Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction
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- 作者：Ashkan Mousapoura ; ash.mousapour@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Alireza Hajipourb ; alirezahajipour@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Mohammad Mehdi Rashidic ; d ; mm_rashidi@tongji.edu.cn" class="auth_mail" title="E-mail the corresponding author ; mm_rashidi@yahoo.com" class="auth_mail" title="E-mail the corresponding author ; Navid Freidoonimehre ; nfreidoonimehr@yahoo.com" class="auth_mail" title="E-mail the corresponding author
- 关键词：Miller cycle ; First-law efficiency ; Second-law efficiency ; Irreversibility ; Finite-time thermodynamics ; Artificial neural network
- 刊名：Energy
- 出版年：2016
- 出版时间：1 January 2016
- 年：2016
- 卷：94
- 期：Complete
- 页码：100-109
- 全文大小：1885 K
文摘
•The performance of an irreversible Miller cycle is investigated using FFT.
•The effects of design parameters on the performance of the cycle are investigated.
•ANN is applied to predict the thermal efficiency and the power output values.
•There is an excellent correlation between FTT and ANN data.
•ANN can be applied to predict data where FTT analysis has not been performed.