A numerical investigation on the wall heat flux in a DI diesel engine fueled with n-heptane using a coupled CFD and ANN approach. (15th January 2015)

• Record Type: Journal Article
• Authors: Taghavifar, Hamid; Taghavifar, Hadi; Mardani, Aref; Mohebbi, Arash; Khalilarya, Shahram
• Is Part Of: Fuel. Volume 140(2015)
• Page Start: 227
• Page End: 236
• Publication Date: 2015-01-15
• Subjects: ANN -- CFD -- Wall heat flux -- Equivalence ratio -- Turbulence kinetic energy; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2014.09.092
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: The wall heat flux modeling of n-heptane fueled direct injection (DI) diesel engine was performed. The coupled computational fluid dynamics (CFD) and artificial neural network (ANN) approach was developed. A 6-17-1 ANN topology yielded the MSE equal to 0.5217 and R 2 equal to 0.99. Abstract: The primitive purpose of the present paper is to address the wall heat flux modeling of n-heptane fueled direct injection (DI) diesel engine with the application of a coupled computational fluid dynamics (CFD) and artificial neural network (ANN) approach. The numerical model was established for a Ford 1.8 l DI diesel engine equipped with a prototype Lucas CAV HPCR system, and an Allied Signal VGT. The turbulent flows within the combustion chamber were simulated using the RNG k – ɛ turbulence model. The input parameters of crank angle, mass flux, liquid mass evaporated, equivalence ratio, turbulence kinetic energy, and pressure were included in the system. It was concluded that more wall heat flux was transferred with fuel injection around TDC and along with combustion initiation for 2000 rpm and the higher pressure can be achieved at the same engine speed. Furthermore, a feed-forward with back propagation learning algorithm and Levenberg–Marquardt training technique were employed for various ANN modeling implementations. At 17 neurons in the hidden layer, the MSE equal to 0.5217 was yielded and the coefficient of determination values of 0.99 and 0.99 were obtained for training and testing phases. The optimum values of the learning rate and momentum were also yielded at 0.6 and 0.7, respectively.