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Publication# Yield Stress of Multimodal Powder Suspensions: An Extension of the YODEL (Yield Stress mODEL)

Abstract

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.

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Related concepts (32)

Multimodal distribution

In statistics, a multimodal distribution is a probability distribution with more than one mode. These appear as distinct peaks (local maxima) in the probability density function, as shown in Figures 1 and 2. Categorical, continuous, and discrete data can all form multimodal distributions. Among univariate analyses, multimodal distributions are commonly bimodal. When the two modes are unequal the larger mode is known as the major mode and the other as the minor mode. The least frequent value between the modes is known as the antimode.

Mixture distribution

In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized. The underlying random variables may be random real numbers, or they may be random vectors (each having the same dimension), in which case the mixture distribution is a multivariate distribution.

Unimodality

In mathematics, unimodality means possessing a unique mode. More generally, unimodality means there is only a single highest value, somehow defined, of some mathematical object. In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. If there is a single mode, the distribution function is called "unimodal".

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2022