输油管道对流换热系数模型

热油管道能耗巨大,其散热问题的研究具有极高的工程应用价值。尽管在很多情况下流体与管壁间的对流换热系数很大,可以在总传热系数计算中忽略对流换热系数的影响,但并不总是如此。主流与近壁边界间的过渡区、即热边界层内的换热是不可忽略的。采用动量热量比拟的方法建立输油管道对流换热系数模型,并在此基础上对总传热系数进行了分析计算。计算结果表明,边界层内的对流换热系数对管道的总传热系数具有较大影响。     

热-力-电耦合下天线罩电性能仿真及分析

在实际飞行工况下,热力载荷会改变高速飞行器天线罩原有的电磁特性,从而影响制导性能。提出了一种基于六面体网格划分的热-力-电耦合模型及仿真方法,可准确表征高速飞行工况下天线罩介电温漂和结构变形对电性能的影响。基于天线罩热-力-电共享网格模型,首先通过瞬态热仿真得到天线罩响应温度场,通过静力分析得到天线罩结构变形场。然后,将天线罩介电温漂和结构变形准确传递到其电磁仿真模型中,并采用三维射线跟踪法计算其电性能。最后通过一个典型算例对高速飞行工况下天线罩电性能的变化进行仿真和分析,结果表明电性能变化非常明显,也进一步说明了所提方法的可行性和研究的必要性。     

基于出行态度的自行车通勤出行市场划分

为识别自行车通勤出行中不同出行者选择偏好的差异,针对基于出行态度的自行车通勤出行市场进行划分.根据南京市居民通勤出行问卷调查,采用结构方程模型从29个观测变量中提取出若干态度潜变量,分析了出行偏好态度变量间的相关性,并采用K-means聚类方法对自行车通勤出行市场进行划分.根据选择自行车通勤出行意愿、对固定出行时刻需求、对出行舒适度需求及环保意识4类态度变量将自行车通勤出行市场划分为6个子市场,同一子市场内个人选择自行车出行意愿近似,不同子市场间选择意愿明显不同.分析了每个子市场内个人通勤出行态度特征以及自行车方式选择特征,针对每个子市场提出了相应政策以引导自行车通勤出行方式的选择.     

液黏离合器摩擦副对流换热系数数值模拟研究

为了预测液黏离合器的温度场分布及热负荷特性,通过数值模拟研究求得摩擦副散热面的对流换热系数。应用计算流体动力学软件CFX建立了摩擦副流固耦合有限元模型,获得了摩擦副的温度场分布,综合考虑换热表面形状、摩擦片转速、油液流速和入口压力、流体物理性质等因素,揭示了各因素与对流换热系数之间的内在联系。结果表明：摩擦副温度从内径到外径逐渐升高,菱形区域中心温度比四周高。摩擦片转速越大对流换热系数越大;油液黏度越小,入口压力越大,对流换热系数越大。可见,油液流速对换热系数的影响最为显著;摩擦片转速、油液的入口压力和黏度会改变流速及流体的运动状态,从而影响对流换热系数。     

饱和黏土的一维热固结特性试验研究

通过室内试验,研究了饱和黏土在不同温度作用下的固结效应.结果表明:温度对土颗粒的膨胀作用和孔隙比的收缩作用影响较小;高温作用下发生的热膨胀在一定程度上阻碍了变形的发生,高温条件下压缩指数较低温条件下有减小的趋势.但渗透性的增强使得该现象逐渐减弱.随着温度的升高,先期固结应力均随之减小.通过反分析得到了两种黏土的材料参数γ.材料参数γ越大,相同温度下的归一化先期固结应力越小.温度越高,水溶液的黏度越低,越容易被排出,其渗透系数越大,超静孔隙水压的消散越快,所以超静孔隙水压随着温度的升高而减小.渗透系数随着孔隙比的升高而升高.粒径较大试样的渗透系数大于粒径较小试样的渗透系数.     

氯代乙酰基乙酰甲胺合成技术的改进

改变了二氯代乙酰基乙酰甲胺的转化剂,提高了转化温度,从而减少了废水量,使转化时间由5.0￣5.5h减少到2.0￣2.5h,缩短了生产周期,提高了生产效率.     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Does a lot help a lot? Forecasting stock returns with pooling strategies in a data‐rich environment

A variety of recent studies provide a skeptical view on the predictability of stock returns. Empirical evidence shows that most prediction models suffer from a loss of information, model uncertainty, and structural instability by relying on low‐dimensional information sets. In this study, we evaluate the predictive ability of various lately refined forecasting strategies, which handle these issues by incorporating information from many potential predictor variables simultaneously. We investigate whether forecasting strategies that (i) combine information and (ii) combine individual forecasts are useful to predict US stock returns, that is, the market excess return, size, value, and the momentum premium. Our results show that methods combining information have remarkable in‐sample predictive ability. However, the out‐of‐sample performance suffers from highly volatile forecast errors. Forecast combinations face a better bias–efficiency trade‐off, yielding a consistently superior forecast performance for the market excess return and the size premium even after the 1970s.     

Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.