Adnan Berber

8743

Mathematical Model for Fuel Flow Performance of Diesel Engine

In this paper, response surface method (RSM) was proposed to determine fuel flow performance of an internal combustion diesel engine by using different specific conditions (injection pressure, engine speed and throttle position). Injection pressure of the diesel engine was chosen as 150 bars for turbocharger and pre-combustion chamber. Experiments were realized at four pressures corresponding to 100, 150, 200 and 250 bars each with throttle positions of 50, 75 and 100%. A mathematical model was used to predict fuel flow performance of engine according to pressures and throttle positions. The optimum performance conditions, for a required fuel flow, were obtained by using response surface method with 3D graphics. The developed prediction equations showed that the linear effect of engine speeds was the most important factor that influenced the fuel flow. Moreover, whether the proposed mathematical model of fuel flow is within the limits of the performance parameters has been considered.
Keywords:

Diesel engine performance, fuel flow, mathematical modeling response surface method,

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  • Abdurrezzak Aktas, Salih Ozer, Biodiesel production from leftover olive cake Energy Education Science and Technology Part A: Energy Science and Research 2012 Volume (issues) 30 (1):89-96.
  • R. Behcet Comparison of exhaust emissions of biodiesel-diesel fuel blends produced from waste oils in Turkey Energy Education Science and Technology Part A: Energy Science and Research 1103-1114 Volume (Issue) 29 (2) July 2012.
  • A. Aktas Effects of using blends of melon kernel oil methyl ester and Diesel fuel on the engine performance and emissions Energy Education Science and Technology Part A: Energy Science and Research 1183-1192.
  • B. Gokalp, H. S. Soyhan Performance of a direct diesel engine using aviation fuels blended with biodiesel Energy Education Science and Technology Part A: Energy Science and Research 463-474 3/9Volume (Issue) 29 (1) April 2012.
  • Singh, D. Pant, S. I. Olsen, P. S. Nigam Key issues to consider in microalgae based biodiesel production Energy Education Science and Technology Part A: Energy Science and Research 687-700 Volume (Issue) 29 (1) April 2012.
  • H. T. Akcay, S. Karslioglu Optimization process for biodiesel production from some Turkish vegetable oils and determination fuel properties Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 28 (2) 861-868 January 2012.
  • M. Ozcanli, A. Keskin, H. Serin, S. Yamacli, D. Ustun Effects of soybean biodiesel on engine vibration and noise emission Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 28 (2) January 2012 949-956.
  • Hasimoglu Performance and emission investigation of a biodiesel fueled low heat rejection diesel engine. Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 28 (2) 1027-1038 January 2012.
  • Ozdalyan The effect using n-butanol-diesel fuel blends at different injection pressureson the performance and emissions of diesel engines 1001-1110 Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 28 (2) January 2012.
  • A. Demirbas Fuels for petroleum, coal and biomass Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 29 (1) April 2012701-705.
  • M. I. Karamangil, B. Erkus, O. Kaynakli, A. Surmen Technical and economic analysis of the problems observed in diesel engines with common rail injection systems in Turkey. Energy Education Science and Technology Part A: Energy Science and Research Volume (Issue) 28 (2) 563-576January 2012.
  • Çelikten İ, Koca A, Arslan MA (2010). Comparison of performance and emissions of diesel fuel, rapeseed and soybean oil methyl esters injected at different pressures Renewable Energy, 35; 4: 814-820
  • Mukherjee I, Ray K( 2006). A review of optimization techniques in metal cutting processes. Comput Indus Eng 8;50:15–34.
  • Satake K, Monaka T, Yamada S, Endo H, Yamagisawa M, Abe T(2008). The rapid development of diesel engine using an optimization of the fuel injection control. Mitsubishi Heavy Industries Limited. Tech Rev 45:6–10.
  • Alonso JM, Alvarruiz F, Desantes JM, Hernandez L, Hernandez V, Molto G ( 2007). Combining neural networks and genetic algorithms to predict and reduce diesel engine emission. IEEE Trans Evol Comput 11:46–55.
  • Seshu PC, Meung JK, Cliff Mirman (2011). Failure analysis of railroad couplers of AAR type E Engineering Failure Analysis 18 :374–385.
  • Noordin MY, Venkatesh VC, Sharif S, Elting S, AbdullahA (2004). Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel Journal of Materials Processing Technology 145: 46–58.
  • Abhang LB, Hameedullah M(2010). Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology Journal of Engineering Science and Technology Review 3 (1) 116-122.
  • Davidsona M, Joseph K, Balasubramanianb GRN, Tagorea ( 2 0 0 8 ). Surface roughness prediction of flow-formed AA6061 alloy by design of experiments journal of materials processing technology 202:41–46.
  • Neşeli S, Yaldız S, Erol Türkeş E (2010). Optimization of tool geometry parameters for turning operations based on the response surface methodology Original Research Article Measurement, In Press, Accepted Manuscript, Available online 6 December.
  • Xingzhong Y, Jia L, Guangming Z, Jingang S, Jingyi T, Guohe H (2008). Optimization of conversion of waste rapeseed oil with high FFA to biodiesel using response surface methodology Renewable Energy 33 :1678–1684.
  • Perez PL, Boehman AL (2010). Performance of a single-cylinder diesel engine using oxygen-enriched intake air at simulated high-altitude conditions Aerospace Science and Technology 14 :83–94.
  • Yang HC, Ryou HS, Jeong YT, Choi YK(1996). Spray characteristics in a direct-injection diesel engine, Atomization and Sprays 6:95-109.
  • Massie DD (2001). Neural-network fundamentals for scientists and engineers. ECOS’0l, 4–6 July, Istanbul, Turkey, 123.
  • Berber A, Gültekin SS, Kulaksız AA, Çelikten İ (2006). Investigation Of Breake Mean Effective Pressure and Fuel Flow Performances of Diesel Engine By Using Artificial Neural Networks. Proceeding of the International Conference on modeling and simulation 2006, 28-30 august, Konya, TURKEY, 2: 577-581.
  • Berber A, Gültekin SS, Kulaksız AA (2010). Investigation of Break Mean Effective Pressure and Spesific Fuel Consumption Performances of Diesel Engine By Using Artificial Neural Networks The Fifth International Ege Energy Symposium and Exhibition (IEESE-5) Pamukkale Universty Denizli Turkey June 27-30.
  • Montgomery DC, (2005). Design and Analysis of Experiments: Response surface method and designs. P. 405. New Jersey: John Wiley and Sons, Inc.
  • Bradley N (2007). The Response Surface Methodology, Department of Mathematical Sciences Indiana University of South Bend Master of science in Applied Mathematics & Computer Science.
International Journal of Automotive Engineering and Technologies-Cover
  • Başlangıç: 2012
  • Yayıncı: Murat CİNİVİZ
Arşiv
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