Academic literature on the topic 'Stochastic accelerations'
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Journal articles on the topic "Stochastic accelerations":
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Borgas, M. S., and B. L. Sawford. "A family of stochastic models for two-particle dispersion in isotropic homogeneous stationary turbulence." Journal of Fluid Mechanics 279 (November 25, 1994): 69–99. http://dx.doi.org/10.1017/s0022112094003824.
Abstract:
A family of Lagrangian stochastic models for the joint motion of particle pairs in isotropic homogeneous stationary turbulence is considered. The Markov assumption and well-mixed criterion of Thomson (1990) are used, and the models have quadratic-form functions of velocity for the particle accelerations. Two constraints are derived which formally require that the correct one-particle statistics are obtained by the models. These constraints involve the Eulerian expectation of the ‘acceleration’ of a fluid particle with conditioned instantaneous velocity, given either at the particle, or at some other particle's position. The Navier-Stokes equations, with Gaussian Eulerian probability distributions, are shown to give quadratic-form conditional accelerations, and models which satisfy these two constraints are found. Dispersion calculations show that the constraints do not always guarantee good one-particle statistics, but it is possible to select a constrained model that does. Thomson's model has good one-particle statistics, but is shown to have unphysical conditional accelerations. Comparisons of relative dispersion for the models are made.
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Ding, Yanqiong, Yongbo Peng, and Jie Li. "A Stochastic Semi-Physical Model of Seismic Ground Motions in Time Domain." Journal of Earthquake and Tsunami 12, no. 03 (August 12, 2018): 1850006. http://dx.doi.org/10.1142/s1793431118500069.
Abstract:
A stochastic function model of seismic ground motions is presented in this paper. It is derived from the consideration of physical mechanisms of seismic ground motions. The model includes the randomness inherent in the seismic source, propagation path and local site. For logical selection of the seismic acceleration records, a cluster analysis method is employed. Statistical distributions of the random parameters associated with the proposed model are identified using the selected data. Superposition method of narrow-band wave groups is then adopted to simulate non-stationary seismic ground motions. In order to verify the feasibility of the proposed model, comparative studies of time histories and response spectra of the simulated seismic accelerations against those of the recorded seismic accelerations are carried out. Their probability density functions, moreover, are readily investigated by virtue of the probability density evolution method.
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Kelly, Patrick, Manoranjan Majji, and Felipe Guzmán. "Estimation and Error Analysis for Optomechanical Inertial Sensors." Sensors 21, no. 18 (September 11, 2021): 6101. http://dx.doi.org/10.3390/s21186101.
Abstract:
A sensor model and methodology to estimate the forcing accelerations measured using a novel optomechanical inertial sensor with the inclusion of stochastic bias and measurement noise processes is presented. A Kalman filter for the estimation of instantaneous sensor bias is developed; the outputs from this calibration step are then employed in two different approaches for the estimation of external accelerations applied to the sensor. The performance of the system is demonstrated using simulated measurements and representative values corresponding to a bench-tested 3.76 Hz oscillator. It is shown that the developed methods produce accurate estimates of the bias over a short calibration step. This information enables precise estimates of acceleration over an extended operation period. These results establish the feasibility of reliably precise acceleration estimates using the presented methods in conjunction with state of the art optomechanical sensing technology.
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Guo, Xiangying, Changkun Li, Zhong Luo, and Dongxing Cao. "Modal Parameter Identification of Structures Using Reconstructed Displacements and Stochastic Subspace Identification." Applied Sciences 11, no. 23 (December 2, 2021): 11432. http://dx.doi.org/10.3390/app112311432.
Abstract:
A method of modal parameter identification of structures using reconstructed displacements was proposed in the present research. The proposed method was developed based on the stochastic subspace identification (SSI) approach and used reconstructed displacements of measured accelerations as inputs. These reconstructed displacements suppressed the high-frequency component of measured acceleration data. Therefore, in comparison to the acceleration-based modal analysis, the operational modal analysis obtained more reliable and stable identification parameters from displacements regardless of the model order. However, due to the difficulty of displacement measurement, different types of noise interferences occurred when an acceleration sensor was used, causing a trend term drift error in the integral displacement. A moving average low-frequency attenuation frequency-domain integral was used to reconstruct displacements, and the moving time window was used in combination with the SSI method to identify the structural modal parameters. First, measured accelerations were used to estimate displacements. Due to the interference of noise and the influence of initial conditions, the integral displacement inevitably had a drift term. The moving average method was then used in combination with a filter to effectively eliminate the random fluctuation interference in measurement data and reduce the influence of random errors. Real displacement results of a structure were obtained through multiple smoothing, filtering, and integration. Finally, using reconstructed displacements as inputs, the improved SSI method was employed to identify the modal parameters of the structure.
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Nava, F. Alejandro. "Assessment of possible peak accelerations through stochastic variations." Terra Nova 3, no. 3 (May 1991): 289–93. http://dx.doi.org/10.1111/j.1365-3121.1991.tb00146.x.
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de Jager, Cornells, Joost Carpay, Alex de Koter, Hans Nieuwenhuijzen, and Erik Schellekens. "Atmospheric dynamics of luminous stars." International Astronomical Union Colloquium 113 (1989): 211–20. http://dx.doi.org/10.1017/s0252921100004474.
Abstract:
AbstractA review is given of data and theories on the motion fields in super and hypergiants with special reference to LBV’s. We show that the radiative momentum flux is incapable of driving the episodical bursts of mass loss of these stars, and that there are several indications that the LBV-phenomenon is hydrodynamically driven. The sum of turbulent and radiative accelerations in the atmospheres of the most luminous stars compensates the gravitational acceleration for stars near the Humphreys-Davidson limit. This explains their atmospheric near-instability. The motion field in the atmosphere of a typical LBV consists mainly of low-order gravity waves, while acoustic waves are rapidly damped. These gravitation waves may be stochastic rather than coherently ordered. These stochastic pulsations are assumed to be responsible for the LBV phenomenon.
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Vajedi, S., K. Gustavsson, B. Mehlig, and L. Biferale. "Inertial-particle accelerations in turbulence: a Lagrangian closure." Journal of Fluid Mechanics 798 (May 31, 2016): 187–200. http://dx.doi.org/10.1017/jfm.2016.305.
