Browsing by Author "Radmehr, Ahmad"

Now showing 1 - 4 of 4
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    Experimental Evaluation of Effect of Leaves on Railroad Tracks in Loss of Braking
    Kumar, Nikhil; Radmehr, Ahmad; Ahmadian, Mehdi (MDPI, 2024-04-29)
    This study aims to comprehensively assess the lubrication effect of leaves on wheel–rail contact dynamics using the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig, which closely simulates field conditions with precision and repeatability. Railway operators grapple with the seasonally recurring challenge of leaf contamination, which can cause partial loss of braking and lead to undesired events such as station overruns. Better understanding the adhesion-reducing impact of leaf contamination significantly improves railway engineering practices to counter their effects on train braking and traction. This experimental study evaluates the reduction in traction and braking forces (collectively called “adhesion”) as a function of leaf volume, using two leaf species that commonly grow along U.S. railroad tracks. The test methods rely on the chosen leaves’ transpiration characteristics while ensuring the result’s reproducibility. Leaves were symmetrically positioned on the wheel surface, centered around the mid-rib area within the wear band, and taped on the edges far from the wear band. The critical test parameters (i.e., wheel load, wheel velocity, and percentage creepage) are kept constant among the tests. At the same time, leaf volume is reduced from a maximum amount that covers the entire wheel surface (100% coverage) to no leaves (0%). The latter is used as the baseline. The percentage creepage is kept constant at an exaggerated amount of 2% to accelerate the test time. The results indicate a nonlinear relationship between leaf volume and the loss of braking. Even a small amount of leaf contamination causes a significant reduction in adhesion by as much as 50% compared with no contamination (i.e., baseline). Increasing leaf volume results in contact saturation, beyond which adhesion is not reduced. The minimum adhesion observed in this study is 20% of the maximum adhesion that occurs when no leaf contamination is present.
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    Experimental Evaluation of Wheel-Rail Interaction
    Radmehr, Ahmad (Virginia Tech, 2021-01-14)
    This study provides a detailed experimental evaluation of wheel-rail interaction for railroad vehicles, using the Virginia Tech Federal Railroad Administration (VT-FRA) Roller Rig. Various contact dynamics that emulate field application of railroad wheels on tracks are set up on the rig under precise, highly-controlled and repeatable conditions. For each setup, the longitudinal and lateral traction (creep) forces are measured for different percent creepages, wheel loads, and angles of attack. The tests are performed using quarter-scaled wheels with different profiles, one cylindrical and the other AAR-1B with a 1:20 taper. Beyond the contact forces, the wheel wear and the deposition of worn materials are measured and estimated as a function of time using a micron-precision laser optics measurement device. The change in traction versus amount of worn material at the contact surface is analyzed and related to wheel-rail friction. It is determined that the accumulation of the worn material at the contact surface, which appears as a fine gray powder, acts as a friction modifier that increases friction. The friction (traction) increase occurs asymptotically. Initially, it increases rapidly with time (and worn material accumulation) and eventually reaches a plateau that defines the maximum friction (traction) at a stable rate. It is estimated that the maximum is reached when the running surface is saturated with the worn material. Prior to the saturation, the friction increases directly with an increasing amount of deposited material. The material that accumulates naturally at the surface—hence, referred to as "natural third-body layer"—is estimated to be a ferrous oxide. It has an opposite effect from the Top of Rail (ToR) friction modifiers that are deposited onto the rail surface to reduce friction in a controlled manner. Additionally, the results of the study indicate that longitudinal traction decreases nonlinearly with increasing angle of attack (AoA), while lateral traction increases, also nonlinearly. The AoA is varied from -2.0 to 2.0 degrees, representing a right- and left-hand curve. Lateral traction increases at a high rate with increasing AoA between 0.0 – 0.5 degrees, and increases at a slow rate beyond 0.5 degree. Similarly, longitudinal traction reduces at a high rate for smaller AoA and at a slower rate for larger AoA. For the tapered wheel, an offset in lateral forces is observed for a right-hand curve versus a left-hand curve. The wheel taper generates a lateral traction that is present at all times. In one direction, it adds to the lateral traction due to the AoA, while in the opposite direction, it subtracts from it, resulting in unequal lateral traction for the same AoA in a right-hand versus a left-hand curve. The change in traction with changing wheel load is nearly linear under steady state conditions. Increasing the wheel load increases both longitudinal and lateral tractions linearly. This is attributed to the friction-like behavior of longitudinal and lateral tractions. An attempt is made to measure the contact shape with wheel load using pressure-sensitive films with various degrees of sensitivity. Additionally, the mathematical modeling of the wheel-roller contact in both pure steel-to-steel contact and in the presence of pressure-sensitive films is presented. The modeling results are in good agreement with the measurements, indicating that the pressure-sensitive films have a measurable effect on the shape and contact patch pressure distribution, as compared with steel-to-steel.
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    In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
    Mirzaei, S. Morteza; Radmehr, Ahmad; Holton, Carvel; Ahmadian, Mehdi (MDPI, 2024-09-30)
    This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.
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    A statistical evaluation of multiple regression models for contact dynamics in rail vehicles using roller rig data
    Hosseini, Sayed Mohammad; Radmehr, Ahmad; Ahangarnejad, Arash Hosseinian; Gramacy, Robert B.; Ahmadian, Mehdi (Taylor & Francis, 2022-01-06)
    A statistical analysis of a large amount of data from experiments conducted on the Virginia Tech-Federal Railroad Administration (VT-FRA) roller rig under various field-emulated conditions is performed to develop multiple regression models for longitudinal and lateral tractions. The experiment-based models are intended to be an alternative to the classical wheel-rail contact models that have been available for decades. The VT-FRA roller rig data is used to develop parametric regression models that efficiently capture the relationship between traction and the combined effects of the influential variables. Single regression models for representing the individual effect of wheel load, creepage, and angle of attack on longitudinal and lateral traction were investigated by the authors in an earlier study. This study extends single regression models to multiple regression models and assesses the interaction among the variables using model selection approaches. The multiple-regression models are then compared with CONTACT, a well-known modelling tool for contact dynamics, in terms of prediction accuracy. The predictions made by both CONTACT and multiple regression models for longitudinal and lateral tractions are in close agreement with the measured data on the VT-FRA roller rig. The multiple regression model, however, offers an algebraic expression that can be solved far more efficiently than a simulation run in CONTACT for a new dynamic condition. The results of the study further indicate that the established multiple regression models are an effective means for studying the effect of multiple parameters such as wheel load, creepage, and angle of attack on longitudinal and lateral tractions. Such data-driven parametric models provide an essential analysis and engineering tool in contact dynamics, just as they have in many other areas of science and engineering.