Dynamic Finite Element Analysis of an Elevated Station-Track Structure Coupled System Under Resonance

In this study, an elevated station and the track structure are regarded as a coupled system, and the vibration performance of the entire system is analyzed under dynamic loading. Special attention is paid to the influence of the mechanical properties of parts of the system on its vibration performance. After a brief literature review of relevant studies on urban elevated rail transit vibration performance, a three-dimensional dynamic analysis model is established considering each component and the connections of the system; furthermore, modal analysis is applied to this model. Based on the model analysis, the vibration performance under harmonic vibration loads and moving train loads are analyzed separately. During the analysis of harmonic vibration, the effects of various parameters (e.g., rail pad stiffness, mass of elastic support block) on system vibration are considered

Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning

The recent mean field game (MFG) formalism facilitates otherwise intractable computation of approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-time finite MFGs subject to finite-horizon objectives. We show that all discrete-time finite MFGs with non-constant fixed point operators fail to be contractive as typically assumed in existing MFG literature, barring convergence via fixed point iteration. Instead, we incorporate entropy-regularization and Boltzmann policies into the fixed point iteration. As a result, we obtain provable convergence to approximate fixed points where existing methods fail, and reach the original goal of approximate Nash equilibria. All proposed methods are evaluated with respect to their exploitability, on both instructive examples with tractable exact solutions and high-dimensional problems where exact methods become intractable. In high-dimensional scenarios, we apply established deep reinforcement learning methods and empirically combine fictitious play with our approximations.Comment: Accepted to the 24th International Conference on Artificial Intelligence and Statistics (AISTATS 2021

Quantifying the effect of matric suction on the compressive properties of two agricultural soil using an osmotic oedometer

International audienceThe compaction of cultivated soils by agricultural machines considerably affects both the structure and physical properties of soil, thus having a major impact on crop production and the environment. The soil mechanical strength to compaction is highly variable both in time and space because it depends on soil type (texture), soil structure (porosity) and soil moisture (suction). This paper is devoted to the effect of soil suction on the compression index Cc which is one of the mechanical parameters that describes the soil mechanical strength to compaction. We used an oedometer compression tests with suction control implemented by using the osmotic technique to study the compression index of a loamy soil and a sandy soil. Soil samples were prepared by compacting soil powder passed through 2 mm sieve, to a dry bulk density of 1.1 or 1.45 Mg m-3. The mechanical stress and the suction ranges considered corresponded to field conditions, with vertical stress less than 800 kPa and suction less than 200 kPa. The results show that the compression index Cc changed little with suctions ranging from 10 to 200 kPa for the two soils at different initial densities. By contrast, the variation of Cc is significant when soil suction is close to zero for the loamy soil at an initial dry bulk density of 1.1 Mg m-3. From a practical point of view, this variation in compression index with suction is a useful result for modelling soil strain due to traffic and predicting the compaction of cultivated soils

Soil compaction by wheeling: change in soil suction due to compression

International audienceSoil compaction due to traffic has been increasingly recognized as a considerable problem facing intensive agriculture. Most of the models used to estimate soil deformation during the passage of machines are based on the concept of total stress: then they have neglected an important stress variable for unsaturated soils, i.e. the matrix suction. The aim of the present work is to evaluate the validity of this hypothesis by studying suction variation during a static compression test. A standard oedometer cell equipped with a tensiometer was used to measure soil suction in situ for different vertical stresses. Measurements were carried out on remoulded soil samples obtained by compacting a loamy soil at different initial water suctions (< 100 kPa). The results showed that the suction remained almost constant until a stress threshold value t beyond which the suction decreased as the stress increased. This stress threshold increased with the initial suction. These results corroborated the hypothesis of a constant suction during deformation usually assumed to model soil compaction during traffic for soils with suction higher than 20 kPa. The results obtained highlighted the effect of soil structure on the stress threshold: t was found to be higher for soil samples with initial aggregates < 2 mm for those with initial aggregates < 0.4 mm. This was interpreted at pore scale by comparing qualitatively the evolution of pore-size distribution and the expected distribution of water in the pores. This interpretation was based on pore-size distribution measurement by mercury intrusion

Low-Frequency Raman Modes and Electronic Excitations In Atomically Thin MoS2 Crystals

Atomically thin MoS$_{2}$ crystals have been recognized as a quasi-2D semiconductor with remarkable physics properties. This letter reports our Raman scattering measurements on multilayer and monolayer MoS$_{2}$, especially in the low-frequency range ($<$50 cm$^{-1}$). We find two low-frequency Raman modes with contrasting thickness dependence. With increasing the number of MoS$_{2}$ layers, one shows a significant increase in frequency while the other decreases following a 1/N (N denotes layer-number) trend. With the aid of first-principle calculations we assign the former as the shear mode $E_{2g}^{2}$ and the latter as the compression vibrational mode. The opposite evolution of the two modes with thickness demonstrates novel vibrational modes in atomically thin crystal as well as a new and more precise way to characterize thickness of atomically thin MoS$_{2}$ films. In addition, we observe a broad feature around 38 cm$^{-1}$ (~5 meV) which is visible only under near-resonance excitation and pinned at the fixed energy independent of thickness. We interpret the feature as an electronic Raman scattering associated with the spin-orbit coupling induced splitting in conduction band at K points in their Brillouin zone.Comment: 5 pages, 4 figure

Learning Sparse Graphon Mean Field Games

Although the field of multi-agent reinforcement learning (MARL) has made considerable progress in the last years, solving systems with a large number of agents remains a hard challenge. Graphon mean field games (GMFGs) enable the scalable analysis of MARL problems that are otherwise intractable. By the mathematical structure of graphons, this approach is limited to dense graphs which are insufficient to describe many real-world networks such as power law graphs. Our paper introduces a novel formulation of GMFGs, called LPGMFGs, which leverages the graph theoretical concept of $L^p$ graphons and provides a machine learning tool to efficiently and accurately approximate solutions for sparse network problems. This especially includes power law networks which are empirically observed in various application areas and cannot be captured by standard graphons. We derive theoretical existence and convergence guarantees and give empirical examples that demonstrate the accuracy of our learning approach for systems with many agents. Furthermore, we extend the Online Mirror Descent (OMD) learning algorithm to our setup to accelerate learning speed, empirically show its capabilities, and conduct a theoretical analysis using the novel concept of smoothed step graphons. In general, we provide a scalable, mathematically well-founded machine learning approach to a large class of otherwise intractable problems of great relevance in numerous research fields.Comment: accepted for publication at the International Conference on Artificial Intelligence and Statistics (AISTATS) 2023; code available at: https://github.com/ChrFabian/Learning_sparse_GMFG