Search Results

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.

The important influence of engine friction on fuel economy has aroused new interest in its accurate measurement. Ricardo have developed a new system of instrumentation capable of measuring mechanical friction under any steady engine running conditions, and isolating the proportion of engine power absorbed by each of the auxiliary drives. Furthermore, auxiliary drive torque is measured instantaneously as a function of crank angle, enabling the dynamics of the drives to be studied. The instrumentation can be easily adapted to fit most engine types and configurations whilst retaining the original auxiliary drive design. Results obtained from gasoline and diesel versions of a 1.6 litre automotive engine using this instrumentation are described. Mechanical friction, pumping losses and auxiliary drive losses were measured with engine load, speed and coolant temperature varied.

The use of cycle simulation computer programs has become an established part of turbocharged diesel engine research and development. However, the utility of these programs has in the past been limited by the need for combustion information at the operating point. This difficulty has been overcome by the development of an empirical correlation simulating the combustion process (heat release) via an analytical expression whose governing parameters are linked to in-cylinder conditions. A method of deriving the governing parameters from only a minimum of experimental test data is presented, but enabling performance to be predicted over a wide range of operating conditions. The use of the combustion correlation enables the effects of changing ambient conditions, turbocharger match, valve timing and other engine design parameters to be predicted automatically, and includes their influence on combustion as well as the turbocharging process.