Sharif Digital Repository

We show that in a microfluidic network with low Reynolds numbers, a system can be irreversible due to hysteresis effects. We simulated a network of pipes that was used in a recent experiment. The network consists of one loop connected to input and output pipes. A train of droplets enters the system at a uniform rate, but the droplets may leave the system in a periodic or even a chaotic pattern. The output pattern depends on the time interval between incoming droplets as well as the network geometry. For some parameters, the system is not reversible  

A new criterion for prediction of train derailment is presented in this paper. A 2 DOF wheel-set model is used to identify the main dynamic parameters that affect wheel-set derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheelset derailment, it requires measurements which are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF was used. The wagon model is a 3-D, non-linear model of a train passenger car. The model includes... 

Macroscale experiments show that a train of two immiscible liquid drops, a bislug, can spontaneously move in a capillary tube because of surface tension asymmetries. We use molecular dynamics simulation of Lennard-Jones fluids to demonstrate this phenomenon for NVT ensembles in submicron tubes. We deliberately tune the strength of intermolecular forces and control the velocity of bislug in different wetting and viscosity conditions. We compute the velocity profile of particles across the tube and explain the origin of deviations from the classical parabolae. We show that the self-generated molecular flow resembles the Poiseuille law when the ratio of the tube radius to its length is less... 

A new criterion for prediction of train derailment is presented in this paper. A 2 DOF wheel-set model is used to identify the main dynamic parameters that affect wheel-set derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheelset derailment, it requires measurements which are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF was used. The wagon model is a 3-D, non-linear model of a train passenger car. The model includes... 

A new criterion for prediction of train derailment is presented in this article. A three-degrees-of-freedom (3 DOF) wheelset model is used to identify the main dynamic parameters that affect wheelset derailment. Using these parameters and conventional definition of derailment coefficient, a new criterion for prediction of wheelset derailment is introduced. The proposed criterion, in addition to providing the required precision in prediction of wheel set derailment, requires measurements that are easy to perform. To evaluate the capability of the new criterion in prediction of derailment, a full wagon model with 48 DOF moving on a track with different random irregularities was used. The track... 

This article presents a new method in determining long train derailment. A new strategy for building train models with large number of wagons has been developed. Previous studies have shown that in a train model, some of the details in the model of the wagons neighboring the wagon under study play minor role in dynamic behavior of the spotted wagon. To reduce the size of the overall model, one would tend to eliminate some of the details of the complex neighboring model having minor effect on overall behavior and trade a small approximation in results for large saving in computation time. In this research this idea is used to develop a train model with one full detail wagon model linked in a...