风洞6_PUS并联支撑机器人运动误差建模与补偿

针对风洞6自由度并联支撑机器人,利用单支链D-H参数方法和摄动法建立了其运动误差模型,编写了误差模型仿真程序。根据风洞实验所需的6种典型运动模式,分析了不同模式下并联支撑机器人输出运动位姿的误差,得到了典型运动模式的误差变化规律。在风洞并联支撑机器人的构件设计和装配过程进行了针对性的误差控制,使设计和制造的并联支撑机器人精度达到了风洞实验的要求,并通过在风洞实验中嵌入与运动误差仿真类似的误差估算程序,再对风洞实验中被试模型的位姿误差进行补偿,实验证明这种方法提高了风洞实验数据的精度。     

泵站虹吸式出水管虹吸形成过程气液两相流数值模拟

为分析虹吸式出水管虹吸形成过程中气液两相流动,基于各向同性假设,采用欧拉多相流模型与RNG k-ε湍流模型对一泵站虹吸式出水管虹吸过程中的气液两相流进行了数值模拟。结果表明:虹吸过程中,在虹吸管驼峰段和下降段易产生上、下气囊,对虹吸作用的完成有不利影响。来流流量是影响虹吸形成时间的主要因素,水力驱气阶段和水力挟气阶段可分别用不同的幂函数来表达时间与流量的关系。     

水稻育秧播种机钵体苗底土压实装置

设计了一种能实现水稻精密育秧播种机钵体软、硬秧盘穴孔底土压实的通用装置,该装置以AT89C51单片机为控制系统核心,采用步进电动机和送盘行程开关实现秧盘供送,以及限位行程开关和对准接近开关实现秧盘穴孔与压实辊指对准,压实辊指与秧盘穴孔内底土相互作用完成底土压实。通过系统的试验研究,确定了该装置的最佳工作参数。压实试验表明,该系统能满足秧盘穴孔底土压实的工作要求,实现了穴孔与压实辊指的精确对准和底土压实,当生产率在500盘/h、提前角对应弧长为1 mm时,对准率为98%,满足钵体苗穴孔底土压实的技术要求;育秧试验表明,增加穴孔底土压实深度,可提高秧苗素质,保持土壤根系坚实不散,有利于栽插作业,压实深度为6 mm时效果最佳。     

面向灌区调水工程的远程自动计量闸门研究

设计了一种基于远程无线通信技术的灌区动态调水系统,由安装于各级渠道上的自动计量闸门集群和运行于调度中心的远程调水控制系统组成。利用明渠测流理论和传感测量技术,实现了闸门的计量功能;对称双轮双向卷拉驱动机构和中空蜂窝闸板结构设计,提高了闸门启闭运动的可靠性和闸门运行能效;开发了基于ARM的闸门终端控制器,实现了闸门终端的智能控制和无线远程通讯,多种闸门工作模式可满足不同的应用需求;设计了太阳能供电与电源管理系统,解决了闸门野外工作的供电问题;开发了远程动态调水控制的应用软件包,实现了对闸门的远程监控与联动调水。实验表明,闸门终端环境适应性强,性能稳定,动态水位误差小于5 mm,闸门定位误差小于1 mm,自由流的测流误差小于4.6%,淹没流测流误差小于8.3%,可满足各类自动调水工程应用。     

Google Maps在农业WebGIS中的应用探索

针对目前国内农业WebGIS存在的信息单一、功能单调等问题,提出了将标准的地理信息系统开发平台和Google Maps同时应用于农业WebGIS的方法。利用Google Maps提供的开放地图接口自定义GoogleMap类可以为农业WebGIS系统提供详细的地理位置信息的显示、标注以及路线查询等功能。最后通过SuperMap.IS.NET开发平台与Google Maps实现对小汤山农业基地的信息管理,证明该方法实用可行。     

基于动态性能的车架结构模型分析研究

客车车架的动态特性是影响整车振动舒适性的关键因素。为探讨纵横梁结构对某轻型客车车架动态特性的影响,建立车架的研究性模型,对研究性模型进行多种方案的计算分析,综合各方案的计算结果,确定影响车架动态特性最大的结构特征,以此为基础对原车架进行改进,提高了车架的模态频率。计算结果表明,该研究方法简便有效,可推广应用于对其它结构的研究。     

