PREDICTION OIL PARAMETER OF CONNECTING ROD SMALL END BEARING OF EF7 ENGINE BY USING ARTIFICIAL NEURAL NETWORK BY SIMULATION IN AVL EXCITE SOFTWARE

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KAZEMI M.; ALIZADENIA S.; TAVAKOLI HASHJIN T.; NAJAFI GH.

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Journal: THE JOURNAL OF ENGINE RESEARCH Year:2010 | Volume:6 | Issue:20 Page(s): 22-29

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Title

PREDICTION OIL PARAMETER OF CONNECTING ROD SMALL END BEARING OF EF7 ENGINE BY USING ARTIFICIAL NEURAL NETWORK BY SIMULATION IN AVL EXCITE SOFTWARE

Author(s)

KAZEMI M. | ALIZADENIA S. | TAVAKOLI HASHJIN T. | NAJAFI GH. | Issue Writer Certificate

Keywords

TC EF7 ENGINEQ3

CONNECTING ROD SMALL ENDQ3

GUDGEON PINQ3

HYDRODYNAMIC LUBRICATIONQ3

TOLERANCEQ2

ARTIFICIAL NEURAL NETWORKQ2

Abstract

In order to reduce wear in GUDGEON PIN, bush and piston boss lubrication is done. Oil used in national engine species was 10W40 with dynamic viscosity 5.5 mPa.s at 140 °C (the working temperature). In order to complete investigation the oil film hydrodynamics analysis in small end of connecting rod, a real full model engine with four cylinders has been simulated by AVL EXCITE5.1 software. In this software, effect of 6 variables consist of; oil temperature, kind of intake, kind of fuel, TOLERANCE bearings between GUDGEON PIN and bronze bush, position of bearing and engine speed on maximum pressure and minimum thickness of film were investigated, and each curves of them has been extracted. The effect of six inputs (oil temperature, intake type, fuel type, TOLERANCE, bearing position, and engine rotation speed) on the lubrication parameters was simulated for four different modes of national engine EF7 by neural networks.The results of AVL EXCITE simulation show that Maximum hydrodynamics pressure of oil film occurs at 3500 rpm in 372° crank angle (combustion moment) in turbocharged engine EF7 with CNG fuel was 446 MPa and 1.83 μm respectively at 140°C working temperature of oil. At same condition, minimum hydrodynamics of oil thickness was 1.83 μm, which bearing wear was the possibility. The best neural network FFBP topology for the prediction lubrication parameters (maximum pressure and minimum thickness) was 6-24- 30-2 structure with learning algorithm trainlm and functions threshold logsig, tansig and is pureline.

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APA: Copy

KAZEMI, M., ALIZADENIA, S., TAVAKOLI HASHJIN, T., & NAJAFI, GH.. (2010). PREDICTION OIL PARAMETER OF CONNECTING ROD SMALL END BEARING OF EF7 ENGINE BY USING ARTIFICIAL NEURAL NETWORK BY SIMULATION IN AVL EXCITE SOFTWARE. THE JOURNAL OF ENGINE RESEARCH, 6(20), 22-29. SID. https://sid.ir/paper/135577/en

Vancouver: Copy

KAZEMI M., ALIZADENIA S., TAVAKOLI HASHJIN T., NAJAFI GH.. PREDICTION OIL PARAMETER OF CONNECTING ROD SMALL END BEARING OF EF7 ENGINE BY USING ARTIFICIAL NEURAL NETWORK BY SIMULATION IN AVL EXCITE SOFTWARE. THE JOURNAL OF ENGINE RESEARCH[Internet]. 2010;6(20):22-29. Available from: https://sid.ir/paper/135577/en

IEEE: Copy

M. KAZEMI, S. ALIZADENIA, T. TAVAKOLI HASHJIN, and GH. NAJAFI, “PREDICTION OIL PARAMETER OF CONNECTING ROD SMALL END BEARING OF EF7 ENGINE BY USING ARTIFICIAL NEURAL NETWORK BY SIMULATION IN AVL EXCITE SOFTWARE,” THE JOURNAL OF ENGINE RESEARCH, vol. 6, no. 20, pp. 22–29, 2010, [Online]. Available: https://sid.ir/paper/135577/en

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