<p>The current work investigated the combustion efficiency of biodiesel engines under diverse ratios of compression (15.5, 16.5, 17.5, and 18.5) and different biodiesel fuels produced from apricot oil, papaya oil, sunflower oil, and tomato seed oil. The combustion process of the biodiesel fuel inside the engine was simulated utilizing ANSYS Fluent v16 (CFD). On AV1 diesel engines (Kirloskar), numerical simulations were conducted at 1500 rpm. The outcomes of the simulation demonstrated that increasing the compression ratio (CR) led to increased peak temperature and pressures in the combustion chamber, as well as elevated levels of CO<sub>2</sub> and NO mass fractions and decreased CO emission values under the same biodiesel fuel type. Additionally, the findings revealed that the highest cylinder temperature was 1007.32 K and the highest cylinder pressure was 7.3 MPa, achieved by biodiesel derived from apricot oil at an 18.5% compression ratio. Meanwhile, the highest NO and CO<sub>2</sub> mass fraction values were 0.000257524 and 0.040167679, respectively, obtained from biodiesel derived from papaya oil at an 18.5% compression ratio. This study explained that the apricot oil biodiesel engine had the highest combustion efficiency with high emissions at a compression ratio of 18:5. On the other hand, tomato seed oil biodiesel engines had low combustion performance and low emissions of NO and CO<sub>2</sub> at a compression ratio of 15:5. The current study concluded that apricot oil biodiesel may be a suitable alternative to diesel fuel operated at a CR of 18:1.</p>
AW Ali T, Journal of the Faculty of Medicine, 2016 - Cited by 1
Maximum likelihood estimation method, uniformly minimum variance unbiased estimation method and minimum mean square error estimation, as classical estimation procedures, are frequently used for parameter estimation in statistics, which assuming the parameter is constant , while Bayes method assuming the parameter is random variable and hence the Bayes estimator is an estimator which minimize the Bayes risk for each value the random observable and for square error lose function the Bayes estimator is the posterior mean. It is well known that the Bayesian estimation is hardly used as a parameter estimation technique due to some difficulties to finding a prior distribution.
The interest of this paper is that
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