Increased pressure on automotive manufactures to reduce overall greenhouse gas emissions from their vehicles has driven research into replacing conventional steel components with carbon fiber reinforced plastics (CFRP). This is due to their high weight specific properties and design freedom. High pressure resin transfer molding (HP-RTM), a derivative of resin transfer molding, is a manufacturing process for CFRP’s that offers high automation potential and low cycle times;​ critical properties of a manufacturing process for the automotive sector.

The goal of the present work is to increase the accuracy and reduce the computational overhead of current RTM infiltration and curing models. These models allow for the reduction in development and testing costs, as they provide critical insight into the flow characteristics and cure development during said stages. First, a geometry dependent orientation calculator for the permeability tensor is proposed to quickly and accurately orient the fiber direction solely using the mold geometry and mesh required by the infiltration solver. The proposed calculator is verified against an 1D analytical solution of Darcy’s law and then validated against fiber orientation data generated from a constitutive draping simulation. Secondly, a local thermal non-equilibrium (LTNE) energy model is proposed. Due to the addition complexity and computational overhead, a non-dimensional analysis is performed to determine the appropriate operating conditions where the added accuracy of the LTNE model is required. Finally, the advancements in energy modelling approaches are highlight. A study is run on a complex, floor geometry where the three main energy modelling approaches are compared (i.e., isothermal, equilibrium, and LTNE). The results showcase the variability in the predicted cure degree development throughout both the infiltration and curing stage, depending on the energy model used. This highlights the importance of accurately capturing the temperature development, as it can lead to significant variations in the cure degree and cure degree rate development. To further reduce the computational cost an adaptive time step formulation, dependent on the maximum cure rate and minimum cure degree, is proposed. This formulation is shown to reduce the overall computational time of the curing simulation by up to 10 times.