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Title: Big data for microstructure-property relationships : a case study of predicting effective conductivities
Authors : Stenzel, Ole
Pecho, Omar
Holzer, Lorenz
Neumann, Matthias
Schmidt, Volker
Published in : American Institute of Chemical Engineers Journal (AIChE)
Volume(Issue) : 63
Issue : 9
Pages : 4224
Pages to: 4232
Publisher / Ed. Institution : John Wiley & Sons, Inc.
Issue Date: 2017
License (according to publishing contract) : Licence according to publishing contract
Type of review: Peer review (Publication)
Language : English
Subjects : Map
Subject (DDC) : 005: Computer programming, programs and data
530: Physics
Abstract: The analysis of big data is changing industries, businesses and research as large amounts of data are available nowadays. In the area of microstructures, acquisition of (3-D tomographic image) data is difficult and time-consuming. It is shown that large amounts of data representing the geometry of virtual, but realistic 3-D microstructures can be generated using stochastic microstructure modeling. Combining the model output with physical simulations and data mining techniques, microstructure-property relationships can be quantitatively characterized. Exemplarily, we aim to predict effective conductivities given the microstructure characteristics volume fraction, mean geodesic tortuosity, and constrictivity. Therefore, we analyze 8119 microstructures generated by two different stochastic 3-D microstructure models. This is — to the best of our knowledge — by far the largest set of microstructures that has ever been analyzed. Fitting artificial neural networks, random forests and classical equations, the prediction of effective conductivities based on geometric microstructure characteristics is possible.
Departement: School of Engineering
Organisational Unit: Institute of Computational Physics (ICP)
Publication type: Article in scientific Journal
DOI : 10.1002/aic.15757
ISSN: 1547-5905
Appears in Collections:Publikationen School of Engineering

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