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An analytical mechanics model was employed to predict the delamination of several thermal-barrier-coated pistons that had been previously tested in a high-output, single-cylinder diesel engine. Some of the coatings delaminated during engine operation. Results are presented for two thicknesses of the same coating material, and for two similar coatings with different levels of stiffness. All the coating thermomechanical properties such as thermal conductivity, density, volumetric heat capacity, thickness, elastic modulus, coefficient of thermal expansion, Poisson ratio and toughness, were measured prior to engine testing. Previous measurements of the piston transient heat flux, based on fast-response surface temperature data, in the same engine were used as an input to calculate the multilayer wall temperature distribution. A theoretical methodology was employed to evaluate and predict the coating durability.

Design of internal combustion (IC) engine pistons is dependent on accurate prediction of the temperature field in the component. Experimental temperature measurements can be taken but are costly and typically limited to a few select locations. High-fidelity computer simulations can be used to predict the temperature at any number of locations within the model, but the models must be calibrated for the predictions to be accurate. The largest barrier to calibration of piston thermal models is estimating the backside boundary conditions, as there is not much literature available for these boundary conditions. Bayesian model calibration is a common choice for model calibration in literature, but little research is available applying this method to piston thermal models. Neural networks have been shown in literature to be effective for calibration of piston thermal models.