基于切比雪夫多项式的数控机床几何误差参数化建模

为了快速精确地建立机床几何误差项数学模型,提出了一种基于切比雪夫多项式的参数化建模方法。首先针对测量得到的机床基本几何项数据,将机床相应运动轴进给量转化为切比雪夫变量。其次将切比雪夫变量代入不同阶次的切比雪夫多项式得到相应的值。然后根据切比雪夫基函数值和切比雪夫变量用多元线性回归方法获得相应的系数,得到关于切比雪夫基函数的数学模型。最后将运动轴进给量与切比雪夫变量之间的转化关系代入得到基本几何误差项的数学模型。建模过程简单且易程序化,切比雪夫多项式的高逼近精度使得建立的模型精度高。将所有几何误差项参数化模型代入机床几何误差模型综合数学模型,从而可得到机床工作空间几何误差场分布。以MV-5A三轴立式加工中心为例,将各个几何误差项参数化模型代入机床几何误差模型中得到该机床综合几何误差数学模型,进而得到该机床工作空间几何误差场分布,为机床设计和误差补偿提供了理论依据。     

田园管理机悬架部分的建模及仿真

主要介绍了虚拟制造技术在田园管理机悬架上的应用,并对田园管理机械的应用及特点作了介绍。针对目前的田园管理机的悬架存在的问题进行了研究,利用大型的三维建模软件Pro/E建立了悬架的三维模型,采用Pro/E与Adams之间的接口程序mech/pro将模型从Pro/E导入Adams,再进行动力学分析,模拟悬架在工作时各个部分的受力及运动状态,为进一步优化分析提供了依据。     

水稻直播机船底板下流体的数值模拟

利用有限元分析软件ANSYS中的计算流体动力学模块(FLOTRANCFD),对船底板下的流体进行模拟和分析,讨论在考虑形状损失和摩擦损失时,船底板下流体的流线和流动情况。通过对流体的仿真得出:船底板横挡板的前倾角对船底板下流体的流线和流量有较大的影响,并得出在倾角30o和60o时有较好的流动情况。     

油菜收获机清选装置虚拟样机的建模与仿真实验

针对油菜收获机清选装置的设计及改进过程中存在的各种问题,以DF-1.5型物料脱粒分离、清选仿真与控制试验台为原型,建立风筛清选装置的虚拟样机模型,并对不同机械系统工作参数下,油菜物料在清选装置中的运动规律进行初步的仿真研究。     

田间土壤特性的空间相关结构分析及其分布描述

本文对雄县试区试验田的土壤特性空间变异性进行经典统计分析，并利用地质统计学半方差分析法定量描述土壤特性的空间相关结构，然后使用Ｋｒｉｇｉｎｇ最优内插估值法得到了田间土壤特性参数的空间分布等值图。分析结果表明，该试验田０～１００ｃｍ土壤范围内的平均含水量不仅在整个空间分布上而且在纵横曲型剖面上都呈现出弱变异性，空间相关结构显著，相关距离约６０～８０ｍ。采用上述结论和等值图不但能减少样本采样的数量和密度，还能根据田间含水量的空间分布特征合理布点采样，实施土壤墒情的科学监测。     

Evaluation of the RZWQM-CERES-Maize hybrid model for maize production

The root zone water quality model (RZWQM) was developed primarily for water quality research with a generic plant growth module primarily serving as a sink for plant nitrogen and water uptake. In this study, we coupled the CERES-Maize Version 3.5 crop growth model with RZWQM to provide RZWQM users with the option for selecting a more comprehensive plant growth model. In the hybrid model, RZWQM supplied CERES with daily soil water and nitrogen contents, soil temperature, and potential evapotranspiration, in addition to daily weather data. CERES-Maize supplied RZWQM with daily water and nitrogen uptake, and other plant growth variables (e.g., root distribution and leaf area index). The RZWQM-CERES hybrid model was evaluated with two well-documented experimental datasets distributed with DSSAT (Decision Support System for Agrotechnology Transfer) Version 3.5, which had various nitrogen and irrigation treatments. Simulation results were compared to the original DSSAT-CERES-Maize model. Both models used the same plant cultivar coefficients and the same soil parameters as distributed with DSSAT Version 3.5. The hybrid model provided similar maize prediction in terms of yield, biomass and leaf area index, as the DSSAT-CERES model when the same soil and crop parameters were used. No overall differences were found between the two models based on the paired t test, suggesting successful coupling of the two models. The hybrid model offers RZWQM users access to a rigorous new plant growth model and provides CERES-Maize users with a tool to address soil and water quality issues under different cropping systems.     

