Towards in-cylinder flow informed engine control strategies using linear stochastic estimation

Many modern I.C. engines rely on some form of active control of injection, timing and/or ignition to help combat tailpipe out emissions, increase the fuel economy and engine drivability. However, development of these strategies is often optimised to suit the average cycle at each condition; an assumption that can lead to sub-optimal performance, especially an increase in particulate (PN) emissions as I.C. engine operation, and in-particular it’s charge motion is subject to cycle-to-cycle variation (CCV). Literature shows that the locations of otherwise repeatable large-scale flow structures may vary by as much 25% of the bore dimension; this could have an impact on fuel break-up and distribution and therefore subsequent combustion performance and emissions. In the presented work, a method is presented that allows full-field flow velocity information to be estimated in real-time from only a limited number of point velocity measurements using linear stochastic estimation (LSE). Three sensor arrangements – single bisecting ‘line-of-sight’, a central cluster and a circumferential ring - which are deemed applicable to implementation in an I.C. engine are compared over all test flow conditions; with all providing useful estimations of the flow field. It is shown how with even a modest number of point measurements it is possible to achieve at least 85% correlation between estimates and original data allowing cycle characterisation to be achieved. Information gathered from this technique could provide inputs to engine control strategies to account for the CCV of the in-cylinder flow.