A new cure kinetics model to simulate thermomechanical behavior of polymeric sealants for automotive applications

Accurate prediction of the cure level of thermoset polymers is essential to simulate the thermomechanical behavior of polymeric thermoset sealants, which is strongly dependent on cure level. Conventional cure kinetics models, however, fail to accurately predict the cure levels of thermoset sealants subjected to a complex temperature program. Herein, we propose a new cure kinetics model that greatly enhances cure level predictability by considering temperature derivatives. The validity of our model was verified by simulating the thermomechanical behavior of a polymeric sealant using a user material subroutine (UMAT) of ABAQUS software. Experimental results from an appropriately designed thermomechanical test were compared with simulation results obtained from the UMAT.

Kinetic modelling of dumpsite leachate treatment using Musa sapientum peels as bio-sorbent

Kinetics models are very vital to dumpsite operators and planners as they provide relevant information for effective treatment of leachates. The aim of this work is to model the kinetic process of treatment of Lagos dumpsite leachate using Musa sapientum peels as bio-sorbent with a view of establishing the kinetic parameters of the treatment process. Musa sapientum peels which were collected from Ayetoro market in Epe Local Government area of Lagos State were used to prepare the bio-sorbent. Kinetic process was carried out using 1 g of the prepared bio-sorbent in 100 ml Lagos dumpsite leachate in different conical flasks and at various contacting time. The kinetic data obtained were fitted to different kinetics models. The kinetics models tested were Fractional power model, Lagregren pseudo first – order model, Pseudo second – order model, Kuo – Lotse kinetic model, Blanchard kinetic model and Elovich kinetic model. Other kinetics models considered were Sobkowsk – Czerwi kinetic model, Intraparticle diffusion (IPD) model, Behnajady – Modirshahla – Ghanbery (BMG) model and Diffusion – Chemisorption model. Coefficient of determination (R2) values and the expected nature of the plots of the models were used to screen the tested models. The results revealed that the Pseudo second – order kinetic model has the best R2 value of 0.99996 and the graph followed the expected nature of the plot hence it was adopted in this work. It was concluded that Pseudo second – order kinetic model can be used to navigate the treatment process of Lagos dumpsite using Musa sapientum peels as bio-sorbent.

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chem- ical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous e?orts in this field have largely been limited to finding a single best-fit set of parame- ters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simul- taneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. The distributions may be used in forward propagation of uncertainty in models that leverage this material.

Study on the Dynamic Recrystallization Behavior of 47Zr-45Ti-5Al-3V Alloy by CA–FE Simulation

The dynamic recrystallization (DRX) behavior of 47Zr-45Ti-5Al-3V alloy was studied by using the experiment and numerical simulation method based on DEFORM-3D software and cellular automata (CA) over a range of deformation temperatures (850 to 1050 °C) and strain rates (10−3 to 100 s−1). The results reveal that the DRX behavior of 47Zr-45Ti-5Al-3V alloy strongly depends on hot-working parameters. With rising deformation temperature (T) and decreasing strain rate (ε˙), the grain size (dDRX) and volume fraction (XDRX) of DRX dramatically boost. The kinetics models of the dDRX and XDRX of DRX grains were established. According to the developed kinetics models for DRX of 47Zr-45Ti-5Al-3V alloy, the distributions of the dDRX and XDRX for DRX grains were predicted by DEFORM-3D. DRX microstructure evolution is simulated by CA. The correlation of the kinetics model is verified by comparing the dDRX and XDRX between the experimental and finite element simulation (FEM) results. The nucleation and growth of dynamic recrystallization grains in 47Zr-45Ti-5Al-3V alloy during hot-working can be simulated accurately by CA simulation, comparing with FEM.