(209b) Pairing experimental and mathematical modelling approaches for the development of highly predictive multiphase flow systems models

Mathematical modelling of multiphase flow systems has been a major and persistent challenge over the last decades. Vast attempts to obtain predictive models can be found reported in literature, where major advances can be recognized in recent years, paired to enhancements in computer science and engineering. Notwithstanding, universally valid models with a mechanistic development are far from being achieved. The current status of modelling any multiphase flow system relies on the model order reduction of purely theoretical models. Such reductions and simplifications become the source of deviations in the predictions of the experimentally measured parameters, and will constrain the applicability of the models. Thus, an insightful multiphase flow model will rely upon a proper selection of sub-models, which should be based on the underlying assumptions for their derivation and their range of applicability. This implies an added level of uncertainty in the mathematical modelling of these systems that cannot be a priori assessed. Therefore, even though mathematical modelling of multiphase flow systems can overcome the limitations discussed for the experimental techniques, there is a fundamental need to validate the models’ predictions and assess their predictive quality, which can be achieved by linking models with reliable experiments. Pairing mathematical modeling and experimental studies, in order to validate the models’ predictive quality, quantifying the deviations and providing a standpoint of the applicability and limitations of the models.

Therefore, in this work it is sought to contribute to the development of insightful multiphase flow models, which provide locally validated predictions and are applicable for extrapolation studies. Such models are achieved by pairing experimental studies conducted by applying our in-house developed advanced measurement techniques with mathematical modelling by Computational Fluid Dynamics techniques. Two cases of study are discussed: i) Trickle Bed Reactors, and ii) Fluidized Bed Reactors