多年生苜蓿地切根补播机低阻松土铲设计与试验

为降低多年生苜蓿地改良多用机械松土铲的水平阻力和土壤扰动量,根据食蚁兽爪趾外缘轮廓曲线模型,设计了一种新型仿生松土铲。基于多年生人工苜蓿地土壤特性,利用EDEM软件建立触土部件土壤相互作用离散元模型,以水平阻力和土壤扰动面积为主要评价指标,在不同作业条件下对仿生松土铲和轻型标准深松铲工作过程进行数值模拟,并进行田间验证试验。结果表明,仿生铲平均减阻率为7.64%,仿真值与实测值误差小于9%。为优化翼铲结构参数,以铲翼倾角和铲翼开角为试验因素,以传感器拉力测量值和沟槽宽度为试验指标,采用响应面分析法(RSM),进行二因素五水平旋转正交组合试验,得到各因素与指标之间的回归数学模型。采用粒子群优化(PSO)算法对回归数学模型进行求解,获取了Pareto最优解集,最终选取传感器拉力测量值为8.679 kN、沟槽宽度为144.2 mm,此时翼铲倾角为20°、翼铲开角为105.6°。田间验证试验表明,实测值与预测值相对误差小于6%,说明基于RSM和PSO的多目标参数优化方法具有科学性和可行性。

Design and Experiment of Low-resistance Soil Loosening Shovel for Cutting Roots and Reseeding in Perennial Alfalfa Field

In order to reduce the tillage resistance and soil disturbance of the multipurpose machine for improving perennial alfalfa field,a new type of bionic ripper was designed based on the contour curve model of the outer edge of the anteater s claw toe.Based on the soil characteristics of the perennial artificial alfalfa field,the discrete element model of the soil contact part-soil interaction was established by using EDEM software.The forward resistance and soil disturbance area were used as the main evaluation indicators.The bionic loosening shovel and the light standard deep loosening were performed under different operating conditions through numerical simulation of the shovel working process and field performance comparison test.The results showed that the average drag reduction rate of the bionic shovel was 7.64%,and the error between the simulated value and the measured value was less than 9%.In order to optimize the structural parameters of the wing shovel,the wing inclination angle and the wing opening angle were used as the test factors,and the sensor tension value and the groove width were used as the test indexes,and the regression mathematical models were obtained.Particle swarm optimization(PSO)was used to solve the regression mathematical model,and the Pareto optimal solution set was obtained.Finally,the sensor tension value was 8.679 kN,the groove width was 144.2 mm,and the wing inclination angle was 20°,and the wing opening angle was 105.6°.Field verification test result showed that the relative error between the actual value and the predicted value was less than 6%.The multi-objective parameter optimization method based on RSM and PSO was scientific and feasible.