水-岩作用下砂岩声发射与红外辐射耦合研究

为探究水-岩作用下的砂岩破裂变形机制,开展天然与饱水砂岩单轴压缩声发射实验与红外辐射观测实验,通过对各变形阶段的轴向应力、声发射能率、分形维数以及红外辐射频数直方图与偏度共同分析了水对砂岩力学性质、声发射以及红外辐射变化的影响.研究结果表明:水对岩石的抗压强度、承载能力等力学性质有明显弱化作用;水抑制了岩石的声发射活动...     

A new approach to predict normalized difference vegetation index using time-delay neural network in the arid and semi-arid grassland

ABSTRACT

Normalized difference vegetation index (NDVI) has been used to conduct important research on plant growth and vegetation productivity. In this paper, a new approach to predict NDVI based on precipitation in the grass-growing season for the arid and semi-arid grassland is proposed using time-delay neural network (TDNN). To intuitively know the ability of TDNN to learn the relationship between NDVI and precipitation and to predict NDVI, the performance of the TDNN model is compared with back propagation neural network (BPNN) trained with the same data. The results indicate that TDNN works well to predict precipitation. Moreover, the relationship between precipitation and NDVI can be predicted accurately by the proposed TDNN model. The results show the goodness-of-fit between the observed NDVI and predicted NDVI measured by the determination coefficient of R² being 0.999 from the TDNN model, with the mean absolute percentage error, mean absolute error, and root-mean-square error to be 0.23%, 0.20, and 0.19, respectively. The analysis shows that the proposed method can result in an accurate NDVI prediction. Thus, the algorithm is applied to predict the NDVI during the grass-growing season for the validation of the approach. This validation results suggest the potential application of this approach for reduction of overgrazing pressure and vegetation restoration in the arid and semi-arid grassland.     

基于掩模经验模态分解的风速组合预测模型

在风电预测中,传统的经验模态分解法将风速信号分解为若干具有不同特征尺度的数据分量时,其所得分量可能存在模态混叠现象,影响风速预测的精度。为此,提出一种基于掩模经验模态分解法和遗传神经网络的风速预测组合模型。首先,通过掩膜信号法（masking signal,MS）对经验模态分解法进行改进,将风速信号分解为频率相对固定、更为平稳的分量。之后,利用遗传神经网络算法分别对这些分量进行预测,将各分量预测结果叠加后得到最终风速预测值。通过C++语言编程进行算法实现,采用实际风场数据进行仿真,其结果表明,所提方法计算时间较短,预测精度较高,特别适用于在线超短期（10 min）和短期（1 h）的风速预测,具有实际的工程应用价值。     

基于发电机扭矩的轴系快速疲劳损伤评价方法研究

通过大型汽轮机组的全尺寸模型模态分析结合轴系扭振基础分析,提出了一种基于发电机扭矩的轴系扭振疲劳损伤评估方法,并采用瞬态分析的方法进行了验证。该方法可绕开测速齿轮和扭振传感器,直接利用发电机端电磁转矩进行疲劳损伤评价,同时也可与基于汽轮机机头扭角数据的轴系扭振损伤评估方法相配合,提高电厂轴系扭振疲劳损伤评估的可靠性和准确性。     

基于比例重复控制的MMC-SST子模块电容纹波电压抑制策略

基于模块化多电平变换器的固态变压器(modular multilevel converter based solid state transformer, MMC-SST)是实现交直流混合配电网柔性互联及能量多向流动的关键装备。针对固态变压器输入级MMC子模块电容纹波电压过大,导致装置的体积和成本增加的问题,提出一种基于比例重复控制的MMC-SST改进纹波电压抑制策略。首先利用基于比例重复控制的电容电压闭环得到调整后的功率移相角。然后,通过双有源桥变换器将子模块电容纹波功率传递到低压直流母线,从而有效抑制MMC子模块的各频次纹波电压,达到减小电容值的目的。最后,仿真结果表明在网侧电压对称或不对称工况下,基于比例重复控制的MMC-SST子模块电容纹波电压抑制策略均具有良好的纹波电压抑制能力。     

Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method

Wind speed is the major factor that affects the wind generation, and in turn the forecasting accuracy of wind speed is the key to wind power prediction. In this paper, a wind speed forecasting method based on improved empirical mode decomposition (EMD) and GA-BP neural network is proposed. EMD has been applied extensively for analyzing nonlinear stochastic signals. Ensemble empirical mode decomposition (EEMD) is an improved method of EMD, which can effectively handle the mode-mixing problem and decompose the original data into more stationary signals with different frequencies. Each signal is taken as an input data to the GA-BP neural network model. The final forecasted wind speed data is obtained by aggregating the predicted data of individual signals. Cases study of a wind farm in Inner Mongolia, China, shows that the proposed hybrid method is much more accurate than the traditional GA-BP forecasting approach and GA-BP with EMD and wavelet neural network method. By the sensitivity analysis of parameters, it can be seen that appropriate settings on parameters can improve the forecasting result. The simulation with MATLAB shows that the proposed method can improve the forecasting accuracy and computational efficiency, which make it suitable for on-line ultra-short term (10 min) and short term (1 h) wind speed forecasting.     

特低渗透油田注空气驱油调研及认识

海塔油田经过近十年的勘探开发,年产油已达80×104t,成为大庆油田外围上产的主产区.但海塔油田探明储量中特低渗透储量比例高,渗透率小于2×10-3μm2的储量接近一半.针对海塔油田特低渗透储量比例大,常规注水开发难以有效动用的实际,积极探索特低渗透油田难采储量有效动用新途径.通过国内外文献调研,对特低渗透油田注空气技术,包括适用条件、注入方式和用量、跟踪分析调整、应用效果、采油工艺及地面设备等进行调研,为特低渗透油藏实施注空气驱油提供借鉴.同时,结合海塔油田的实际,在国内海拉尔油田优选出先导试验区,开展注空气驱现场试验,见到了初步效果.试验结果表明,海拉尔油田注空气驱油具有一定的可行性,可以有效动用部分难采储量,加快海塔油田上产步伐.可见,对于特低渗透油藏,空气驱的技术经济效益明显好于水驱,注空气开发技术可作为一项战略性技术储备,为提高特低渗透油藏最终采收率提供技术支撑.     

澳洲白羊与小尾寒羊杂交F1代背最长肌肉品质与皮下脂肪组织膻味物质分析

选取8月龄澳洲白羊与小尾寒羊的杂交一代母羊(澳寒羊,n=10)并与其同月龄母本小尾寒羊为对照(n=7),测定其屠宰性状、肉质指标、脂肪酸组成和膻味物质含量,探究澳洲白羊与小尾寒羊杂交后其后代的肉品品质和膻味物质的变化.结果表明:在肉品质方面,澳寒羊的a*值显著高于小尾寒羊,肌内脂肪含量和pH0值显著(P<0.05)低于...     

高通量测序分析益生菌对冷食小鼠肠道菌群的干预

采用高通量测序技术分析益生菌对冷食小鼠肠道菌群的影响。将实验小鼠随机分为模型组、对照组和低中高剂量组,分别灌喂等量的普通冰淇淋、107 cfu/m L益生菌液和107、108、109 cfu/m L的益生菌冰淇淋。连续灌喂15d,记录并计算小鼠的稀便率和腹泻指数,15 d后,收集各组小鼠粪便进行高通量测序分析。发现食用冷食会导致部分小鼠腹泻,在剂量范围内,益生菌可以缓解冷凉性敏感小鼠的腹泻症状,高剂量组小鼠的稀便率降低了3.11%,腹泻指数(10-1)降低了0.33,抑制效果较其它各组最为明显。服用了益生菌的小鼠粪便菌群多样性较高(高剂量组ACE值为69987.29,Chao1值为27098.77),乳酸菌属、艾克曼氏菌属、Blautia等菌属占比增大,菌群结构发生改变。因此益生菌可以提高冷凉性腹泻敏感小鼠对冷食的接受度,冷冻载体下的益生菌可以在小鼠肠道中定植并对小鼠肠道起到调节作用。