This paper is intended to compare the hazard rate from the Bayesian approach with the hazard rate from the maximum likelihood estimate (MLE) method. The MLE of a parameter is appropriate as long as there are sufficient data. For various reasons, however, sufficient data may not be available, which may make the result of the MLE method unreliable. In order to resolve the problem, it is necessary to rely on judgment about unknown parameters. This is done by adopting the Bayesian approach. The hazard rate of a mixture model can be inferred from a method called Bayesian estimation. For eliciting a prior distribution which can be used in deriving a Bayesian estimate, a computerized-simulation method is introduced. Finally, a numerical example is given to illustrate the potential benefits of the Bayesian approach.

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The objective of this work was to investigate the effects of injection conditions and swirl on D.I. diesel combustion using a transparent engine system. The test engine is equipped with a common rail injection system to control injection conditions and to obtain split injection characteristics. A combustion analysis and steady flow test were conducted to measure the heat release rate due to cylinder pressure and the swirl ratio. In addition, spray and diffusion flame images were obtained using a high speed camera. The LII & LIS methods were also used to obtain 2-D soot and droplet distributions. High injection pressure was found to shorten ignition delay, as well as to enhance peak pressure. The results also revealed that the heat release rate in the premixed combustion region was markedly reduced through the use of pilot injection, while the soot distribution and the heat release rate in the diffusion combustion region were increased. The swirl effect was found to shorten ignition delay at certain injection timings, and to enhance the heat release rate in all experimental conditions.