刚性-压力敏感性材料界面I型准静态扩展裂纹尖端场

为了研究船用工程复合材料的界面裂纹特性,建立了刚性-压力敏感粘弹塑性材料Ⅰ型准静态扩展裂纹的力学模型.在稳态扩展阶段,应力和应变具有相同的奇异量级,即(σ,ε)∝γ<'-1/(n-1)>.引入Airy应力函数,通过渐近分析得出了裂纹尖端应力和应变的分离变量形式的渐近解,并采用打靶法求得了裂纹尖端应力和应变的数值结果.数值计算结果表明,界而裂尖场主要受材料的泊松比和幂硬化指数的控制.通过对裂纹尖端场的渐近分析,从应变角度出发,提出了刚性一压力敏感性材料界面Ⅰ型准静态扩展裂纹的断裂判据.     

三维频域格林函数的高效率计算方法研究

传统的频域格林函数计算方法主要采用级数或渐进展开式对其进行数值逼近,但这种方法需要非常细致的分区,计算过程复杂,循环次数也较多,极大地影响计算效率.将高斯积分引入到频域格林函数及其导数的数值计算,并将其计算结果与已有参考文献进行对比,证明该方法在满足足够精度的基础上,使计算过程大大简化,减少了分区,提高了计算效率.     

任意信源的一类渐近均匀分割性定理的研究

研究了任意信源在可列状态空间下的Shannon—McMillan定理．采用构造相容分布与非负上鞅的方法结合一些重要不等式,研究任意随机变量序列相对熵密度的强极限定理,即渐近均匀分割性．得出了若干任意信源、m阶马氏信源、无记忆信源的渐进均匀分割性定理,并将已有的关于离散信源的结果进行了推广．     

多学科设计优化算法比较及其在船舶和海洋平台设计上的应用

多学科设计优化(Multidiseiplinary Design Optimization,简称MDO)是一种通过充分探索和利用系统中的相互作用的协同机制来设计复杂系统工程和子系统的方法论.多学科设计优化算法是其核心部分,也是研究最活跃的领域.文中首先介绍了MDO算法的定义、分类和发展,然后从算法的来源和目的、优化过程、优缺点、改进方法和应用情况等五个方面对四种基于分解技术的MDO算法进行了综述,进而对比了这四种算法的异同点.最后,针对船舶和海洋平台设计的具体特点,归纳了适合于船舶或海洋平台多学科设计优化的MDO算法所需要具备的特征,并建议使用基于近似模型的协同优化算法或BUSS 2000算法进行船舶和海洋平台的多学科设计优化.     

几种典型海滩平衡剖面模型中参数意义的探讨

海滩平衡剖面是海岸近岸过程研究的一个重要概念,在海岸工程中有应用价值。通过分析认为粤东后江湾的海滩剖面处于动态平衡状态。应用Dean、Bodge和Lee等3种海滩平衡剖面模型对后江湾的海滩剖面数据进行拟合,并从地形动力学的角度对拟合结果进行了分析解释,结果表明3个模型中的参数分别包含有波浪周期或波长、剖面变化渐近深度、海滩反射性等物理意义。这可为海岸工程设计确定近岸水下地形轮廓提供重要的依据。     

Continuum signalized junction model for dynamic traffic networks: Offset,spillback, and multiple signal phases

This paper extends the continuum signalized intersection model exhaustively studied in Han et al. (2014) to more accurately account for three realistic complications: signal offsets, queue spillbacks, and complex signal phasing schemes. The model extensions are derived theoretically based on signal cycle, green split, and offset, and are shown to approximate well traffic operations at signalized intersections treated using the traditional (and more realistic) on-and-off model. We propose a generalized continuum signal model, which explicitly handles complex vehicle spillback patterns on signalized networks with provable error estimates. Under mild conditions, the errors are small and bounded by fixed values that do not grow with time. Overall, this represents a significant improvement over the original continuum model, which had errors that grew quickly with time in the presence of any queue spillbacks and for which errors were not explicitly derived for different offset cases. Thus, the new model is able to more accurately approximate traffic dynamics in large networks with multiple signals under more realistic conditions. We also qualitatively describe how this new model can be applied to several realistic intersection configurations that might be encountered in typical urban networks. These include intersections with multiple entry and exit links, complex signal phasing, all-red times, and the presence of dedicated turning lanes. Numerical tests of the models show remarkable consistency with the on-and-off model, as expected from the theory, with the added benefit of significant computational savings and higher signal control resolution when using the continuum model.     

Infrastructure deployment under uncertainties and competition: The biofuel industry case

Technological paradigm shifts often come with a newly emerging industry that seeks a viable infrastructure deployment plan to compete against established competitors. Such phenomenon has been repeatedly seen in the field of transportation systems, such as those related to the booming bioenergy production, among others. We develop a game-theoretic modeling framework using a continuum approximation scheme to address the impacts of competition on the optimal infrastructure deployment. Furthermore, we extend the model to incorporate uncertainties in supply/demand and the risk of facility disruptions. Analytical properties of the optimal infrastructure system are obtained, based on which fast numerical solution algorithms are developed. Several hypothetical problem instances are used to illustrate the effectiveness of the proposed algorithms and to quantify the impacts of various system parameters. A large-scale biofuel industry case study for the U.S. Midwest is conducted to obtain additional managerial insights.     

The dynamic shortest path problem with time-dependent stochastic disruptions

The dynamic shortest path problem with time-dependent stochastic disruptions consists of finding a route with a minimum expected travel time from an origin to a destination using both historical and real-time information. The problem is formulated as a discrete time finite horizon Markov decision process and it is solved by a hybrid Approximate Dynamic Programming (ADP) algorithm with a clustering approach using a deterministic lookahead policy and value function approximation. The algorithm is tested on a number of network configurations which represent different network sizes and disruption levels. Computational results reveal that the proposed hybrid ADP algorithm provides high quality solutions with a reduced computational effort.     

Benders Decomposition for Discrete–Continuous Linear Bilevel Problems with application to traffic network design

We propose a new fast solution method for linear Bilevel Problems with binary leader and continuous follower variables under the partial cooperation assumption. We reformulate the Bilevel Problem into a single-level problem by using the Karush–Kuhn–Tucker conditions. This non-linear model can be linearized because of the special structure achieved by the binary leader decision variables and subsequently solved by a Benders Decomposition Algorithm to global optimality. We illustrate the capability of the approach on the Discrete Network Design Problem which adds arcs to an existing road network at the leader stage and anticipates the traffic equilibrium for the follower stage. Because of the non-linear objective functions of this problem, we use a linearization method for increasing, convex and non-linear functions based on continuous variables. Numerical tests show that this algorithm can solve even large instances of Bilevel Problems.     

基于实测数据的路段可靠性近似算法研究

利用北京市快速路与主干路的流量与车速实测数据,分析交通流量的变化规律,拟合流量-速度关系;分别采用车速和流量计算可靠度与饱和度,得到欠饱和与超饱和状态下可靠度与饱和度之间的一一对应关系,从而建立可靠度与饱和度之间的关系模型;最后利用实测数据对该模型进行了验证,结果表明快速路最大误差为0．0885,主干路最大误差为0．0923．