In this paper, a model for controlling paddy drying by incorporating accumulated temperature is introduced and defined. Drying experiments using freshly harvested paddy were conducted at different levels of drying air parameters including temperature (T = 27°C, 31°C, 35°C, 39°C, and 43°C), relative humidity (RH = 45%, 50%, 55%, 60%, and 65%), initial moisture content (M 0 = 17%, 19%, 21%, 23%, and 25%) and airspeed (V = 0.4, 0.5, 0.6, 0.7, and 0.8 m s −1). When T = 31°C, RH = 60%, M 0 = 19%, and V = 0.5 ms −1 , the crack additional percentage reached a minimum value of 0.508%, with the average precipitation rate at 0.719%•h. Further, an accumulated temperature and quality chart was provided in this paper. The x-coordinate of this chart is temperature, and the initial moisture content represents the y-coordinate. It covers the drying conditions of the actual dryer well and has a wide range of applicability for real-world environment. The model developed in this study not only provides a scientific reference for precise drying and intelligent control of paddy but also guide the actual drying operation.
Mechanical drying significantly affects the mechanical properties of corn kernels. Improper drying may result in material losses and in a decline in quality due to pressure, collisions, and other factors during subsequent storage and transport operations. A literature survey revealed that at time of writing the characteristics of dried corn kernels have not been systematically and fully studied. In this paper, an orthogonal rotation combination test scheme was designed. Using a multiparameter controllable thin layer drying test bench, corn was dried under different conditions (temperature 30-60°C, relative humidity 30-60%, air velocity 0.46-0.94 m s-1 , initial moisture content of corn of 20-30% w.b., tempering ratio 0-3). Then, a texture analyser was used to measure the mechanical properties (rupture force, rupture energy, modulus of elasticity and brittleness) of the dried corn kernels. Relationship models were established for the rupture force, rupture energy, modulus of elasticity and brittleness and drying conditions of corn kernels. An increase in the drying temperature from 30 to 60°C increased the rupture energy, elastic modulus, and brittleness of the corn kernels by 19.11, 11.76, and 4.02%, respectively; an increase in the drying relative humidity from 30 to 60% increased the rupture force, energy, modulus of elasticity and brittleness by 15.
The fast swimming speed, flexible cornering, and high propulsion efficiency of diving beetles are primarily achieved by their two powerful hind legs. Unlike other aquatic organisms, such as turtle, jellyfish, fish and frog et al., the diving beetle could complete retreating motion without turning around, and the turning radius is small for this kind of propulsion mode. However, most bionic vehicles have not contained these advantages, the study about this propulsion method is useful for the design of bionic robots. In this paper, the swimming videos of the diving beetle, including forwarding, turning and retreating, were captured by two synchronized high-speed cameras, and were analyzed via SIMI Motion. The analysis results revealed that the swimming speed initially increased quickly to a maximum at 60% of the power stroke, and then decreased. During the power stroke, the diving beetle stretched its tibias and tarsi, the bristles on both sides of which were shaped like paddles, to maximize the cross-sectional areas against the water to achieve the maximum thrust. During the recovery stroke, the diving beetle rotated its tarsi and folded the bristles to minimize the cross-sectional areas to reduce the drag force. For one turning motion (turn right about 90 degrees), it takes only one motion cycle for the diving beetle to complete it. During the retreating motion, the average acceleration was close to 9.8 m/s2 in the first 25 ms. Finally, based on the diving beetle's hind-leg movement pattern, a kinematic model was constructed, and according to this model and the motion data of the joint angles, the motion trajectories of the hind legs were obtained by using MATLAB. Since the advantages of this propulsion method, it may become a new bionic propulsion method, and the motion data and kinematic model of the hind legs will be helpful in the design of bionic underwater unmanned vehicles.
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