Theoretical analysis of Er<Superscript>3+</Superscript>-doped telluride glass fiber amplifier for 2.700 μm laser amplification

The rate equations and power evolution equations of erbium-doped telluride glass fiber amplifier for both 1.530 and 2.700 μm lasers are solved numerically, the dependences of gain spectra on fiber length, dopant concentration and pump power are analyzed, and the gain of 2.700 μm laser is calculated and compared with the experimental result from reference. The numerical analysis shows that with 8 × 10²⁴ ion/m³ erbium ion concentration, 5m fiber length and 600mW pump power, the gains at 1.530 and 2.700 μm may achieve 23dB or so. With larger power pump and higher dopant concentration, a net gain of 17 dB is obtained from the Er³⁺-doped telluride glass fiber amplifier for 110mW input signal. This fiber amplifier is promising for both 1.530 μm signal amplification and 2.700 μm laser amplification.     

Substructuring technique for dynamics analysis of flexible beams with large deformation

In this paper, the substructuring technique is extended for the dynamics simulation of flexible beams with large deformation. The dynamics equation of a spatial straight beam undergoing large displacement and small deformation is deduced by using the Jourdain variation principle and the model synthesis method. The longitudinal shortening effect due to the transversal deformation is taken into consideration in the dynamics equation. In this way, the geometric stiffening effect, which is also called stress stiffening effect, is accounted for in the dynamics equation. The transfer equation of the flexible beam is obtained by assembling the dynamics equation and the kinematic relationship between the two connection points of the flexible beam. Treating a flexible beam with small deformation as a substructure, one can solve the dynamics of a flexible beam with large deformation by using the substructuring technique and the transfer matrix method. The dynamics simulation of a flexible beam with large deformation is carried out by using the proposed approach and the results are verified by comparing with those obtained from Abaqus software.     

Fine sediment deposits in shelf seas

From field observations it appears that the top layer of a shelf bottom in general exhibits an intricate geographical pattern of sediment formations. Sediments of different composition are confined in distinct regions. This contradicts the idea that current and wave forces stir up bottom sediment and disperse it in a random way over the shelf; the dispersal process is counteracted by sorting mechanisms. In this paper the bottom patterns of fine cohesive sediments are considered. A specific sorting mechanism is studied which may explain the patchy structure of fine sediment deposits. It is shown that fine sediments can be trapped in bottom deposits which contain a fine sediment fraction high enough to prevent pore water motion in the shelf bed. This mechanism opposes sediment dispersal away from existing deposits. It may also explain the formation or the preservation of mud patches, even in regions where the bottom shear stress is relatively high.     

模拟流体-弹性结构相互作用分析的ISPH-TLSPH耦合方法研究（英文）

A fully Lagrangian algorithm for numerical simulation of fluid-elastic structure interaction(FSI) problems is developed based on the Smoothed Particle Hydrodynamics(SPH) method. The developed method corresponds to incompressible fluid flows and elastic structures. Divergence-free(projection based) incompressible SPH(ISPH) is used for the fluid phase, while the equations of motion for structural dynamics are solved using Total Lagrangian SPH(TLSPH) method.The temporal pressure noise can occur at ...     

A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.     

Job accessibility and employment outcomes: which income groups benefit the most?

Improving job accessibility can increase the probability for individual persons to be employed and reduce their commutes. Empirical research suggests that the relationship between job accessibility and employment outcomes differ across income groups, but no research has investigated the difference or explored which income groups benefit the most from job accessibility improvements. This research fills the gap by examining six income groups in the Los Angeles metropolitan area. Results show that job accessibility affects the employment status of medium-to-low income groups (household income between US$25,000 and US$75,000). For the lowest-income group (household income lower than US$25,000), owning a car significantly improves their chances to be employed, but job accessibility has no effect. On the other hand, higher job accessibility is associated with shorter commuting distance for the other five income groups, but not for the lowest-income group. These results suggest that transportation and land use policies need to address the specific needs of distinct population groups and underscore the importance of spatial access for the middle-class, which tends to be overlooked in the literature on transportation equity.     

