There is a large quantity of literature available on longitudinal train dynamics and risk assessment but nothing that combines these two topics. This thesis is focused at assessing derailment risks developed due to longitudinal train dynamics. A key focus of this thesis is to identify strategies that can be field implemented to correctly manage these risks. This thesis quantifies derailment risk and allows a datum for comparison. A derailment risk assessment on longitudinal train dynamics was studied for a 107 vehicle train consist travelling along the Monto and North Coast Lines in Queensland, Australia. The train consisted of 103 wagons and 4 locomotives with locomotives positioned in groups of two in lead and mid train positions. The wagons were empty hopper wagons on a track gauge of 1067mm. The scenarios studied include: the effect of longitudinal impacts on wagon dynamics in transition curves; and the effects of longitudinal steady forces on wagon dynamics on curves. Simulation software packages VAMPIRE and CRE-LTS were used. The effects of longitudinal impacts from in-train forces on wagon dynamics in curves were studied using longitudinal train simulation and detailed wagon dynamics simulation. In-train force impacts were produced using a train control action. The resulting worst-case in-train forces resulting from these simulations were applied to the coupler pin of the wagon dynamics simulation model. The wagon model was used to study the effect of these in-train forces when applied in curves and transitions at an angle to the wagon longitudinal axis. The effects of different levels of coupler impact forces resulting from different levels of coupling slack were also studied. Maximum values for wheel unloading and L/V ratio for various curve radii and coupler slack conditions were identified. The results demonstrated that the derailment criteria for wheel unloading could be exceeded for a coupler slack of 50mm and 75mm on sharper curves, up to 400m radii. A detailed study of the effect of steady in-train forces on wagon dynamics on curves also was completed. Steady in-train forces were applied to a three wagon model using VAMPIRE. Maximum and minimum values of wheel unloading and L/V ratio were identified to demonstrate the level of vehicle stability for each scenario. The results allowed the worse cases of wheel unloading and L/V ratio to be studied in detail. Probability density functions were constructed for the occurrence of longitudinal forces and coupler angles for the Monto and North Coast Lines. Data was simulated for a coupler slack of 25, 50 and 75mm and force characteristics were further classified into the occurrences of impact and non-impact forces. These probability density functions were analysed for each track section to investigate the effects of coupler slack, track topography and gradient on wagon dynamics. The possible wagon instability in each of these scenarios was then assessed to give a measure of the potential consequences of the event. Risk assessment techniques were used to categorise levels of risk based on the consequences and likelihood of each event. It was found that for the train configuration simulated, the Monto Line has a higher derailment risk than the North Coast Line for many of the scenarios studies in this thesis. For a coupler slack of 25mm no derailment risks were identified, 50mm coupler slack derailment risks were only identified on the Monto track and the majority of derailment risks were identified for a 75mm coupler slack.

The application of Model Reference Adaptive Control (MRAC) for train dynamic braking is investigated in order to control dynamic braking forces while remaining within the allowable adhesion and coupler forces.  This control method can accurately determine the train braking distance.  One of the critical factors in Positive Train Control (PTC) is accurately estimating train braking distance under different operating conditions.  Accurate estimation of the braking distance will allow trains to be spaced closer together, with reasonable confidence that they will stop without causing a collision.  This study develops a dynamic model of a train consist based on a multibody formulation of railcars, trucks (bogies), and suspensions.   The study includes the derivation of the mathematical model and the results of a numerical study in Matlab.  A three-railcar model is used for performing a parametric study to evaluate how various elements will affect the train stopping distance from an initial speed.  Parameters that can be varied in the model include initial train speed, railcar weight, wheel-rail interface condition, and dynamic braking force.  Other parameters included in the model are aerodynamic drag forces and air brake forces. An MRAC system is developed to control the amount of current through traction motors under various wheel/rail adhesion conditions while braking.  Minimizing the braking distance of a train requires the dynamic braking forces to be maximized within the available wheel/rail adhesion.  Excessively large dynamic braking can cause wheel lockup that can damage the wheels and rail.  Excessive braking forces can also cause large buff loads at the couplers.  For DC traction motors, an MRAC system is used to control the current supplied to the traction motors.  This motor current is directly proportional to the dynamic braking force.  In addition, the MRAC system is also used to control the train speed by controlling the synchronous speed of the AC traction motors.  The goal of both control systems for DC and AC traction motors is to apply maximum available dynamic braking while avoiding wheel lockup and high coupler forces.  The results of the study indicate that the MRAC system significantly improves braking distance while maintaining better wheel/rail adhesion and coupler dynamics during braking.  Furthermore, according to this study, the braking distance can be accurately estimated when MRAC is used.  The robustness of the MRAC system with respect to different parameters is investigated, and the results show an acceptable robust response behavior. / Ph. D.