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Abstract Ice air jet technology is one non-destructive, residue-free and environmentally friendly machining process. It is an efficient method to form ice particles by atomizing water droplets in ultra-low temperature environment. The freezing characteristics of water droplets in cryogenic gas and liquid nitrogen environment are visually analyzed, and the effects of droplet volume, ambient temperature are also studied. The results show that when water droplets freeze in a cryogenic gas environment, four distinct stages are observed, namely pre-cooling stage, recalescence stage, solidification stage, and deep cooling stage. However, when water droplets freeze in liquid nitrogen, the recalescence stage cannot be observed. For a 5 µl water droplet, it takes 68 s for water droplets to freeze into ice particles at − 20.36 °C, while it takes only 1.7 s in liquid nitrogen. During the freezing process, the water droplets form an ice shell outside and freeze inwardly. Ice particles may break up due to differences in solubility and density. With the increase of volume the time spent on pre-cooling stage and freezing stage both increases. For the large latent heat of water phase transformation, the solidification stage time is greatly affected by the volume of water droplets. When the ambient temperature drops from − 10.67 °C to − 24.68 °C, the freezing time of 5 µl water droplets decreases by 45.5%, indicating that the ambient temperature has a great influence on the freezing time. The results of the study can significantly contribute to the development of ice air jet technology.

With the development of agriculture, the complexity and dynamism of orchard environments pose challenges to the perception and positioning of inter-row environments for agricultural vehicles. This paper proposes a method for extracting navigation lines and measuring pedestrian obstacles. The improved YOLOv5 algorithm is used to detect tree trunks between left and right rows in orchards. The experimental results show that the average angle deviation of the extracted navigation lines was less than 5 degrees, verifying its accuracy. Due to the variable posture of pedestrians and ineffective camera depth, a distance measurement algorithm based on a four-zone depth comparison is proposed for pedestrian obstacle distance measurement. Experimental results showed that within a range of 6 m, the average relative error of distance measurement did not exceed 1%, and within a range of 9 m, the maximum relative error was 2.03%. The average distance measurement time was 30 ms, which could accurately and quickly achieve pedestrian distance measurement in orchard environments. On the publicly available TUM RGB-D dynamic dataset, YOLOD-SLAM2 significantly reduced the RMSE index of absolute trajectory error compared to the ORB-SLAM2 algorithm, which was less than 0.05 m/s. In actual orchard environments, YOLOD-SLAM2 had a higher degree of agreement between the estimated trajectory and the true trajectory when the vehicle was traveling in straight and circular directions. The RMSE index of the absolute trajectory error was less than 0.03 m/s, and the average tracking time was 47 ms, indicating that the YOLOD-SLAM2 algorithm proposed in this paper could meet the accuracy and real-time requirements of agricultural vehicle positioning in orchard environments. [ABSTRACT FROM AUTHOR]

Dissolved oxygen is an important environmental factor for prawn breeding. In order to improve the prediction accuracy of dissolved oxygen concentration in prawn pond, and solve the problem of low prediction accuracy of different frequency domain modal classification after empirical modal decomposition of nonlinear time series data when there are few training samples, an combination prediction model based on empirical mode decomposition (EMD), random forest (RF) and long short term memory neural network (LSTM) was proposed in this research. Firstly, the time series data of prawn breeding dissolved oxygen concentration were decomposed at multiple scales by EMD to obtain a set of stationary intrinsic mode function (IMF). Secondly, with fewer training samples, poor predicts effects on the low-frequency were verified component by LSTM. Then, IMF1－IMF4 were divided into high-frequency components through test results and used for LSTM model. IMF5－IMF7, Rn were divided for RF model, the EMD-RF-LSTM combination model was constructed to improve the prediction accuracy. Modeled low-frequency and high-frequency components IMF using RF and LSTM, then predictions of each component were accumulated and the prediction value of dissolved oxygen of sequence data were got. Finally, the performance of the model was compared with the limit learning machine (ELM), RF, standard LSTM, EMD-ELM and EMD-RF, EMD-LSTM, etc. In the test based on real dataset, the EMD-ELM model contrasted with ELM model, reduced the mean absolute error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) by 30.11%, 29.60% and 32.95%, respectively. The MAPE, RMSE, MAE for the proposed models were 0.0129,0.1156,0.0844, respectively. MAPE decreased by 84.07%, 57.57%, and 49.81% compared with EMD-ELM, EMD-RF and EMD-LSTM, respectively, the prediction accuracy was significantly improved. The results show that the proposed model EMD-RF-LSTM has good prediction performance and generalization ability, which is meets the actual demand of accurate prediction of dissolved oxygen concentration in prawn culture, and can provide reference for the prediction and early warning of prawn pond water quality.

Radial jet drilling (RJD) technology has been proved to be an economical and efficient stimulation technology for oil and gas, geothermal, hydrate, etc. but conventional RJD technology adopts pure water jet to break rock and form laterals, which has low rock breaking efficiency and is unable to effectively break hard rock such as shale. Swirling abrasive jet is proposed to promote the development of RJD. Here, the characteristics of the flow field of the swirling abrasive jet nozzle and the influence of the key impeller parameters are studied by numerical simulation. The distribution and development of axial velocity, tangential velocity, and radial velocity of water and abrasive are analyzed. The results show that the swirling abrasive jet has no constant velocity core, has stronger diffusivity, and can form a larger impact area than the direct jet. Abrasive particles and water can acquire large tangential and radial velocity which can break rock under the action of shear and tensile stress efficiently. With the increase of the spinning angle, the axial velocity of the fluid decreases, and the tangential velocity increases gradually. With the increase of blade thickness, the axial velocity decreases, and the tangential velocity increases. With the increase of the number of blades, the axial velocity decreases, and the tangential velocity increases. The spinning direction almost has no effect on the flow field. Therefore, the spinning angle is recommended to be no less than 270°, blade thickness is 2.5 mm, and number of blades are 3. The research results provide theoretical guidance for the structural design of swirling abrasive jet nozzles.