基于CFD-DEM的谷物联合收获机清选损失监测占比补偿方法研究

Compensation Method for Cleaning Loss Monitoring Proportion of Grain Combine Harvesters Based on CFD-DEM

摘要:

为得到谷物收获过程中精确的清选损失信息,不仅需要误差小、稳定性好的清选损失监测传感器硬件作为基 础,还需要明晰传感器监测值与实际清选损失量之间的数学关系,本文基于课题组前期研发的清选损失监测传感器,首先构建了水稻、小麦、油菜等典型作物的脱出混合物各成分颗粒模型,并根据实际工况建立了多组清选装置三维流道模型,通过CFD-DEM耦合方法开展了作物种类、喂入量、筛片开度、风机进风口面积等单因素及多因素 仿真,分析不同作业工况下清选损失监测值与实际值之间的关系及其变化规律,并利用田间接料试验对仿真结果进行验证。研究结果表明,作物种类、筛片开度、风机进风口面积等是影响清选损失监测占比的主要因素,喂入量在一定范围内对占比影响不显著;由耦合仿真得出的清选损失监测占比数值相较于实际田间试验值偏大,偏大程度数值的标准差为0. 97% ,验证了仿真分析的有效性。结合仿真样本数据构建了多种典型非线性拟合模型,通过对拟合结果进行对比分析并考虑实际工程应用,选取二次多项式回归模型作为清选损失监测占比补偿模型(R2 adj大于0. 991、RMSE小于0. 216% )。最后将该模型与嵌入式系统相结合,实现清选损失监测占比的在线补偿,模型补偿值与实测值之间的差值不超过1. 08% 。该研究有效提升了总清选损失计算的准确性和适应性,减少了对人工接料盒试验的依赖,同时可为收获作业参数智能化调控及整机性能评价提供有力支撑。

Abstract:

Aiming to achieve high-precision monitoring of cleaning loss during grain harvesting, it is essential not only to develop cleaning loss monitoring sensors with low error and high stability but also to establish an accurate mathematical mapping between the sensor readings and the actual cleaning loss. Focusing on a cleaning loss monitoring sensor previously developed and systematically investigated methods to improve monitoring accuracy, three-dimensional particle models representing components of the threshing mixture, such as grains, short straw, and impurities, were constructed for typical crops, including rice, wheat, and rapeseed, based on their physical and compositional characteristics. Multiple three-dimensional flow channel models of cleaning devices were then established according to actual operating conditions. Using a coupled CFD-DEM simulation approach, numerical simulations of gas- solid two-phase flow were conducted under both single-factor and multi-factor conditions, including crop type, feeding rate, sieve opening, and fan inlet area. The relationship and variation patterns between sensor readings and actual cleaning loss were analyzed in detail under different operating scenarios. Field catching tests were subsequently performed to validate the simulation results. The findings indicated that crop type, sieve opening, and fan inlet area were the primary factors influencing the proportion of cleaning loss detected by the sensor. In contrast, feeding rate had an insignificant effect within a certain range. The monitoring proportion values of cleaning loss obtained from the simulation were larger than that of the actual field test values, with a standard deviation of 0. 97% in the extent of overestimation, which verified the effectiveness of the simulation analysis. Subsequently, several representative nonlinear fitting models were established based on the simulated sample data. Through comparative analysis of the fitting results and considering practical engineering applicability, a quadratic polynomial regression model was selected as the compensation model for cleaning loss monitoring ratio, achieving an adjusted R2 greater than 0. 991 and a root mean square error lower than 0. 216% . Finally, the model was integrated with an embedded system to realize online compensation of the cleaning loss monitoring ratio, with the deviation between the compensated values and the measured values not exceeding 1. 08% . This research significantly improved the accuracy and adaptability of cleaning loss monitoring, reduced reliance on manual catching box tests, and provided robust technical support for intelligent operational parameter control and comprehensive performance evaluation of combine harvesters.