Validation of Heat Transfer, Thermal Decomposition, and Container Pressurization of Polyurethane Foam Using Mean Value and Latin Hypercube Sampling Approaches

Polymer foam encapsulants provide mechanical, electrical, and thermal isolation in engineered systems. It can be advantageous to surround objects of interest, such as electronics, with foams in a hermetically sealed container in order to protect them from hostile environments or from accidents such as fire. In fire environments, gas pressure from thermal decomposition of foams can cause mechanical failure of sealed systems. In this work, a detailed uncertainty quantification study of polymeric methylene diisocyanate (PMDI)-polyether-polyol based polyurethane foam is presented and compared to experimental results to assess the validity of a 3-D finite element model of the heat transfer and degradation processes. In this series of experiments, 320 kg/m3 PMDI foam in a 0.2 L sealed steel container is heated to 1,073 K at a rate of 150 K/min. The experiment ends when the can breaches due to the buildup of pressure. The temperature at key location is monitored as well as the internal pressure of the can. Both experimental uncertainty and computational uncertainty are examined and compared. The mean value method (MV) and Latin hypercube sampling (LHS) approach are used to propagate the uncertainty through the model. The results of the both the MV method and the LHS approach show that while the model generally can predict the temperature at given locations in the system, it is less successful at predicting the pressure response. Also, these two approaches for propagating uncertainty agree with each other, the importance of each input parameter on the simulation results is also investigated, showing that for the temperature response the conductivity of the steel container and the effective conductivity of the foam, are the most important parameters. For the pressure response, the activation energy, effective conductivity, and specific heat are most important. The comparison to experiments and the identification of the drivers of uncertainty allow for targeted development of the computational model and for definition of the experiments necessary to improve accuracy.