Compression moulding of sheet moulding compound: Modelling with computational fluid dynamics and validation

Abstract

Compression moulding experiments of sheet moulding compound, visual observations of a vacuum test with prepregs and numerical models with two main approaches for computational fluid dynamics simulations of the mould filling phase are presented. One assumes that there are layers near the mould surfaces with much less viscosity and the other only use one viscosity model. The numerical experiments showed that the pressure could be accurately predicted with both approaches. The property necessary to predict correct pressure with altered mould closing velocities was that the bulk material had to obey shear-thinning effects. Preheating effects before compression were neglected, but altering the heating time until the prepreg was assumed to start flow had a significant effect. The experiments confirmed that the pressure is predominantly affected by the mould closing velocity. Regardless of the considered process settings, a first pressure top always appeared approximately at the logarithmic strain 0.25. A second top was associated with a slowdown of the press. The location of this was affected by the velocity and the vacuum, the latter indicating that vacuum assistance prevents a build-up of back pressure. Furthermore, heated prepreg above a critical temperature is observed to swell immediately as vacuum assistance is applied.

Review of the Numerical Modeling of Compression Molding of Sheet Molding Compound

“…in which η 0 is the initial viscosity of the charge and T is the temperature. A model based on Equation (6) was implemented numerically in Marjavaara et al [60] and expanded with Equations (4) and (5) in Kluge et al [61]. In both these works, the goal was to numerically determine the pressure field, and those models were then compared to experimental data with relatively good agreement.…”

“…A subject that has been discussed is whether the viscosity model should take into account differences between the bulk of the flow and the layers closest to the mold walls. Kluge et al [61], for instance, evaluated a number of different models with different formulations both with the same equation for viscosity through the charge and with a separate equation for the viscosity of the skin closest to the walls and for the cases studied it was concluded that a one equation model was sufficient to predict the pressure in the mold. More recently a model was suggested by Bertóti and Böhlke [62], in which the viscosity of the flow was shown to be affected by the orientation of the fibers.…”

Heat and mass transfer model to predict the operational performance of a steam sterilisation autoclave including products

“…The methodology presented utilised controlling parameters in conjunction with steady-state expressions whereby the controlling parameters were tuned using measured data. Similar modelling approach could be found in other engineering studies where constants were utilised with established expressions and validated using measured data [14]. Various methodologies have been developed and used in food processing industry to further understand the detail heat and mass transfer phenomena within a cooking process including the loads being heated up.…”

“…Various methodologies have been developed and used in food processing industry to further understand the detail heat and mass transfer phenomena within a cooking process including the loads being heated up. Kannan et al [14] presented a computational fluid dynamic (CFD) model for estimating the temperature of liquid within a can during a food retorting process. The model was for water, mainly describing the methodology for modelling of convective heat transfer within the can.…”

Discrete and continuous modelling of convective heat transport in a thin porous layer of mono sized spheres

“…Heat transfer in porous media is of great importance for a large number of applications including heat exchangers [1][2][3], pelletization of different products like iron ore pellets [4][5][6][7], wood pellets for bio energy [8], flow in blast furnaces [9,10], solar dryers for food and crop drying [11], pebble bed reactors [12], foods processing [13][14][15] and composites manufacturing [16].…”