf during coal mining may leak into the working face under the airflow influence, which is possibly causing disasters such as methane gas excessive at the working face and seriously threatening the mine safety. This paper first established a three-dimensional numerical model of the mining area under U-shaped ventilation, introducing the gas state equation, continuity equation, momentum equation, porosity evolution equation, and permeability evolution equation to simulate the airflow field and gas concentration field in the mining area under the natural state. The reliability of the numerical simulations is then verified by the measured air volumes at the working face. The areas in the mining area where gas is likely to accumulate are also delineated. Subsequently, the gas concentration field in goaf under the gas extraction state was theoretically simulated for different locations of large-diameter borehole. The maximum gas concentration in goaf and the gas concentration trend in the upper corner were analyzed in detail, and the critical borehole location (17.8 m from the working face) was determined as the optimum location for gas extraction from the upper corner. Finally, a gas extraction test was carried out on-site to evaluate the application effect. The results show that the measured airflow rate has a small error with the simulated results. The gas concentration in the area without gas extraction is high, with the gas concentration in the upper corner being over 1.2%, which is greater than the critical value of 0.5%. The maximum reduction in gas concentration was 43.9%, effectively reducing the gas concentration in the extraction area after employing a large borehole to extract methane gas. The gas concentration in the upper corner and the distance of the borehole from the working face are expressed as a positive exponential function. The field engineering results show that the implementation of the large borehole at a distance of less than 17.8 m from the working face can control the gas in the upper corner to less than 0.5%, effectively reducing the risk of gas in the upper corner. The numerical simulation work in this paper can provide some basic support for the design of an on-site borehole to extract gas from the mining void and reduce the gas hazard in coal mines.

In order to study the problems of unreasonable airflow distribution and serious dust pollution in a heading surface, an experimental platform for forced ventilation and dust removal was built based on the similar principles. Through the similar experiment and numerical simulation, the distribution of airflow field in the roadway and the spatial and temporal evolution of dust pollution under the conditions of forced ventilation were determined. The airflow field in the roadway can be divided into three zones: jet zone, vortex zone and reflux zone. The dust concentration gradually decreases from the head to the rear of the roadway. Under the forced ventilation conditions, there is a unilateral accumulation of dust, with higher dust concentrations away from the ducts. The position of the equipment has an interception effect on the dust. The maximum error between the test value and the simulation result is 12.9%, which verifies the accuracy of the experimental results. The research results can provide theoretical guidance for the application of dust removal technology in coal mine.

This study optimized the ventilation structure of a fan-driven corn circulating grain storage bin. By combining computational fluid dynamics, orthogonal experiments, and response surface optimization experiments, the internal airflow field of the corn circulating ventilation of grain storage bin was simulated and the structural parameters were optimized using Fluent simulation. The optimization function of the Design-expert 13 software was utilized to determine the optimal parameter combination as follows: the diameter of the circulating inlet was 20 mm, the number of circulation layers is 13, the diameter of the bottom ventilation opening was 6 mm, and the number of ventilation openings was 7. Using the evaluation index of relative standard deviation CV, the wind speed uniformity of the storage area before and after optimization was compared. After optimization, the relative standard deviation CV of the internal flow field of the grain storage bin increased by 24.1% compared to the initial ventilation structure of the bin. This study provides important data for the optimization of the ventilation structure of corn grain storage.

Aiming at the heating problem of 12kV switchgear, the model of its main circuit module was established and simplified. Then, the temperature field and airflow field were simulated by finite element analysis software ANSYS. Besides, the distribution of temperature and airflow in the circuit breaker room was analyzed respectively and the heat dissipation structure of the switchgear was improved. Finally, two axial fans were installed at the top of the switchgear and the ventilation hole was designed at the bottom. The simulation results showed that this method can accelerate the air convection and reduce the temperature rise effectively. With the continuous increase of distribution network capacity, switchgear would have overheating problem during the long-term operation. If this problem cannot be solved, it would affect its insulation performance and even cause safety accidents. This study provides a reference for the design of switchgear and solves its overheating problem.

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.

The melt-blowing process involves high velocity airflow and fiber motion, which have a significant effect on fiber attenuation. In this paper, the three-dimensional airflow field for a melt-blowing slot die was measured using the hot-wire anemometry in an experiment. The fiber motion was captured online using a high-speed camera. The characteristics of the airflow distribution and fiber motion were analyzed. The results show that the melt-blowing airflow field is asymmetrically distributed. The centerline air velocity is higher than that around it and decays quickly. The maximum airflow velocity exists near the die face, in the range of 130–160 m/s. In the region of −0.3 cm < y < 0.3 cm and 0 < z < 2 cm, the airflow has a high velocity (>100 m/s). As the distance of z reaches 5 cm and 7 cm, the maximum airflow velocity reduces to 70 m/s. The amplitude of fibers is calculated, and it increases with the increase in air dispersion area which has a significant influence on fiber attenuation. At z = 1.5 cm, 2.5 cm, 4 cm, and 5.5 cm, the average fiber amplitudes are 1.05 mm, 1.71 mm, 2.83 mm, and 3.97 mm, respectively. In the vicinity of the die, the fibers move vertically downward as straight segments. With the increase in distance from the spinneret, the fiber appears to bend significantly and forms a fiber loop. The fiber loop morphology affects the velocity of the fiber movement, causing crossover, folding, and bonding of the moving fiber. The study investigated the interaction between the fiber and airflow fields. It indicates that the airflow velocity, velocity difference, and dispersion area can affect the motion of fiber which plays an important role in fiber attenuation during the melt-blowing process.