李丹, 朱德兰, 刘柯楠, 王方同, 祝鹏. 平移式喷灌机行走阻力的确定及验证[J]. 农业工程学报, 2019, 35(17): 19-27. DOI: 10.11975/j.issn.1002-6819.2019.17.003
    引用本文: 李丹, 朱德兰, 刘柯楠, 王方同, 祝鹏. 平移式喷灌机行走阻力的确定及验证[J]. 农业工程学报, 2019, 35(17): 19-27. DOI: 10.11975/j.issn.1002-6819.2019.17.003
    Li Dan, Zhu Delan, Liu Kenan, Wang Fangtong, Zhu Peng. Determination and application verification for driving resistance of lateral move sprinkling machine[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(17): 19-27. DOI: 10.11975/j.issn.1002-6819.2019.17.003
    Citation: Li Dan, Zhu Delan, Liu Kenan, Wang Fangtong, Zhu Peng. Determination and application verification for driving resistance of lateral move sprinkling machine[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(17): 19-27. DOI: 10.11975/j.issn.1002-6819.2019.17.003

    平移式喷灌机行走阻力的确定及验证

    Determination and application verification for driving resistance of lateral move sprinkling machine

    • 摘要: 行走阻力对平移式喷灌机的动力配置具有重要影响,为了确定不同土壤条件下喷灌机的行走阻力,为机组提供合理的动力配置依据。针对黄绵土和塿土2种土壤类型,以土壤含水率和容重为因素,选取表征机组行走阻力的5个土壤力学参数即土壤黏聚变形模数、摩擦变形模数、沉陷指数、黏聚力和内摩擦角为指标,采用二元二次通用旋转组合试验设计,通过平板沉陷试验与直接剪切试验,建立了2种土壤类型含水率、容重与5个参数间的回归模型,从而得出喷灌机所需行走阻力,并以此为依据对喷灌机行走驱动系统进行配置和田间行走试验。结果表明,含水率、容重和二者交互作用对2种土壤的5个参数均有显著影响(P<0.05);随着含水率和容重增加,黄绵土黏聚变形模数呈减小趋势而塿土呈先增大后减小趋势,且2种土壤的摩擦变形模数均减小,沉陷指数均呈先减小后增大趋势,黏聚力与内摩擦角均增大。采用上述方法进行的驱动系统配置满足喷灌机动力需求,由5个参数的回归模型所得驱动功率计算值与田间试验功率实测值最大相对误差分别为6.43%、7.73%,模型合理。研究可为平移式喷灌机动力配置提供依据。

       

      Abstract: Abstract: Pivot sprinkler irrigation is efficient and able to improve irrigation uniformity and water conservation, especially in arid and semi-arid regions in northwest China. The motor to move the sprinkler machine is one of the key components of the machine. Most methods available for calculating the power demand to move the sprinkler system is empirical and lack of scientific basis, despite its importance in sprinkler application and improving work efficiency. To bridge this gap, we investigated the dependence of this resistance on soil water content and soil bulk density via their impact on soil cohesion modulus, internal friction modulus, sinkage-exponent, cohesion and internal friction angle. We tested a loess soil and a Lou soil, two common soils in arid and semi-arid regions in northwest China. In the experiment, the soil were first air-dried and the deigned soil water content was achieved by evenly spraying a volume of water, calculated based on the water content, over the arid-dry soil followed by a thorough mixture to ensure homogeneity. Binary quadratic general rotation combination tests were then carried out. The change in the five soil mechanical parameters with water content and bulk density were calculated based on results obtained from plate-subsidence test and direct-shear box test, from which we calculated the variation of the resistance with moisture content and bulk density of the soils. Regression models linking each of the five mechanical parameters to moisture and bulk density were established and were used to configure the motor of the sprinkler machine. We verified the models against field tests conducted at Yangling and Yulin in Shannxi province. In the test, we measured the soil moisture and bulk density at 0, 50 and 100 m from the edge of the tested plot. The results showed that the cohesion modulus, internal friction modulus, sinkage-exponent, cohesion and internal friction angle of both soils changed significantly with both soil moisture and bulk density (P<0.05). With water content and bulk density increasing, the cohesion modulus of the loess soil decreased monotonically, while of the Lou soil increased first followed by a decrease. We also found that, as soil moisture and bulk density increased, the internal friction modulus of both soils decreased, and their sinkage-exponent decreased first followed by an increase. The regression model fitted the experimental results of both soils well. In particular, the regression model was quadratic for the cohesion modulus and sinkage-exponent, and linear for the internal friction modulus, cohesion and internal friction angle. The motor in the sprinkler machine configured from the regression model meet the power demand, with its rotating speed and torque for moving the sprinkling machine being 500 W, 2.6 N·m and 1800 r/min for the loess soil, and 400 W, 2.2 N·m and 1800 r/min for the Lou soil. Compared to the measurement, the maximum relative error of the calculated power demand for the Lou soil and loess soil was 6.43% and 7.73%, respectively, proving the accuracy of the model. Our results provide a basis for configuring the power of the motor to move sprinkling machine over different soils in field.

       

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