On the coupling between an ideal fluid and immersed particles

In this paper we use Lagrange-Poincare reduction to understand the coupling between a fluid and a set of Lagrangian particles that are supposed to simulate it. In particular, we reinterpret the work of Cendra et al. by substituting velocity interpolation from particle velocities for their principal connection. The consequence of writing evolution equations in terms of interpolation is two-fold. First, it gives estimates on the error incurred when interpolation is used to derive the evolution of the system. Second, this form of the equations of motion can inspire a family of particle and hybrid particle-spectral methods where the error analysis is "built-in". We also discuss the influence of other parameters attached to the particles, such as shape, orientation, or higher-order deformations, and how they can help with conservation of momenta in the sense of Kelvin's circulation theorem.Comment: to appear in Physica D, comments and questions welcom

Alien Registration- Mathieu, Henry L. (Lewiston, Androscoggin County)

https://digitalmaine.com/alien_docs/28941/thumbnail.jp

Multi-Objective Design Optimization of the Leg Mechanism for a Piping Inspection Robot

This paper addresses the dimensional synthesis of an adaptive mechanism of contact points ie a leg mechanism of a piping inspection robot operating in an irradiated area as a nuclear power plant. This studied mechanism is the leading part of the robot sub-system responsible of the locomotion. Firstly, three architectures are chosen from the literature and their properties are described. Then, a method using a multi-objective optimization is proposed to determine the best architecture and the optimal geometric parameters of a leg taking into account environmental and design constraints. In this context, the objective functions are the minimization of the mechanism size and the maximization of the transmission force factor. Representations of the Pareto front versus the objective functions and the design parameters are given. Finally, the CAD model of several solutions located on the Pareto front are presented and discussed.Comment: Proceedings of the ASME 2014 International Design Engineering Technical Conferences \& Computers and Information in Engineering Conference, Buffalo : United States (2014

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Photometric variability in the old open cluster M 67. II. General Survey

We use differential CCD photometry to search for variability in BVI among 990 stars projected in and around the old open cluster M 67. In a previous paper we reported results for 22 cluster members that are optical counterparts to X-ray sources; this study focuses on the other stars in our observations. A variety of sampling rates were employed, allowing variability on time scales ranging from \sim 0.3 hours to \sim 20 days to be studied. Among the brightest sources studied, detection of variability as small as sigma approx 10 mmag is achieved (with > 3 sigma confidence); for the typical star observed, sensitivity to variability at levels sigma approx 20 mmag is achieved. The study is unbiased for stars with 12.5 < B < 18.5, 12.5 < V < 18.5, and 12 < I < 18 within a radius of about 10 arcmin from the cluster centre. In addition, stars with 10 < BVI < 12.5 were monitored in a few small regions in the cluster. We present photometry for all 990 sources studied, and report the variability characteristics of those stars found to be variable at a statistically significant level. Among the variables, we highlight several sources that merit future study, including stars located on the cluster binary sequence, stars on the giant branch, blue stragglers, and a newly discovered W UMa system.Comment: 12 pages, including 6 figures and 5 tables. Tables 1 and 3 only available in electronic version of paper. Accepted by A&

The Influence of Photosynthesis on the Number of Metamers per Growth Unit in GreenLab Model

GreenLab Model is a functional-structural plant growth model that combines both organogenesis (at each cycle, new organs are created with respect to genetic rules) and photosynthesis (organs are filled with the biomass produced by the leaves photosynthesis). Our new developments of the model concern the retroaction of photosynthesis on organogenesis. We present here the first step towards the total representation of this retroaction, where the influence of available biomass on the number of metamers in new growth units us modelled. The theory is introduced and applied to a Corner model tree. Different interesting behaviours are pointed out