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In one-dimensional engine simulation programs the simulation of engine performance is mostly done by parameter fitting in order to match simulations with experimental data. The extensive fitting procedure is especially needed for emissions formation - CO, HC, NO, soot - simulations. An alternative to this approach is, to calculate the emissions based on detailed kinetic models. This however demands that the in-cylinder combustion-flow interaction can be modeled accurately, and that the CPU time needed for the model is still acceptable. PDF based stochastic reactor models offer one possible solution. They usually introduce only one (time dependent) parameter - the mixing time - to model the influence of flow on the chemistry. They offer the prediction of the heat release, together with all emission formation, if the optimum mixing time is given.

The aerodynamic optimization of an AUDI Q5 vehicle is presented using the continuous adjoint approach within the OpenFOAM framework. All calculations are performed on an unstructured automatically generated mesh. The primal flow, which serves as input for the adjoint method, is calculated using the standard CFD process at AUDI. It is based on DES calculations using a Spalart-Allmaras turbulence model. The transient results of the primal solution are time averaged and fed to a stationary adjoint solver using a frozen turbulence assumption. From the adjoint model, drag sensitivity maps are computed and measures for drag reduction are derived. The predicted measures are compared to CFD simulations and to wind tunnel experiments at 1:4 model scale. In this context, general challenges, such as convergence and accuracy of the adjoint method are discussed and best practice guidelines are demonstrated.