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http://hdl.handle.net/11452/33311
Başlık: | Spline regression modelling of PTO performance of tractor fuelled with different biodiesels |
Yazarlar: | Aybek, Ali Üçok, Serdar Üçgül, Mustafa Uludağ Üniversitesi/Ziraat Fakültesi/Biyosistem Mühendisliği Bölümü. 0000-0001-8528-7490 Aslan, Selçuk T-2708-2018 7006604572 |
Anahtar kelimeler: | Agriculture Biodiesel Performance characteristics Power take-off Tractor Spline modelling Diesel-engine Blends Oil |
Yayın Tarihi: | May-2017 |
Yayıncı: | Chinese Acad Agricultural Engineering |
Atıf: | Aybek, A. vd. (2017). ''Spline regression modelling of PTO performance of tractor fuelled with different biodiesels''. International Journal of Agricultural and Biological Engineering, 10(3), 115-120. |
Özet: | The objective of this study was to investigate the possibility of fitting spline regression models for power take-off (PTO) performance characteristics of an agricultural tractor tested with four different fuels, including diesel fuel (B0) and three biodiesel blends made of canola oil (B10: 10% biodiesel + 90% petro-diesel blend; B20: 20% biodiesel + 90% petro-diesel blend; B30: 30% biodiesel + 90% petro-diesel blend). The performance characteristics evaluated were PTO power, engine torque, engine fuel consumption, and specific fuel consumption. Due to sharp slopes in measured quantities around the nominal engine speed (2200 r/min), compared to the standard regression method, the spline regression models suited well to the experimental data with coefficient of determination R-2= 0.99 for PTO power and engine torque. R-2 varied between 0.97 and 0.99 for fuel consumption and 0.91 and 0.95 for specific fuel consumption. The weaker correlation found for specific fuel consumption could be attributed to profound fluctuations in measured data causing atypical pattern in the corresponding graphs around the nominal engine speed. It was concluded that splines were useful to accurately predict the measured PTO power and engine torque. Neither standard methods nor splines were sufficient to obtain very good regression models for specific fuel consumption. |
URI: | https://doi.org/10.3965/j.ijabe.20171003.3220 http://hdl.handle.net/11452/33311 |
ISSN: | 1934-6344 1934-6352 |
Koleksiyonlarda Görünür: | Scopus Web of Science |
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