Turbocharger turbine map scaling for virtual engine use

Abstract

Emissions requirements, performance expectations, and expanding range of fuels for off-road engines are broadening engine air system requirements. As part of reducing engine development cost and risk, virtual engine simulation is used to select architecture and components capable of covering a broad range of requirements through low risk changes to the turbocharger turbine. A method is presented for predicting turbine performance changes due to turbine housing area change, the most effective of these low risk changes, from available turbine map data. The method, developed from a mean line model, learns four simplified geometry parameters, three loss coefficients, and two maximum efficiency rotor inlet incidence coefficients from a training turbine map data set, then predicts a scaled turbine map for changes to a simplified geometry parameter associated with housing area. Reduced mass flow RMS errors less than 4.3% and efficiency RMS errors less than 5.2% are expected when generating scaled turbine maps with this method, which is additional 2% error for flow and 3% error for efficiency over that expected when modeling actual turbine map data.