On Active Suspension in Rail Vehicles

The topic of this PhD thesis is active suspension in rail vehicles whichis usually realized through sensors, controllers and actuation components.A well established example of an active suspension is the tiltingcontrol system used to tilt the carbody in curves to reduce centrifugalacceleration felt by passengers. Active suspension for rail vehicles is beingstudied since 1970s and in this PhD thesis it has been tried to expandon some aspects of this topic.This study extends the research field by both experimental and theoreticalstudies. In the first phase of the study which led to a licentiatedegree the focus was more on experimental work with active verticalsuspension (AVS). This was implemented by introducing actuators inthe secondary suspension of a Bombardier test train, Regina 250, in thevertical direction. The aim has been to improve vertical ride comfort bycontrolling bounce, pitch and roll motions.In the second phase after the licentiate, the studies have been moretheoretical and can be divided into two parts. The first part of the workhas been more focused on equipping two-axle rail vehicles with differentactive suspension solutions for improving the vehicle performanceregarding comfort and wheel-rail interaction. Three papers are writtenon active suspension for two-axle rail vehicles. Two of the papers discussthe use of H¥ control for wheelset guidance in curves to reducewheel-rail damage. The third paper shows that by use of active verticaland lateral suspension (AVS and ALS) in two-axle rail vehicles goodcomfort can be achieved as well. The paper then studies how the threeactive suspension systems (ALS, AVS, and ASW) interact once implementedtogether on a two-axle rail vehicle.The second part is a study on safety of active suspension systems.The study discusses a possible procedure to ensure that a designed activesuspension for a rail vehicle will be safe in all possible failure situations.QC 20170602</p

Analytical and Numerical Analysis of the Acoustics of Shallow Flow Reversal Chambers