A behavioral choice model of the use of car-sharing and ride-sourcing services

There are a number of disruptive mobility services that are increasingly finding their way into the marketplace. Two key examples of such services are car-sharing services and ride-sourcing services. In an effort to better understand the influence of various exogenous socio-economic and demographic variables on the frequency of use of ride-sourcing and car-sharing services, this paper presents a bivariate ordered probit model estimated on a survey data set derived from the 2014–2015 Puget Sound Regional Travel Study. Model estimation results show that users of these services tend to be young, well-educated, higher-income, working individuals residing in higher-density areas. There are significant interaction effects reflecting the influence of children and the built environment on disruptive mobility service usage. The model developed in this paper provides key insights into factors affecting market penetration of these services, and can be integrated in larger travel forecasting model systems to better predict the adoption and use of mobility-on-demand services.     

The role of socio-economic,built environment and psychological factors in parental mode choice for their children in an Iranian setting

This research investigated the role of parental psychological and socio-economic factors as well as built environment for the choice of their children’s (primary school pupils, aged 7–9 years) travel mode to school in Rasht, Iran. A total of 1078 questionnaires were distributed (return rate of 80 percent) among pupils in nine primary schools in January 2014. A mixed logit (ML) model was employed due to its ability to test heterogeneity among parents and also to determine its possible sources. Results of random coefficient ML modelling showed that several psychological, socio-economic and built environment characteristics were significant factors in parental mode choice. Only walking time perception to school had a significant random normal distribution coefficient and no other psychological and socio-economic variable had a random effect. Further investigation by random coefficient analysis showed that the possible source of household preference heterogeneity could be to own two or more cars. Regarding psychological variables, strong parental worry about their children walking alone to school had a negative impact on allowing them to walk to school. Parents who evaluated poor contextual and design preconditions for walking tended to choose school service more than private car and walking. Parents with stronger environmental personal norms were more willing to allow their children to walk. The findings suggest that infrastructural measures, such as sidewalk facilities, neighborhood security and safety, encourage parents to allow children to walk to school. Information campaigns targeting environmental norms may increase walking among pupils in an Iranian setting.     

Incentivizing alternative fuel vehicles: the influence of transport policies,attitudes and perceptions

This paper aims to evaluate the influence of policies, attitudes and perceptions when incentivizing alternative fuel vehicles. The impact of possible policies such as gasoline taxes increases, purchase price subsidies, tax exemptions, and increases in fuel recharging station availability for alternative fuelled vehicles is evaluated using hybrid choice models. The models also allow assessing the sensitivity of latent variables (i.e., attitudes and perceptions) in the car purchase behaviour. The models are estimated using data from a stated choice survey collected in five Colombian cities. The latent variables are obtained from the rating of statements related to the transport system, environmental concern, vehicle preferences, and technology. The modelling approach includes regression between latent variables. Results show that environmental concern and the support for green transport policies have a positive influence on the intention to purchase alternative fuel vehicles. Meanwhile, people who reveal to be car-dependent prefer to buy standard fuelled vehicles. The analysis among cities shows similar trends in individual behaviour, although there are differences in attribute sensitivities. The policy scenario analysis revealed high sensitivity to capital cost and the need for extensive investments in refuelling stations for alternative fuel vehicles to become attractive. Nevertheless, all policies should not only be directed at infrastructure and vehicles but also be focused on user awareness and acceptance of the alternative fuel vehicles. The analysis suggests that in an environmentally conscious market, people prefer alternative fuels. However, if the transport policies support private transport, the market shares of alternative fuel vehicles will decrease.     

Identification and quantification of link vulnerability in multi-level public transport networks: a passenger perspective

Robust public transport networks are important, since disruptions decrease the public transport accessibility of areas. Despite this importance, the full passenger impacts of public transport network vulnerability have not yet been considered in science and practice. We have developed a methodology to identify the most vulnerable links in the total, multi-level public transport network and to quantify the societal costs of link vulnerability for these identified links. Contrary to traditional single-level network approaches, we consider the integrated, total multi-level PT network in the identification and quantification of link vulnerability, including PT services on other network levels which remain available once a disturbance occurs. We also incorporate both exposure to large, non-recurrent disturbances and the impacts of these disturbances explicitly when identifying and quantifying link vulnerability. This results in complete and realistic insights into the negative accessibility impacts of disturbances. Our methodology is applied to a case study in the Netherlands, using a dataset containing 2.5 years of disturbance information. Our results show that especially crowded links of the light rail/metro network are vulnerable, due to the combination of relatively high disruption exposure and relatively high passenger flows. The proposed methodology allows quantification of robustness benefits of measures, in addition to the costs of these measures. Showing the value of robustness, our work can support and rationalize the decision-making process of public transport operators and authorities regarding the implementation of robustness measures.