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An Adaptive Neuro-Fuzzy Inference System (ANFIS) model for prediction of thermal contact conductance between exhaust valve and its seat.pdf
Applied Thermal Engineering 105 (2016) 613–621
Contents lists available at ScienceDirect
Applied Thermal Engineering
journal homepage: /locate/apthermeng
Research Paper
An Adaptive Neuro-Fuzzy Inference System (ANFIS) model for prediction of thermal contact conductance between exhaust valve and its seat
Mohsen Motahari-Nezhad a,?, Mohammad Sh. Mazidi b
a Young Researchers and Elite Club, South Tehran Branch, Islamic Azad University, Tehran, Iran b Optimization and Development of Energy Technologies Division, Research Institute of Petroleum Industry (RIPI), Tehran, Iran
highlights
Thermal Contact Conductance (TCC) between exhaust valve and its seat is estimated. The inverse method is used to estimate the heat transfer rate. An Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for prediction of TCC. The results show that the gaussmf function provided the best performance.
article info
Article history: Received 8 December 2015 Accepted 12 March 2016 Available online 21 March 2016
Keywords: Exhaust valve Thermal Contact Conductance (TCC) Neuro-fuzzy systems ANFIS network Inverse method
abstract
Exhaust valve and its seat in an internal combustion engine reach high temperatures due to the hot gases exit through the engine exhaust port. This high temperature must decrease in order to avoid damaging of the combustion chamber and the engine itself. This high amount of heat can be taken away from the exhaust valve to its seat when they are in contact, then it will be transferred to the coolant. Therefore, study of heat transfer between the exhaust valve and its seat is necessary. In this paper, the capabilities of a neuro-fuzzy approach, namely ANFIS (Adaptive Network based Fuzzy Inference System) have been studied for estimating the rate of the heat transfer between the exhaust valve and its seat. The ANFIS model is formed by means of input–output data set taken from the experimental study and the inverse solution using the Conjugate Gradient Method (CGM) with a
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