Materials Map

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2024A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen6citations
  • 2015Tomographic image analysis and processing to simulate micro-petrophysical experimentscitations

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Macaulay, Euan
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Pitchers, Rhys
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Turner, Michael
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Tsang, Matthew
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2024
2015

Co-Authors (by relevance)

  • Macaulay, Euan
  • Pitchers, Rhys
  • Turner, Michael
  • Tsang, Matthew
  • Sakellariou, Arthur
  • Kingston, Andrew
  • Varslot, Trond
  • Arns, Christoph
  • Sok, Robert
  • Latham, Shane
  • Sheppard, Adrian
  • Senden, Timothy
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article

A three-dimensional feature extraction-based method for coal cleat characterization using X-ray μCT and its application to a Bowen Basin coal specimen

  • Macaulay, Euan
  • Knackstedt, Mark
  • Pitchers, Rhys
  • Turner, Michael
  • Tsang, Matthew
Abstract

<p>Cleats are the dominant micro-fracture network controlling the macro-mechanical behavior of coal. Improved understanding of the spatial characteristics of cleat networks is therefore important to the coal mining industry. Discrete fracture networks (DFNs) are increasingly used in engineering analyses to spatially model fractures at various scales. The reliability of coal DFNs largely depends on the confidence in the input cleat statistics. Estimates of these parameters can be made from image-based three-dimensional (3D) characterization of coal cleats using X-ray micro-computed tomography (μCT). One key step in this process, after cleat extraction, is the separation of individual cleats, without which the cleats are a connected network and statistics for different cleat sets cannot be measured. In this paper, a feature extraction-based image processing method is introduced to identify and separate distinct cleat groups from 3D X-ray μCT images. Kernels (filters) representing explicit cleat features of coal are built and cleat separation is successfully achieved by convolutional operations on 3D coal images. The new method is applied to a coal specimen with 80 mm in diameter and 100 mm in length acquired from an Anglo American Steelmaking Coal mine in the Bowen Basin, Queensland, Australia. It is demonstrated that the new method produces reliable cleat separation capable of defining individual cleats and preserving 3D topology after separation. Bedding-parallel fractures are also identified and separated, which has historically been challenging to delineate and rarely reported. A variety of cleat/fracture statistics is measured which not only can quantitatively characterize the cleat/fracture system but also can be used for DFN modeling. Finally, variability and heterogeneity with respect to the core axis are investigated. Significant heterogeneity is observed and suggests that the representative elementary volume (REV) of the cleat groups for engineering purposes may be a complex problem requiring careful consideration.</p>

Topics
  • impedance spectroscopy
  • extraction
  • tomography