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Big data for microstructure‐property relationships: A case study of predicting effective conductivities
Author(s) -
Stenzel Ole,
Pecho Omar,
Holzer Lorenz,
Neumann Matthias,
Schmidt Volker
Publication year - 2017
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15757
Subject(s) - microstructure , tortuosity , big data , geodesic , market microstructure , materials science , property (philosophy) , artificial neural network , computer science , data mining , artificial intelligence , geometry , mathematics , porosity , metallurgy , composite material , philosophy , epistemology , finance , order (exchange) , economics
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. © 2017 American Institute of Chemical Engineers AIChE J , 63: 4224–4232, 2017

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