Abstract:
The distribution of particle accelerations in turbulence is intermittent, with non-Gaussian tails that are quite different for light and heavy particles. In this article we analyse a closure scheme for the acceleration fluctuations of light and heavy inertial particles in turbulence, formulated in terms of Lagrangian correlation functions of fluid tracers. We compute the variance and the flatness of inertial-particle accelerations and we discuss their dependency on the Stokes number. The closure incorporates effects induced by the Lagrangian correlations along the trajectories of fluid tracers, and its predictions agree well with results of direct numerical simulations of inertial particles in turbulence, provided that the effects induced by inertial preferential sampling of heavy/light particles outside/inside vortices are negligible. In particular, the scheme predicts the correct functional behaviour of the acceleration variance, as a function of $St$, as well as the presence of a minimum/maximum for the flatness of the acceleration of heavy/light particles, in good qualitative agreement with numerical data. We also show that the closure works well when applied to the Lagrangian evolution of particles using a stochastic surrogate for the underlying Eulerian velocity field. Our results support the conclusion that there exist important contributions to the statistics of the acceleration of inertial particles independent of the preferential sampling. For heavy particles we observe deviations between the predictions of the closure scheme and direct numerical simulations, at Stokes numbers of order unity. For light particles the deviation occurs for larger Stokes numbers.
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Yan, Guang Hui, and Shuo Zhang. "Research on Modeling and Optimization Control of Heavy Truck Cab Active Suspension System." Applied Mechanics and Materials 687-691 (November 2014): 359–62. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.359.
Abstract:
In order to meet the ride comfort of the heavy truck cab, the 1/2 heavy truck cab active suspension model established. Based on this model the LQG optimization control was selected for the active control of a 1/2 heavy truck cab suspension system. The road disturbance is integral white noise stochastic signal. By the example simulation in Matlab/Simulink, the results show that the cab active suspension with LQG control strategy can decrease the vertical accelerations, the roll angle and roll angle acceleration of the truck cab, the active suspension can improve both the ride comfort and driving safety.
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Chen, Na, Meng Wang, Tom Alkim, and Bart van Arem. "A Robust Longitudinal Control Strategy of Platoons under Model Uncertainties and Time Delays." Journal of Advanced Transportation 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/9852721.
Abstract:
Automated vehicles are designed to free drivers from driving tasks and are expected to improve traffic safety and efficiency when connected via vehicle-to-vehicle communication, that is, connected automated vehicles (CAVs). The time delays and model uncertainties in vehicle control systems pose challenges for automated driving in real world. Ignoring them may render the performance of cooperative driving systems unsatisfactory or even unstable. This paper aims to design a robust and flexible platooning control strategy for CAVs. A centralized control method is presented, where the leader of a CAV platoon collects information from followers, computes the desired accelerations of all controlled vehicles, and broadcasts the desired accelerations to followers. The robust platooning is formulated as a Min-Max Model Predictive Control (MM-MPC) problem, where optimal accelerations are generated to minimize the cost function under the worst case, where the worst case is taken over the possible models. The proposed method is flexible in such a way that it can be applied to both homogeneous platoon and heterogeneous platoon with mixed human-driven and automated controlled vehicles. A third-order linear vehicle model with fixed feedback delay and stochastic actuator lag is used to predict the platoon behavior. Actuator lag is assumed to vary randomly with unknown distributions but a known upper bound. The controller regulates platoon accelerations over a time horizon to minimize a cost function representing driving safety, efficiency, and ride comfort, subject to speed limits, plausible acceleration range, and minimal net spacing. The designed strategy is tested by simulating homogeneous and heterogeneous platoons in a number of typical and extreme scenarios to assess the system stability and performance. The test results demonstrate that the designed control strategy for CAV can ensure the robustness of stability and performance against model uncertainties and feedback delay and outperforms the deterministic MPC based platooning control.
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Haerendel, Gerhard. "Magnetic energy conversion in the Corona and Magnetosphere." Highlights of Astronomy 10 (1995): 302. http://dx.doi.org/10.1017/s1539299600011278.
Abstract:
Five different categories of magnetic energy conversion will be discussed, and their manifestations in the ionosphere-magnetosphere and chromosphere - corona will be compared. They are: (1) Ohmic dissipation of d.c. currents, (2) Damping of Alfvén waves, (3) Magnetic pumping, (4) Reconnection, and (5) Electrostatic acceleration parallel B. Manifestations are: (1) Ionospheric heating by Peder sen currents and generation of chromospheric faculae and plages by dissipation of field-aligned currents; (2) Generation of spicules by collisional damping of Alfvén-waves and Alfvén-wave damping in the corona by resonant absorption; (3) Drift resonance acceleration of radiation belt particles and stochastic acceleration of high-energy flare particles by Alfvén waves; (4) Reconnection at the magnetopause and in the tail, plasmoid formation and a wide variety of configuration changes and mhd instabilities in the corona for which reconnection plays a decisive role; (5) Acceleration by field-aligned potential drops as origin of auroral arcs and of > 10 MeV electron and ion beams in solar flares. Some of these mechanisms, e.g. plages, spicules, nanoflares and field-aligned electrostatic accelerations will be selected for more detailed discussion.
Dissertations / Theses on the topic "Stochastic accelerations":
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Fossà, Alberto. "Propagation multi-fidélité d’incertitude orbitale en présence d’accélérations stochastiques." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0009.
