Abstract
Thermal Barrier Coatings (TBC) have been investigated both experimentally and through simulation for mixing controlled combustion (MCC) concepts as a method for reducing heat transfer losses and increasing cycle efficiency, but it is still a very active research area. Early studies were inconclusive, with different groups discovering obstacles to realizing the theoretical potential. Nuanced papers have shown that coating material properties, thickness, microstructure, and surface morphology/roughness all can impact the efficacy of the TBC and must be accounted for. Adding to the complexities, there is a strong spatial and temporal heat flux inhomogeneity for mixing controlled combustion imposed onto the surfaces from the impinging flame jets. In support of the United States Department of Energy SuperTruck II program goal to achieve 55% brake thermal efficiency on a heavy-duty Diesel engine, this study seeks to develop a deeper insight into the inhomogeneous heat flux from MCC on TBC’s and infer concrete guidance for designing coatings, a co-simulation approach has been developed that couples high fidelity Computational Fluid Dynamics (CFD) modeling of in-cylinder processes and combustion and Finite Element Analysis (FEA) modeling of the TBC and metal engine components to resolve spatial and temporal thermal boundary conditions. The models interface at the surface of the combustion chamber; FEA modeling predicts the spatially resolved surface temperature profile, while CFD is used to develop insights into the effect of the TBC on the combustion process and the boundary conditions on the gas side. The paper demonstrates the capability of the framework to estimate cycle impacts as well as identify critical locations on the piston/TBC that exhibit the highest charge temperature and highest heat fluxes. In addition, the FEA results include predictions of thermal stresses, thus enabling insight into factors affecting coating durability. An example of the capability of the framework is provided to illustrate its use for investigating novel coatings and provide deeper insights to guide future coating design.