高次指数组合非线性模型在土壤侵蚀中的应用

土壤侵蚀是全球性的严重环境问题。揭示土壤侵蚀过程，建立土壤侵蚀模型将为土地合理利用规划、水土保持措施优化配置、水土流失监测及水土保持减水减沙效益评价等生产实践提供重要科学依据和技术工具。土壤侵蚀预报模型按建模方法可分为经验模型与物理模型等，其中，经验模型主要是基于土壤侵蚀观测资料，采取统计分析的方法模拟侵蚀产沙量与降雨、植被、土壤、地形等影响因子之间的关系。自1953年刘善建在国内首次建立了坡面土壤侵蚀预报方程以来，不同学者根据各地的实际情况，建立了诸多不同的土壤侵蚀经验模型。由于国内土壤侵蚀物理模型研究起步晚，还很不成熟，而经验模型在侵蚀预报中一直处于重要地位，继续强化对它的研究，无疑可以进一步满足水土流失定量评价及水土保持决策等不同生产实践的迫切需求。     

单时相双极化ENVISAT ASAR数据水稻识别

许多研究已表明合成孔径雷达(SAR)对水稻识别及作物长势监测很有潜力。但是，以往的研究多是采用单极化多时相SAR数据进行水稻监测的。该文本着探讨多极化方式的优势以及降低数据购买成本和减少数据处理量的目的，对单时相双极化的ENVISAT ASAR APP数据的水稻识别能力进行了评价。在水稻生长季节，获取了覆盖江苏洪泽县的ASAR APP时间序列数据。首先，分析比较不同地物的后向散射系数，选择出最能区分水稻与非水稻的单时相数据；然后，采用决策阈值法将水稻信息从图像中提取出来；最后，利用DGPS实测的样地数据对水稻识别进行精度验证。结果表明，利用水稻齐穗期至近成熟期的HH和VV极化的ENVISAT ASAR APP图像能较好区分水稻与非水稻， 水稻识别精度可达86％以上。     

Distribution of light and heavy fractions of soil organic carbon as related to land use and tillage practice

Mass distributions of different soil organic carbon (SOC) fractions are influenced by land use and management. Concentrations of C and N in light- and heavy fractions of bulk soils and aggregates in 0–20 cm were determined to evaluate the role of aggregation in SOC sequestration under conventional tillage (CT), no-till (NT), and forest treatments. Light- and heavy fractions of SOC were separated using 1.85 g mL⁻¹ sodium polytungstate solution. Soils under forest and NT preserved, respectively, 167% and 94% more light fraction than those under CT. The mass of light fraction decreased with an increase in soil depth, but significantly increased with an increase in aggregate size. C concentrations of light fraction in all aggregate classes were significantly higher under NT and forest than under CT. C concentrations in heavy fraction averaged 20, 10, and 8 g kg⁻¹ under forest, NT, and CT, respectively. Of the total SOC pool, heavy fraction C accounted for 76% in CT soils and 63% in forest and NT soils. These data suggest that there is a greater protection of SOC by aggregates in the light fraction of minimally disturbed soils than that of disturbed soil, and the SOC loss following conversion from forest to agriculture is attributed to reduction in C concentrations in both heavy and light fractions. In contrast, the SOC gain upon conversion from CT to NT is primarily attributed to an increase in C concentration in the light fraction.     

Soil compaction and forest floor removal reduced microbial biomass and enzyme activities in a boreal aspen forest soil

Soil enzymes are linked to microbial functions and nutrient cycling in forest ecosystems and are considered sensitive to soil disturbances. We investigated the effects of severe soil compaction and whole-tree harvesting plus forest floor removal (referred to as FFR below, compared with stem-only harvesting) on available N, microbial biomass C (MBC), microbial biomass N (MBN), and microbial biomass P (MBP), and dehydrogenase, protease, and phosphatase activities in the forest floor and 0–10 cm mineral soil in a boreal aspen (Populus tremuloides Michx.) forest soil near Dawson Creek, British Columbia, Canada. In the forest floor, no soil compaction effects were observed for any of the soil microbial or enzyme activity parameters measured. In the mineral soil, compaction reduced available N, MBP, and acid phosphatase by 53, 47, and 48%, respectively, when forest floor was intact, and protease and alkaline phosphatase activities by 28 and 27%, respectively, regardless of FFR. Forest floor removal reduced available P, MBC, MBN, and protease and alkaline phosphatase activities by 38, 46, 49, 25, and 45%, respectively, regardless of soil compaction, and available N, MBP, and acid phosphatase activity by 52, 50, and 39%, respectively, in the noncompacted soil. Neither soil compaction nor FFR affected dehydrogenase activities. Reductions in microbial biomass and protease and phosphatase activities after compaction and FFR likely led to the reduced N and P availabilities in the soil. Our results indicate that microbial biomass and enzyme activities were sensitive to soil compaction and FFR and that such disturbances had negative consequences for forest soil N and P cycling and fertility.