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In this paper a practical methodology for automated tuning of simulation models is introduced, which is widely and successfully adapted in IAV. For this, stochastic optimization algorithms (like Genetic Algorithms or Particle Swarm Optimization), and appropriate algorithms for optimization tasks with very long computation time (e.g. Adaptive Surrogate-Model Optimization or Adaptive Hybrid Strategies) are used in combination with commercial and internal simulation tools. Often it is necessary to evaluate several contradictory objectives at the same time which leads to multi-criterion optimization. Effective post processing methods (mathematical decision aids) are used to select the best compromises for the problem. As a practical example, this automated tuning methodology is applied to an engine performance simulation model developed in GT-Power.

High pressure EGR provides NOx emission reduction even at low exhaust temperatures. To maintain a safe EGR system operation over a required lifetime, the EGR cooler fouling must not exceed an allowable level, even if the engine is operated under worst-case conditions. A reliable fouling simulation model represents a valuable tool in the engine development process, which validates operating and calibration strategies regarding fouling tendency, helping to avoid fouling issues in a late development phase close to series production. Long-chained hydrocarbons in the exhaust gas essentially impact the fouling layer formation. Therefore, a simulation model requires reliable input data especially regarding mass flow of long-chained hydrocarbons transported into the cooler. There is a huge number of different hydrocarbon species in the exhaust gas, but their individual concentration typically is very low, close to the detection limit of standard in-situ measurement equipment like GC-MS.