Rolling contact fatigue (RCF) is a major damage mechanism in wheel-rail systems, with strong implications for safety and life-cycle costs. This paper presents an automated framework that combines multibody simulations in Simpack with Python-based pre- and post-processing to quantify RCF and wear under realistic operating conditions. The workflow generates parametric Simpack models from a passenger coach, runs large numbers of simulations in parallel on mainline routes with realistic speed profiles and measured track irregularities, and applies RCF damage models to the resulting contact data. Global RCF models, namely the Whole Life Rail Model and the Surface Fatigue Index approach, are implemented for rails and wheels and compared to an empirical grinding guideline. Parameter studies vary one quantity at a time: rail profile (60E1 versus 60E2), friction coefficient (0.1–0.5), and carbody mass (five loading states), to isolate the influence of design parameters and operating conditions. The results show that the 60E2 rail profile systematically reduces RCF damage and wear compared with 60E1. Higher friction and carbody mass increase damage, with the Surface Fatigue Index model exhibiting pronounced sensitivity to friction. Comparing simulated damage with the grinding limit according to the Heyder model indicates that visible cracks are expected on some sections before grinding, whereas on other sections extended grinding intervals may be feasible. The framework is generic and suitable for future studies and calibration of RCF models.
A two-degree-of-freedom mechanical model of human balancing on rolling balance board in the sagittal plane is investigated with rolling radius and board elevation as two adjustable parameters. The balancing process is modeled as a closed-loop state feedback mechanism with reaction delay, hence the underlying mathematical model is a delay-differential equation. Connection is established between the stabilizability criteria by means of critical delay and the success of balancing trials by human subjects. While the mathematical model shows that the rolling radius and the board elevation equally contribute to the difficulty of the balancing task, experiments suggest that a change of rolling radius has a more significant effect on difficulty than a change of board elevation. Furthermore, it is found that experimental balancing success overlaps with plausible stabilizability of the model if the intrinsic ankle stiffness of the human subject is negligible.
This investigation evaluates the combined influence of Taguchi-optimized friction stir welding (FSW) parameters and post-weld heat treatment (PWHT) on the microstructural evolution and tensile strength of AA3103 aluminium alloy joints, complemented by Artificial Neural Network (ANN) modeling for mechanical property prediction. A Taguchi L9 orthogonal array was implemented independently for three tool pin geometries (square, circular, and triangular), incorporating shoulder-to-pin diameter ratios (D/d = 3, 4, 5), spindle speeds (355, 710, 1400 RPM), and welding speeds (20, 40, 80 mm/min), resulting in 27 experiments. A two-stage PWHT was applied, consisting of annealing at 350 °C for 2 h to promote dislocation recovery and residual stress relaxation, followed by artificial ageing at 200–225 °C and water quenching. Tensile properties, evaluated according to ASTM E8/E8M-22, were statistically analysed using S/N ratios, ANOVA, regression model, and ANN. The D/d ratio emerged as the dominant factor governing strength retention, showing the highest S/N delta for ultimate tensile strength and yield strength, and representing the only statistically significant parameter for yield strength. Spindle speed primarily influenced ductility stability. The optimized parameter set (circular pin, D/d = 3, 1400, 40 mm/min) demonstrated superior strength and microstructure. PWHT reduced regression residual errors to within ±8% and promoted fine equiaxed grain stabilization in the stir zone with a moderated hardness gradient across the TMAZ and HAZ.
This paper investigates the collision of different bearing balls with a massive steel cuboid in order to determine the restitution parameter experimentally. A test bench is constructed to measure the rebound distances of the different balls using slow-motion camera. The numerical analysis of the collision with energy dissipation assumes nonlinear viscoelastic property with Hertzian contact. The hysteresis damping factors are determined from the expression of the Flores model. First, second and fourth order numerical methods are compared to each other as regards the running times and accuracies for finding the most suitable technique.
The thermal performance of jacketed reactors is strongly influenced by the thermophysical properties of the applied heat transfer fluid. While analytical methods provide rapid engineering estimates, they are often unable to describe local flow structures and temperature distributions within the jacket space. In the present study, a three-dimensional computational fluid dynamics (CFD) model was developed in ANSYS CFX to investigate the flow and heat transfer behavior of two commonly used coolant solutions: methanol-water and ethylene glycol-water mixtures. The numerical simulations were performed for two jacketed reactor geometries under identical thermal boundary conditions. The velocity field, temperature distribution, pressure drop, and heat transfer coefficient were evaluated and compared with previously published analytical calculations. The results showed that the methanol-based coolant provided higher heat transfer coefficients in both investigated geometries. The calculated differences between the two coolant solutions reached 23.0% and 20.7%, respectively, which is in good agreement with the trends predicted by the analytical model. The CFD simulations confirmed the main conclusions of the analytical investigation while providing additional insight into the underlying flow and temperature fields. The proposed methodology can support the comparative evaluation of heat transfer fluids and assist engineering decision-making during the thermal design of jacketed reactor systems.
