3D thermo-fluid dynamic simulations of high-speed-extruded starch based products - Cubeddu, Alessandro; Rauh, Cornelia; Delgado, Antonio (Open Journal of Fluid Dynamics, 2014)
This paper aims to investigate a method to perform non-isothermal flow simulations in a complex geometry for generalised Newtonian fluids. For this purpose, 3D numerical simulations of starch based products are performed. The geometry of a co-rotating twin-screw extruder is considered. Process conditions concern high rotational speed (up to 1800 rpm), different flow rates (30, 40 and 60 kg/h) and water contents (22% and 36%), for a total of 54 simulations. To cope with the geometry complexity a Mesh Superposition Technique (MST) was adopted. The pseudoplastic behaviour of the fluid is taken into account by considering viscosity as function of shear rate (Ostwaldde Waele relationship) and temperature (Arrhenius law). Simulated temperature variations are compared with measurements at same process conditions for validation. Qualitative behaviour of temperature T and shear stress 𝛕 along the screw are analysed and comparisons of different process conditions are presented. By these simulations a database is formed to develop a process control strategy for novel extruder operating points in food technolog
UnivIS-Import:2015-03-09:Pub.2014.tech.ITC.stmmec.3dther
Keywords: High Speed Extrusion; Non-Isothermal Numerical Simulation; Ostwald-De Waele Model

Hybrid artificial neural network for prediction and control of process variables in food extrusion - Cubeddu, Alessandro; Rauh, Cornelia; Delgado, Antonio (Innovative Food Science & Emerging Technologies, 2014)
Two hybrid Artificial Neural Network (ANN) chains have been implemented and tested for configuration and control of a food extrusion process. The first architecture (Configuration Chain) predicts the process variables screw speed N and water content w starting from product characteristics. It calculates also energy and flow variables as Specific Mechanical Energy, shaft torque, shear stress, pressure and temperature variations. The second architecture (Control Chain) is a closed loop ANN-chain in which a differential neurocontroller regulates the extrusion process by calculating adjustments of N and w. Both chains are feedforward multilayer ANNs trained using the Back Propagation Algorithm. The ANNs in each chain are built using the programme MemBrain and are triggered and automated by an AngelScript code. A total of 42 patterns have been used (24 for training and 18 for verification). For both ANN-chains the quality of each ANN is presented as well as proof of concept of the whole chain. Industrial relevance The present study is intended to offer a method to efficiently optimise in an innovative way the process of extrusion in the food industy. In particular, the research aims to develop two Artificial Neural Network chains for (1) prediction of optimal process variables (screw speed and water content are considered) starting from desired product characteristics and (2) prevent process instabilities on the predicted variables by using a neurocontroller. The advantages for the industry are the opportunity to select the best machine variables for each reasonable desired product and the possibility to stabilise and regulate in a connectionistic way a multivariable system like the extrusion process which is difficult to be regulated in a deterministic manner. © 2013 Elsevier Ltd.
UnivIS-Import:2015-03-09:Pub.2014.tech.ITC.stmmec.hybrid
Keywords: Artificial neural networks; Differential neurocontroller; Extrusion process control; Process variable prediction

Simulations of bubble growth and interaction in high viscous fluids using the lattice Boltzmann method - Cubeddu, Alessandro; Rauh, C.; Ulrich, V. (International Journal of Multiphase Flow, 2017)
Phenomena of growth, coalescence and breakdown of bubbles within high viscous fluids are of great interest in the fluid dynamics of multiphase fluids because of their industrial relevance, e.g. in polymer, metal alloy and food processing fields. The dynamics of multiple bubble growth in hot viscous fluids is a complex issue governed by pressure forces, vapour diffusion, surface tension and viscous forces. Effects of water evaporation from the mixture surface are responsible for phenomena like glass transition, viscous increase and dough solidification. This article presents Lattice Boltzmann simulations of nucleating bubbles with large density ratio, that grow and interact in a hot high-viscous fluid. The work focuses on the first phases of the bubble expansion, neglecting the effects of evaporation. The simulations are performed using the Lattice Boltzmann Method (LBM). The Free Surface method is used to reduce a liquid/gas two-phase flow to a single-phase flow. The interface layer between gas and fluid is tracked using the volume of fluid (VOF) method. To avoid numerical instabilities due to the high viscosity (n = 100 Pa s), the problem is scaled from physical to LB-units through non-dimensional quantities. The bubbles are initially punched randomly into the domain with a dimension comparable with the dimension of nucleation and are allowed to grow under an internal over-pressure. The simulated final structure of the bubbles is compared with images of a pure starch fluid, extruded under same conditions. It is shown as the final bubble distribution, matrix dimension and bubble diameters in the simulation are in good agreement with the real final conformation.
EAM Import::2019-03-13