微波快速测量球团铁精矿混合原料水分新方法

对目前几种测量水分的方法进行了评述，研究了用微波快速测量球团混合铁精矿原料水分的新方法，并分析和讨论了不同混合铁精矿原料水分含量对检测结果的影响。实验结果为：当球团混合铁精矿原料的含水量为6．97％-9．81％时，检测误差小于0．17％。该研究有望为球团混合铁精矿原料水分快速测量提供一种新力法．     

酯硬化水玻璃再生砂循环使用后的性能变化及应对策略

分析了酯硬化水玻璃砂多次再生使用后,其Na2O含量较大升高、溃散性显著下降、生产厚大铸型(芯)时硬透性变差并出现蠕变现象的原因,探讨了再生砂性能变化后的应对策略。认为:研究开发适于干法再生水玻璃砂的改性水玻璃、对再生砂实施表面处理、对旧砂进行高脱膜率的再生等,应是解决酯硬化水玻璃砂多次再生使用后溃散性恶化问题的主要措施;而控制再生砂的含水量、适当增加有机酯的加入量、CO2气体补充硬化等,是增加型(芯)硬透性和克服蠕变现象的主要方法。     

DA471球铁凸轮轴热处理工艺研究

采用覆砂铁型生产了DA471型QT700—2球铁凸轮轴，其铸态显微组织由50％～85％的珠光体和球状石墨组成，球化率大于85％。论述了正火（雾冷和强风冷却）和回火温度对凸轮轴中显微组织和力学性能的影响。结果表明，正火加回火后凸轮轴的珠光体含量大于95％，抗拉强度和伸长率分别大于700MPa和3％。该凸轮轴已经大量使用。     

衡水市土壤墒情变化特征分析

衡水水文局对衡水市15处土壤墒情站进行墒情监测,分别对水浇地作物、水浇地白地、旱地作物、旱地白地4种土地情况进行墒情变化分析。通过土壤墒情变化规律分析,为农业抗旱提供科学依据。     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

基于PLC的灌溉施肥控制系统的设计与实现

针对传统农田灌溉施肥存在的浪费水资源、液体肥不均匀等问题,提出利用欧姆龙SYSMAC C系列可编程控制器和组态王6.55监控软件设计一套PLC灌溉施肥控制系统,利用传感器采集数据控制农田按需灌溉、按量施肥,实现自动灌溉施肥和监控管理,节省人力、实用性强。     

土壤含水量空间变异性的频域分析方法

基于频域法分析理论原理,给出了基于二维信号采样与重建的频域法插值公式,对土壤特性的空间变异性进行表征.对一块麦田采用20m×20m的网格进行土壤含水量采样,利用频域法对15×15个采样值进行分析,结果表明含水量分布的有效带宽为0.01,说明空间变异主要由周期大于100m的频率分量组成;通过改变采样间距的"重采样"方法,...     

基于粒子群优化RBF神经网络原油含水率预测

原油含水率预测对于确定油井水、油层位以及估计原油产量有着非常重要意义。BP神经网络是最近常用的原油含水率预测方法,然而,由于BP神经网络存在容易陷入局部极小值、收敛速度慢等问题,影响了其预测的实用性和准确性,对此,提出基于粒子群优化RBF神经网络(PSO-RBFNN)的原油含水率预测方法,粒子群优化算法用于RBF神经网络参数优化。在分析原油含水率预测的影响因素基础上,建立粒子群优化RBF神经网络的原油含水率预测模型。实验结果表明,在原油含水率预测中,基于粒子群优化RBF神经网络比BP神经网络有着更高的预测精度。     

Temporal persistence and stability of surface soil moisture in a semi-arid watershed

Satellite soil moisture products, such as those from Advanced Microwave Scanning Radiometer (AMSR), require diverse landscapes for validation. Semi-arid landscapes present a particular challenge to satellite remote sensing validation using traditional techniques because of the high spatial variability and potentially rapid rates of temporal change in moisture conditions. In this study, temporal stability analysis and spatial sampling techniques are used to investigate the representativeness of ground observations at satellite scale soil moisture in a semi-arid watershed for a long study period (March 1, 2002 to September 13, 2005). The watershed utilized, the Walnut Gulch Experimental Watershed, has a dense network of 19 soil moisture sensors, distributed over a 150 km² study region. In conjunction with this monitoring network, intensive gravimetric soil moisture sampling conducted as part of the Soil Moisture Experiment in 2004 (SMEX04), contributed to the calibration of the network for large-scale estimation during the North American Monsoon System (NAMS). The sensor network is shown to be an excellent estimator of the watershed average with an accuracy of approximately 0.01 m³/m³ soil moisture. However, temporal stability analysis indicated that while much of the network is stable, the soil moisture spatial pattern, as represented by mean relative difference, is not replicated by the network mean relative difference pattern. Rather, the network is composed of statistical samples. Geophysical aspects of the watershed, including topography and soil type are also examined for their influence on the soil moisture variability and stability. Soil type, as characterized by bulk density, clay and sand content, was responsible for nearly 50% of the temporal stability. Topographic effects were less important in defining representativeness and stability.     

Aircraft based soil moisture retrievals under mixed vegetation and topographic conditions

An unresolved issue in global soil moisture retrieval using passive microwave sensors is the spatial integration of heterogeneous landscape features to the nominal 50 km footprint observed by most low frequency satellite systems. One of the objectives of the Soil Moisture Experiments 2004 (SMEX04) was to address some aspects of this problem, specifically variability introduced by vegetation, topography and convective precipitation. Other goals included supporting the development of soil moisture data sets that would contribute to understanding the role of the land surface in the concurrent North American Monsoon System. SMEX04 was conducted over two regions: Arizona — semi-arid climate with sparse vegetation and moderate topography, and Sonora (Mexico) — moderate vegetation with strong topographic gradients. The Polarimetric Scanning Radiometer (PSR/CX) was flown on a Naval Research Lab P-3B aircraft as part of SMEX04 (10 dates of coverage over Arizona and 11 over Sonora). Radio Frequency Interference (RFI) was observed in both PSR and satellite-based (AMSR-E) observations at 6.92 GHz over Arizona, but no detectable RFI was observed over the Sonora domain. The PSR estimated soil moisture was in agreement with the ground-based estimates of soil moisture over both domains. The estimated error over the Sonora domain (SEE = 0.021 cm³/cm³) was higher than over the Arizona domain (SEE = 0.014 cm³/cm³). These results show the possibility of estimating soil moisture in areas of moderate and heterogeneous vegetation and high topographic variability.