Thermal-Mechanical Fatigue Prediction of Aluminum Cylinder Head with Integrated Exhaust Manifold of a Turbo Charged Gasoline Engine 2016-01-1085
The present paper describes a CAE analysis approach to evaluate the thermal-mechanical fatigue (TMF) of the cylinder head of a turbo charged GDI engine with integrated exhaust manifold. It allows design engineers to identify structural weakness at the early stage or to find the root cause of cylinder head TMF failures.
At SAIC Motor, in test validation phase a newly developed engine must pass a strict durability test on test bed under thermal cycling conditions so that the durability characteristics can be evaluated. The accelerated dynamometer test is so designed that it gives equivalent cumulative damage as what would occur in the field. The duty cycle includes rated speed full load, rated speed motored and idle speed conditions.
A transient none-linear finite element method is used to calculate the plastic deformation and thermal mechanical behaviors of the cylinder head assembly during thermal cycling. The finite element model includes cylinder head, block, bolts, valves, valve seats, valve guides and gasket.
A transient heat transfer simulation is performed to provide thermal boundary conditions for the nonlinear stress/strain analysis. The thermal boundary conditions are determined with the help of CFD simulations of gas side and coolant side.
A TMF prediction approach is employed, which is based on elasto-viscoplastic behaviour and damage models from thermal-mechanical tests. Relative risky locations of the cylinder head are identified by this simulation methodology. The predicted life span results are compared with the outcome from the thermal cycle durability test. The durability of the cylinder head is validated by the engine thermal shock test.
Citation: Chen, M., Wang, Y., Wu, W., Cui, Q. et al., "Thermal-Mechanical Fatigue Prediction of Aluminum Cylinder Head with Integrated Exhaust Manifold of a Turbo Charged Gasoline Engine," SAE Technical Paper 2016-01-1085, 2016, https://doi.org/10.4271/2016-01-1085. Download Citation
Ming Chen, Yanjun Wang, Wenrui Wu, Quan Cui, Kai Wang, Lingfang Wang