Search Results

Turbocharging has become the favored approach for downsizing internal combustion engines to reduce fuel consumption and CO2 emissions, without sacrificing performance. Matching a turbocharger to an engine requires a balance of various design variables in order to meet the desired performance. Once an initial selection of potential compressor and turbine options is made, corresponding performance maps are evaluated in 1D engine cycle simulations to down-select the best combination. This is the conventional matching procedure used in industry and is ‘passive’ since it relies on measured maps, thus only existing designs may be evaluated. In other words, turbine characteristics cannot be changed during matching so as to explore the effect of design adjustments. Instead, this paper presents an ‘adaptive’ matching methodology for the turbocharger turbine.

Mandated pollutant emission levels are shifting light-duty vehicles towards hybrid and electric powertrains. Heavy-duty applications, on the other hand, will continue to rely on internal combustion engines for the foreseeable future. Hence there remain clear environmental and economic reasons to further decrease IC engine emissions. Turbocharged diesels are the mainstay prime mover for heavy-duty vehicles and industrial machines, and transient performance is integral to maximizing productivity, while minimizing work cycle fuel consumption and CO2 emissions. 1D engine simulation tools are commonplace for “virtual” performance development, saving time and cost, and enabling product and emissions legislation cycles to be met. A known limitation however, is the predictive capability of the turbocharger turbine sub-model in these tools.

Internal combustion engines are routinely developed using 1D engine simulation tools. A well-known limitation is the accuracy of the turbocharger compressor and turbine sub-models, which rely on hot gas bench-measured maps to characterize performance. Such discrete map data is inherently too sparse to be used directly in simulation, and so a preprocessing algorithm interpolates and extrapolates the data to generate a wider, more densely populated map. Methods used for compressor map interpolation vary. They may be mathematical or physical in nature, but there is no unified approach, except that they typically operate on input map data in SAE format. For decades it has been common practice for turbocharger suppliers to share performance data with engine OEMs in this form. This paper describes a compressor map interpolation technique based on the nondimensional compressor flow and loading coefficients, instead of SAE-format data.