拖拉机－挂车机组非线性系统随机响应分析

推导了随机激励下参数频变系统的统计线性化方法。利用该方法对铁牛－６５４拖拉机挂车机组非线性系统进行了平稳激励下随机振动响应的计算，计算结果表明，把拖拉机挂车机组作为非线性系统来处理可显著地提高振动分析的精度。     

土壤水分特征曲线几种标定的对比分析

土壤水分特征曲线的标定是土壤水的能量和数量关系在不同尺度间转换的方法,也是植被生态需水机理研究的重要组成部分。通过对传统的迭代法、一次法和建立的2种混合遗传算法进行比较,标定的结果表明,多项式遗传算法能综合考虑2种方差的影响,并能给出一致性的标定结果,这种经验式混合遗传算法对传统的标定算法进行了改进。     

华北冬小麦-夏玉米农田水分动态模拟研究

冬小麦-夏玉米连作是华北地区主要的粮食作物种植模式。根据华北季节性冻土区的特点,将全年划分为作物生长期与越冬期,分别建立了作物生长条件下农田水分运移模型、冻融条件下土壤水热运移模型。前一模型主要包括参照腾发量计算、腾发量分配、作物根系吸水、土壤表面蒸发、土壤水分特征参数和土壤水分运动等子模型;后一模型主要包括冻土水热耦舍迁移、地气水热交换等子模型。应用以上模型对冬小麦-夏玉米连作条件下的土壤水分过程进行模拟,根据北京永乐店试验资料对模型进行检验。模拟了不同降水水平年、不同灌溉处理下的农田灌溉制度及土壤水分过程,分析了降水、灌溉对农田蒸散、土壤水利用、深层渗漏等的影响。     

汽车动力总成液压悬置元件特性研究

液压悬置具有低频大阻尼、高刚度等特点，可以有效隔离和衰减发动机的怠速振动，降低车内噪声，提高乘坐舒适性。为此，应用流体一结构耦合理论的方法建立了某车动力总成悬置系统的动力学分析模型，并运用Adams软件对其动特性进行了仿真。仿真结果与已知实验数据基本吻合，从而证明了该模型建立的正确性。     

基于B样条的水泵复杂特性曲线拟合方法

为适应水泵复杂特性曲线中存在大挠度和非等距测点的情况，探索了基于三次均匀B样条的曲线拟合方法。通过局部预测的方法引入新的型值点以克服曲线尖点拟合的困难。根据离散数据点连线的几何形状进行分段，基于最小二乘法分步优化节点的横坐标和纵坐标，得到了最优逼近的B样条拟合曲线。通过实例计算表明，该方法可以高精度地将全区域范围的水泵试验数据拟合成光滑的特性曲线，并能够消除因试验随机误差所引起的曲线扰动。     

基于气温预报和神经网络的参考作物腾发量预报

采用反向传播人工神经网络(BP-ANN)逼近气象因子-参考作物腾发量ET0函数关系,以天气预报中的最高和最低气温为输入进行短期ET0预报。收集了南京站实测的2010年7月1日至2013年7月7日逐日气象数据和2012年7月1日至2013年6月30日逐日对未来7d的气象预报数据,以最高、最低气温及相应的日序数为3个输入因子,ET0为输出建立一个包含一个隐含层的3层BP网络,以2010年7月1日至2012年6月30日实测气象数据及通过FAO-56PM公式计算的ET0进行网络,以2012年7月1日至2013年6月30日实测气象数据及通过FAO-56PM公式计算的ET0进行网络验证。将2012年7月1日至2013年6月30日逐日对未来7d的气象预报中的最高、最低气温输入训练及验证后的网络,得到2012年7月1日至2013年6月30日逐日对未来7d的ET0预报值,并与FAO-56PM公式计算的ET0值进行比较以验证预报精度。结果表明,预见期1~7d内,预报的ET0和计算的ET0变化趋势基本一致,预报精度随着预见期的增加而降低;平均准确率(±1.5mm/d以内)达88.08%,相关系数为0.77,均方根误差为1.28mm/d,显示出了较高的预报精度。在局部时间段内出现的ET0,PM和预报ET0的较大差别的原因是该时段内的ET0更多地受到除了日最高和最低气温之外的其他因素的影响。提出的方法 ET0预报,随着气象预报准确度的提高,可实现较为精确的ET0预报。     

