Big data for microstructure‐property relationships: A case study of predicting effective conductivities. Issue 9 (3rd May 2017)

• Record Type: Journal Article
• Authors: Stenzel, Ole; Pecho, Omar; Holzer, Lorenz; Neumann, Matthias; Schmidt, Volker
• Is Part Of: AIChE journal. Volume 63:Issue 9(2017)
• Page Start: 4224
• Page End: 4232
• Publication Date: 2017-05-03
• Subjects: big data -- effective conductivity -- geodesic tortuosity -- microstructure characteristics -- predictive simulation -- stochastic microstructure modeling; Chemical engineering -- Periodicals; Génie chimique -- Périodiques; 660.28
• DOI: 10.1002/aic.15757
• Languages: English
• ISSNs: 0001-1541

Abstract

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. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4224–4232, 2017