پیش بینی پارامترهای روغن کاری یاتاقان چشم کوچک شاتون موتور ملی EF۷ به کمک شبکه های عصبی با شبیه سازی در نرم افزار AVL EXCITE
محل انتشار: فصلنامه تحقیقات موتور، دوره: 20، شماره: 20
سال انتشار: 1389
نوع سند: مقاله ژورنالی
زبان: فارسی
مشاهده: 191
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استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_ENGIN-20-20_003
تاریخ نمایه سازی: 11 اردیبهشت 1401
چکیده مقاله:
In order to reduce wear in gudgeon pin, bush and piston boss lubrication is done. Oil used in national engine
species was ۱۰W۴۰ with dynamic viscosity ۵.۵ mPa.s at ۱۴۰ °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 EXCITE۵.۱ software. In this software, effect of ۶
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 EF۷ by neural networks.
The results of AVL EXCITE simulation show that Maximum hydrodynamics pressure of oil film occurs
at ۳۵۰۰ rpm in ۳۷۲° crank angle (combustion moment) in turbocharged engine EF۷ with CNG fuel was ۴۴۶
MPa and ۱.۸۳ μm respectively at ۱۴۰°C working temperature of oil. At same condition, minimum hydrodynamics
of oil thickness was ۱.۸۳ μm, which bearing wear was the possibility. The best neural network FFBP
topology for the prediction lubrication parameters (maximum pressure and minimum thickness) was ۶-۲۴-
۳۰-۲ structure with learning algorithm trainlm and functions threshold logsig, tansig and is pureline.
کلیدواژه ها:
TC EF۷ Engine ، Connecting Rod Small End ، Gudgeon Pin ، Hydrodynamic Lubrication ، Tolerance ، Artificial Neural Network ، TC EF۷ Engine ، Connecting Rod Small End ، Gudgeon Pin ، Hydrodynamic Lubrication ، Tolerance ، Artificial Neural Network