Interesting magnetic properties of Fe1−x_{1-x}Cox_xSi alloys

Solid solution between nonmagnetic narrow gap semiconductor FeSi and diamagnetic semi-metal CoSi gives rise to interesting metallic alloys with long-range helical magnetic ordering, for a wide range of intermediate concentration. We report various interesting magnetic properties of these alloys, including low temperature re-entrant spin-glass like behaviour and a novel inverted magnetic hysteresis loop. Role of Dzyaloshinski-Moriya interaction in the magnetic response of these non-centrosymmetric alloys is discussed.Comment: 11 pages and 3 figure

Fluid leadership in a multi-user virtual environment educational project with teenagers: Schome Park

This paper examines leadership practices in a virtual community, the Schome Park project. Schome Park, based at the Open University, UK, was the first European closed (i.e. protected) island in Teen Second Life, a multi-user 3D virtual environment. This fully realised, complex interactive 3D environment has no imposed narrative and offers significant engagement for educational projects. The Schome (‘not school not home’) third space community – i.e. not placed in the first space of home or second space of work/school (Oldenburg, 1989) - was set up with the explicit aim of challenging the instructional models and pedagogic practices of the formal, state educational system. In this disembodied environment identities, represented in the virtual world by personalised avatars, possess usefully ambivalent valences. Often adults will join ‘inworld’ educational events organised and delivered by the younger members of the community. Schome makes flexible use of a wiki (collaboratively designed website), asynchronous discussion fora and other communicative media to support learning processes and enhance the development of a physically distanced yet authentic learning community. The authors propose that the community design in these new spaces created an opportunity for leaders to emerge regardless of contextual hierarchy and to forge a developing culture. The paper makes use of evidence from varied datasets to examine manifestations of leadership in the community and issues arising. Young people have been engaged in proposing, planning, executing and reflecting on teaching and learning and governance without deference to adults. Our analysis contributes to understandings of the development of leadership within carefully designed educational online communities and some of the challenges involved for adults in facilitating an appropriately supportive environment for young people. While aware that this innovative experiment continues to face many challenges, we propose that the design of the project offers much to encourage an approach to education in which collaborative, situated engagement in learning and teaching is perceived as a more fruitful model for the twenty-first century than reproduction of traditional hierarchies of teachers and the taught of conventional classrooms

Fluid passage-time calculation in large Markov models

Recent developments in the analysis of large Markov models facilitate the fast approximation of transient characteristics of the underlying stochastic process. So-called fluid analysis makes it possible to consider previously intractable models whose underlying discrete state space grows exponentially as model components are added. In this work, we show how fluid approximation techniques may be used to extract passage-time measures from performance models. We focus on two types of passage measure: passage-times involving individual components; as well as passage-times which capture the time taken for a population of components to evolve. Specifically, we show that for models of sufficient scale, passage-time distributions can be well approximated by a deterministic fluid-derived passage-time measure. Where models are not of sufficient scale, we are able to generate approximate bounds for the entire cumulative distribution function of these passage-time random variables, using moment-based techniques. Finally, we show that for some passage-time measures involving individual components the cumulative distribution function can be directly approximated by fluid techniques

Fluid-particle interaction force for polydisperse systems from lattice boltzmann simulations

Gas-solid fluidized beds are almost always polydisperse in industrial\ud application. However, to describe the fluid-particle interaction\ud force in models for large-scale gas-solid flow, relations\ud are used which have been derived for monodisperse system, for\ud which ad-hoc modifications are made to account for polydispersity.\ud Recently it was shown, on the basis of detailed lattice\ud Boltzmann simulations, that for bidisperse systems these\ud modifications predict a drag force which can be factors different\ud from the true drag force. In this work fluid-particle interaction\ud forces for polydisperse system are studied by means of\ud lattice Boltzmann simulation, using a grid that is typically an\ud order of magnitude smaller than the sphere diameter. Two different\ud lognormal size distributions are considered for this study.\ud The systems consist of polydisperse random arrays of spheres\ud in the diameter range of 8-24 grid spacing and 8-40 grid spacing,\ud a solid volume fraction of 0.5 and 0.3 and Reynolds number\ud 0.1 to 500. The data confirms the observations made for bidisperse\ud systems, namely that an extra correction factor for the\ud drag force is required to adequately capture the effect of polydispersity.\ud It was found that the correction factor derived by van\ud der Hoef et al (J. Fluid Mech. 528 (2005) 233) on the basis of\ud bidisperse simulation data, applies also to general polydisperse\ud system

Numerical Investigation of Wind Turbine Airfoils under Clean and Dusty Air Conditions