Abstract:
Le problème de la propagation non linéaire d’incertitude est crucial en astrodynamique, car tous les systèmes d’intérêt pratique, allant de la navigation à la détermination d’orbite et au suivi de cibles, impliquent des non-linéarités dans leurs modèles dynamiques et de mesure. Un sujet d’intérêt est la propagation précise d’incertitude à travers la dynamique orbitale non linéaire, une exigence fondamentale dans plusieurs applications telles que la surveillance de l’espace, la gestion du trafic spatial et la fin de vie des satellites. Étant donnée une représentation dimensionnelle finie de la fonction de densité de probabilité (pdf) de l’état initial, l’objectif est d’obtenir une représentation similaire de cette pdf à tout moment futur. Ce problème a été historiquement abordé avec des méthodes linéarisées ou des simulations de Monte Carlo (MC), toutes deux inadaptées pour satisfaire la demande d’un nombre croissant d’applications. Les méthodes linéarisées sont très performantes, mais ne peuvent pas gérer de fortes non-linéarités ou de longues fenêtres de propagation en raison de la validité locale de la linéarisation. En revanche, les méthodes MC peuvent gérer tout type de non-linéarité, mais sont trop coûteuses en termes de calcul pour toute tâche nécessitant la propagation de plusieurs pdf. Au lieu de cela, cette thèse exploite des méthodes multi-fidélité et des techniques d’algèbre différentielle (DA) pour développer des méthodes efficaces pour la propagation précise des incertitudes à travers des systèmes dynamiques non linéaires. La première méthode, appelée low-order automatic domain splitting (LOADS), représente l’incertitude avec un ensemble de polynômes de Taylor du deuxième ordre et exploite une mesure de non-linéarité basée sur la DA pour ajuster leur nombre en fonction de la dynamique locale et de la précision requise. Un modèle adaptatif de mélange Gaussien (GMM) est ensuite développé en associant chaque polynôme à un noyau pondéré pour obtenir une représentation analytique de la pdf d’état. En outre, une méthode multi-fidélité est proposée pour réduire le coût computationnel des algorithmes précédents tout en conservant une précision similaire. La méthode GMM est dans ce cas exécutée sur un modèle dynamique à faible fidélité, et seules les moyennes des noyaux sont propagées ponctuellement dans une dynamique à haute fidélité pour corriger la pdf à faible fidélité. Si les méthodes précédentes traitent de la propagation d’une incertitude initiale dans un modèle dynamique déterministe, les effets des forces mal ou non modélisées sont enfin pris en compte pour améliorer le réalisme des statistiques propagées. Dans ce cas, la méthode multi-fidélité est d’abord utilisée pour propager l’incertitude initiale dans un modèle dynamique déterministe de faible fidélité. Les propagations ponctuelles sont ensuite remplacées par une propagation polynomiale des moments de la pdf dans un système dynamique stochastique. Ces moments modélisent les effets des accélérations stochastiques sur les moyennes des noyaux, et couplés à la méthode GMM, ils fournissent une description de la pdf qui tient compte de l’incertitude initiale et des effets des forces négligées. Les méthodes proposées sont appliquées au problème de la propagation d’incertitude en orbite, et leurs performances sont évaluées dans différents régimes orbitaux. Les résultats démontrent leur efficacité pour une propagation précise de l’incertitude initiale et des effets du bruit du processus à une fraction du coût de calcul des simulations MC. La méthode LOADS est ensuite utilisée pour résoudre le problème de la détermination initiale d’orbite en exploitant les informations sur l’incertitude des mesures, et pour développer une méthode de prétraitement des données qui améliore la robustesse des algorithmes de détermination d’orbite. Ces outils sont enfin validés sur des observations réelles d’un objet en orbite de transfert géostationnaireThe problem of nonlinear uncertainty propagation (UP) is crucial in astrodynamics since all systems of practical interest, ranging from navigation to orbit determination (OD) and target tracking, involve nonlinearities in their dynamics and measurement models. One topic of interest is the accurate propagation of uncertainty through the nonlinear orbital dynamics, a fundamental requirement in several applications such as space surveillance and tracking (SST), space traffic management (STM), and end-of-life (EOL) disposal. Given a finite-dimensional representation of the probability density function (pdf) of the initial state, the main goal is to obtain a similar representation of the state pdf at any future time. This problem has been historically tackled with either linearized methods or Monte Carlo (MC) simulations, both of which are unsuitable to satisfy the demand of a rapidly growing number of applications. Linearized methods are light on computational resources, but cannot handle strong nonlinearities or long propagation windows due to the local validity of the linearization. In contrast, MC methods can handle any kind of nonlinearity, but are too computationally expensive for any task that requires the propagation of several pdfs. Instead, this thesis leverages multifidelity methods and differential algebra (DA) techniques to develop computationally efficient methods for the accurate propagation of uncertainties through nonlinear dynamical systems. The first method, named low-order automatic domain splitting (LOADS), represents the uncertainty with a set of second-order Taylor polynomials and leverages a DA-based measure of nonlinearity to adjust their number based on the local dynamics and the required accuracy. An adaptive Gaussian mixture model (GMM) method is then developed by associating each polynomial to a weighted Gaussian kernel, thus obtaining an analytical representation of the state pdf. Going further, a multifidelity method is proposed to reduce the computational cost of the former algorithms while retaining a similar accuracy. The adaptive GMM method is in this case run on a low-fidelity dynamical model, and only the expected values of the kernels are propagated point-wise in high-fidelity dynamics to compute a posteriori correction of the low-fidelity state pdf. If the former methods deal with the propagation of an initial uncertainty through a deterministic dynamical model, the effects of mismodeled or unmodeled forces are finally considered to further enhance the realism of the propagated statistics. In this case, the multifidelity GMM method is used at first to propagate the initial uncertainty through a low-fidelity, deterministic dynamical model. The point-wise propagations are then replaced with a DA-based algorithm to efficiently propagate a polynomial representation of the moments of the pdf in a stochastic dynamical system. These moments model the effects of stochastic accelerations on the deterministic kernels’ means, and coupled with the former GMM provide a description of the propagated state pdf that accounts for both the uncertainty in the initial state and the effects of neglected forces. The proposed methods are applied to the problem of orbit UP, and their performance is assessed in different orbital regimes. The results demonstrate the effectiveness of these methods in accurately propagating the initial uncertainty and the effects of process noise at a fraction of the computational cost of high-fidelity MC simulations. The LOADS method is then employed to solve the initial orbit determination (IOD) problem by exploiting the information on measurement uncertainty and to develop a preprocessing scheme aimed at improving the robustness of batch OD algorithms. These tools are finally validated on a set of real observations for an object in geostationary transfer orbit (GTO)
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Wolf, Christian [Verfasser]. "Advanced acceleration techniques for Nested Benders decomposition in stochastic programming / Christian Wolf." Paderborn : Universitätsbibliothek, 2014. http://d-nb.info/1046905090/34.
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McEvoy, Erica L., and Erica L. McEvoy. "A Numerical Method for the Simulation of Skew Brownian Motion and its Application to Diffusive Shock Acceleration of Charged Particles." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/625664.
Abstract:
Stochastic differential equations are becoming a popular tool for modeling the transport and acceleration of cosmic rays in the heliosphere. In diffusive shock acceleration, cosmic rays diffuse across a region of discontinuity where the up- stream diffusion coefficient abruptly changes to the downstream value. Because the method of stochastic integration has not yet been developed to handle these types of discontinuities, I utilize methods and ideas from probability theory to develop a conceptual framework for the treatment of such discontinuities.
Using this framework, I then produce some simple numerical algorithms that allow one to incorporate and simulate a variety of discontinuities (or boundary conditions) using stochastic integration. These algorithms were then modified to create a new algorithm which incorporates the discontinuous change in diffusion coefficient found in shock acceleration (known as Skew Brownian Motion). The originality of this algorithm lies in the fact that it is the first of its kind to be statistically exact, so that one obtains accuracy without the use of approximations (other than the machine precision error).
