芯板可压缩的夹层加筋板固有频率分析

推导了一种考虑芯板垂向压缩变形影响的双向加筋的约束阻尼夹层板有限元单元.其中,夹层板面遵循Mindlin一阶剪切变形理论的假定;芯板采用基于厚板理论的非线性位移模式, 各向位移沿板厚成抛物线分布, 并考虑了芯板的横向压缩变形;加强筋采用Timoshenko梁模型,考虑了其剪切变形的影响.根据层间位移连续和板、梁位移连续假设,将芯板和加强筋的位移用上下面板位移表示,推导了相应的位移应变关系, 继而根据Hamilton原理建立了控制方程.数值计算结果表明约束阻尼夹层加筋板有限元单元的推导是正确的;在约束阻尼夹层加筋板的固有频率研究中,考虑夹层板芯层的垂向压缩变形的影响是必要的.还讨论了芯板和加强筋的各个参数对板固有频率的影响.     

基于离散元方法的极地浮碎冰区船舶冰阻力研究

本文采用离散元方法建立浮/碎冰模型,结合欧拉多相流VOF方法数值模拟船舶与浮/碎冰间的接触碰撞过程。重点对比分析了船冰作用过程中三种接触力计算模型：Hertz-Mindlin模型、Linear Spring模型及Walton Braun模型对船冰作用形式及冰阻力数值的影响。得出在同一工况下,三种接触碰撞模型对船体周围碎冰运动模式变化影响一致。对于船体所受冰阻力,通过分析船长、船宽方向冰阻力数值并将计算结果与DuBrovin经验公式进行对比,得出使用Linear Spring接触力计算模型计算的阻力幅值发散低,数值相对稳定且计算误差较小。本文研究工作对极地冰区船舶与浮碎冰相互作用的阻力计算具有一定的指导意义。     

Calibration of nonlinear car-following laws for traffic oscillation prediction

Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver’s car-following behavior but also the vehicle trajectory’s time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell’s car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.     

Traffic evacuation simulation based on multi-level driving decision model

Traffic evacuation is a critical task in disaster management. Planning its evacuation in advance requires taking many factors into consideration such as the destination shelter locations and numbers, the number of vehicles to clear, the traffic congestions as well as traffic road configurations. A traffic evacuation simulation tool can provide the emergency managers with the flexibility of exploring various scenarios for identifying more accurate model to plan their evacuation. This paper presents a traffic evacuation simulation system based on integrated multi-level driving-decision models which generate agents’ behavior in a unified framework. In this framework, each agent undergoes a Strategic, Cognitive, Tactical and Operational (SCTO) decision process, in order to make a driving decision. An agent’s actions are determined by a combination, on each process level, of various existing behavior models widely used in different driving simulation models. A wide spectrum of variability in each agent’s decision and driving behaviors, such as in pre-evacuation activities, in choice of route, and in the following or overtaking the car ahead, are represented in the SCTO decision process models to simulate various scenarios. We present the formal model for the agent and the multi-level decision models. A prototype simulation system that reflects the multi-level driving-decision process modeling is developed and implemented. Our SCTO framework is validated by comparing with MATSim tool, and the experimental results of evacuation simulation models are compared with the existing evacuation plan for densely populated Beijing, China in terms of various performance metrics. Our simulation system shows promising results to support emergency managers in designing and evaluating more realistic traffic evacuation plans with multi-level agent’s decision models that reflect different levels of individual variability of handling stress situations. The flexible combination of existing behavior and decision models can help generating the best evacuation plan to manage each crisis with unique characteristics, rather than resorting to a fixed evacuation plan.     

Validation of vessel seakeeping model tuning algorithm based on measurements at model scale

Wave-induced vessel motion prediction plays a critical role in ensuring safe marine operations. The operational limiting criteria can usually be calculated by applying presumed linearized vessel motion transfer functions based on the specified vessel loading condition, which may deviate from the real vessel condition when the operation is executed. Reducing the uncertainties of the onboard vessel loading condition can therefore improve the accuracy of vessel motion prediction and hence improve the safety and cost-efficiency for marine operations. However, parameters related to the onboard vessel loading condition can be difficult to measure directly, such as the center of gravity and moments of inertia. In addition, the hydrodynamic viscous damping terms are always subject to significant uncertainties and sometimes become critical for accurate vessel motion predictions. A very promising algorithm for the tuning of these important uncertain vessel parameters based on the unscented Kalman filter (UKF) that uses onboard vessel motion measurements and synchronous wave information was proposed and demonstrated previously by application to synthetic data. The present paper validates the UKF-based vessel seakeeping model tuning algorithm by considering measurements from model-scale seakeeping tests. Validation analyses demonstrate rational tuning results. The observed random errors and bias in relation to the measurement functions due to the applied simplification and linearization in the seakeeping simulations can lead to biased tuning. The importance of designing the state space and the measurement space is demonstrated by case studies. Due to the nonlinear relationship between the uncertain vessel parameters and the vessel motions, the tuning is shown to be sensitive to the mean state vector and selection of the surrounding sigma points.     

