12毫米厚铝合金板的搅拌摩擦焊研究(英文)

Most of the investigations regarding friction stir welding(FSW)of aluminum alloy plates have been limited to about 5 to 6mm thick plates.In prior work conducted the various aspects concerning the process parameters and the FSW tool geometry were studied utilizing friction stir welding of 12 mm thick commercial grade aluminum alloy.Two different simple-tomanufacture tool geometries were used.The effect of varying welding parameters and dwell time of FSW tool on mechanical properties and weld quality was examined.It was observed that in order to achieve a defect free welding on such thick aluminum alloy plates,tool having trapezoidal pin geometry was suitable.Adequate tensile strength and ductility can be achieved utilizing a combination of high tool rotational speed of about 2000 r/min and low speed of welding around 28 mm/min.At very low and high dwell time the ductility of welded joints are reduced significantly.     

Analytical Wheel Models for Ride Dynamic Simulation of Off-road Tracked Vehicles

This paper outlines various analytical approaches of varying complexities to model the wheel in the ride dynamic formulation of off-road tracked vehicles. In addition to a proposed model, four analytical models available in the literature are compared to study their effectiveness in modeling the wheel/track-terrain interaction for ride dynamic evaluation of typical high mobility tracked vehicles. The ride dynamic model used in this study describes the bounce-pitch plane motion of an armoured personnel carrier (Ml 13 APC) traversing over an arbitrary rigid terrain profile at constant speed. The ride dynamic response of the tracked vehicle is evaluated with different wheel models, and compared against field-measured ride data. The relative performance of different wheel models are assessed based on the accuracy of response prediction and associated computational time. The proposed wheel model is found to perform very well in comparison, and is equally applicable for the case of wheeled vehicles.     

Roll-induced bifurcation in ship maneuvering under model uncertainty

In this paper, a mathematical model is developed for the maneuvering motion of a naval ship and bifurcations of its equilibrium are identified in roll-coupled motion. The subject ship is a high-speed surface combatant with twin-propeller twin-rudder system. Captive model tests are conducted for the ship using planar motion mechanism. Maneuvering coefficients are calculated by polynomial curve fitting of the test data. Uncertainty distribution in the coefficients is assumed same as that of the curve fitting errors. Uncertainty in the model coefficients is propagated to full-scale simulation results by the stochastic response surface method (SRSM). This method is computationally efficient as compared to standard Monte Carlo simulation technique. The SRSM uses polynomial chaos expansion of orthogonal to fit any probability distribution. Bifurcation analysis of the mathematical model is performed by varying the vertical center of gravity as the bifurcation parameter. Hopf bifurcation is identified. It is found that the bifurcations occur due to the coupling of roll motion with sway, yaw motion and rudder angle. In the presence of wind, roll angle response in bifurcation diagram is discussed.     

A hybrid model based method for bus travel time estimation

Providing accurate information about bus arrival time to passengers can make the public transport system more attractive. Such information helps the passengers by reducing the uncertainty on waiting time and the associated frustrations. However, accurate estimation of bus travel time is still a challenging problem, especially under heterogeneous and lane-less traffic conditions. The accuracy of such information provided to passengers depends mainly on the estimation method used, which in turns depends on the input data used. Hence, developing suitable estimation methods and identifying the most significant/appropriate input data are important. The present study focused on these aspects of development of estimation methods that can accurately estimate travel time by using significant inputs. In order to identify significant inputs, a data mining technique, namely the k-NN classifying algorithm, was used. It is based on the similarity in pattern between the input and historic data. These identified inputs were then used in a hybrid model that combined exponential smoothing technique with recursive estimation scheme based on the Kalman Filtering (KF) technique. The optimal values of the smoothing parameter were dynamically estimated and were updated using the latest measurements available from the field. The performance of the proposed algorithm showed a clear improvement in estimation accuracy when compared with existing methods.     

大型加强板结构焊接顺序的效果研究(英文)

Welding sequence has a significant effect on distortion pattern of large orthogonally stiffened panels normally used in ships and offshore structures. These deformations adversely affect the subsequent fitup and alignment of the adjacent panels. It may also result in loss of structural integrity. These panels primarily suffer from angular and buckling distortions. The extent of distortion depends on several parameters such as welding speed, plate thickness, welding current, voltage, restraints applied to the job while welding, thermal history as well as sequence of welding. Numerical modeling of welding and experimental validation of the FE model has been carried out for estimation of thermal history and resulting distortions. In the present work an FE model has been developed for studying the effect of welding sequence on the distortion pattern and its magnitude in fabrication of orthogonally stiffened plate panels.     

Evacuation Modelling in the United States: Does the Demand Model Choice Matter?

Abstract

This paper reviews the literature on the evacuation demand problem, with an emphasis on the impact of various modelling approaches on network‐wide evacuation performance measures. First, a number of important factors that affect evacuee behaviour are summarized. Evacuation software packages and tools are also investigated in terms of the demand generation model they use. The most widely used models are then selected for performing sensitivity analysis. Next, a cell‐transmission‐based system optimal dynamic traffic assignment (SO‐DTA) model is employed to assess the effects of the demand model choice on the clearance time and average travel time. It is concluded that evacuation demand models should be selected with care, and policy makers should make sure the selected demand curve can replicate real‐life conditions with relatively high fidelity for the study region to be able to develop reliable and realistic evacuation plans.     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.