日光温室作物蒸发蒸腾量的计算方法研究及其评价

对FAO推荐计算参考作物蒸发蒸腾量的Penman-Monteith(缩写为P-M)公式,在日光温室微气候的条件应用作了详细的分析。将P-M公式分为2个部分,即辐射项(ETrad)和空气动力学项(ETaero),推导出了计算温室内参考作物蒸发蒸腾量的P-M修正公式,解决了P-M公式假定温室内风速为“0”所引起的一系列问题。并根据2004年和2005年温室内实测气象数据和水面蒸发对其进行了验证,通过相关分析得出用修正后的P-M公式计算作物蒸发蒸腾量比FAO推荐的P-M公式计算值误差小、精度高。建议在日光温室里使用修正后的P-M公式计算参考作物的蒸发蒸腾量。     

Remote sensing of biotic and abiotic stress for irrigation management of cotton

The applicability of commercially available remote sensing instrumentation was evaluated for site-specific management of abiotic and biotic stress on cotton (Gossypium hirsutum L.) grown under a center pivot low energy precision application (LEPA) irrigation system. This study was conducted in a field where three irrigation regimes (100%, 75%, and 50% ET_c) were imposed on areas of Phymatotrichum (root rot) with the specific objectives to (1) examine commercial remote sensing instrumentation for locating areas showing biotic and abiotic stress symptomology in a cotton field, (2) compare data obtained from commercial aerial infrared photography to that collected by infrared transducers (IRTs) mounted on a center pivot, (3) evaluate canopy temperature changes between irrigation regimes and their relationship to lint yield with IRTs and/or IR photography, and (4) explore the use of deficit irrigation and the use of crop coefficients for irrigation scheduling. Pivot-mounted IRTs and an IR camera were able to differentiate water stress among irrigation regimes. The IR camera distinguished between biotic (root rot) and abiotic (drought) stress with the assistance of groundtruthing. The 50% ET_c regime had significantly higher canopy temperatures than the other two regimes, which was reflected in significantly lower lint yields when compared to the 75% and 100% ET_c regimes. Deficit irrigation down to 75% ET_c had no impact on lint yield, indicating that water savings were possible without reducing yield.     

Measuring versus estimating net radiation and soil heat flux: Impact on Penman–Monteith reference ET estimates in semiarid regions

The standardized ASCE Penman–Monteith and FAO-56 equations were used to estimate reference evapotranspiration (ET₀) using estimated and measured net radiation (R_n) and soil heat flux (G), based on hourly and daily meteorological data. The estimates were evaluated against lysimeter measurements. The results indicate that using measured or estimated values of R_n and G can have significant effect on the accuracy of the ET₀ estimations, especially when calculations were made on an hourly basis. The FAO-56 version performed very well during the irrigation season on a daily basis. The use of measured R_n and G did not improve ET₀ estimation on a daily basis, therefore, the use of estimated R_n and G appears to be dependable when calculations are based on 24-h weather data. When daily ET₀ was calculated from hourly estimations, the results were different depending on the version used. The ASCE version was more accurate, especially when R_n and G were measured. Therefore, measurement of R_n and G may have potential to improve estimation only when daily ET₀ is calculated from hourly estimations. The PM FAO-56 version was always a little less accurate than the ASCE version. For hourly calculations, using a constant surface resistance (as in FAO-56 version), the PM method underpredicted for high evaporative demand and vice versa. The ASCE version performed better than PM FAO-56 version when R_n and G were measured and estimated. Therefore, ASCE version tended to provide quite accurate values of hourly ET₀, even using estimated values of R_n and G. As conclusion, the methods proposed by FAO-56 for estimating R_n and G tended to produce accurate estimates for daily and hourly ET₀ under semiarid conditions and can be used with some degree of confidence for estimating ET₀. In addition, results suggest that the ASCE standardized equation on an hourly basis improved the accuracy of ET₀ estimation with respect to the FAO-56 version.     

Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches

Different methods have been developed to estimate evapotranspiration from remote sensing data, from empirical approaches such as the simplified relationship to complex methods based on remote sensing data assimilation along with SVAT models. The simplified relationship has been applied from small spatial scale using airborne TIR images to continental scale with NOAA data. Assimilation procedures often require remote sensing data over different spectral domains to retrieve input parameters which characterize surface properties such as albedo, emissivity or Leaf Area Index. A brief review of these different approaches is presented, with a discussion about the main physical bases and assumptions of various models. The paper reports also some examples and results obtained over the experimental area of the Alpilles Reseda project, where various types of models have been applied to estimate surface fluxes from remote sensing data.