摘要
为了提高链勺式马铃薯排种装置排种性能,该研究基于离散单元法理论,使用EDEM软件建立了排种装置数值模型,在对排种过程中种薯运动规律仿真分析的基础上,设计了具有双层种箱结构的排种装置,以空种率和重种率为性能指标,试验研究了排种速度、种勺直径和充种高度对充种性能的影响规律,利用回归方程和多目标优化方法对双层种箱式排种装置进行了参数的优化设计,结果为:1)排种速度0.67 m/s、种勺直径48.6 mm、充种高度0.28 m时,空种率和重种率分别是3.8%和8.8%;2)排种速度0.36 m/s≤v≤0.96 m/s、种勺直径44 mm≤d≤56 mm、充种高度0.15 m≤h≤0.28 m时,空种率小于10%,重种率小于20%。种薯运动规律表明:增大高效充种区、增强种薯流动性可以有效提高充种成功率。试验结果表明:与单层种箱式排种装置相比,双层种箱式排种装置空种率降低50%,重种率降低24.5%;排种速度提高92%时,仍可保证排种性能。该研究为链勺式马铃薯排种装置的优化设计提供指导。
Potato is the fourth staple food product in China, and Chinese potato planting area of 6.7 million hm2 ranks first worldwide in 2013. However, the mechanized potato seeding percentage in China is only 23%, which is limited by poor performance of potato planter. Cup-chain metering device is the most commonly used for potato planting, whereas it is troubled with high miss-seeding and re-seeding problems. To solve these problems, the simulation model of potato planter is firstly built, and this model is used to analyze potato seed movement during metering process; then a new type of metering device with double-deck seed tank is designed, and finally the parameters of seeding speed, seed cup diameter and seed filling height are optimized. 1) Potato planter simulation: Potato cup-chain metering planter is simulated based on the software of EDEM(enhanced discrete element method). On the one hand, Hertz-Mindlin non-sliding contact model between 2 seeds is built with the recovery coefficient of 0.3, static friction coefficient of 0.56 and dynamic friction coefficient of 0.15; on the other hand, the model between seed and seeder is built with the recovery coefficient of 0.52, static friction coefficient of 0.5 and dynamic friction coefficient of 0.1. 2) Potato seed movement analysis: During the metering process, movement is originated from only a small part of seeds instead of most of them that are immobile. The small part of moving seeds determine the seed charging performance. The major movement area of potato seed is around the chain presenting the shape of inverted cone, which contains 3 areas of high-efficiency charging area, low-efficiency charging area and no seed area. The seed charging performance is directly affected by the volume of high-efficiency charging area and the seed mobility. 3) Seed planter design: Aiming to enlarge high-efficiency charging area and enhance seed mobility, planter with double-deck seed tank is designed, which doubles the volume of high-efficiency charging area and enhance seed mobility. A planter test platform with double-deck seed tank is modified from a 1 220 type potato planter provided by Zhongji Meno Polytron Technology. 4) Performance evaluation and parameter optimization: 2 indicators are employed to evaluate the planter performance, including miss-seeding percentage and re-seeding percentage. The influence of seeding speed, seed cup diameter and seed filling height in the seed tank on seed charging performance is studied. Regression equation and multi-objective optimization method are employed for parameter optimization. In the Chinese national standard for potato planter, miss-seeding percentage is no more than 10% and re-seeding percentage is no more than 20%, the designed planter can operate at the seeding speed of 0.96 m/s to satisfy this national standard. To balance the planting performance, including miss-seeding percentage and re-seeding percentage, and planting efficiency of seeding speed, the parameters of seeding speed, seed cup diameter and seed filling height in the seed tank are optimized as 0.67 m/s, 48.6 mm and 0.28 m, respectively, showing the miss-seeding percentage of 3.8% and re-seeding percentage of 8.8%. This paper demonstrates the potential for a high-performance cup-chain metering device with double-deck seed tank for potato planting.
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第20期32-39,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
公益性行业(农业)科研专项(201503124)
国家自然科学基金项目(31301241)
关键词
农业机械
设计
试验
马铃薯种植机
链勺式排种装置
多目标优化
回归方法
离散元方法
agriculture machinery
design
experiments
potato planter
cup-chain metering device
multi-objective optimization
regression method
discrete element method