Inverse Modeling to Quantify Irrigation System Characteristics and Operational Management

Remotely sensed (RS) data is a major source to obtain spatialdata required for hydrological models. The challenge for thefuture is to obtain besides the more direct observable data(landcover, leaf area index, digital elevation model andevapotranspiration), non-visible data such as soilcharacteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inversemodeling to obtain these non-RS-visible data. For a commandarea in Haryana, India, we applied for the 2000–2001 rabiseason a RS-GIS-combined inverse modeling approach to derivenon-RS-visible data required in the regional application ofhydrological models. A Genetic Algorithm loaded stochasticphysically based soil-water-atmosphere-plant model (SWAP) wasdeveloped for the inverse problem and used in the study. Theresults showed good agreement with the inventoried data suchas soil hydraulic properties, sowing dates, groundwaterdepths, irrigation practices and water quality. The deriveddata could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluateoperational management strategies.     

Predicting effects of drainage water management in Iowa's subsurface drained landscapes

Long-term hydrologic simulations are presented predicting the effects of drainage water management on subsurface drainage, surface runoff and crop production in Iowa's subsurface drained landscapes. The deterministic hydrologic model, DRAINMOD was used to simulate Webster (fine-loamy, mixed, superactive, mesic) soil in a Continuous Corn rotation (WEBS_CC) with different drain depths from 0.75 to 1.20 m and drain spacing from 10 to 50 m in a combination of free and controlled drainage over a weather record of 60 (1945-2004) years. Shallow drainage is defined as drains installed at a drain depth of 0.75 m, and controlled drainage with a drain depth of 1.20 m restricts flow at the drain outlet to maintain a water table at 0.60 m below surface level during the winter (November-March) and summer (June-August) months. These drainage design and management modifications were evaluated against conventional drainage system installed at a drain depth of 1.20 m with free drainage at the drain outlet. The simulation results indicate the potential of a tradeoff between subsurface drainage and surface runoff as a pathway to remove excess water from the system. While a reduction of subsurface drainage may occur through the use of shallow and controlled drainage, these practices may increase surface runoff in Iowa's subsurface drained landscapes. The simulations also indicate that shallow and controlled drainage might increase the excess water stress on crop production, and thereby result in slightly lower relative yields. Field experiments are needed to examine the pathways of water movement, total water balance, and crop production under shallow and controlled drainage in Iowa's subsurface drained landscapes.     

An entropy approach to spatial disaggregation of agricultural production

While agricultural production statistics are reported on a geopolitical – often national – basis we often need to know the status of production or productivity within specific sub-regions, watersheds, or agroecological zones. Such re-aggregations are typically made using expert judgments or simple area-weighting rules. We describe a new, entropy-based approach to making spatially disaggregated assessments of the distribution of crop production. Using this approach, tabular crop production statistics are blended judiciously with an array of other secondary data to assess the production of specific crops within individual ‘pixels’ – typically 25–100 square kilometers in size. The information utilized includes crop production statistics, farming system characteristics, satellite-derived land cover data, biophysical crop suitability assessments, and population density. An application is presented in which Brazilian state level production statistics are used to generate pixel level crop production data for eight crops. To validate the spatial allocation we aggregated the pixel estimates to obtain synthetic estimates of municipality level production in Brazil, and compared those estimates with actual municipality statistics. The approach produced extremely promising results. We then examined the robustness of these results compared to short-cut approaches to allocating crop production statistics and showed that, while computationally intensive, the cross-entropy method does provide more reliable estimates of crop production patterns.     

基于Pro/E的播种机零部件参数化造型

针对旱作节水播种机设计中存在的设计手段落后、设计效率低下等问题，应用基于特征的参数化造型软件Pro/E对旱作节水播种机的零部件进行参数化造型，在对该类播种机开沟器、排种器、地轮等零部件的造型设计中探讨了技术要点，提高了旱作节水播种机修改设计和系列化设计的效率。