Daniele Suzzi

Nucleation and transport of particles in multiphase reactors are two important mechanisms that significantly influence the final product properties. Modeling these mechanisms is, however, extremely challenging and different approaches exist for the treatment of the particle ...

ABSTRACT The numerical prediction of underhood local temperatures represents a challenging field in the development of passenger cars. The reliability of computational fluid dynamics (CFD) results is depending upon numerics and employing appropriate physical models which can strongly affect the quality of results and the overall simulation time. The purpose of this paper is to analyze and validate the simulation of heat transfer phenomena in a vehicle’s underbody through the comparison with experimental data. The measurements are obtained from thermal sensors mounted on a passenger car during a highway drive. Furthermore, the radiative boundary conditions are defined through the post-processing of thermographic camera images. The numerical results of a commercial CFD code are finally compared with the experimental data on surface temperatures of the vehicle’s underbody.

A combined Quality by Design (QbD) and Discrete Element Model (DEM) simulation-approach is presented to characterize a blending unit operation by evaluating the impact of formulation parameters and process variables on the blending quality and blending end point. Understanding the variability of both the API and the excipients, as well as their impact on the blending process are critical elements for blending QbD. In a first step, the QbD-methodology is systematically used to (1) establish the critical quality attribute content uniformity and to link this CQA to its surrogate blend homogeneity, (2) identify potentially critical input factors that may affect blending operation quality and (3) risk-rank these factors to define activities for process characterization. Subsequently, a DEM-simulation-based characterization of the blending process is performed. A statistical evaluation is finally presented, relating blend homogeneity of systems with low particle number to the regulatory requirements. Data are then used to map out a three-dimensional knowledge space, providing parameters to define a design space and set up an appropriate control strategy.