This paper focuses on the simulation of the airflow around wind turbine airfoils (S809 and S814) under both clean and dusty air conditions by using Computational Fluid Dynamics (CFD). The physical geometries of the airfoils and the meshing processes are completed in the ANSYS Mesh package ICEM. The simulation is done by ANSYS FLUENT. For clean air condition, Spalart– Allmaras (SA) model and realizable k-ε model are used. The results are compared with the experimental data to test which model agrees better. For dusty air condition, simulation of the two-phase flow is operated by realizable k-ε model and discrete phase model (DPM) in different concentration of dust particles (1% and 10% in volume). The results are compared with the data of clean air to illustrate the effect of dust contamination on the lift and drag characteristics of the airfoil

A Guide To Gasketing Principles And Best Practices

TutorialLeakage from static joints has been found to be a major contributor of emissions in many plants. With the collaboration from the world’s leading gasket manufacturers, the Gasket Division of the Fluid Sealing Association has created a training presentation that will assist all personnel dealing with modern gasketing applications and issues. What may appear to be a simple and easy to install component actually requires knowledge and understanding of its working principles and characteristics. This paper will start with basic gasketing concepts and then proceed into details regarding installation & assembly. It will also address equipment and fastener considerations, material selection and common uses. Finally, field failure analysis techniques will be explained so that errors in selection or installation procedures can be corrected. As the original format of this paper is based on a digital slide presentation, the format that follows will follow that layout

Mammalian Sperm Motility: Observation and Theory

Mammalian spermatozoa motility is a subject of growing importance because of rising human infertility and the possibility of improving animal breeding. We highlight opportunities for fluid and continuum dynamics to provide novel insights concerning the mechanics of these specialized cells, especially during their remarkable journey to the egg. The biological structure of the motile sperm appendage, the flagellum, is described and placed in the context of the mechanics underlying the migration of mammalian sperm through the numerous environments of the female reproductive tract. This process demands certain specific changes to flagellar movement and motility for which further mechanical insight would be valuable, although this requires improved modeling capabilities, particularly to increase our understanding of sperm progression in vivo. We summarize current theoretical studies, highlighting the synergistic combination of imaging and theory in exploring sperm motility, and discuss the challenges for future observational and theoretical studies in understanding the underlying mechanics.\ud Acronyms and Definitions\ud Acrosome: the cap of the sperm head containing enzymes allowing penetration of the zona pellucida via the acrosome reaction\ud Adenosine triphosphate (ATP): the currency unit of chemical energy transfer in living cells\ud Axoneme: a phylogenetically conserved structure within the eukaryotic flagellum consisting of a ring of nine microtubule doublets and a central pair, frequently referred to as 9 + 2\ud Bending moment density: the moment per unit length associated with flagellar bending; it can be divided into a hydrodynamic moment, an elastic moment (from the flagellar bending stiffness), an active moment (generated by dyneins exerting forces between adjacent microtubule doublets), and a passive moment resisting shear\ud Capacitation: the physiological state of a sperm required for fertilization, which is accompanied by the motility patterns associated with hyperactivation, characterized in saline by high-amplitude asymmetric beating\ud Central pair: a pair of microtubules along the length of the axoneme, symmetrically and slightly offset from the axoneme centerline\ud Cumulus oophorus: the outer vestment of the mammalian egg consisting of hundreds of cells radiating out from the egg embedded within a non-Newtonian hyaluronic acid gel\ud Dynein: a molecular motor within the axoneme, attached between adjacent microtubule doublets, that exerts a shearing force to induce axonemal bending\ud Flagellum: a motile cellular appendage that drives the swimming of sperm and other cells; this article focuses on the eukaryotic flagellum\ud Microtubule doublet: a pair of proteinaceous filament structures running the length of the axoneme; dyneins drive their bending, which induces flagellar motion\ud Mid-piece: the region of a sperm flagellum with a mitochondrial sheath, where ATP is generated\ud Oocyte: the egg\ud Outer dense fibers and fibrous sheath: accessory structures reinforcing the mammalian sperm flagellum; the combined axoneme and accessory structures are referred to as 9+9+2\ud Resistive-force theory: an approximation for the local drag of a slender filament element in Stokes flow (or a viscoelastic generalization thereof)\ud Rheotaxis: directed motility in response to the influence of fluid flow\ud Shear: in the context of the flagellum, the relative movement of adjacent microtubule doublets\ud Slender-body theory: an improved approximation for the local drag on a slender filament element in Stokes flow (or a viscoelastic generalization thereof)\ud Zona pellucida: a tough glycoprotein coat between the human egg and the cumulus oophorus, which a sperm must penetrate for successful fertilizatio

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202