The problems of low computational efficiency, small-scale simulation and limited to qualitative research exist on a single CPU computation using phase-field method.
This computational obstacle was recently overcome for coarse (non-cohesive) particles via the identification of a system-size-independent measurement, thereby allowing for the direct comparison between small-scale simulations and large-scale experiments (LaMarche et al., 2015b).
This ability to directly compare small-scale simulations and large-scale experiments via a system-size independent measurement (defluidization) allows for the quantitative validation of the particle-level models in DEM CFD without resorting to particle-level measurements and/or unrealistically small experimental systems.
We explore the sensitivity of our model and illustrate its performance with respect to standard logistic analysis and CART in a small simulation study and in our case study.
The performance of the proposed method was compared with the approximation using the probit ordinal regression model in a small simulation study and real data sets (GLS and Septoria data sets) using a four- and five-point ordinal scale.
A small simulation study and results from analyses of lung cancer and diabetes datasets are given in Sections 3 and 4, respectively.
The usefulness of the proposed estimation and hypothesis testing procedures is illustrated by means of a small simulation study and a real-life dataset arising from physiology.
The approach was demonstrated to perform well in a small simulation study and for two experiments, using a wheat MAGIC population.
Large simulations are able to sustain larger carnivores than smaller simulations and in very small simulations with low food density there may not be enough space to support a viable population of unicells.
