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A full calibration exercise of a diesel engine air path can take months to complete (depending on the number of variables). Model-based calibration approach can speed up the calibration process significantly. This paper discusses the overall calibration process of the air-path of the Cat® C7.1 engine using statistical machine learning tool. The standard Cat® C7.1 engine's twin-stage turbocharger was replaced by a VTG (Variable Turbine Geometry) as part of an evaluation of a novel air system. The changes made to the air-path system required a recalculation of the air path's boost set point and desired EGR set point maps. Statistical learning processes provided a firm basis to model and optimize the air path set point maps and allowed a healthy balance to be struck between the resources required for the exercise and the resulting data quality.

Recent work on thermo-electric (TE) systems has highlighted the need for refined heat transfer design as well as the long standing need for improved materials performance. Recent work on heat transfer for TE systems has shown that enhanced heat transfer is needed over and above what would normally be seen in a vehicle exhaust system. In particular a better understanding of flow development and boundary layer behaviour is needed to support new design proposals. In the meantime, recent work in TE materials suggests that with the use of skutterudites significant performance benefits can accrue over existing materials. The current generation of TE materials have non-dimensional thermoelectric figure of merit (ZT) values of around 1. Skutterudites have been demonstrated to have ZT values of about 1.4 and can maintain these values over a wider temperature range than do existing materials through the engineering of the TE device.