9,921 research outputs found
Urban wind energy conversion: the potential of ducted turbines
The prospects for urban wind power are discussed. A roof-mounted ducted wind turbine, which uses pressure differentials created by wind flow around a building, is proposed as an alternative to more conventional approaches. Outcomes from tests at model and prototype scale are described, and a simple mathematical model is presented. Predictions from the latter suggest that a ducted turbine can produce very high specific power outputs, going some way to offsetting its directional sensitivity. Further predictions using climate files are made to assess annual energy output and seasonal variations, with a conventional small wind turbine and a photovoltaic panel as comparators. It is concluded that ducted turbines have significant potential for retro-fitting to existing buildings, and have clear advantages where visual impact and safety are matters of concern
Deployment of quality assurance procedures for digital library programmes
Many digital library programmes have a development philosophy based on use of open standards. In practice, however, projects may not have procedures in place to ensure that project deliverables make use of appropriate open standards. In addition there will be occasions when open standards are not sufficiently mature for deployment in a service environment or use of open standards will require expertise or resources which are not readily available
Nonlinear stability of the ensemble Kalman filter with adaptive covariance inflation
The Ensemble Kalman filter and Ensemble square root filters are data
assimilation methods used to combine high dimensional nonlinear models with
observed data. These methods have proved to be indispensable tools in science
and engineering as they allow computationally cheap, low dimensional ensemble
state approximation for extremely high dimensional turbulent forecast models.
From a theoretical perspective, these methods are poorly understood, with the
exception of a recently established but still incomplete nonlinear stability
theory. Moreover, recent numerical and theoretical studies of catastrophic
filter divergence have indicated that stability is a genuine mathematical
concern and can not be taken for granted in implementation. In this article we
propose a simple modification of ensemble based methods which resolves these
stability issues entirely. The method involves a new type of adaptive
covariance inflation, which comes with minimal additional cost. We develop a
complete nonlinear stability theory for the adaptive method, yielding Lyapunov
functions and geometric ergodicity under weak assumptions. We present numerical
evidence which suggests the adaptive methods have improved accuracy over
standard methods and completely eliminate catastrophic filter divergence. This
enhanced stability allows for the use of extremely cheap, unstable forecast
integrators, which would otherwise lead to widespread filter malfunction.Comment: 34 pages. 4 figure
Novel nanocomposite automotive temperature sensing technology
In recent years, automotive emissions legislation has been introduced and is rapidly becoming more stringent. With alternative vehicular propulsion methods far from becoming mainstream reality, leading automotive providers have intensified efforts in the direction of reducing the harmful footprint of their products. This is being accomplished via smaller, more optimally designed internal-combustion engines. A crucial means to that end is exhaust gas temperature monitoring and control. To enable such control, a mass-produced sensor, capable of operating reliably in the harsh automotive combustion environment, comprising a broad spectrum of high temperatures, severe shocks and a chemically aggressive ambient, has been used widely in the past decade, with performance demands growing constantly in line with advances in engine performance. This paper presents a technology overview of the potential of novel nano composite sensor design and manufacture using materials in an innovative way towards industrialising such a sensing solution. The presented sensor design implements the state-of-the-art in thick and thin film technology incorporating nano materials for improved strength, fabrication and performance properties
Fiscal Decentralisation and Economic Growth: A Bayesian Model Averaging Approach
This article re-examines the relationship between fiscal decentralisation and economic growth by employing Bayesian model averaging (BMA). BMA enables the consideration of a range of measures of fiscal decentralisation and allows the incorporation of model uncertainty into the empirical methodology. Posterior coefficient estimates suggest that not straightforward relationship exists between fiscal decentralisation and economic growth based on time-series data for Australia.
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