Periodically Forced Nonlinear Oscillators With Hysteretic Damping

We perform a detailed study of the dynamics of a nonlinear, one-dimensional oscillator driven by a periodic force under hysteretic damping, whose linear version was originally proposed and analyzed by Bishop in [1]. We first add a small quadratic stiffness term in the constitutive equation and construct the periodic solution of the problem by a systematic perturbation method, neglecting transient terms as $t\rightarrow \infty$. We then repeat the analysis replacing the quadratic by a cubic term, which does not allow the solutions to escape to infinity. In both cases, we examine the dependence of the amplitude of the periodic solution on the different parameters of the model and discuss the differences with the linear model. We point out certain undesirable features of the solutions, which have also been alluded to in the literature for the linear Bishop's model, but persist in the nonlinear case as well. Finally, we discuss an alternative hysteretic damping oscillator model first proposed by Reid [2], which appears to be free from these difficulties and exhibits remarkably rich dynamical properties when extended in the nonlinear regime.Comment: Accepted for publication in the Journal of Computational and Nonlinear Dynamic

Stability Properties of 1-Dimensional Hamiltonian Lattices with Non-analytic Potentials

We investigate the local and global dynamics of two 1-Dimensional (1D) Hamiltonian lattices whose inter-particle forces are derived from non-analytic potentials. In particular, we study the dynamics of a model governed by a "graphene-type" force law and one inspired by Hollomon's law describing "work-hardening" effects in certain elastic materials. Our main aim is to show that, although similarities with the analytic case exist, some of the local and global stability properties of non-analytic potentials are very different than those encountered in systems with polynomial interactions, as in the case of 1D Fermi-Pasta-Ulam-Tsingou (FPUT) lattices. Our approach is to study the motion in the neighborhood of simple periodic orbits representing continuations of normal modes of the corresponding linear system, as the number of particles $N$ and the total energy $E$ are increased. We find that the graphene-type model is remarkably stable up to escape energy levels where breakdown is expected, while the Hollomon lattice never breaks, yet is unstable at low energies and only attains stability at energies where the harmonic force becomes dominant. We suggest that, since our results hold for large $N$, it would be interesting to study analogous phenomena in the continuum limit where 1D lattices become strings.Comment: Accepted for publication in the International Journal of Bifurcation and Chao