I then apply this algorithm to model the problem of diffusive shock acceleration, modifying it to incorporate the additional effect of the discontinuous flow speed profile found at the shock. A steady-state solution is obtained that accurately simulates this phenomenon. This result represents a significant improvement over previous approximation algorithms, and will be useful for the simulation of discontinuous diffusion processes in other fields, such as biology and finance.
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Wilhelm, Alina [Verfasser], Martin [Akademischer Betreuer] Pohl, Christoph [Gutachter] Pfrommer, and Julia [Gutachter] Tjus. "Stochastic re-acceleration of particles in supernova remnants / Alina Wilhelm ; Gutachter: Christoph Pfrommer, Julia Tjus ; Betreuer: Martin Pohl." Potsdam : Universität Potsdam, 2021. http://d-nb.info/123972909X/34.
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Trimeloni, Thomas. "Accelerating Finite State Projection through General Purpose Graphics Processing." VCU Scholars Compass, 2011. http://scholarscompass.vcu.edu/etd/175.
Abstract:
The finite state projection algorithm provides modelers a new way of directly solving the chemical master equation. The algorithm utilizes the matrix exponential function, and so the algorithm’s performance suffers when it is applied to large problems. Other work has been done to reduce the size of the exponentiation through mathematical simplifications, but efficiently exponentiating a large matrix has not been explored. This work explores implementing the finite state projection algorithm on several different high-performance computing platforms as a means of efficiently calculating the matrix exponential function for large systems. This work finds that general purpose graphics processing can accelerate the finite state projection algorithm by several orders of magnitude. Specific biological models and modeling techniques are discussed as a demonstration of the algorithm implemented on a general purpose graphics processor. The results of this work show that general purpose graphics processing will be a key factor in modeling more complex biological systems.
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Hall, Eric Joseph. "Accelerated numerical schemes for deterministic and stochastic partial differential equations of parabolic type." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8038.
Abstract:
First we consider implicit finite difference schemes on uniform grids in time and space for second order linear stochastic partial differential equations of parabolic type. Under sufficient regularity conditions, we prove the existence of an appropriate asymptotic expansion in powers of the the spatial mesh and hence we apply Richardson's method to accelerate the convergence with respect to the spatial approximation to an arbitrarily high order. Then we extend these results to equations where the parabolicity condition is allowed to degenerate. Finally, we consider implicit finite difference approximations for deterministic linear second order partial differential equations of parabolic type and give sufficient conditions under which the approximations in space and time can be simultaneously accelerated to an arbitrarily high order.
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SANTOS, FELIPE SILVA PLACIDO DOS. "ACCELERATING BENDERS STOCHASTIC DECOMPOSITION FOR THE OPTIMIZATION OF PARTIAL BACKORDER CONTROL FOR PERIODIC REVIEW (R, S) INVENTORY SYSTEM WITH UNCERTAIN DEMAND." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=31326@1.
Abstract:
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIROINSTITUTO MILITAR DE ENGENHARIA
CENTRO TECNOLÓGICO DO EXÉRCITO
Este trabalho apresenta uma proposta de aceleração da decomposição de Benders aplicada a uma versão mais geral e compacta (menos restrições e variáveis) do modelo de gestão de estoques, otimizado via programação estocástica de dois estágios que considera uma camada, um item, demanda incerta e política de controle (R, S). De maneira a ser possível considerar problemas de grande porte, foram aplicados os métodos L-Shaped tradicional com corte único e a sua forma estendida com múltiplos cortes. Resultados computacionais preliminares mostraram um substancial melhor desempenho computacional do método L-Shaped tradicional em relação à sua forma multi-cut L-Shaped, mesmo o primeiro necessitando de mais iterações para convergir na solução ótima. Tal observação motivou o desenvolvimento de uma nova técnica de aceleração da decomposição de Benders e de um conjunto de desigualdades válidas. Experimentos numéricos mostram que a abordagem proposta de combinar a técnica de aceleração elaborada com as desigualdades válidas desenvolvidas provê significativa redução do tempo computacional necessário para a solução de instâncias de grande porte.
This dissertation presents a speed up proposal for the Benders decomposition applied to a more general and compact version (less constraints and variables) of inventory management model, optimized via two-stage stochastic programming, which considers one layer, one item, uncertain demand and control policy (R, S). In order to be possible to consider large scale problems, the L-Shaped traditional method with single cuts and its extended form with multiple cuts were applied. Preliminary computational results showed a substantially better computational performance of the traditional L-Shaped method in comparison to the multi-cut L-Shaped method, even with the first requiring more iterations to converge on optimum solutions. This observation led to the development of a new technique to accelerate the decomposition of Benders and a set of valid inequalities. Numerical experiments show that the proposed approach of combining the elaborate acceleration technique with the developed valid inequalities, provide significant reduction in the computational time required to solve large scale instances.
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Flammarion, Nicolas. "Stochastic approximation and least-squares regression, with applications to machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE056/document.
