Train safety and operational efficiency are enhanced by the ability to understand the behavior of trains under varying conditions. Under the direction of the Federal Railroad Administration (FRA), a longitudinal train dynamics and operation simulation software — Train Energy and Dynamics Simulator (TEDS) — has been developed. TEDS is capable of modeling modern train operations and equipment, and is an effective tool for studying train operations safety and performance as affected by equipment, train makeup, train handling, track conditions, operating practices and environmental conditions. TEDS simulates the dynamics of longitudinal train action and incorporates the dynamic effects of various different types of draft gears and end-of-car cushioning units including mismatched devices coupled together, the transient response of locomotive tractive and dynamic braking effort, as well as a fluid dynamic representation of the air brake system with the capability to model conventional pneumatic and ECP brake systems. The capabilities of TEDS are described and demonstrated with several examples. The validation effort undertaken is described at both the component and system level. Comparisons of TEDS simulations of impact tests with the test results are shown to verify the draft gear and end-of-car cushioning unit models. The air brake model predictions are verified by comparing brake rack test results to TEDS simulations of braking behavior.

One of the fundamental problems arising in kinematics is that of determining object position, velocity and acceleration from given point position, velocity and acceleration data. This type of problem is frequently encountered in robotics, biomechanics, real-time control of space structures, automatic guided vehicles, etc. Complications arise when redundant data are used and when the data have errors. Chutakanonta and Gupta proposed two simple and elegant methods for the estimation of object position from the given point position data. The present work is an extension of these methods for estimating the object velocity and acceleration states from the given point position, velocity and acceleration data. The method proposed herein uses Singular Value Decomposition (SVD) to effectively estimate the object velocity and acceleration states. Such matrix decompositions can be performed by using readily available matrix-oriented software like MATLAB and can be successfully used to simplify the solution of the over-determined system of equations encountered in these types of problems. Several hypothetical examples and examples that simulate practical situations are presented to determine the effectiveness, robustness and applicability of the proposed method. The method is found to be very effective in estimating the object velocity and acceleration states in the presence of imprecise and redundant data as well as for nearly co-planar point data.

Limiting harmful locomotive exhaust emissions is important to the Nation’s health and safety. The Environmental Protection Agency (EPA) has comprehensive gaseous exhaust emissions (or referred to as emissions hereto) testing requirements in place. All current tests are conducted on stationary locomotives. This paper discusses the development of an efficient stationary emissions measurement system that is compact, portable, easy to use, and applicable to onboard locomotives for in-use, over-the-road testing. More efficient locomotive emissions testing and better understanding of in-use emissions would be beneficial to all stakeholders. Sharma & Associates, Inc., (SA) adapted an off-the-shelf, portable, on-road, heavy-duty diesel truck emissions analyzer for locomotive use. This process included development of the necessary peripheral equipment and a computer program to take the raw emissions and report them as brake-specific emissions rates and duty cycle emissions. This paper describes the use of this system on a stationary locomotive. The system is currently being fitted and tested for over-the-road use. The measurement of particulate matter and smoke opacity were out of scope of the phase of the project that this paper is based on and not addressed hereto.

One of the fundamental problems arising in kinematics is that of determining object position, velocity and acceleration from given point position, velocity and acceleration data. This type of problem is frequently encountered in robotics, biomechanics, real-time control of space structures, automatic guided vehicles, etc. Complications arise when redundant data are used and when the data have errors. Chutakanonta and Gupta proposed two simple and elegant methods for the estimation of object position from the given point position data. The present work is an extension of these methods for estimating the object velocity and acceleration states from the given point position, velocity and acceleration data. The method proposed herein uses Singular Value Decomposition (SVD) to effectively estimate the object velocity and acceleration states. Such matrix decompositions can be performed by using readily available matrix-oriented software like MATLAB and can be successfully used to simplify the solution of the over-determined system of equations encountered in these types of problems. Several hypothetical examples and examples that simulate practical situations are presented to determine the effectiveness, robustness and applicability of the proposed method. The method is found to be very effective in estimating the object velocity and acceleration states in the presence of imprecise and redundant data as well as for nearly co-planar point data.

There is a significant increase in the transportation by rail of hazardous materials such as crude oil and ethanol in the North American market. Several derailment incidents associated with such transport have led to a renewed focus on improving the performance of tank cars against the potential for puncture under derailment conditions. Proposed strategies for improving puncture resistance have included design changes to tank cars, as well as, operational considerations such as reduced speeds. Given the chaotic nature of derailment events, it has been difficult to quantify globally, the overall ‘real-world’ safety improvement resulting from any given proposed change. A novel and objective methodology for quantifying and characterizing reductions in risk that result from changes to tank car designs or the tank car operating environment is outlined in this paper. The proposed methodology captures several parameters that are relevant to tank car derailment performance, including multiple derailment scenarios, derailment dynamics, impact load distributions, impactor sizes, operating conditions, tank car designs, etc., and combines them into a consistent probabilistic framework to estimate the relative merit of proposed mitigation strategies.