基于LS-DYNA的45号钢铣削SPH算法仿真分析

基于LS-DYNA软件的SPH算法对45号钢的铣削过程进行仿真分析,得出45号钢在切削过程中的变形及切屑的形成过程,控制切削速度、进给速度、切削深度等参数,以求得切削参数对切削力的影响.     

基于云平台和BFAST算法的地表变化检测方法

准确识别地表变化的时空信息,有助于探究地表自然环境和生态系统发展演变的规律,支撑相关的科研与行政管理工作。本文以河南某生态保护修复工程部分实施范围为研究区域,基于Google Earth Engine(GEE)云平台,以2013—2020年的98景Landsat8/OLI遥感影像作为数据源,应用Breaks for additive season and trend(BFAST)算法对地表变化进行了信息提取和制图。首先基于GEE云平台对Landsat8/OLI地表反射率数据集进行调用和预处理,基于CFMask算法对遥感数据集进行云影掩膜,开展光谱指数(植被指数NDVI)的计算以及时间序列数据集的构建。其次基于时序数据集与BFAST算法构建由趋势项、季节项和残差项组成的广义线性回归模型,通过最小二乘法求解模型中的未知参数集,以此进一步构建时序拟合模型,而后基于残差的Moving sums(MOSUM)方法对时序结构变化进行检测。最后从检测结果中抽取像元样点,通过与Google Earth高分辨率影像数据叠置和目视解译,开展结果验证和精度评价。结果表明,本文提出的方法在研究区的时序地表变化...     

河套灌区控制排水对油葵生长与养分利用的影响

为系统地从土壤水分、盐分、养分和油葵生长的变化来揭示不同排水方式的调控效应,设置4个处理,生育期暗管控制排水深度分别为40cm（K1）、70cm（K2）、100cm（K3）,春灌排水深度均为100cm,选择明沟排水（深度150cm）作为对照处理（CK）,开展了田间试验。结果表明：K1处理自油葵开花期到收获1m土层平均储水量比K2、K3处理提高了0.01%~4.53%,为作物生长后期提供了有效的水分。K1处理稳定了土壤水消耗的速率,削弱了水平方向土壤水分的消耗差异。春灌后K1、K2、K3处理平均脱盐率分别为49.02%、50.43%、49.70%,处理间无显著差异,而明沟排水仅为35.52%。暗管排水处理暗管中间点与暗管上土壤盐分淋洗率相差7.1~8.2个百分点,处理间无显著差异。CK处理盐分淋洗差异性相对较小,距明沟0.4m处与明沟中间点相差2.8个百分点。至生育后期（开花期）不同处理存在土壤返盐情况,K1、K2、K3、CK较春灌前平均返盐率分别为28.63%、24.20%、20.83%、22.07%。K1、K2处理返盐程度相对较高,但其含盐量不影响油葵后期正常生长。K1处理在现蕾期铵态氮含量显著高于其他处理(P<0.05),较K2、K3、CK处理高30.43%、45.90%、14.83%;开花期铵态氮含量由大到小依次为K1、CK、K2、K3,差异性小于成熟期;成熟期K1、K2处理铵态氮含量与CK处理无显著差异。硝态氮含量在现蕾期、开花期和成熟期含量K1处理最高,K1处理较K2、K3、CK处理分别高13.62%~30.80%、14.33%~53.09%、7.17%~28.10%(P<0.05)。K1处理可减小地下水位波动,使氮素以稳定形态存在,减少硝态氮流失。暗管排水可以提高油葵出苗率2.5~2.7个百分点。K1处理增加有效株占比2.3～5.0个百分点;油葵出苗50d后能显著增加株高5.10%~14.87%、茎粗6.29%~22.46%;提高水分利用效率1.16%~10.8%;提高氮磷钾肥料偏生产力7.69%~11.16%;增产4.52%~11.14%;并且有效地提高了叶片光合能力。从对土壤控盐、保肥、稳产与水肥利用效率多角度综合分析,春灌排水深度100cm,生育期控制排水深度40cm（K1）的控制排水方式是适宜的选择。     

