Fubin Zhang, Zhitao Luo, Weixun Li, Enlai Zheng, Dongchuan Pan, Jin Qian, Haoping Yao, Xiaochan Wang
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引用次数: 0
Abstract
In this paper, to improve the operational performance of crushing and strip-laying device in no-till planter, both the logarithmic helix curved shell and parabolic guide structures are first designed. Then, a lightweight discrete element particle model of flexible wet-adhesive rice straw is developed and the corresponding calibration method of contact model parameters is also proposed. Furthermore, a discrete element model of the whole straw-crushing and strip-laying device based on the JKR contact model is also established. It’s demonstrated that compared to the traditional straw model, the laying strip mass predicted by the developed straw model agrees better with experimental results. Moreover, the parabolic guide structure can reduce the variation coefficient of straw mass in the strip by 24.16 %, and the steady state flow of straw in the outlet area of the device is 0.205 kg/s. Finally, the response surface method is employed to determine the optimal crusher shaft speed (1617 rpm), inlet clearance (30 mm) and ground clearance (189 mm) of the straw-crushing and strip-laying device. A comparative analysis of bench test also reveals the optimization design can further reduce the variation coefficient of straw mass in the laying strip by 2.34 % and increase the cleanliness rate in the seed strip by 2.89 %.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.