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Research and development (R&D) programmes to optimise engine performance and emissions involve a large number of experimental variables and the optimum solution will normally be a trade-off between several measured responses (e.g. fuel consumption, exhaust emissions and combustion noise). The increasing number of experimental variables and the search for smaller improvements make identification of optimum configurations and robust solutions more demanding. Empirical models are routinely used to explore the trade-offs and identify the optimum engine hardware build and parameter settings. The use of Bayesian methods enables prior engineering knowledge to be explicitly incorporated into the model generation process, which allows useful models to be developed at an earlier stage in the test programme. It also enables a sequential approach to experimental design to be adopted in which the ultimate engineering objectives can be more effectively taken into account.

The search for improvements in fuel economy, specific power, exhaust emissions and refinement leads to an increasing number of experimental variables and flexibility in their operating strategy. This makes identification of optimum settings and robust solutions more demanding. New test and analysis techniques are required to identify underlying trends and sensitivities in order to determine, with some confidence, the technical and commercial benefits of changes to one or more parameters. Greater emphasis on experimental design will therefore be essential for any engine research and development programme. Design of experiments (DoE) techniques have been widely used for identifying optimum settings. Traditional experimental designs can be improved by including engineering knowledge more directly into the design process and by providing information on a continuous basis during the test programme.