Comptes Rendus Mecanique最新文献

This paper is concerned with the selection of the geometries of test specimens for bulk metal characterization tests. Simulations of characterization tests on cylindrical, notched, and shear specimens were conducted to allow for the analysis of stress state evolution during the tests and to evidence the impact of geometry on stress state. Both stress triaxiality and Lode parameter were considered for selecting representative specimens. Three choice criterions were regarded: the diversification of stress states as well as the constancy and the nearness of stress state indicators to the theoretical values along the deformation.

Studies dedicated to the homogenization approach of microcracked media are largely focused on the determination of effective elastic properties. Some works investigate other properties, but most of them consider only open cracks. This paper intends to provide effective thermal properties related to the conduction problem taking into account the unilateral effect (opening/closure of cracks). Such analysis considers steady-state heat transfer within an initially isotropic media weakened by randomly oriented cracks. According to the boundary conditions, estimates and bounds based on Eshelby-like formalism are developed to derive closed-form expressions for effective thermal conductivity and resistivity in a fixed microcracking state.

An infinitely variable transmission (IVT) is a system that allows for a continuous (non-discrete) variation (including zero) in transmission ratio between two rotating elements. In this paper, a novel ratcheting-type IVT mechanism is presented and its geometrical design and kinematic analysis are studied in details. The proposed system contains two identical units. Each unit includes a cam with a follower, oscillatory slotted links pivoted at a shaft that can be moved vertically by a hydraulic ram (alterable transmission ratio), and a grooved wheel with an actuating rod. The input rotational motion is converted through each unit to an oscillatory angular motion of controlled amplitude. This resulting motion is rectified using a ratchet to get a unidirectional output rotational motion. Therefore, the system output motion will have a different velocity and acceleration than those of the system input. The kinematic analysis revealed that the transmission ratio can be varied continuously in a range from zero to infinity. The analysis also showed that, for particular transmission ratios, the system gives uniform output (angular velocity and acceleration) for a corresponding uniform input.

The present work aims at performing a molecular dynamics modeling of suspensions composed of flexible linear molecules. Molecules are represented by a series of connected beads, whose dynamics is governed by three potentials: the extension potential affecting the elongation of segments connecting consecutive beads, the one governing the molecule bending and finally the Lennard-Jones describing the interaction of non-consecutive beads. A population of non-interacting molecules is simulated in elongation and shear flows for different flow and molecule parameters. The flow-induced conformation is analyzed in the different considered situations. Finally a model for predicting the evolution of the population conformation will be obtained by using deep-learning.

A data set over space and time is assumed to have a low-rank representation in separated spatial and temporal modes. The problem of evaluating these modes from a temporal series of partial measurements is considered. Each elementary instantaneous measurement captures only a “window” (in space) of the observed data set, but the position of this window varies in time so as to cover the entire region of interest and would allow for a complete measurement would the scene be static. A novel procedure, alternative to the Gappy Proper Orthogonal Decomposition (GPOD) methodology, is introduced. It is a fixed-point iterative procedure where modes are evaluated sequentially. Tested upon very sparse acquisition (1% of measurements being available) and very noisy synthetic data sets (10% noise), the proposed algorithm is shown to outperform two variants of the GPOD algorithm, with much faster convergence, and better reconstruction of the entire data set.

The work introduces new advanced numerical tools for data assimilation in structural mechanics. Considering the general Bayesian inference context, the proposed approach performs real-time and robust sequential updating of selected parameters of a numerical model from noisy measurements, so that accurate predictions on outputs of interest can be made from the numerical simulator. The approach leans on the joint use of Transport Map sampling and PGD model reduction into the Bayesian framework. In addition, a procedure for the dynamical and data-based correction of model bias during the sequential Bayesian inference is set up, and a procedure based on sensitivity analysis is proposed for the selection of the most relevant data among a large set of data, as encountered for instance with full-field measurements coming from digital image/volume correlation (DIC/DVC) technologies. The performance of the overall numerical strategy is illustrated on a specific example addressing structural integrity on damageable concrete structures, and dealing with the prediction of crack propagation from a damage model and DIC experimental data.

In the past decade, data science became trendy and in-demand due to the necessity to capture, process, maintain, analyze and communicate data. Multiple regressions and artificial neural networks are both used for the analysis and handling of data. This work explores the use of meta-heuristic optimization to find optimal regression kernel for data fitting. It is shown that optimizing the regression kernel improve both the fitting and predictive ability of the regression. For instance, Tabu-search optimization is used to find the best least-squares regression kernel for different applications of buckling of straight columns and artificially generated data. Four independent parameters were used as input and a large pool of monomial search domain is initially considered. Different input parameters are also tested and the benefits of using of independent input parameters is shown.

The aim of this paper is to present a new classification and regression algorithm based on Artificial Intelligence. The main feature of this algorithm, which will be called Code2Vect, is the nature of the data to treat: qualitative or quantitative and continuous or discrete. Contrary to other artificial intelligence techniques based on the “Big-Data,” this new approach will enable working with a reduced amount of data, within the so-called “Smart Data” paradigm. Moreover, the main purpose of this algorithm is to enable the representation of high-dimensional data and more specifically grouping and visualizing this data according to a given target. For that purpose, the data will be projected into a vectorial space equipped with an appropriate metric, able to group data according to their affinity (with respect to a given output of interest). Furthermore, another application of this algorithm lies on its prediction capability. As it occurs with most common data-mining techniques such as regression trees, by giving an input the output will be inferred, in this case considering the nature of the data formerly described. In order to illustrate its potentialities, two different applications will be addressed, one concerning the representation of high-dimensional and categorical data and another featuring the prediction capabilities of the algorithm.