种子重力分选机预分层喂料系统设计与试验

为了提高种子重力分选机分选性能,降低设备使用调节难度,提出了种子进入分选台面的预分层工艺,设计了配套的分层喂料系统,计算确定了结构参数。在SolidWorks中建立了系统模型,采用SolidWorks-Simulation对模型工作应力和位移进行分析。采用SolidWorks-Flow Simulation对流场进行仿真和系统结构参数优化。在5TZX-50型重力分选机上进行试验,利用Design Expert 软件进行Box-Behnken响应面分析,建立了性能指标与影响因素的数学回归模型,选出了气流速度为3.40m/s、振动频率为9.75Hz、导流板倾角为29.65°的最佳组合,该组合下除轻杂率和除重杂率试验值为95.12%和96.37%。试验表明,除杂率与目标优化预测值的误差不大于1.36%,分层喂料系统满足设计要求。     

浙江高温热害气象下水稻孕穗阶段热害概率的拟合优度检验方法

为避免或降低高温热害对水稻孕穗阶段的影响,建立了一种热害概率预测模型,并采用卡方检验法判断其拟合度。结果表明,预测温度值与实际温度值的拟合度较高,该模型能够有效预测稻田的热害发生概率以及划分高温热害风险等级。      

农业机械化与农民增收关系的交互动态响应分析——基于吉林省统计核算数据

为探究农业机械化发展与农民增收之间的关系,以吉林省1998—2018年的时间序列数据为例,通过灰色关联分析和构建VAR模型,利用脉冲响应函数和方差分解分别探讨农业机械化与农民增收之间的关系。结果表明:农业机械化发展与农民人均纯收入之间的灰色关联度为0.8345,关系比较密切;吉林省农业机械化发展与农民人均纯收入之间存在长期均衡关系。从长期来看,农业机械总动力每增加1%,农民人均纯收入会上涨1.4561%;吉林省农业机械化发展与农民人均纯收入之间存在双向因果关系,两者之间相互影响,而且农业机械化发展对农民人均纯收入增长的影响程度更大。最后,为更好地促进吉林省农业机械化发展提出相关建议。     

苹果园春季土施尿素的利用及其在土壤中的累积

 以7年生嘎拉苹果/平邑甜茶为试材, 利用¹⁵N示踪技术, 研究了苹果生产体系中氮素年周期 的利用、残留、损失及在土壤中的迁移动态。结果表明, 对春季土施尿素的利用率盛花期较低, 为11.38% , 至采收后达到最高。土壤氮素残留率盛花期最高, 为57.10% , 果实采后残留率最低。年周期中各土层氮素残留量不同, 盛花期20～40 cm土层残留量最高, 采收后最低, 0～20 cm土层在新梢旺长期残留量最高, 果实成熟期最低, 而40～60 cm土层在果实膨大期残留量达到最高, 在采收后0～20 cm土层残留有所增加, 其余各土层残留量均达到最低值。氮素损失与土壤残留呈相反的变化趋势, 氮素损失率在盛花期最低为31.53% , 随物候期的推迟逐渐升高, 在果实采后高达58.40%     

Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

Transferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO₂ sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.     

Comparing fire spread algorithms using equivalence testing and neutral landscape models

We demonstrate a method to evaluate the degree to which a meta-model approximates spatial disturbance processes represented by a more detailed model across a range of landscape conditions, using neutral landscapes and equivalence testing. We illustrate this approach by comparing burn patterns produced by a relatively simple fire spread algorithm with those generated by a more detailed fire behavior model from which the simpler algorithm was derived. Equivalence testing allows objective comparisons of the output of simple and complex models, to determine if the results are significantly similar. Neutral landscape models represent a range of landscape conditions that the model may encounter, allowing evaluation of the sensitivity and behavior of the model to different landscape compositions and configurations. We first tested the model for universal applicability, then narrowed the testing to assess the practical domain of applicability. As a whole, the calibrated simple model passed the test for significant equivalence using the 25% threshold. When applied to a range of landscape conditions different from the calibration scenarios, the model failed the tests for equivalence. Although our particular model failed the tests, the neutral landscape models were helpful in determining an appropriate domain of applicability and in assessing the model sensitivity to landscape changes. Equivalence testing provides an effective method for model comparison, and coupled with neutral landscapes, our approach provides an objective way to assess the domain of applicability of a spatial model.