Μπεϋζιανές μέθοδοι και εκτίμηση μη γραμμικών δυναμικών συστημάτων

This thesis is concerned with the interplay between Bayesian Statistics and Nonlinear Dynamical Systems. Specifically, the main goal of the thesis is the development of new Markov Chain Monte Carlo (MCMC) methods that have applications in the general field of nonlinear dynamics. The motivation for this approach is the decomposition of the modeling procedure into two interacting parts: the deterministic part and the stochastic noise process. Using this kind of modeling, we are able to capture a wide variety of phenomena, utilizing the complex behavior of the nonlinear part and the new characteristics emerging from the interaction with the noise process. The proposed methods are nonparametric, based on the Geometric Stick-Breaking process as a prior over the space of probability measures. An important aspect of this work is the relaxation of a very common assumption in the literature: the normality of the noise distribution. In the first two Chapters we present basic notions, definitions and results of the fields of Bayesian statistics and dynamical systems. In Chapter 3 we propose a Bayesian nonparametric framework, the Geometric Stick Breaking Reconstruction (GSBR) model, suitable for the full reconstruction and prediction of dynamically-noisy corrupted time series, when the additive noise may exhibit significant departures from normality. We have used the Geometric Stick Breaking process as a prior over the unknown noise density, showing that it yields almost indistinguishable results from the more commonly used, but computationally more expensive, Dirichlet Process prior. The GSBR model is also generalized in order to include arbitrary number of finite lag terms and finally extended in the multivariate case, where the noise process is modeled as an infinite mixture of multivariate Normal kernels with unknown precision matrices, using Wishart distributions. In Chapter 4, the thesis proceeds with the proposal of a new Bayesian nonparametric method, the Dynamic Noise Reduction Replicator (DNRR) model, suitable for noise reduction over a given chaotic time series, subjected to the effects of (the perhaps non-Gaussian) additive dynamical noise. By using the DNRR, we are also able to relate the regions of primary homoclinic tangencies of the associated deterministic system, with regions of persistent high determinism deviations. Further, in relating the random dynamical systems with their associated deterministic parts, in Chapter 5 we present an extension of the GSBR sampler, in order to provide a MCMC-based stochastic approximation of the global stable manifold. Specifically, we have introduced the Backward GSBR (BGSBR) model, in order to estimate past unobserved observations, namely performing prediction in reversed time. The BGSBR sampler can be applied multiple times over proper subsets of the noisy observations, each time generating posterior samples for the various initial conditions. Then the global stable manifold of the associated deterministic map can be stochastically approximated as the union of the supports of the posterior marginal distributions. The method is parsimonious and efficient both in invertible and non-invertible maps. In Chapter 6 we present the main conclusions and address some relevant topics for further research, based on the results obtained during this thesis.Η παρούσα διατριβή αφορά τη διάδραση μεταξύ Μπεϋζιανής στατιστικής και μη γραμμικών δυναμικών συστημάτων. Ειδικότερα, ο βασικός στόχος της διατριβής είναι η ανάπτυξη νέων μεθόδων Markov Chain Monte Carlo (MCMC) με εφαρμογές στο ευρύτερο πεδίο της μη γραμμικής δυναμικής. Το κίνητρο για την ανάπτυξη τέτοιων μεθόδων, αφορά την διάκριση της διαδικασίας μοντελοποίησης σε δύο βασικά διαδραστικά μέρη: το αιτιοκρατικό (ντετερμινιστικό) μέρος και τη στοχαστική διαδικασία θορύβου. Μέσω μιας τέτοιου είδους μοντελοποίησης, επιτυγχάνεται η σύλληψη μιας ευρείας συλλογής φαινομένων, αξιοποιώντας την πολυπλοκότητα της δυναμικής συμπεριφοράς λόγω του μη γραμμικού μέρους και τα νέα χαρακτηριστικά που αναδεικνύονται λόγω της εμπλοκής των στοχαστικών διαταραχών. Οι προτεινόμενες στατιστικές μέθοδοι είναι μη παραμετρικές και βασίζονται στη χρήση τυχαίων μέτρων πιθανότητας με γεωμετρικά βάρη (Geometric stick breaking process (GSB)) ως εκ των προτέρων κατανομές στο χώρο των μέτρων πιθανότητας. Μια σημαντική πτυχή των προτεινόμενων μεθόδων είναι η επίτευξη της χαλάρωσης μιας πολύ συχνής υπόθεσης στη βιβλιογραφία: της κανονικότητας της διαδικασίας θορύβου. Στα δύο πρώτα Κεφάλαια γίνεται αναφορά σε βασικές έννοιες της Μπεϋζιανής στατιστικής και της θεωρίας των δυναμικών συστημάτων. Στο Κεφάλαιο 3 κατασκεύαζουμε ένα μη παραμετρικό Μπεϋζιανό μοντέλο κατάλληλο για αναδόμηση των δυναμικών εξισώσεων και πρόγνωση μελλοντικών τιμών από παρατηρηθείσες χρονοσειρές μολυσμένες με προσθετικό δυναμικό θόρυβο: το μοντέλο geometric stick-breaking reconstruction (GSBR). Το GSBR μοντέλο βασίζεται στο τυχαίο μέτρο με γεωμετρικά βάρη (GSB), ενώ γίνεται επίσης παρουσίαση του αντίστοιχου μοντέλου Dirichlet process reconstruction (DPR) βασισμένου στο τυχαίο μέτρο DP, καθώς και η μεταξύ τους σύγκριση. Η μεθοδολογία επεκτείνεται ώστε να γίνει εφικτή η μοντελοποίηση χρησιμοποιώντας αυθαίρετο πεπερασμένο πλήθος όρων χρονικών υστερήσεων (lags), καθώς και στην πολυδιάστατη περίπτωση μέσω της άπειρης μίξης πολυδιάστατων κανονικών πυρήνων με άγνωστους πίνακες αποκρίσεων, χρησιμοποιώντας ως μέτρο μίξης το τυχαίο μέτρο GSB και μέτρο βάσης (base measure) μια κατανομή Wishart. Στο Κεφάλαιο 4, προτείνεται μια μη παραμετρική Μπεϋζιανή μεθοδολογία βασιζόμενη επίσης στο τυχαίο μέτρο GSB, με σκοπό τη μείωση δυναμικού θορύβου σε διαθέσιμα δεδομένα μη γραμμικών χρονοσειρών με προσθετικό θορυβο. Το μοντέλο Dynamic Noise Reduction Replicator (DNRR) επιτυγχάνει μεγάλη ακρίβεια στην αναδόμηση των δυναμικών εξισώσεων, ώστε να αναπαράγει την υποκείμενη δυναμική σε περιβάλλον ασθενέστερου δυναμικού θορύβου. Μέσω της εφαρμογής του DNRR είναι δυνατή η σύνδεση των περιοχών υψηλών αποκλίσεων από τον ντετερμινισμό με τις περιοχές των πρωταρχικών ομοκλινικών εφαπτομενικοτήτων του υποκείμενου ντετερμινιστικού συστήματος. Συσχετίζοντας τα στοχαστικά δυναμικά συστήματα με τα αντίστοιχα ντετερμινιστικά τους μέρη, στο Κεφάλαιο 5 παρουσιάζεται μία επέκταση του μοντέλου GSBR, με σκοπό τη στοχαστική προσέγγιση της ολικής ευσταθούς πολλαπλότητας (global stable manifold), με χρήση μεθόδου MCMC. Ειδικότερα, γίνεται παρουσίαση του οπισθοδρομικού (backward) GSBR μοντέλου BGSBR, μέσω του οποίου επιτυγχάνεται πρόβλεψη σε αντεστραμμένο χρόνο. Με κατάλληλες πολλαπλές εφαρμογές του BGSBR χρησιμοποιώντας υποσύνολα των διαθέσιμων δεδομένων, δείχνουμε ότι η ένωση των στηριγμάτων των περιθώριων κατανομών για τις διάφορες αρχικές συνθήκες παρέχουν μια στοχαστική προσέγγιση της ευσταθούς πολλαπλότητας του υποκείμενου ντετερμινιστικού συστήματος. Η μεθοδολογία είναι εφαρμόσιμη τόσο σε αντιστρέψιμες όσο και σε μη αντιστρέψιμες απεικονίσεις. Στο Κεφάλαιο 6 γίνεται σύνοψη των αποτελεσμάτων των προηγούμενων Κεφαλαίων και αναφορά σε θέματα για μελλοντική έρευνα, τα οποία προέκυψαν κατά τη διάρκεια εκπόνησης της παρούσας Διατριβής