Abstract:
De multiples problèmes en apprentissage automatique consistent à minimiser une fonction lisse sur un espace euclidien. Pour l’apprentissage supervisé, cela inclut les régressions par moindres carrés et logistique. Si les problèmes de petite taille sont résolus efficacement avec de nombreux algorithmes d’optimisation, les problèmes de grande échelle nécessitent en revanche des méthodes du premier ordre issues de la descente de gradient. Dans ce manuscrit, nous considérons le cas particulier de la perte quadratique. Dans une première partie, nous nous proposons de la minimiser grâce à un oracle stochastique. Dans une seconde partie, nous considérons deux de ses applications à l’apprentissage automatique : au partitionnement de données et à l’estimation sous contrainte de forme. La première contribution est un cadre unifié pour l’optimisation de fonctions quadratiques non-fortement convexes. Celui-ci comprend la descente de gradient accélérée et la descente de gradient moyennée. Ce nouveau cadre suggère un algorithme alternatif qui combine les aspects positifs du moyennage et de l’accélération. La deuxième contribution est d’obtenir le taux optimal d’erreur de prédiction pour la régression par moindres carrés en fonction de la dépendance au bruit du problème et à l’oubli des conditions initiales. Notre nouvel algorithme est issu de la descente de gradient accélérée et moyennée. La troisième contribution traite de la minimisation de fonctions composites, somme de l’espérance de fonctions quadratiques et d’une régularisation convexe. Nous étendons les résultats existants pour les moindres carrés à toute régularisation et aux différentes géométries induites par une divergence de Bregman. Dans une quatrième contribution, nous considérons le problème du partitionnement discriminatif. Nous proposons sa première analyse théorique, une extension parcimonieuse, son extension au cas multi-labels et un nouvel algorithme ayant une meilleure complexité que les méthodes existantes. La dernière contribution de cette thèse considère le problème de la sériation. Nous adoptons une approche statistique où la matrice est observée avec du bruit et nous étudions les taux d’estimation minimax. Nous proposons aussi un estimateur computationellement efficaceMany problems in machine learning are naturally cast as the minimization of a smooth function defined on a Euclidean space. For supervised learning, this includes least-squares regression and logistic regression. While small problems are efficiently solved by classical optimization algorithms, large-scale problems are typically solved with first-order techniques based on gradient descent. In this manuscript, we consider the particular case of the quadratic loss. In the first part, we are interestedin its minimization when its gradients are only accessible through a stochastic oracle. In the second part, we consider two applications of the quadratic loss in machine learning: clustering and estimation with shape constraints. In the first main contribution, we provided a unified framework for optimizing non-strongly convex quadratic functions, which encompasses accelerated gradient descent and averaged gradient descent. This new framework suggests an alternative algorithm that exhibits the positive behavior of both averaging and acceleration. The second main contribution aims at obtaining the optimal prediction error rates for least-squares regression, both in terms of dependence on the noise of the problem and of forgetting the initial conditions. Our new algorithm rests upon averaged accelerated gradient descent. The third main contribution deals with minimization of composite objective functions composed of the expectation of quadratic functions and a convex function. Weextend earlier results on least-squares regression to any regularizer and any geometry represented by a Bregman divergence. As a fourth contribution, we consider the the discriminative clustering framework. We propose its first theoretical analysis, a novel sparse extension, a natural extension for the multi-label scenario and an efficient iterative algorithm with better running-time complexity than existing methods. The fifth main contribution deals with the seriation problem. We propose a statistical approach to this problem where the matrix is observed with noise and study the corresponding minimax rate of estimation. We also suggest a computationally efficient estimator whose performance is studied both theoretically and experimentally
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Kulunchakov, Andrei. "Optimisation stochastique pour l'apprentissage machine à grande échelle : réduction de la variance et accélération." Thesis, Université Grenoble Alpes, 2020. http://www.theses.fr/2020GRALM057.
Abstract:
Cette thèse vise à explorer divers sujets liés à l'analyse des méthodes de premier ordre appliquées à des problèmes stochastiques de grande dimension. Notre première contribution porte sur divers algorithmes incrémentaux, tels que SVRG, SAGA, MISO, SDCA, qui ont été analysés de manière approfondie pour les problèmes avec des informations de gradient exactes. Nous proposons une nouvelle technique, qui permet de traiter ces méthodes de manière unifiée et de démontrer leur robustesse à des perturbations stochastiques lors de l'observation des gradients. Notre approche est basée sur une extension du concept de suite d'estimation introduite par Yurii Nesterov pour l'analyse d'algorithmes déterministes accélérés.Cette approche permet de concevoir de façon naturelle de nouveaux algorithmes incrémentaux offrant les mêmes garanties que les méthodes existantes tout en étant robustes aux perturbations stochastiques.Enfin, nous proposons un nouvel algorithme de descente de gradient stochastique accéléré et un nouvel algorithme SVRG accéléré robuste au bruit stochastique. Dans le dernier cas il s'agit essentiellement de l'accélération déterministe au sens de Nesterov, qui préserve la convergence optimale des erreurs stochastiques.Finalement, nous abordons le problème de l'accélération générique. Pour cela, nous étendons l'approche multi-étapes de Catalyst, qui visait à l'origine l'accélération de méthodes déterministes. Afin de l'appliquer aux problèmes stochastiques, nous le modifions pour le rendre plus flexible par rapport au choix des fonctions auxiliaires minimisées à chaque étape de l'algorithme. Finalement, à partir d'une méthode d'optimisation pour les problèmes fortement convexes, avec des garanties standard de convergence, notre procédure commence par accélérer la convergence vers une région dominée par le bruit, pour converger avec une vitesse quasi-optimale ensuite. Cette approche nous permet d'accélérer diverses méthodes stochastiques, y compris les algorithmes à variance réduite. Là encore, le cadre développé présente des similitudes avec l'analyse d'algorithmes accélérés à l'aide des suites d'estimation. En ce sens, nous essayons de combler l'écart entre l'optimisation déterministe et stochastique en termes d'accélération de Nesterov. Une autre contribution est une analyse unifiée d'algorithmes proximaux stochastiques lorsque l'opérateur proximal ne peut pas être calculé de façon exacte.Ensuite, nous étudions des propriétés d'algorithmes stochastique non-Euclidiens appliqués au problème d'estimation parcimonieuse. La structure de parcimonie permet de réduire de façon significative les effets du bruit dans les observation du gradient. Nous proposons un nouvel algorithme stochastique, appelé SMD-SR, permettant de faire meilleur usage de cette structure. Là encore, la méthode en question est une routine multi-étapes qui utilise l'algorithme stochastique de descente en miroir comme élément constitutif de ses étapes. Cette procédure comporte deux phases de convergence, dont la convergence linéaire de l'erreur pendant la phase préliminaire, et la convergence à la vitesse asymptotique optimale pendant la phase asymptotique. Par rapport aux solutions existantes les plus efficaces aux problèmes d’optimisation stochastique parcimonieux, nous proposons une amélioration sur plusieurs aspects. Tout d'abord, nous montrons que l'algorithme proposé réduit l'erreur initiale avec une vitesse linéaire (comme un algorithme déterministe de descente de gradient, utilisant l'observation complète du gradient), avec un taux de convergence optimal par rapport aux caractéristiques du bruit. Deuxièmement, nous obtenons ce taux pour une grande classe de modèles de bruit, y compris les distributions sous-gaussiennes, de Rademacher, de Student multivariées, etc. Enfin, ces résultats