Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 9Citation - Scopus: 10Experimental Studies of Autoignition and Soot Formation of Diesel Surrogate Fuels(SAGE Publications Inc., 2013) Diez, Alvaro; Crookes, Roy J.; Lovas, TereseComputational simulation has undergone vast development for internal-combustion engine research as a time- and costsaving tool. Yet combustion simulation for conventional hydrocarbon petroleum fuels faces difficult challenges since such fuels have very complex compositions, consisting of many different molecular species, for which data are sparse. The use of surrogate fuels for combustion simulation could provide a solution to this problem. In this investigation, n-heptane and mixtures of n-heptane and toluene were studied within a broad range of potential surrogate diesel fuels, and the ignition delay and soot formation trends were compared with those of diesel fuel. Ignition delays show good agreement with those for diesel fuel and it was also possible to replicate partially the soot formation behaviour for certain engine conditions. Further investigation is needed to find a surrogate fuel that closely matches over the range of operating conditions of a diesel engine.Article Citation - WoS: 11Citation - Scopus: 13Near Infrared Spectroscopic Determination of Diesel Fuel Parameters Using Genetic Multivariate Calibration(Taylor and Francis Ltd., 2008) Özdemir, DurmuşThe use of full spectral region from near infrared spectroscopic analysis does not always end up with a good multivariate calibration model as many of the wavelengths do not contain necessary information. Due to the complexity of the spectra, some of the wavelengths or regions may, in fact, disturb the model-building step. Genetic algorithms are one of the useful tools for solving wavelength selection problems and may improve the predictive ability of conventional multivariate calibration methods. This study demonstrates application of genetic algorithm-based multivariate calibration to near infrared spectroscopic determination of several diesel fuel parameters. The parameters studied are cetane number, boiling and freezing point, total aromatic content, viscosity, and density. Multivariate calibration models were generated using genetic inverse least squares (GILS) method and used to predict the diesel fuel parameters based on their near infrared spectra. For each property, a different data set was used and in all cases the number of samples was around 250. Overall, percent standard error of prediction (%SEP) values ranged between 2.48 and 4.84% for boiling point, total aromatics, viscosity, and density. However, %SEP results for cetane number and freezing point were 11.00% and 14.86%, respectively.