The cores comprised three replicates of two contrasting substrate types at two different ages: unused virgin samples and 5-year-old samples from existing green roof test beds. The physical properties of 12 green roof substrate cores have been evaluated using non-invasive X-ray microtomography (XMT) imaging. This study presents a non-invasive technique which allows for repeatable determination of key substrate characteristics over time, and evaluates the impact of observed substrate changes on hydrological performance. However, there is very limited knowledge concerning the evolution of green roof hydrological performance with system age. Green roofs contribute to stormwater management through the retention of rainfall and the detention of runoff.

Although we only used binary and ternary mixtures (40 different mixtures in total) in this study, the trend should be applicable to multi-component mixtures and therefore useful as a design aide for powder formulation. The expended work of impeller shows the clearest trend with the mixture Bond number when it is expressed in terms of the arithmetic mean. The mixture Bond number is dependent on the interfacial surface energy, particle size and density and a wide range of Bond number is covered in our study by varying all three. This is done by introducing a weighting factor based on the fractional surface area of each component of the mixture.

Three commonly used methods for calculating a mean have been explored to express the mixture Bond number, namely arithmetic, geometric and harmonic mean. In this study, we present our work on prediction of flow characteristics of binary and ternary mixtures under dynamic conditions using Discrete Element Method and simulating the expended mechanical work of a rotating impeller penetrating a packed bed. Flow behaviour of powders plays an important role in defining their performance in many industries.

Cao lattice energy