sont obtenus sous la condition optimale sur le niveau de parcimonie qui peut approcher le nombre total d'iterations de l'algorithme (à un facteur logarithmique près)A goal of this thesis is to explore several topics in optimization for high-dimensional stochastic problems. The first task is related to various incremental approaches, which rely on exact gradient information, such as SVRG, SAGA, MISO, SDCA. While the minimization of large limit sums of functions was thoroughly analyzed, we suggest in Chapter 2 a new technique, which allows to consider all these methods in a generic fashion and demonstrate their robustness to possible stochastic perturbations in the gradient information.Our technique is based on extending the concept of estimate sequence introduced originally by Yu. Nesterov in order to accelerate deterministic algorithms.Using the finite-sum structure of the problems, we are able to modify the aforementioned algorithms to take into account stochastic perturbations. At the same time, the framework allows to derive naturally new algorithms with the same guarantees as existing incremental methods. Finally, we propose a new accelerated stochastic gradient descent algorithm and a new accelerated SVRG algorithm that is robust to stochastic noise. This acceleration essentially performs the typical deterministic acceleration in the sense of Nesterov, while preserving the optimal variance convergence.Next, we address the problem of generic acceleration in stochastic optimization. For this task, we generalize in Chapter 3 the multi-stage approach called Catalyst, which was originally aimed to accelerate deterministic methods. In order to apply it to stochastic problems, we improve its flexibility on the choice of surrogate functions minimized at each stage. Finally, given an optimization method with mild convergence guarantees for strongly convex problems, our developed multi-stage procedure, accelerates convergence to a noise-dominated region, and then achieves the optimal (up to a logarithmic factor) worst-case convergence depending on the noise variance of the gradients. Thus, we successfully address the acceleration of various stochastic methods, including the variance-reduced approaches considered and generalized in Chapter 2. Again, the developed framework bears similarities with the acceleration performed by Yu. Nesterov using the estimate sequences. In this sense, we try to fill the gap between deterministic and stochastic optimization in terms of Nesterov's acceleration. A side contribution of this chapter is a generic analysis that can handle inexact proximal operators, providing new insights about the robustness of stochastic algorithms when the proximal operator cannot be exactly computed.In Chapter 4, we study properties of non-Euclidean stochastic algorithms applied to the problem of sparse signal recovery. A sparse structure significantly reduces the effects of noise in gradient observations. We propose a new stochastic algorithm, called SMD-SR, allowing to make better use of this structure. This method is a multi-step procedure which uses the stochastic mirror descent algorithm as a building block over its stages. Essentially, SMD-SR has two phases of convergence with the linear bias convergence during the preliminary phase and the optimal asymptotic rate during the asymptotic phase.Comparing to the most effective existing solution to the sparse stochastic optimization problems, we offer an improvement in several aspects. First, we establish the linear bias convergence (similar to the one of the deterministic gradient descent algorithm, when the full gradient observation is available), while showing the optimal robustness to noise. Second, we achieve this rate for a large class of noise models, including sub-Gaussian, Rademacher, multivariate Student distributions and scale mixtures. Finally, these results are obtained under the optimal condition on the level of sparsity which can approach the total number of iterations of the algorithm (up to a logarithmic factor)
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Rassou, Sébastien. "Accélération d'électrons par onde de sillage laser : Développement d’un modèle analytique étendu au cas d’un plasma magnétisé dans le régime du Blowout." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLS066/document.
Abstract:
Une impulsion laser intense se propageant dans un plasma sous-dense (ne< 10¹⁸ W.cm⁻²) et de durée très courte (τ₀< 100 fs), , on atteint le régime de la bulle. Les champs électriques dans ces bulles, de l’ordre de 100 GV/m, peuvent accélérer un faisceau d’électrons jusqu’au GeV sur des distances de l’ordre du centimètre. Dans ce régime, les électrons expulsés par la force pondéromotrice du laser forment une fine et dense couche à la surface d'une cavité d'ions restés immobiles. Les propriétés de ce régime sont examinées par l’intermédiaire d’un modèle analytique, que nous avons développé en nous inspirant du travail de W. Lu et S. Yi. En nous plaçant dans ce régime prometteur, nous avons étudié les mécanismes d’injection et de piégeage dans l'onde de sillage. Dans l’injection optique, les polarisations parallèles ou circulaires positives conduisent respectivement à une injection mettant en jeu du chauffage stochastique, ou à l’injection froide. Un paramètre de similarité est introduit, celui-ci permet de déterminer la méthode d’injection la plus appropriée pour maximiser la charge injectée. Enfin, le modèle analytique présenté en première partie est étendu afin d’étudier l’onde de sillage dans le régime de la bulle lorsqu’un champ magnétique longitudinal initial est appliqué au plasma. Lorsque le plasma est magnétisé deux phénomènes remarquables se manifestent, d'une part une ouverture apparaît à l'arrière de la bulle et d'autre part un mécanisme d'amplification du champ magnétique longitudinale est induit par la variation du flux magnétique. Les prédictions de notre modèle analytique sont confrontées aux résultats de simulations PIC 3D issues du code CALDER-Circ. La conséquence immédiate de la déformation de l'onde de sillage est la réduction, voire la suppression de l'auto-injection. L’application d’un champ magnétique longitudinal, combinée à un choix judicieux des paramètres laser-plasma, permet de réduire la dispersion en énergie des faisceaux d’électrons produits après injection optiqueAn intense laser pulse propagating in an under dense plasma (ne< 10¹⁸ W.cm⁻²) and short(τ₀< 100 fs), the bubble regime is reached. Within the bubble the electric field can exceed 100 GV/m and a trapped electron beam is accelerated to GeV energy with few centimetres of plasma.In this regime, the electrons expelled by the laser ponderomotive force are brought back and form a dense sheath layer. First, an analytic model was derived using W. Lu and S. Yi formalisms in order to investigate the properties of the wakefield in the blowout regime. In a second part, the trapping and injection mechanisms into the wakefield were studied. When the optical injection scheme is used, electrons may undergo stochastic heating or cold injection depending on the lasers’ polarisations. A similarity parameter was introduced to find out the most appropriate method to maximise the trapped charge. In a third part, our analytic model is extended to investigate the influence of an initially applied longitudinal magnetic field on the laser wakefield in the bubble regime. When the plasma is magnetized two remarkable phenomena occur. Firstly the bubble is opened at its rear, and secondly the longitudinal magnetic field is amplified - at the rear of the bubble - due to the azimuthal current induced by the variation of the magnetic flux. The predictions of our analytic model were shown to be in agreement with 3D PIC simulation results obtained with Calder-Circ. In most situations the wake shape is altered and self-injection can be reduced or even cancelled by the applied magnetic field. However, the application of a longitudinal magnetic field, combined with a careful choice of laser-plasma parameters, reduces the energy spread of the electron beam produced after optical injection
Books on the topic "Stochastic accelerations":
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Möhl, Dieter. Stochastic Cooling of Particle Beams. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
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Möhl, Dieter. Stochastic Cooling of Particle Beams. Springer, 2013.
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Möhl, Dieter. Stochastic Cooling of Particle Beams. Springer, 2013.