Dynamics and Statistics of Weak Chaos in a 4–D Symplectic Map

The important phenomenon of “stickiness” of chaotic orbits in low dimensional dynamical systems has been investigated for several decades, in view of its applications to various areas of physics, such as classical and statistical mechanics, celestial mechanics and accelerator dynamics. Most of the work to date has focused on two-degree of freedom Hamiltonian models often represented by two-dimensional (2D) area preserving maps. In this paper, we extend earlier results using a 4–dimensional extension of the 2D MacMillan map, and show that a symplectic model of two coupled MacMillan maps also exhibits stickiness phenomena in limited regions of phase space. To this end, we employ probability distributions in the sense of the Central Limit Theorem to demonstrate that, as in the 2D case, sticky regions near the origin are also characterized by “weak” chaos and Tsallis entropy, in sharp contrast to the “strong” chaos that extends over much wider domains and is described by Boltzmann Gibbs statistics. Remarkably, similar stickiness phenomena have been observed in higher dimensional Hamiltonian systems around unstable simple periodic orbits at various values of the total energy of the system

Energy transport in one-dimensional oscillator arrays with hysteretic damping

Energy transport in one-dimensional oscillator arrays has been extensively studied to date in the conservative case, as well as under weak viscous damping. When driven at one end by a sinusoidal force, such arrays are known to exhibit the phenomenon of supratransmission, i.e. a sudden energy surge above a critical driving amplitude. In this paper, we study one-dimensional oscillator chains in the presence of hysteretic damping, and include nonlinear stiffness forces that are important for many materials at high energies. We first employ Reid’s model of local hysteretic damping, and then study a new model of nearest neighbor dependent hysteretic damping to compare their supratransmission and wave packet spreading properties in a deterministic as well as stochastic setting. The results have important quantitative differences, which should be helpful when comparing the merits of the two models in specific engineering applications

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors

Parametric Quasi-Static Study of the Effect of Misalignments on the Path of Contact, Transmission Error, and Contact Pressure of Crowned Spur and Helical Gear Teeth Using a Novel Rapidly Convergent Method

Quasi-static modelling of non-conjugate contact of tooth-modified spur and helical gears has been studied at length, but existing models are hindered by convergence problems and require a brute-force numerical approach. Here, a novel, computationally efficient, and stable and unconditionally convergent model is developed for non-conjugate tooth contact in three dimensions and applied to crowned spur and helical gears to assess parametrically the sensitivity of various in- and out-of-plane misalignments on the path of contact, transmission error, and contact pressure. Performance metrics are defined, and comparisons are made between three different crowning modification functions

Parametric Quasi-Static Study of the Effect of Misalignments on the Path of Contact, Transmission Error, and Contact Pressure of Crowned Spur and Helical Gear Teeth Using a Novel Rapidly Convergent Method

Quasi-static modelling of non-conjugate contact of tooth-modified spur and helical gears has been studied at length, but existing models are hindered by convergence problems and require a brute-force numerical approach. Here, a novel, computationally efficient, and stable and unconditionally convergent model is developed for non-conjugate tooth contact in three dimensions and applied to crowned spur and helical gears to assess parametrically the sensitivity of various in- and out-of-plane misalignments on the path of contact, transmission error, and contact pressure. Performance metrics are defined, and comparisons are made between three different crowning modification functions