Gulab Malunjkar1,Hieu McElroy2,Adebola Ogunniyi2,Mirella Coroneo3
Dow Chemical International Pvt. Ltd1,The Dow Chemical Company2,Dow Italia S.R.L.3
Gulab Malunjkar1,Hieu McElroy2,Adebola Ogunniyi2,Mirella Coroneo3
Dow Chemical International Pvt. Ltd1,The Dow Chemical Company2,Dow Italia S.R.L.3
Metal-faced sandwich panels with a rigid polyurethane (PUR) or polyisocyanurate (PIR) core are used in construction as insulating panels. The discontinuous process for their production consists of injecting a reacting polyurethane (PU) mixture in a mold, usually in a single point, where the liquid flows up to 12 m in length and reacts, growing in volume and filling the cavity. The injection is well known to be a source of defects and of non-uniform foam properties, which are potentially critical factors for the final performance of the panel. The ability of a foam formulation to flow and fill the cavity of a sandwich panel can have a significant impact on the orientation of the foam cells, affecting insulation and structural properties of the foam, and affecting the tendency of a foam panel to blister when installed in the field. Developing a better way to distribute foam in a panel could, therefore, enable the reduction of defects in panels during production.<br/><br/>A Finite Element Method (FEM) was used to predict the filling of a mold cavity with PU foam, using different distribution devices. An advanced numerical model was used to simulate the foam injection and expansion processes in a single step. Foam flow simulation solvers were coupled with an advanced global stabilization algorithm and a multi-criteria adaptive meshing technology. These new technologies enabled a realistic prediction of foam flow and foam expansion. The finite element model was validated for an accurate foam molding solution. PU foams were then characterized independently to generate the modeling parameters for the simulation. The foam filling simulation shows realistic flow pattern inside the mold and used to predict optimal filling conditions (i.e. temperatures, gating and venting). The simulation accounts for the pressure effect inside and outside of the mold. It also predicts the density distribution in foam. With this approach, we can now perform <i>in silico</i> optimization of the foam manufacturing process to achieve defect-free foam panels.