Book chapters on the topic "Stochastic accelerations":
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Lisitsa, Valery S. "The Influence of Regular and Stochastic Accelerations on Atomic Spectra." In Atoms in Plasmas, 261–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-78726-3_12.
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Petrosian, Vahé. "Stochastic Acceleration by Turbulence." In Particle Acceleration in Cosmic Plasmas, 535–56. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-6455-6_17.
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Schwaha, P., M. Nedjalkov, S. Selberherr, J. M. Sellier, I. Dimov, and R. Georgieva. "Stochastic Formulation of Newton’s Acceleration." In Large-Scale Scientific Computing, 178–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43880-0_19.
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Bogdan, T. J., and R. Schlickeiser. "Stochastic Electron Acceleration in Stellar Coronae." In Radio Stars, 33–34. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5420-5_2.
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Schlickeiser, Reinhard. "Stochastic Particle Acceleration in Cosmic Objects." In Cosmic Radiation in Contemporary Astrophysics, 27–55. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-5488-5_2.
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Yang, J. N., Z. Li, and S. C. Liu. "Instantaneous Optimal Control with Acceleration and Velocity Feedback." In Stochastic Structural Dynamics 2, 287–306. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84534-5_16.
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Minty, Michiko G., and Frank Zimmermann. "Cooling." In Particle Acceleration and Detection, 263–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-08581-3_11.
Abstract:
AbstractMany applications of particle accelerators require beam cooling, which refers to a reduction of the beam phase space volume or an increase in the beam density via dissipative forces. In electron and positron storage rings cooling naturally occurs due to synchrotron radiation, and special synchrotron-radiation damping rings for the production of low-emittance beams are an integral part of electron-positron linear colliders. For other types of particles different cooling techniques are available. Electron cooling and stochastic cooling of hadron beams are used to accumulate beams of rare particles (such as antiprotons), to combat emittance growth (e.g., due to scattering on an internal target), or to produce beams of high quality for certain experiments. Laser cooling is employed to cool ion beams down to extremely small temperatures. Here the laser is used to induce transitions between the ion electronic states and the cooling exploits the Dopper frequency shift. Electron beams of unprecedentedly small emittance may be obtained by a different type of laser cooling, where the laser beam acts like a wiggler magnet. Finally, designs of a future muon collider rely on the principle of ionization cooling. Reference [1] gives a brief review of the principal ideas and the history of beam cooling in storage rings; a theoretical dicussion and a few practical examples can be found in [2].
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Dröge, W. "Stochastic Particle Acceleration at Magnetohydrodynamic Shock Waves." In Interstellar Magnetic Fields, 255–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-72621-7_43.
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Yang, J. N., Z. Li, and S. C. Liu. "Optimal Aseismic Hybrid Control of Nonlinear and Hysteretic Structures using Velocity and Acceleration Feedbacks." In Nonlinear Stochastic Mechanics, 531–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-84789-9_46.
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Pollock, Sara. "Anderson Acceleration for Degenerate and Nondegenerate Problems." In Deterministic and Stochastic Optimal Control and Inverse Problems, 197–216. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003050575-9.
Conference papers on the topic "Stochastic accelerations":
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He, Peng, Philippe Cardou, and André Desbiens. "Estimating the Orientation of a Game Controller Moving in the Vertical Plane Using Inertial Sensors." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70446.
Abstract:
This paper presents a novel method of estimating the orientation of a rigid body in the vertical plane from point-acceleration measurements, by discerning its gravitational and inertial components. In this method, a simple stochastic model of the human-hand motions is used in order to distinguish between the two types of acceleration. Two mathematical models of the rigid-body motion are formulated as distinct state-space systems, each corresponding to a proposed method. In both two cases, the output is a nonlinear function of the state, which calls for the application of the extended Kalman filter (EKF). The proposed filter is shown to work efficiently through two simulated trajectories, which are representative of human-hand motions. A comparison of the orientation estimates obtained from the proposed method shows that the filter offers more accuracy than a tilt sensor under high accelerations, and avoids the drift obtained by the time-integration of gyroscope measurements.
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Amirouche, Farid M. L., Rick T. Tong, and L. Palkovics. "Human Body Vibration Control and Ride Comfort: A Two State Semi-Active Suspension Design for Cabs and Seats." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-1115.
Abstract:
Abstract This paper presents a computer controlled cab and driver’s seat suspension model for heavy trucks and off-road vehicles. There are two models representing the vehicle with a cab suspension and a seat with an operator modeled as a lumped mass system. The vehicle is modeled as an 8 degrees-of-freedom lumped mass with suspension characteristics of 3 axles vehicle and three tires with masses and stiffnesses. The seat is supported by linear spring and damper whose characteristics are time dependent. The ride comfort is measured by an index of relative acceleration between parts of the driver and the vehicle under stochastic road excitation. First, a sky hook control semi-active controller is designed to seek optimum cab suspension. The velocity and displacement of the cab is then used as an input to the seat-operator model to minimize the accelerations at the lower torso, Head, and neck as well as the work performed by the body active forces. This paper shows how the energy transmitted to the seat can be minimized at the cab level. Furthermore the input to seat becomes small making the seat suspension more realistic and attainable.
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Türkay, Semiha, and Aslı S. Leblebici. "Vibration Control of a Rigid and Flexible High-Speed Railway Vehicle." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8096.
Abstract:
Abstract In this paper, the vertical carbody dynamics of the railway vehicle excited by random track inputs are investigated. The multi-objective ℋ∞ controllers for carbody weight of the actual, heavy and a mass confined in a polytopic range have been designed with the aim of reducing the wheel forces, heave, pitch and roll body accelerations of the vehicle. Later, the carbody mass is modelled as a free-free Euler Bernoulli beam and the low frequency flexural vibrations of the train body are examined. An omnibus ℋ∞ controller is synthesized to suppress both the rigid and low frequencies flexible modes of the railway vehicle. The performances of the ℋ∞ controllers are verified by using the passive and active suspension responses on the right and left rail track disturbances that are represented by the power spectral density functions authenticated for the stochastic real track data collected from the Qinhuangdao-Shenyang passenger railway line in China. Simulation results showed that all controllers exhibit a very good performance by effectively reducing the car-body accelerations in vicinity of the resonanat frequencies while keeping the wheel-rail forces in the allowable limit.
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Poursina, Mohammad. "An Efficient Application of Polynomial Chaos Expansion for the Dynamic Analysis of Multibody Systems With Uncertainty." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35226.
