水稻芽种离散元主要接触参数仿真标定与试验

水稻播种时芽种含水率高、种芽长度差异大,表现出与其他作物种子不同的物理特性。为减小仿真分析时芽种参数设置不准确而形成的模拟系统误差,本文基于水稻芽种摩擦角的试验与仿真测定,标定出不同含水率下的水稻芽种离散元主要接触参数。通过芽种的摩擦角(两种休止角及滑动摩擦角)离散元仿真试验,建立了芽种-不锈钢板静摩擦因数、芽种-芽种静摩擦因数、芽种-芽种滚动摩擦因数与3个摩擦角之间的三元回归方程。以芽种的3个摩擦角试验结果作为修正指标,对回归方程数值求解,得到当仿真结果与试验结果的拟合误差达到允许范围内的3个主要接触参数。将标定后的参数进行试验验证,分别对不同含水率下的芽种摩擦角的仿真测定与实测结果进行对比分析,相对误差均小于2.75%,表明水稻芽种离散元模型与实际颗粒物料体现出相同的摩擦特性,建立的回归模型满足不同含水率芽种参数的标定要求。对其他5个品种的水稻芽种摩擦角实测值与仿真结果进行对比分析,相对误差均在5.54%以内。该水稻芽种的离散元模型及接触参数可为水稻精密播种装置的动态仿真提供参考。     

颗粒肥料离散元仿真边界参数系统化研究

离散元边界参数对仿真精确性具有重要的影响。为提高离散元仿真精度,以大颗粒尿素颗粒为研究对象,利用Plackett-Burman休止角仿真试验进行了重要边界参数的筛选,确定影响显著的参数依次为尿素颗粒间滚动摩擦因数、颗粒间静摩擦因数和颗粒与ABS板间静摩擦因数,且3种边界参数影响尿素颗粒堆积特性,休止角随着3种边界参数的增大而增大。利用自制静摩擦因数测量仪和虚拟仿真标定方法分别对颗粒与ABS板间的静摩擦因数、颗粒间静摩擦因数和颗粒间滚动摩擦因数进行研究,并对确定值进行了堆积过程的仿真和试验验证,仿真休止角与实际试验休止角相对误差仅为0.36%,不同含水率下的实际试验休止角与标定参数下的仿真休止角相对误差均不大于3.25%,表明仿真确定的边界参数和仿真模型的有效性。     

Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model

The DSSAT-CSM-CERES-Wheat V4.0 model was calibrated for yield and irrigation scheduling of wheat with 2004–2005 data and validated with 13 independent data sets from experiments conducted during 2002–2006 at the Punjab Agricultural University (PAU) farm, Ludhiana, and in a farmer's field near PAU at Phillaur, Punjab, India. Subsequently, the validated model was used to estimate long-term mean and variability of potential yield (Y_p), drainage, runoff, evapo-transpiration (ET), crop water productivity (CWP), and irrigation water productivity (IWP) of wheat cv. PBW343 using 36 years (1970–1971 to 2005–2006) of historical weather data from Ludhiana. Seven sowing dates in fortnightly intervals, ranging from early October to early January, and three irrigation scheduling methods [soil water deficit (SWD)-based, growth stage-based, and ET-based] were evaluated. For the SWD-based scheduling, irrigation management depth was set to 75 cm with irrigation scheduled when SWD reached 50% to replace 100% of the deficit. For growth stage-based scheduling, irrigation was applied either only once at one of the key growth stages [crown root initiation (CRI), booting, flowering, and grain filling], twice (two stages in various combinations), thrice (three stages in various combinations), or four times (all four stages). For ET-driven irrigation, irrigations were scheduled based on cumulative net ETo (ETo-rain) since the previous irrigation, for a range of net ETo (25, 75, 125, 150, and 175 mm). Five main irrigation schedules (SWD-based, ET-driven with irrigation applied after accumulation of either 75 or 125 mm of ETo, i.e., ET75 or ET125, and growth stage-based with irrigation applied at CRI plus booting, or at CRI plus booting plus flowering stage) were chosen for detailed analysis of yield, water balance, and CWP and IWP. Nitrogen was non-limiting in all the simulations.Mean Y_p across 36 years ranged from 5.2 t ha⁻¹ (10 October sowing) to 6.4 t ha⁻¹ (10 November sowing), with yield variations due to seasonal weather greater than variations across sowing dates. Yields under different irrigation scheduling, CWP and IWP were highest for 10 November sowing. Yields and CWP were higher for SWD and ET75-based irrigations on both soils, but IWP was higher for ET75-based irrigation on sandy loam and for ET150-based irrigation on loam. Simulation results suggest that yields, CWP, and IWP of PBW343 would be highest for sowing between late October and mid-November in the Indian Punjab. It is recommended that sowing be done within this planting period and that irrigation be applied based on the atmospheric demand and soil water status and not on the growth stage. Despite the potential limitations recognised with simulation results, we can conclude that DSSAT-CSM-CERES-Wheat V4.0 is a useful decision support system to help farmers to optimally schedule and manage irrigation in wheat grown in coarse-textured soils under declining groundwater table situations of the Indian Punjab. Further, the validated model and the simulation results can also be extrapolated to other areas with similar climatic and soil environments in Asia where crop, soil, weather, and management data are available.     

