The Sand Coated Die

The sand coated die is composed of a casting and a die which are separated by a layer of variable sand thickness. Increasing sand thickness will reduce the chilling influence of the die and hence augment the solidification time of the casting. A computer model has been developed which accurately predicts the relative solidification time in the sand coated die. This model, validated for several cast metals, is in close agreement with the experimental data of the present research as well as with the ones published previously in literature. At the interface sand-die no perfect conduction contact exists. This may be explained by a simplified model of sand grains packing

An Information-Centric Communication Infrastructure for Real-Time State Estimation of Active Distribution Networks

© 2010-2012 IEEE.The evolution toward emerging active distribution networks (ADNs) can be realized via a real-time state estimation (RTSE) application facilitated by the use of phasor measurement units (PMUs). A critical challenge in deploying PMU-based RTSE applications at large scale is the lack of a scalable and flexible communication infrastructure for the timely (i.e., sub-second) delivery of the high volume of synchronized and continuous synchrophasor measurements. We address this challenge by introducing a communication platform called C-DAX based on the information-centric networking (ICN) concept. With a topic-based publish-subscribe engine that decouples data producers and consumers in time and space, C-DAX enables efficient synchrophasor measurement delivery, as well as flexible and scalable (re)configuration of PMU data communication for seamless full observability of power conditions in complex and dynamic scenarios. Based on the derived set of requirements for supporting PMU-based RTSE in ADNs, we design the ICN-based C-DAX communication platform, together with a joint optimized physical network resource provisioning strategy, in order to enable the agile PMU data communications in near real-time. In this paper, C-DAX is validated via a field trial implementation deployed over a sample feeder in a real-distribution network; it is also evaluated through simulation-based experiments using a large set of real medium voltage grid topologies currently operating live in The Netherlands. This is the first work that applies emerging communication paradigms, such as ICN, to smart grids while maintaining the required hard real-time data delivery as demonstrated through field trials at national scale. As such, it aims to become a blueprint for the application of ICN-based general purpose communication platforms to ADNs

SunPy - Python for Solar Physics

This paper presents SunPy (version 0.5), a community-developed Python package for solar physics. Python, a free, cross-platform, general-purpose, high-level programming language, has seen widespread adoption among the scientific community, resulting in the availability of a large number of software packages, from numerical computation (NumPy, SciPy) and machine learning (scikit-learn) to visualisation and plotting (matplotlib). SunPy is a data-analysis environment specialising in providing the software necessary to analyse solar and heliospheric data in Python. SunPy is open-source software (BSD licence) and has an open and transparent development workflow that anyone can contribute to. SunPy provides access to solar data through integration with the Virtual Solar Observatory (VSO), the Heliophysics Event Knowledgebase (HEK), and the HELiophysics Integrated Observatory (HELIO) webservices. It currently supports image data from major solar missions (e.g., SDO, SOHO, STEREO, and IRIS), time-series data from missions such as GOES, SDO/EVE, and PROBA2/LYRA, and radio spectra from e-Callisto and STEREO/SWAVES. We describe SunPy's functionality, provide examples of solar data analysis in SunPy, and show how Python-based solar data-analysis can leverage the many existing tools already available in Python. We discuss the future goals of the project and encourage interested users to become involved in the planning and development of SunPy

An Efficient Algorithm for Computing Entropic Measures of Feature Subsets

International audienceEntropic measures such as conditional entropy or mutual information have been used numerous times in pattern mining, for instance to characterize valuable itemsets or approximate functional dependencies. Strangely enough the fundamental problem of designing efficient algorithms to compute entropy of subsets of features (or mutual information of feature subsets relatively to some target feature) has received little attention compared to the analog problem of computing frequency of itemsets. The present article proposes to fill this gap: it introduces a fast and scalable method that computes entropy and mutual information for a large number of feature subsets by adopting the divide and conquer strategy used by FP-growth-one of the most efficient frequent itemset mining algorithm. In order to illustrate its practical interest, the algorithm is then used to solve the recently introduced problem of mining reliable approximate functional dependencies. It finally provides empirical evidences that in the context of non-redundant pattern extraction, the proposed method outperforms existing algorithms for both speed and scalability