Self-assembled granular walkers

Mechanisms of locomotion in microscopic systems are of great interest not only for technological applications, but also for the sake of understanding, and potentially harnessing, processes far from thermal equilibrium. Down-scaling is a particular challenge, and has led to a number of interesting concepts including thermal ratchet systems and asymmetric swimmers. Here we present a system which is particularly intriguing, as it is self-assembling and uses a robust mechanism which can be implemented in various settings. It consists of small spheres of different size which adhere to each other, and are subject to an oscillating (zero average) external force eld. An inherent nonlinearity in the mutual force network leads to force rectication and hence to locomotion. We present a model that accounts for the observed behaviour and demonstrates the wide applicability and potential scalability of the concept.Comment: 17 pages, 4 figure

A Non-Scaling FFAG Gantry Design for the PAMELA Project

A gantry is re­quired for the PAMELA pro­ject using non-scal­ing Fixed Field Al­ter­nat­ing Gra­di­ent (NS-FFAG) mag­nets. The NS-FFAG prin­ci­ple of­fers the pos­si­bil­i­ty of a gantry much small­er, lighter and cheap­er than con­ven­tion­al de­signs, with the added abil­i­ty to ac­cept a wide range of fast chang­ing en­er­gies. This paper will build on pre­vi­ous work to in­ves­ti­gate a de­sign which could be used for the PAMELA pro­ject

Detection and predictive modeling of chaos in finite hydrological time series

International audienceThe ability to detect the chaotic signal from a finite time series observation of hydrologic systems is addressed in this paper. The presence of random and seasonal components in hydrological time series, like rainfall or runoff, makes the detection process challenging. Tests with simulated data demonstrate the presence of thresholds, in terms of noise to chaotic-signal and seasonality to chaotic-signal ratios, beyond which the set of currently available tools is not able to detect the chaotic component. The investigations also indicate that the decomposition of a simulated time series into the corresponding random, seasonal and chaotic components is possible from finite data. Real streamflow data from the Arkansas and Colorado rivers are used to validate these results. Neither of the raw time series exhibits chaos. While a chaotic component can be extracted from the Arkansas data, such a component is either not present or can not be extracted from the Colorado data. This indicates that real hydrologic data may or may not have a detectable chaotic component. The strengths and limitations of the existing set of tools for the detection and modeling of chaos are also studied