Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
The prediction of the rheological properties of concentrated suspensions is of great importance both in industrial processes (ceramics, cements, and pharmaceutics) and natural phenomena (debris flow, soil erosion). In a previous paper, we presented a new model (YODEL) that can predict the yield stress of concentrated particulate suspensions. The model is based on first principles and takes into account particle size distribution, interparticle forces, and microstructural features. It was validated using data from the literature on four different alumina powder suspensions. The current paper extends the application field of the YODEL, successfully, to multimodal distributions of much interest in the cement and concrete field. The key parameter governing the predictive capacity of the YODEL for multimodal distributions was shown to be the maximum packing fraction of the powder mixtures. The de Larrard compressive packing model was used to provide a maximum packing fraction for mixtures from their particle size distributions. The YODEL can predict yield stresses of multimodal suspensions within 10% of the experimental results. Further improvement of the maximum packing fraction prediction should help in our goal of yield stress prediction from basic powder and suspension characteristics.
Nicola Marzari, Lorenzo Monacelli
Giovanni Pizzi, Ivano Eligio Castelli, Francisco Fernando Ramirez