An RFID-based inventory management framework for emergency relief operations

In the aftermath of super storm Sandy, a large region from North Carolina to Maine endured food shortages, power outages, and long lines at gas stations forced to ration fuel due to low supply and high demand. These issues were largely the result of the affected transportation network’s inability to effectively cope with random and highly dynamic changes, and a lack of available resources and suppliers who were capable of enacting adequate emergency response measures. These problems experienced during super storm Sandy further underscored the need for a robust emergency inventory management system, where planning policies can be integrated with real-time on-line inventory management strategies to keep track of fluctuations of vital commodities such as food, water, medicine, fuel and power supplies. Motivated by this important problem, this paper investigates a comprehensive feedback-based emergency management framework for disasters such as super storm Sandy that provides integration with an emerging intelligent transportation systems technology, namely Radio Frequency Identification Devices (RFID). Within this framework, the offline-planning problem is solved by the stochastic humanitarian inventory management approach; and the online modeling strategy includes the application of a continuous time model predictive control technique. After introducing the mathematical background, the proposed framework is discussed using case studies built based on super storm Sandy in order to understand the efficiency and practicality of this RFID-based methodology. Results suggest that the methodology can properly account for and react to the rapidly changing needs for vital supplies that occur during the emergency relief operations. Based on this approach, planners and decision makers can be aware of the time delay that can happen due to disaster-related disruptions and thus maintain a safe level of buffer for vital supplies.     

沙漏模式在无线电陆空通话英语教学中的应用

本文主要介绍在国外流行的沙漏教学模式的由来及其框架结构。并阐述了这一教学模式中涉及到的语言呈现阶段、语言强调阶段和语言练习阶段引入到飞行专业《无线电陆空通话》英语教学实践中的运用,从而证明沙漏教学模式在《无线电陆空通话》英语教学中的实用性和有效性。     

Predictive path following with arrival time awareness for waterborne AGVs

Large ports are seeking innovative logistical ways to improve their competitiveness world-wide. This article proposes waterborne AGVs, inspired by conventional automated guided vehicles and autonomous surface vessels, for transport over water. A predictive path following with arrival time awareness controller is proposed for such waterborne AGVs. The controller is able to achieve smooth tracking and energy efficiency with arrival time awareness for transport oriented applications. Tracking errors are conveniently formulated with vessel dynamics modeled in connected reference path coordinate systems and a coordinate transformation at switching coordinate systems. Binary decision variables and logic constraints based on an along-track state are proposed for modeling switches in the framework of Model Predictive Control (MPC) so that overshoots are avoided. Moreover, timing-aware along-track references are generated by a two-level double integrator scheme. The lower level is embedded in online MPC optimizations for smooth tracking. The higher level solves a mixed-integer quadratic programming problem considering distance-to-go and time-to-go before each MPC optimization. References over the next prediction horizon are generated being aware of the requirements on arrival time. Furthermore, successive linearizations of nonlinear vessel dynamics about a shifted previous optimal system trajectory are implemented to maintain a trade-off between computational complexity and optimality. Simulation results of two industrially relevant Inter Terminal Transport case studies illustrate the effectiveness of the proposed modeling and control design for waterborne AGVs.     

Case studies of shipping along Arctic routes. Analysis and profitability perspectives for the container sector

Arctic sea routes have for long attracted interest from observers and shipping companies because of their shorter distances between the Atlantic and the Pacific. The prevalence of sea ice prevented the real development of a significant traffic, but did not prevent research from trying to assess the economic viability of these routes. With the actual present melting of sea ice in the Arctic, this effort at modeling the profitability of Arctic shipping routes received a new impetus. However, the conclusions of these studies vary widely, depending on the chosen parameters and their value. What can be said of these models, from 1991 until 2013, and to what extent can a model be drawn, capitalizing on twenty years of simulations?     

Repeated anticipatory network traffic control using iterative optimization accounting for model bias correction

Anticipatory signal control in traffic networks adapts the signal timings with the aim of controlling the resulting (equilibrium) flows and route choice patterns in the network. This study investigates a method to support control decisions for successful applications in real traffic systems that operate repeatedly, for instance from day to day, month to month, etc. The route choice response to signal control is usually predicted through models; however this leads to suboptimality because of unavoidable prediction errors between model and reality. This paper proposes an iterative optimizing control method to drive the traffic network towards the real optimal performance by observing modeling errors and correcting for them. Theoretical analysis of this Iterative Optimizing Control with Model Bias Correction (IOCMBC) on matching properties between the modeled optimal solution and the real optimum is presented, and the advantages over conventional iterative schemes are demonstrated. A local convergence analysis is also elaborated to investigate conditions required for a convergent scheme. The main innovation is the calculation of the sensitivity (Jacobian) information of the real route choice behavior with respect to signal control variables. To avoid performing additional perturbations, we introduce a measurement-based implementation method for estimating the operational Jacobian that is associated with the reality. Numerical tests confirm the effectiveness of the proposed IOCMBC method in tackling modeling errors, as well as the influence of the optimization step size on the reality-tracking convergence.