Accurate estimation of the coefficient of friction (CoF) is essential to accurately modeling railroad dynamics, reducing maintenance costs, and increasing safety factors in rail operations. The assumption of a constant CoF is popularly used in simulation studies for ease of implementation, however many evidences demonstrated that CoF depends on various dynamic parameters and instantaneous conditions. In the real world, accurately estimating the CoF is difficult due to effects of various uncertain parameters, such as wheel and rail materials, rail roughness, contact patch, and so on. In this study, the newly developed 3-D nonlinear CoF model for the dry rail condition is introduced and the CoF variation is tested using this model with dynamic parameters estimated from the wheel-rail simulation model. In order to account for uncertain parameters, a stochastic analysis using the polynomial chaos (poly-chaos) theory is performed using the CoF and wheel-rail dynamics models. The wheel-rail system at a right traction wheel is modeled as a mass-spring-damper system to simulate the basic wheel-rail dynamics and the CoF variation. The wheel-rail model accounts for wheel-rail contact, creepage effect, and creep force, among others. Simulations are performed at train speed of 20 m/s for 4 sec using rail roughness as a unique excitation source. The dynamic simulation has been performed for the deterministic model and for the stochastic model. The dynamics results of the deterministic model provide the starting point for the uncertainty analysis. Six uncertain parameters have been studied with an assumption of 50% uncertainty, intentionally imposed for testing extreme conditions. These parameters are: the maximum amplitude of rail roughness (MARR), the wheel lateral displacement, the track stiffness and damping coefficient, the sleeper distance, and semi-elliptical contact lengths. A symmetric beta distribution is assumed for these six uncertain parameters. The PDF of the CoF has been obtained for each uncertain parameter study, for combinations of two different uncertain parameters, and also for combinations of three different uncertain parameters. The results from the deterministic model show acceptable vibration results for the body, the wheel, and the rail. The introduced CoF model demonstrates the nonlinear variation of the total CoF, the stick component, and the slip component. In addition, it captures the maximum CoF value (initial peak) successfully. The stochastic analysis results show that the total CoF PDF before 1 sec is dominantly affected by the stick phenomenon, while the slip dominantly influences the total CoF PDF after 1 sec. Although a symmetric distribution has been used for the uncertain parameters considered, the uncertainty in the response obtained displayed a skewed distribution for some of the situations investigated. The CoF PDFs obtained from simulations with combinations of two and three uncertain parameters have wider PDF ranges than those obtained for only one uncertain parameter. FFT analysis using the rail displacement has been performed for the qualitative validation of the stochastic simulation result due to the absence of the experimental data. The FFT analysis of the deterministic rail displacement and of the stochastic rail displacement with uncertainties demonstrates consistent trends commensurate with loss of tractive efficiency, such as the bandwidth broadening, peak frequency shifts, and side band occurrence. Thus, the FFT analysis validates qualitatively that the stochastic modeling with various uncertainties is well executed and is reflecting observable, real-world results. In conclusions, the development of an effective model which helps to understand the nonlinear nature of wheel-rail friction is critical to the progress of railroad component technology and rail safety. In the real world, accurate estimation of the CoF at the wheel-rail interface is very difficult since it is influenced by several uncertain parameters as illustrated in this study. Using the deterministic CoF value can cause underestimation or overestimation of CoF values leading to inaccurate decisions in the design of the wheel-rail system. Thus, the possible PDF ranges of the CoF according to key uncertain parameters must be considered in the design of the wheel-rail system. / Ph. D.

