中大型客滚船的线型与推进系统研究

本文介绍了当今世界上典型的中大型客滚渡船传统的线型设计方法和推进系统,以及近年来针对大型客滚渡船研发的新推进理念和方法。详细介绍这些新的推进理念,并将其与传统的方式进行比较,对将来的实际应用提出了建议。     

合肥北绕城高速公路石灰改良膨胀土的试验研究

膨胀土是一种吸水膨胀、失水收缩、胀缩变形显著的粘性土,其主要工程性质对公路有比较强的破坏作用。要减少膨胀土对路基路面的破坏,必须从设计上采取措施加以处理。     

雾化液滴尺寸和速度分布函数的推导

为了增强雾化液滴质量和热量的传递,有效预测与评价喷雾质量,应用最大熵原理和动量守恒定律推导出了新的雾化液滴尺寸和速度的概率密度分布函数,包括数目分布函数、数目微分分布函数、体积分布函数、体积微分分布函数与总体积公式。当不考虑速度分布时,推导的喷雾液滴尺寸分布与曹建明先前推导的尺寸分布具有完全相同的形式,其分布函数是Nukiyama-Tanasawa型雾化液滴尺寸分布函数的一种特定形式,推导结果具有普适意义。     

公路货物运输抽样调查中样本量的确定

在分析我国现行公路货物运输抽样调查方案的基础上,结合分层随机抽样中样本量的确定方法,以广东省为例,对公路货物运输抽样中的样本量进行测算,可进一步验证公路货物运输抽样调查中样本量确定的科学性,同时能为交通运输行业或其他行业类似的抽样调查提供有益的实践经验.     

斜拉桥箱形主梁尺寸的优化分析

针对斜拉桥中采用的双箱式主梁,改变斜拉桥箱形主梁的底板厚度、斜腹板厚度以及斜腹板倾斜角度,利用结构有限元分析程序ANSYS,对不同情况下的箱形主梁建立了有限元模型。考虑到梁段以外附近区域的作用,在其两端截面上施加了由平面杆系结构分析所得的端面内力,另外,索力和预加力(梁纵向、横隔梁横向、斜腹板竖向)也施加在相应的位置,分析了不同工况下箱形主梁在自重、索力和预应力作用下的空间应力效应。给出了斜拉桥箱形主梁的底板厚度、斜腹板厚度及斜腹板倾斜角度的合理化建议。分析表明:当底板厚度为30 cm左右,斜腹板厚度为30cm左右,斜腹板倾斜角度为150°~152°时,主梁的应力分布比较合理。     

浅析大客车气压制动装置制动失灵原因

介绍引起大客车制动失灵的一些故障，针对这些故障给出了预防措施和解决的方案。     

一种三角形网格自动生成技术

在研究总结目前流行的三角形网格自动生成法的基础上,提出前沿生成与Delaunay三角化组合起来的三角形自动生成方法。该方法解决了Delaunay法中离散点域的构造问题,不会产生重合点,不用进行点集排序和坏点剔除,同时避免了前沿生成法中过多的校验。该法计算效率高,对复杂边界适应性好,可实现三角形尺度的渐变过渡。应用该方法成功进行了三峡电站日调节数学模型计算域的自动剖分。     

粘土含量对粉沙质泥沙沉降速度的影响

通过对泥沙沉降试验结果进行统计整理和理论分析,研究了粘土含量对粉沙质泥沙沉降速度的影响。结果表明:泥沙沉降速度同时受泥沙中值粒径d50和粘土含量p0.004控制;沉降速度ω随泥沙中值粒径d50增大而增大;粘土含量较小时,沉速随p0.004增大而减小,粘土含量较大时,沉速随粘土含量增大而增大;粘土含量对泥沙沉速影响的分界点为20%~25%。     

Sampling of alternatives for route choice modeling

This paper presents a new paradigm for choice set generation in the context of route choice model estimation. We assume that the choice sets contain all paths connecting each origin–destination pair. Although this is behaviorally questionable, we make this assumption in order to avoid bias in the econometric model. These sets are in general impossible to generate explicitly. Therefore, we propose an importance sampling approach to generate subsets of paths suitable for model estimation. Using only a subset of alternatives requires the path utilities to be corrected according to the sampling protocol in order to obtain unbiased parameter estimates. We derive such a sampling correction for the proposed algorithm.Estimating models based on samples of alternatives is straightforward for some types of models, in particular the multinomial logit (MNL) model. In order to apply MNL for route choice, the utilities should also be corrected to account for the correlation using, for instance, a path size (PS) formulation. We argue that the PS attribute should be computed based on the full choice set. Again, this is not feasible in general, and we propose a new version of the PS attribute derived from the sampling protocol, called Expanded PS.Numerical results based on synthetic data show that models including a sampling correction are remarkably better than the ones that do not. Moreover, the Expanded PS shows good results and outperforms models with the original PS formulation.     

Experimental design influences on stated choice outputs: An empirical study in air travel choice

Discrete choice experiments are conducted in the transport field to obtain data for investigating travel behaviour and derived measures such as the value of travel time savings. The multinomial logit (MNL) and other more advanced discrete choice models (e.g., the mixed MNL model) have often been estimated on data from stated choice experiments and applied for planning and policy purposes. Determining efficient underlying experimental designs for these studies has become an increasingly important stream of research, in which the objective is to generate stated choice tasks that maximize the collected information, yielding more reliable parameter estimates. These theoretical advances have not been rigorously tested in practice, such that claims on whether the theoretical efficiency gains translate into practice cannot be made. Using an extensive empirical study of air travel choice behaviour, this paper presents for the first time results of different stated choice experimental design approaches, in which respective estimation results are compared. We show that D-efficient designs keep their promise in lowering standard errors in estimating, thereby requiring smaller sample sizes, ceteris paribus, compared to a more traditional orthogonal design. The parameter estimates found using an orthogonal design or an efficient design turn out to be statistically different in several cases, mainly attributed to more or less dominant alternatives existing in the orthogonal design. Furthermore, we found that small designs with a limited number of choice tasks performs just as good (or even better) than a large design. Finally, we show that theoretically predicted sample sizes using the so-called S-estimates provide a good lower bound. This paper will enable practitioners in better understanding the potential benefits of efficient designs, and enables policy makers to make decisions based on more reliable parameter estimates.