A distributed process for the aerospace industry

In response to significant changes within the aerospace industry during recent years, a new approach is proposed for developing complex products in a globally distributed environment. Current approaches favour collocating project teams, but collocation is often not practical, nor economically justifiable. The Macro Concept is a process whereby products can be developed by globally distributed task teams, managed by a Core team. In the development of this concept, particular attention is paid to the human factors, the product decomposition and the infrastructure that supports the product introduction process in a globalized industry.

Macro - A tool to support distributed MDO

This paper addresses a new way for handling distributed design know as the Macro concept. It is based round the assumption that future design teams will become more distributed in nature as industry exploits the Internet and other integrated communication and data exchange systems. The paper notes that this concept is part of an attack on the problems associated with the total process of Distribute Multi-Disciplinary design and Optimisation. The concepts rely on the creation of distributed self-building and self-organising teams made up from members who are globally distributed. The paper describes both the approach adopted and its implementation in a prototype software system operating over the Internet. In essence the work presented is describing a novel method for implementing a distributed design process which is far from complete but which is producing challenging ideas. © 2000 by Cranfield University.

Mechanically tunable conjugated polymer distributed feedback lasers

Herein we report a low-threshold organic laser device based on semiconducting poly(9, 9′ -dioctylfluoren-2,7-diyl-alt-benzothiadiazole) (F8BT) encapsulated in a mechanically stretchable polydimethylsiloxane (PDMS) matrix. We take advantage of the natural flexibility of PDMS to alter the periodicity of the distributed feedback grating which in turn tunes the gain wavelength at which the resonant feedback is obtained. This way, we demonstrate that low-threshold lasing [6.1 μJ cm-2 (5.3 nJ)] is maintained over a large stretching range of 0%-7% which translates into a tuning range of about 20 nm. © 2010 American Institute of Physics.

Transient Natural Ventilation of a Room with a Distributed Heat Source

We report on an experimental and theoretical study of the transient flows which develop as a naturally ventilated room adjusts from one temperature to another. We focus on a room heated from below by a uniform heat source, with both high- and low-level ventilation openings. Depending on the initial temperature of the room relative to (i) the final equilibrium temperature and (ii) the exterior temperature, three different modes of ventilation may develop. First, if the room temperature lies between the exterior and the equilibrium temperature, the interior remains well-mixed and gradually heats up to the equilibrium temperature. Secondly, if the room is initially warmer than the equilibrium temperature, then a thermal stratification develops in which the upper layer of originally hot air is displaced upwards by a lower layer of relatively cool inflowing air. At the interface, some mixing occurs owing to the effects of penetrative convection. Thirdly, if the room is initially cooler than the exterior, then on opening the vents, the original air is displaced downwards and a layer of ambient air deepens from above. As this lower layer drains, it is eventually heated to the ambient temperature, and is then able to mix into the overlying layer of external air, and the room becomes well-mixed. For each case, we present new laboratory experiments and compare these with some new quantitative models of the transient flows. We conclude by considering the implications of our work for natural ventilation of large auditoria.

Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.