Jet-milling is a particle engineering process widely adopted in industrial manufacturing to reduce the particle size of powders. Computational Fluid Dynamics simulations (CFD) coupled with Discrete Element Modelling (DEM) proved to be a valuable tool to tackle the complexity and the non-linearity of particle breakage and classification occurring in mills. To date however, they have been employed to address only single aspects of process design being unable to reproduce it completely. The coupled CFD-DEM simulations presented in this work are for the first time capable of simultaneously describe particle fragmentation, particle-gas interaction and classification. Through coarse-graining, realistic amounts of powder can be simulated allowing to demonstrate/study how the hold-up mass slows down the milling gas affecting classification and thus the milling performance. Bottlenecks/limitations of model and methodology are critically examined to understand what is currently preventing us from creating a digital twin of the milling process.

The use of computational techniques in the design of dry powder inhalers (DPI), as well as in unravelling the complex mechanisms of drug aerosolization, has increased significantly in recent years. Computational fluid dynamics (CFD) is used to study the air flow, inside the DPI, during the patient inspiratory act while discrete element methods (DEM) are used to simulate the dispersion and aerosolization of the drug product powder particles. In this work we discuss the possibility to validate a coupled CFD-DEM model for the NextHaler® DPI device against previously published experimental data. The approximations and assumptions made are deeply discussed. The comparison between computational and experimental results is detailed both for fluid and powder flows. Finally, the potential and possible applications of a calibrated DPI model are discussed as well as the missing elements necessary to achieve a fully quantitative predictive computational model.

Predicting the outcome of jet-milling based on the knowledge of process parameters and starting material properties is a task still far from being accomplished. Given the technical difficulties in measuring thermodynamics, flow properties and particle statistics directly in the mills, modelling and simulations constitute alternative tools to gain insight in the process physics and many papers have been recently published on the subject. An ideal predictive simulation tool should combine the correct description of non-isothermal, compressible, high Mach number fluid flow, the correct particle-fluid and particle-particle interactions and the correct fracture mechanics of particle upon collisions but it is not currently available. In this paper we present our coupled CFD-DEM simulation results; while comparing them with the recent modelling and experimental works we will review the current understating of the jet-mill physics and particle classification. Subsequently we analyze the missing elements and the bottlenecks currently limiting the simulation technique as well as the possible ways to circumvent them towards a quantitative, predictive simulation of jet-milling.

Shear cell tests have been conducted on twenty different lactose powders, most of which commercially available for oral or inhalation purposes, spanning a wide range of particle sizes, particle morphologies, production processes. The aims of the investigation were: i) to verify the reliability of the technique in evaluating and classifying the flowability of powders; ii) to understand the connection between the flowability of a powder and the morphological properties of its particles; iii) to find a general mathematical relationship able to predict the yield locus shape given the particle size, shape and consolidation state of a lactose powder. These aspects and their limitations are detailed in the manuscript together with other interesting findings on the stick-slip behavior observed in most of the lactose powders examined.