Flow reversal chambers are mainly used to accomplish a compact silencer design needed on a vehicle. Generally in this configuration the inlet and outlet ports are on the same face and the flow direction is reversed. During many years different authors have tried to develop 1D and 3D models for evaluating the acoustic performance of circular and rectangular reversing chambers. Ih [1] categorizes four methods for evaluating the acoustic performance of the reversing chamber. The first involves utilizing analysis techniques for other types of muffler elements having similar acoustic performances [2]. Analysis techniques for extended inlet/outlet expansion chambers may be used to approximate the behavior of a reversing chamber in which the length-to-diameter ratio is large. When the length-to-diameter ratio is small, the reversing chamber approximates the behavior of a short expansion chamber. In this case, exact predictions of the acoustic performances cannot be made and, moreover, the method itself is a trial-and-error one. The second is a mode-matching method at the discontinuities [3-5], but this is tedious to formulate and the transmission matrix for this type of muffler has not been obtained. A simplified version (third method) of this method has been developed for plane wave propagation, in which the sound pressures and particle velocities at the area discontinuities are matched [6, 7]. However, this method is restricted to a very small frequency range below the cut-off frequency of the first asymmetric mode, i.e., the (1, 0) mode, and the peaks of the transmission loss curves are not correctly predicted due to the disregard of the higher order modes. Furthermore, when the length-to-diameter ratio is small, the actual acoustic performance deviates appreciably from the theoretical transmission loss predicted by this one-dimensional analysis method. The fourth method involves using numerical methods such as finite element analysis [8] and the finite difference method [9], or possibly, the boundary element method. These numerical techniques have some merits in the treatment of more complicated geometries, such as that of an elliptic cross-section and/or a chamber with a pass tube [10], but a great many mesh points or mesh elements are required to deal with the high frequency range, so that the execution time for computation is long and the costs are high. It is also difficult to describe the total exhaust system by incorporating the transmission matrix of each silencer element.Lindborg et al. [11] modeled the flow reversal chamber by two port method. The system under study is broken down into a set of linear subcomponents that are described individually and then assembled in a network. Each component is treated as a black box that is defined at the inlet and outlet ports where plane waves are assumed. This is an efficient tool, but for complicated geometries such as the flow reversal chamber the decomposition into subcomponents is not obvious. Three different approaches are used for the two port modeling of a flow reversal [11]; 1- Large quarter wave resonator 2- More detailed representation consisting of cones and quarter wave resonators 3- A simplification of the second approach into a simple Pipe 6 From the results of this study, it can be concluded that the acoustic characteristics of shallow flow reversal chambers can be modeled, with engineering precision, up to cut on frequency of the first higher order mode using simple two-port elements. Good results were achieved modeling the flow reversal chamber as a simple straight duct connecting the inlet and the outlet. Munjal [12] devised a numerical collocation method. This method is easily applicable to rectangular as well as circular expansion chambers, but is limited to integer multiple area expansion ratios due to its inherent concept of discrete geometrical partitioning. Analytical methods have been introduced over the years. These methods fall into two main groups, one-dimensional and three-dimensional models. However as Ih [13] has mentioned, if the length of the chamber is much shorter than its width, then a large number of modes should be counted for calculating transmission loss even for the very low frequency range and this fact, arising mainly from the higher order acoustic modes generated at area discontinuities which do not fully decay before they reach the counterpart port, because the inlet and outlet are very close to each other. This leakage phenomenon means that the one-dimensional models are quite far from the actual performances even in the low frequency region. Three-dimensional models provide a very simple and exact approach to theoretical prediction of acoustical performance of plenum and reversing chambers. A three-dimensional mathematical formulation for mufflers with circular or rectangular cross-section with arbitrary location of inlet/outlet is derived by using the Eigen function expansion technique by Ih [13, 14]. The same problem is solved by the use of Green's function by Kim and Kang [15] for circular chambers and by Venkatesham et al. [16] for rectangular chambers. These methods take into account the effect of higher order modes which is necessary for successful analysis of a flow reversal chamber. The basic idea for these models stems from the fact that these chambers are in general regular in shape, which permits the use of series of orthogonal eigenfunctions. However, mufflers used in industry are not exactly rectangles or cylinders. Usually they are a bit curved at the edges to increase the stiffness. It is of interest for industry to know how this difference can alter the TL curve. This problem can be solved by FEM, however this method would be expensive and time consuming. One purpose of this thesis work is to investigate other methods for predicting TL of such chambers. One method could be to approximate the chamber which is curved at the edges with one which has sharp edges and then use the available theoretical models like the Green's function method to get TL curve. In the present study we want to find out how to do this approximation. The other possible method can be Neural Network. However this method needs some training data to train the neural network. Data for training can be obtained either through experiment or FEM. The effect of mean flow velocity is not studied here; However it has been found to be of negligible effect when Mach number is smaller than about 0.03 [17]. Besides, when the mean flow velocity is smaller than about M = 0.1, the convective contributions can be considered as negligible second order quantities and flow-generated noise may often be neglected. Further, if the mean flow velocity is small, the flow-generated 7 noise as well as pressure losses can be greatly reduced without degradation of the acoustic performance by streamline guidance: i.e., by using special l/O connecting geometries such as bell mouths and perforated bridges with high perforation ratios over 20% [14]

Development and on-Track Tests of Active Vertical Secondary Suspension for Passenger Trains

QC 20150204</p

Unsupervised rail vehicle running instability detection algorithm for passenger trains (iVRIDA)

Intelligently identifying rail vehicle faults instigating running instability from carbody floor acceleration is essential to ensure operational safety and reduce maintenance costs. However, the vehicle-track interaction's nonlinearities and scarcity of running instability occurrences complicate the task. The running instability is an anomaly in the vehicle-track interaction. Thus, we propose unsupervised anomaly detection and clustering algorithms based iVRIDA framework to detect and identify running instability and corresponding root cause. We deploy and compare the performance of the PCA-AD (baseline), Sparse Autoencoder (SAE-AD), and LSTM-Encoder-Decoder (LSTMEncDec-AD) model to detect the running instability occurrences. Furthermore, we deploy a k-means algorithm on latent space to identify clusters associated with root causes instigating instability. We deployed the iVRIDA framework on simulated and measured accelerations of European high-speed rail vehicles where SAE-AD and LSTMEncDec-AD models showed 97% accuracy. The proposed method contributes to smart maintenance by intelligently identifying anomalous vehicle-track interaction events.QC 20230607PIVOT

