Field Test Validation

Train Makeup Study

KipWatch - Condition-based monitoring system

SA has developed a train simulation model for studying longitudinal dynamics of train including the effects of train handling, train makeup, equipment design, route changes and stopping distances.

To establish the predictive accuracy of the SA model, train speed, coupler forces and air brake system response from the model have been compared with the available published test data.  The premise of this comparison is that the model should be able to predict an observed event correctly, it should replicate the trend in the event and estimate the magnitude of the involved parameter (speed, force, pressure etc.) reasonably well.

The throttle and dynamic brake positions along with the train and track data are used as input. An event with six locomotives and 100-plus car long train negotiating an undulating terrain under throttle and dynamic braking is selected as an example.  The predicted train speed and coupler forces on Car #1 and Car #37 are shown on the left along with the train handling, speed and coupler forces from the field.  It can bee seen that as postulated in the premise, model predictions reproduce the coupler force occurrence, trends and peak levels quite accurately for the speed as well as the coupler forces.

To establish the accuracy of air brake model in the model, an event for the same train, a braking event for a train stop was simulated.  The throttle and locomotive brake pipe and brake cylinder pressures are shown here on the right.  In this event, braking was initiated a bit early, so brakes were released and re-applied with a full service reduction.  The locomotive brake pipe and brake cylinder pressures along with the throttle position are shown on the right.  The correlation between the model predictions and the test data shown for the locomotive, Car#1 and Car #77 (in the last quarter of the train) displays the same premise as for the coupler forces in the undulating terrain negotiation.

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The train simulation model developed by SA can be used to study train makeup and the implications of train weight distribution and coupling system characteristics such as placement of cushioning unit equipped car blocks and placement of empty cars between loaded car blocks.

Such a study was conducted using the model for a client to predict the behavior of empty cars placed in the back of train between loaded cars.  This long train as shown in the side charts had a large number of empty cars in the front 3rd of the train with many cushioned cars and an empty car placed between heavily loaded cars in the last quartile of the train.

The simulation was conducted for an event on a descending grade on a mildly curved territory.  Maximum buff and draft coupler forces and car longitudinal acceleration were monitored through out the train.  Special attention was paid for the empty car (Car#88) in the back of train while negotiating the curved segment.

The maximum draft and buff forces and the car acceleration are shown for Car#72 and Car#88.  As shown in the graphs, though a loaded car (Car #72) experienced a maximum buff coupler force of ~ 220,000 lbs, the overall acceleration on this car was less than 0.1gs.  At the same time, an empty car (Car # 88) in the same vicinity experienced a maximum buff force of ~150,000 lbs but saw longitudinal acceleration 1.7gs.  Such high accelerations in presence of track defects can create a significant potential for wheel lift and/or wheel-climb derailments, especially, in curved territories.

This application of the SA model proved significantly useful for the client as it identified potentially dangerous train makeup under some specific operating scenarios.

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The AAR’s Stress State Reduction Initiative concerning wheel flats & out-of-round wheels and the impacts such defects make on rail is in effect. Railroads have networks of impact recorders across the country to register and document wheel impacts.  The car owners are responsible for correcting high-impact wheel sets or they face increased billing and surcharges.

SA developed kipWatch, a condition-based monitoring system that gathers impact alerts from the railroads, analyzes the data for the car owner, and submits a report in a timely and efficient manner.  kipWatch determines which cars in the fleet are more problematic, and helps investigate the cause(s) of the problems and how best to address them.  This effort helps the car owner avoid extra billing, adhere to regulations, and also recognize significant savings through reduced change-outs.

kipWatch helps fleetowners meet the rail industry requirements through data collection and analysis, trend reports, and result comparisons.

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