Yapay Sinir Ağı Kullanarak Kanola Yağı Biyodizel Karışımları İçin Motor Performansı, Yanma ve Emisyon Tahmini

Şen, Mehmet Umut

doi:10.29130/dubited.551230

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…It also requires special tools. To overcome these difficulties, ANN can be used to estimate performance parameters in internal combustion engines [21,18,22,23]. It is thought that a well-trained ANN model provides fast and consistent results and makes it an easy-to-use tool in preliminary studies for such engineering problems [24].…”

Section: Discussionmentioning

confidence: 99%

Prediction of Specific Fuel Consumption of 60 HP 2WD Tractor Using Artificial Neural Networks

Küçüksariyildiz

Çarman

Sabancı

2021

International Journal of Automotive Science and Technology

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In this study, specific fuel consumption was determined at different axle load, tire pressure and drawbar force for a 60 HP tractor. In addition, the results were also estimated with the help of Artificial Neural Networks (ANN), which is one of the machine learning methods. The testings were carried out on the Tractor Draft Test Track. In the study; three different drive tire inner pressures (P) (160 kPa, 120 kPa and 80 kPa), four different dynamic axle loads (W) (1796 daN, 2076 daN, 2276 daN, 2476 daN) and four different traction forces (500 daN,1000 daN, 1500 daN, 2000 daN) were tested. Specific fuel consump-tion values varied between 290.7-542.1 g/kWh depending on the draft force in the testings Despite the 38% increase in the axle load, a 3.5% decrease occurred in the specific fuel consumption values. Specific fuel consumption values in-creased when increased tire inner pressure. Specific fuel consumption in-creased by 1.03% when a 100% increase in tire pressure. In ANN, the most suc-cessful model was determined by trying different training algorithms, transfer functions and the number of neurons in the hidden layer. In the most successful network model, MAE, RMSE values for the prediction of specific fuel consump-tion were found to be 0.005331, 0.007551 respectively.

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Section: Discussionmentioning

confidence: 99%