扬州地区TDR法田间测定不同土壤含水量的标定

使用美国Spectrum TDR 100便携式水分测定仪,在扬州地区不同土壤,找出TDR法和烘干法测定土壤含水量之间的关系。通过试验证明,容重在1.3 g/cm3处TDR测定值接近真实值,容重越小或越大,其偏差就越大。粘粒含量为0~200 g/kg,砂粒含量在300~500 g/kg范围内,TDR测定值接近于烘干法。利用TDR测定土壤水分,砂土和壤土可以不用调整,而粘壤土和粘土要进行系数换算,粘壤土的测定结果要除以1.18,粘土的测定结果要除以1.44。     

薄层热风干燥过程含水率在线测量装置研制与试验

针对薄层热风干燥过程自动称量精度受温度波动、气流扰动及机械振动等因素影响的问题，该研究研制了薄层热风干燥装置及其控制系统，以实现干燥过程物料质量及含水率的自动获取、及时查看和数据存储。系统改进了传统称量结构，采用悬吊称量的方式，将称量传感器置于干燥装置外，与干燥环境相隔离以提高称量传感器使用寿命；采用变频器输出频率表征风速的方式，有效避免了热式风速传感器在变温环境中的测量误差；通过称量传感器温度-质量的归一化偏差校正方法及“停风-稳定-称量-恢复”准静态的自动称量流程，开展了温度波动、气流扰动误差特性及装置验证试验，保证了干燥过程中物料含水率的准确获取。试验表明：恒载标定时，采用温度-质量归一化偏差校正方法校正后的质量与恒载质量最大偏差值为0.368 g，与实测质量相比，平均绝对百分比误差降低了84.4%，测量不确定度降低了72.2%；采用停风检测方案后，实测质量与恒载质量的最大偏差值由37.1 g降至0.31 g；物料加载时，在干燥温度35、45及55℃条件下，以手动称量方式下获取的质量及含水率为标准值，自动称量方式下的质量绝对误差分别为0.337、0.415和0.472 g，含水率...     

基于定标模型云共享的奶牛粪水微型NIR现场速测系统

针对还田利用中粪水成分现场获取困难的问题，该研究设计开发了基于定标模型云共享的奶牛粪水微型近红外（Near-Infrared，NIR）现场速测系统，并对系统进行了验证。该系统主要包括微型近红外光谱传感器、云服务器、Android客户端。微型近红外传感器采集被测样品光谱数据，通过蓝牙协议将数据传输给Android客户端，再通过移动网络将光谱数据传送到云服务器；云服务器利用部署在云端的定标模型对接收到光谱数据进行计算、分析得到定量预测结果，并将预测结果回送Android客户端，可以实现总氮（Total Nitrogen，TN）、总磷（Total Phosphorus，TP）、总钾（Total Potassium，TK）、铵态氮（Ammonium Nitrogen，NH4+-N）、硝态氮（Nitrate Nitrogen，NO3--N）、酰胺态氮（Amide Nitrogen，CONH2-N），有效磷（Available Phosphorus，AP）、有效钾（Available Potassium，AK）、有机质（Organic Matter，OM）、pH值共10种粪水成分的快速检测。结果表明：对粪水TN、TP、TK、NH4+-N、NO3--N、CONH2-N、AP、AK、OM、pH值的预测相对误差分别约为9.251%、4.261%、8.238%、8.906%、17.825%、15.123%、9.829%、5.507%、10.558%、2.969%。微型近红外光谱技术结合定标模型云共享能够实现粪水中多种成分的现场速测，完成了定量模型的资源共享，且因微型近红外光谱传感器的便携性、Android客户端的操作简便性、定标模型云共享的低成本和无需用户具备专业知识要求等优点，具有广阔的市场前景和重要的现实应用价值。     

基于Tavares模型的多面体棉籽离散元参数标定与试验

针对基于离散元法的棉籽压缩破碎过程中缺少准确仿真模型的问题,本文以新陆早84号棉籽为研究对象,结合物理试验与仿真试验,对棉籽参数进行标定。基于三维扫描技术,使用Solidworks 2022中的网格建模功能,快速建立棉籽多面体模型。采用堆积角试验对棉籽种间参数进行标定,得到棉籽-棉籽碰撞恢复系数、棉籽-棉籽静摩擦因数、棉籽-棉籽滚动摩擦因数的最优参数组合为0.106、0.248、0.105,仿真堆积角与实际堆积角相对误差为0.28%,证明棉籽种间参数准确。通过单颗粒压缩试验对Tavares模型参数进行标定,以棉籽破碎力与破碎能为指标进行验证,结果表明棉籽破碎力与破碎能相对误差分别为2.37%和2.87%,说明构建的棉籽模型和Tavares模型参数可以表征棉籽压缩破碎过程。