Investigating the effect of the equivalent conicity function's nonlinearity on the dynamic behaviour of a rail vehicle under typical service conditions

Generally, the equivalent conicity function (ECF) is denoted by equivalent conicity at 3mm (λ3mm) and a Nonlinearity Parameter (NP). NP describes the nonlinearity of the ECF and its influence on a vehicle design is explored thoroughly, however, NP’s role in vehicle and track maintenance is not researched yet. This paper investigates the influence of track maintenance actions on vehicle dynamics with help of NP vs λ3mm scatter plots of ECF database. The ECF database is constructed by combining measured worn wheel and rail profile pairs of the Swedish high-speed vehicle and rail network, respectively. The ECF database revealed an inverse relationship between λ3mm and NP, i.e., NP is negative for larger λ3mm values. The combination of negative NP and high λ3mm causes reduction in the vehicle’s nonlinear critical speed and vehicle often exhibit the unstable running on the Swedish rail network. Thus, the occurrence of ECF with negative NP and high λ3mm is undersirable and the undesirable ECF can be converted into desirable ECF by grinding the rail, which converts ECF’s into positive NP and low λ3mm combinations. Thus, the NP parameter along with the λ3mm must be considered in track maintenance decisions.QC 20210813IN2TRACK

iVRIDA: intelligent Vehicle Running Instability Detection Algorithm for high-speed rail vehicles using Temporal Convolution Network : – A pilot study

Intelligent fault identification of rail vehicles from onboard measurements is of utmost importance to reduce the operating and maintenance cost of high-speed vehicles. Early identification of vehicle faults responsible for an unsafe situation, such as the instable running of highspeed vehicles, is very important to ensure the safety of operating rail vehicles. However, this task is challenging because of the nonlinear dynamics associated with multiple subsystems of the rail vehicle. The task becomes more challenging with only accelerations recorded in the carbody where, nevertheless, sensor maintenance is significantly lower compared to axlebox accelerometers. This paper proposes a Temporal Convolution Network (TCN)-based intelligent fault detection algorithm to detect rail vehicle faults. In this investigation, the classifiers are trained and tested with the results of numerical simulations of a high-speed vehicle (200 km/h). The TCN based fault classification algorithm identifies the rail vehicle faults with 98.7% accuracy. The proposed method contributes towards digitalization of rail vehicle maintenance through condition-based and predictive maintenance.QC 20220726part of proceedings ISBN 9781936263363</p

Monitoring of lateral and cross level track geometry irregularities through onboard vehicle dynamics measurements using machine learning classification algorithms

In recent years, significant studies have focused on monitoring the track geometry irregularities through measurements of vehicle dynamics acquired onboard. Most of these studies analyse the vertical irregularity and the vertical vehicle dynamics since the lateral direction is much more challenging due to the non-linearities caused by the contact between the wheels and the rails. In the present work, a machine learning-based fault classifier for the condition monitoring of track irregularities in the lateral direction is proposed. The classifiers are trained with a dataset composed of numerical simulation results and validated with a dataset of measurements acquired by a diagnostic vehicle on the straight track sections of a high-speed line (300 km/h). Classifiers based on decision tree, linear and Gaussian support vector machine algorithms are developed and compared in terms of performance: good results are achieved with the three algorithms, especially with the Gaussian support vector machine. Even though classifiers are data driven, they retain the essence of lateral dynamics.QC 20200302</p