Abstract:
In this paper the mathematical framework of an advanced algorithm is presented to efficiently form and solve the equations of motion of a multibody system involving uncertainty in the system parameters and/or the excitations. The uncertainty is introduced to the system through the application of the polynomial chaos expansion. In this scheme, states of the system, nondeterministic parameters, and the constraint loads are expanded using modal values as well as orthogonal basis functions. Computational complexity of the application of traditional methods to solve the stochastic equations of motion of a multibody system drastically grows as a cubic function of the number of the states of the system, uncertain parameters and the maximum degree of the polynomial chosen for the basis function. The presented method forms the equation of motion of the system without forming the entire mass and Jacobian matrices. In this strategy, the stochastic governing equations of motion of each individual body as well as the one associated with the kinematic constraint at the connecting joint are developed in terms of the basis functions and modal coordinates. Then sweeping the system in two passes assembly and disassembly, one can form and solve the stochastic equations of motion. In the assembly pass the non-deterministic equations of motion of the assemblies are obtained. In the disassembly process, these equations are then recursively solved for the modal values of the spatial accelerations and the constraints loads. In the serial and parallel implementations, computational complexity of the method increases as a linear and logarithmic functions of the number of the states of the system, uncertain variables, and the maximum degree of the basis functions used in the expansion.
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Georgiadis, L., A. M. Ruiz-Teran, and P. J. Stafford. "Comparison of the Structural Behaviour between Under-Deck Cable-Stayed and Under-Deck Suspension Footbridges under Pedestrian Action." In IABSE Symposium, Wroclaw 2020: Synergy of Culture and Civil Engineering – History and Challenges. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/wroclaw.2020.0765.
Abstract:
<p>Under-deck cable-stayed (UDCS) and under-deck suspension (UDS) footbridges are slender structures supported by cables located below the deck and, despite the similarities in their appearance, they represent two different engineering concepts. In the present work, their structural behaviour has been investigated in detail and their response under static and dynamic pedestrian loading has been compared. A static analysis has been conducted first. Then a modal analysis has been performed, followed by a full time-history dynamic analysis under the action of a stochastic pedestrian load model. The influence of geometric non-linearity in both static and dynamic analyses has been examined. Results show that although the bending moments and deflections in UDS footbridges are smaller compared to UDCS footbridges, the level of accelerations, which is the governing design criterion for the bridge deck in order to satisfy comfort, is similar.</p>
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Miller, James A., and Reuven Ramaty. "Stochastic acceleration in impulsive solar flares." In Particle acceleration in cosmic plasmas. AIP, 1992. http://dx.doi.org/10.1063/1.42732.
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Yu, Xiaotian, Irwin King, Michael R. Lyu, and Tianbao Yang. "A Generic Approach for Accelerating Stochastic Zeroth-Order Convex Optimization." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/422.
Abstract:
In this paper, we propose a generic approach for accelerating the convergence of existing algorithms to solve the problem of stochastic zeroth-order convex optimization (SZCO). Standard techniques for accelerating the convergence of stochastic zeroth-order algorithms are by exploring multiple functional evaluations (e.g., two-point evaluations), or by exploiting global conditions of the problem (e.g., smoothness and strong convexity). Nevertheless, these classic acceleration techniques are necessarily restricting the applicability of newly developed algorithms. The key of our proposed generic approach is to explore a local growth condition (or called local error bound condition) of the objective function in SZCO. The benefits of the proposed acceleration technique are: (i) it is applicable to both settings with one-point evaluation and two-point evaluations; (ii) it does not necessarily require strong convexity or smoothness condition of the objective function; (iii) it yields an improvement on convergence for a broad family of problems. Empirical studies in various settings demonstrate the effectiveness of the proposed acceleration approach.
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Fitzgerald, P. C., E. J. OBrien, A. Malekjafarian, and L. J. Prendergas. "Acceleration-based Bridge Scour Monitoring." In Proceedings of the 8th International Conference on Computational Stochastic Mechanics (CSM 8). Singapore: Research Publishing Services, 2018. http://dx.doi.org/10.3850/978-981-11-2723-6_24-cd.
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Nakamura, Tatsufumi. "High energy electron acceleration by stochastic acceleration mechanism." In SCIENCE OF SUPERSTRONG FIELD INTERACTIONS: Seventh International Symposium of the Graduate University for Advanced Studies on Science of Superstrong Field Interactions. AIP, 2002. http://dx.doi.org/10.1063/1.1514300.
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Han Jiling. "High energy particles from stochastic acceleration." In IEEE Conference Record - Abstracts. 1997 IEEE International Conference on Plasma Science. IEEE, 1997. http://dx.doi.org/10.1109/plasma.1997.605042.
Reports on the topic "Stochastic accelerations":
1
Carr, Dustin Wade, and Roy H. Olsson. A digital accelerometer array utilizing suprathreshold stochastic resonance for detection of sub-Brownian noise floor accelerations. Office of Scientific and Technical Information (OSTI), December 2004. http://dx.doi.org/10.2172/920745.
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Burby, J. W. The Hamiltonian Mechanics of Stochastic Acceleration. Office of Scientific and Technical Information (OSTI), July 2013. http://dx.doi.org/10.2172/1087712.
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Dimits, A. M., and J. A. Krommes. Stochastic particle acceleration and statistical closures. Office of Scientific and Technical Information (OSTI), October 1985. http://dx.doi.org/10.2172/5111904.
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Geiger, Cathleen A., Chandra Kambhamettu, S. L. McNutt, and Mani Thomas. Stochastic Analysis of Satellite-derived Arctic Sea Ice Information and Acceleration Proposal to Support N00014-02-1-0244. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada615525.
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Hair. L52003 Application of the Crack Layer Theory for Understanding and Modeling of SCC in High Pressure. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2003. http://dx.doi.org/10.55274/r0010893.
Abstract:
A stochastic features of SCC colony, such as corrosion pit distribution, SC crack initiation from the pits and SC crack aspect ratio, SC crack cluster formation, SC cluster interaction and instability, are observed and characterized. A concept of a single crack equivalent to a cluster of cracks is introduced to simplify computational work on clusters evolution and instability. Various criteria of equivalence for different stages of clusters evolution are discussed. An accelerated test with a number of accelerating factors has been designed and performed for simulation of individual SC crack growth. Corrosion products at each stage of single crack propagation are investigated by means of Raman and FTIR analysis. The crack layer theory is adopted for modeling of SC crack growth. It provides the formalism for modeling of the effect of such processes as electro-chemical reactions, hydrogen embrittlement, and mechanical loading rates on crack growth rate. Finally, a computer simulation of SC crack growth was performed and validated by the available set of experimental data.