Les nouvelles attentes vis-à-vis des nouveaux trains à grande vitesse sont nombreuses: on les voudrait plus rapides, plus confortables, plus stables, tout en étant moins consommateur d'énergie, moins agressif vis-à-vis des voies, moins bruyants… Afin d'optimiser la conception de ces trains du futur, il est alors nécessaire de pouvoir se baser sur une connaissance précise de l'ensemble des conditions de circulations qu'ils sont susceptibles de rencontrer au cours de leur cycle de vie. Afin de relever ces défis, la simulation a un très grand rôle à jouer. Pour que la simulation puisse être utilisée dans des perspectives de conception, de certification et d'optimisation de la maintenance, elle doit alors être tout à fait représentative de l'ensemble des comportements physiques mis en jeu. Le modèle du train, du contact entre les roues et le rail, doivent ainsi être validés avec attention, et les simulations doivent être lancées sur des ensembles d'excitations qui sont réalistes et représentatifs de ces défauts de géométrie. En ce qui concerne la dynamique, la géométrie de la voie, et plus particulièrement les défauts de géométrie, représentent une des principales sources d'excitation du train, qui est un système mécanique fortement non linéaire. A partir de mesures de la géométrie d'un réseau ferroviaire, un paramétrage complet de la géométrie de la voie et de sa variabilité semblent alors nécessaires, afin d'analyser au mieux le lien entre la réponse dynamique du train et les propriétés physiques et statistiques de la géométrie de la voie. Dans ce contexte, une approche pertinente pour modéliser cette géométrie de la voie, est de la considérer comme un champ aléatoire multivarié, dont les propriétés sont a priori inconnues. En raison des interactions spécifiques entre le train et la voie, il s'avère que ce champ aléatoire n'est ni Gaussien ni stationnaire. Ce travail de thèse s'est alors particulièrement concentré sur le développement de méthodes numériques permettant l'identification en inverse, à partir de mesures expérimentales, de champs aléatoires non Gaussiens et non stationnaires. Le comportement du train étant très non linéaire, ainsi que très sensible vis-à-vis de la géométrie de la voie, la caractérisation du champ aléatoire correspondant aux défauts de géométrie doit être extrêmement fine, tant du point de vue fréquentiel que statistique. La dimension des espaces statistiques considérés est alors très importante. De ce fait, une attention toute particulière a été portée dans ces travaux aux méthodes de réduction statistique, ainsi qu'aux méthodes pouvant être généralisées à la très grande dimension. Une fois la variabilité de la géométrie de la voie caractérisée à partir de données expérimentales, elle doit ensuite être propagée au sein du modèle numérique ferroviaire. A cette fin, les propriétés mécaniques d'un modèle numérique de train à grande vitesse ont été identifiées à partir de mesures expérimentales. La réponse dynamique stochastique de ce train, soumis à un très grand nombre de conditions de circulation réalistes et représentatives générées à partir du modèle stochastique de la voie ferrée, a été ainsi évaluée. Enfin, afin d'illustrer les possibilités apportées par un tel couplage entre la variabilité de la géométrie de la voie et la réponse dynamique du train, ce travail de thèse aborde trois applications / High speed trains are currently meant to run faster and to carry heavier loads, while being less energy consuming and still ensuring the safety and comfort certification criteria. In order to optimize the conception of such innovative trains, a precise knowledge of the realm of possibilities of track conditions that the train is likely to be confronted to during its life cycle is necessary. Simulation has therefore a big to play in this context. However, to face these challenges, it has to be very representative of the physical behavior of the system. From a general point of view, a railway simulation can be seen as the dynamic response of a non-linear mechanical system, the train, which is excited by a complex multivariate spatial function, the track geometry. Therefore, the models of the train, of the wheel/rail contact forces have thus to be fully validated and the simulations have to be raised on sets of excitations that are realistic and representative of the track geometry. Based on experimental measurements, a complete parametrization of the track geometry and of its variability would be of great concern to analyze the complex link between the train dynamics and the physical and statistical properties of the track geometry. A good approach to characterize this variability is to model the track geometry as a multivariate random field, for which statistical properties are only known through a set of independent realizations. Due to the specific interactions between the train and the track, this random field is neither stationary nor Gaussian. In order to propagate the track geometry variability to the train response, methods to identify in inverse, from a finite set of experimental data, the statistical properties of non-stationary and non-Gaussian random fields were analyzed in this thesis. The train behavior being very non-linear and very sensitive to the track geometry, the random field has to be described very precisely from frequency and statistical points of view. As a result, the statistical dimension of this random field is very high. Hence, a particular attention is paid in this thesis to statistical reduction methods and to statistical identification methods that can be numerically applied to the high dimensional case. Once the track geometry variability has been characterized from experimental data, it has to be propagated through the model. To this end, a normalized multibody model of a high speed train, whose mechanical parameters have been carefully identified from experimental measurements, has been made run on sets of realistic and representative running conditions. The commercial software Vampire was used to solve these dynamic equations. At last, three applications are proposed to illustrate to what extent such a railway stochastic modeling opens new possibilities in terms of virtual certification, predictive maintenance and optimization of the railway system