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Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction

Authors :
Ashkan Mousapour
Mohammad Mehdi Rashidi
A. Hajipour
Navid Freidoonimehr
Source :
Energy. 94:100-109
Publication Year :
2016
Publisher :
Elsevier BV, 2016.

Abstract

In this paper, the first and second-laws efficiencies are applied to performance analysis of an irreversible Miller cycle. In the irreversible cycle, the linear relation between the specific heat of the working fluid and its temperature, the internal irreversibility described using the compression and expansion efficiencies, the friction loss computed according to the mean velocity of the piston and the heat-transfer loss are considered. The effects of various design parameters, such as the minimum and maximum temperatures of the working fluid and the compression ratio on the power output and the first and second-laws efficiencies of the cycle are discussed. In the following, a procedure named ANN is used for predicting the thermal efficiency values versus the compression ratio, and the minimum and maximum temperatures of the Miller cycle. Nowadays, Miller cycle is widely used in the automotive industry and the obtained results of this study will provide some significant theoretical grounds for the design optimization of the Miller cycle.

Details

ISSN :
03605442
Volume :
94
Database :
OpenAIRE
Journal :
Energy
Accession number :
edsair.doi...........7c8c2e3af23526b5aa12b592f5fc2e43
Full Text :
https://doi.org/10.1016/j.energy.2015.10.073