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Structural aerospace composite parts are commonly cured through autoclave processing. To optimize the autoclave process, manufacturing process simulations have been increasingly used to investigate the thermal behavior of the cure assembly. Performing such a simulation, computational fluid dynamics (CFD) coupled with finite element method (FEM) model can be used to deal with the conjugate heat transfer problem between the airflow and solid regions inside the autoclave. A transient CFD simulation requires intensive computing resources. To avoid a long computing time, a quasi-transient coupling approach is adopted to allow a significant acceleration of the simulation process. This approach has been validated for a simple geometry in a previous study. This paper provides an experimental and numerical study on heat transfer in a medium-sized autoclave for a more complicated loading condition and a composite structure, a curved shell with three stringers, that mocks the fuselage structure of an aircraft. Two lumped mass calorimeters are used for the measurement of the heat transfer coefficients (HTCs) during the predefined curing cycle. Owing to some uncertainty in the inlet flow velocity, a correction parameter and calibration method are proposed to reduce the numerical error. The simulation results are compared to the experimental results, which consist of thermal measurements and temperature distributions of the composite shell, to validate the simulation model. This study shows the capability and potential of the quasi-transient coupling approach for the modeling of heat transfer in autoclave processing with reduced computational cost and high correlation between the experimental and numerical results.

The resin transfer molding (RTM) process shows considerable advantages in composite manufacturing. Nevertheless, the part quality manufactured by RTM is sensitive to material and process variations during the preform impregnation. To improve the process robustness and achieve better process control, a methodology for resin flow monitoring based on a combination of a sensing system and a neural network model is proposed, which can be easily implemented into a generic RTM process. Using pressure data provided by a limited number of sensors distributed over the mold surface, the proposed method allows the prediction of flow-front patterns at any impregnation time. The dataset for training is generated by physical-based simulations. Considering the permeability changes caused by uncertainty conditions, the permeability tensor is modeled with random variations. The network parameters are obtained by trial-and-error. Furthermore, the sensor distribution scheme and the dataset size are identified as the sensitive factors of the model. Finally, the predicted results are verified by numerical solutions. This method can be used to avoid the formation of voids and improve the final part quality.