Track buckling due to excessive rail temperature is a major cause of derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While the slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX), ENSCO Inc. conducted a model verification study by comparing actual rail temperatures measured by wayside sensors installed on railroad track near Folkston, GA, with the rail temperatures predicted by the model based on weather forecast data over the course of summer 2011. The paper outlines the procedure of the verification process together with correlation results, which are favorable. The paper also presents results of several case studies conducted on derailments attributed to track buckling. These investigations improve our understanding of conditions and temperature patterns leading to increased risk of rail buckles and validate further use of the Rail Temperature Prediction Model as track buckling prediction tool and as an aid to the railroads in making more informed decisions on slow order issuing process.

Track buckling due to excessive rail temperature may cause derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order management process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX) and FRA, ENSCO Inc. conducted a comprehensive model verification study by comparing actual rail temperatures measured by wayside sensors installed at 23 measurement sites located across the CSX network with the rail temperatures predicted by the model based on weather forecast data over the course of spring and summer 2012. In addition to the correlation analysis, detection theory was used to evaluate the model’s ability to correctly identify instances when rail temperatures are elevated above a wide range of thresholds. Detection theory provides a good way of comparing the performance of the model to the performance of the current industry practice of estimating rail temperature based on constant offsets above predicted daily peak ambient air temperatures. As a next step in order to quantify the impact of implementation of the model on CSX operations, heat slow orders issued by CSX in 2012 on 10 selected subdivisions were compared to theoretical heat slow orders generated by the model. The paper outlines the analysis approach together with correlation, detection theory and slow order comparison results. The analysis results along with investigation of past heat related track buckle derailments indicate that the railroad would benefit from adopting the rail temperature prediction model along with flexible rail temperature thresholds. The implementation of the model will have a positive impact on safety by allowing for issuing of advance heat slow orders in more accurate, effective and targeted way.

This paper documents results of fuel consumption and exhaust emission tests performed on a 1,500 kW EMD GP38-2 locomotive equipped with an auxiliary power unit (APU) designed to minimize main engine idling time by providing stand-by services normally provided by the main EMD 16-645-E engine at idle. The purpose of these tests was to perform an evaluation of the exhaust emissions and fuel consumption of both the EMD 16-645-E engine and the APU. The APU diesel engine was a 2.0L, 4-cylinder, turbocharged, Kubota model V2003-TEBG rated at 30.6 kW. The APU was tested using an external load box over a range of load conditions, ranging from unloaded (0 kW) through 16 kW, which was the maximum APU load expected as installed in the locomotive. Fuel consumption and exhaust emissions are compared between an idling EMD 16-645-E engine and the APU engine at a “typical” stand-by condition with the coolant and lubricating oil heaters operating and the locomotive control cab air conditioner turned off. Test results showed that the APU fuel consumption and exhaust emissions are dramatically lower than the idling EMD locomotive engine. Because the APU is designed to automatically start and stop as a function of the locomotive water temperature, and therefore operates only a portion of the time that the EMD engine would otherwise be idling. Reductions in fuel consumption and exhaust emissions over an extended period of time would be even more dramatic.