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Content archived on 2024-05-21

Improving engine performance and efficiency by minimisation of knock probability (MINKNOCK)

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Eliminating engine knock in automobiles

Faced with increasingly stringent emission standards, Europe's car producers are constantly striving to improve fuel economy and engine performance. This was the aim of numerous engine tests and computer simulations performed during the MINKNOCK project.

Industrial Technologies icon Industrial Technologies

European car manufacturers have established a strong reputation in one of the world's most competitive industries. In order to maintain a leading position in the twenty-first century, the focus must shift to building fuel-efficient, environmentally-friendly vehicles. One way to achieve this is to reduce engine knock, which causes increased fuel consumption and emissions of air pollutants and greenhouse gases. The participants in the MINKNOCK project, which included car producers, fuel companies and research institutes, set out to do just that. Engineers with Ford Werke AG in Germany began by testing a production engine with several different types of fuel over a wide range of operating conditions. They found that performance depended on the Research and Motor octane numbers (RON and MON respectively), but other factors such as the stoichiometric air-fuel ratio also played an important role. In addition, the relationship between each fuel octane rating and knock sensitivity was investigated, as well as the link to spark advance. Ultimately, it was shown that an index proposed by Shell incorporating both RON and MON was the most appropriate for predicting knock sensitivity. the engine tests were complemented by extensive simulations using Computational fluid dynamics (CFD) models. Specifically, the STAR-CD, FIRE and Shell Auto-Ignition models were applied, in some cases in combination with an Extended coherent flame model (ECFM). The Ford engineers experimented with various parameterisations of heat transfer, turbulence and other phenomena in order to optimise agreement between the model results and experimental data.

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