IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v94y2016icp100-109.html
   My bibliography  Save this article

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

Author

Listed:
  • Mousapour, Ashkan
  • Hajipour, Alireza
  • Rashidi, Mohammad Mehdi
  • Freidoonimehr, Navid

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.

Suggested Citation

  • Mousapour, Ashkan & Hajipour, Alireza & Rashidi, Mohammad Mehdi & Freidoonimehr, Navid, 2016. "Performance evaluation of an irreversible Miller cycle comparing FTT (finite-time thermodynamics) analysis and ANN (artificial neural network) prediction," Energy, Elsevier, vol. 94(C), pages 100-109.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:100-109
    DOI: 10.1016/j.energy.2015.10.073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544215014413
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2015.10.073?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chen, Lingen & Sun, Fengrui & Wu, Chih, 2004. "Optimal performance of an irreversible dual-cycle," Applied Energy, Elsevier, vol. 79(1), pages 3-14, September.
    2. Paul, Christopher J. & Engeda, Abraham, 2015. "Modeling a complete Stirling engine," Energy, Elsevier, vol. 80(C), pages 85-97.
    3. Sun, Heng & Zhu, Hongmei & Liu, Feng & Ding, He, 2014. "Simulation and optimization of a novel Rankine power cycle for recovering cold energy from liquefied natural gas using a mixed working fluid," Energy, Elsevier, vol. 70(C), pages 317-324.
    4. Smrekar, J. & Assadi, M. & Fast, M. & Kuštrin, I. & De, S., 2009. "Development of artificial neural network model for a coal-fired boiler using real plant data," Energy, Elsevier, vol. 34(2), pages 144-152.
    5. Lin, Jiann-Chang & Hou, Shuhn-Shyurng, 2007. "Influence of heat loss on the performance of an air-standard Atkinson cycle," Applied Energy, Elsevier, vol. 84(9), pages 904-920, September.
    6. Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    7. Chen, Lingen & Zeng, Fanming & Sun, Fengrui & Wu, Chih, 1996. "Heat-transfer effects on net work and/or power as functions of efficiency for air-standard diesel cycles," Energy, Elsevier, vol. 21(12), pages 1201-1205.
    8. Bejan, Adrian, 1980. "Second law analysis in heat transfer," Energy, Elsevier, vol. 5(8), pages 720-732.
    9. Wu, Zhi-Jun & Yu, Xiao & Fu, Le-Zhong & Deng, Jun & Hu, Zong-Jie & Li, Li-Guang, 2014. "A high efficiency oxyfuel internal combustion engine cycle with water direct injection for waste heat recovery," Energy, Elsevier, vol. 70(C), pages 110-120.
    10. Ge, Yanlin & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2006. "Performance of an Atkinson cycle with heat transfer, friction and variable specific-heats of the working fluid," Applied Energy, Elsevier, vol. 83(11), pages 1210-1221, November.
    11. Rashidi, M.M. & Ali, M. & Freidoonimehr, N. & Nazari, F., 2013. "Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm," Energy, Elsevier, vol. 55(C), pages 497-510.
    12. Lior, Noam & Zhang, Na, 2007. "Energy, exergy, and Second Law performance criteria," Energy, Elsevier, vol. 32(4), pages 281-296.
    13. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng & Qu, Shuan, 2015. "Comparative analysis and evaluation of turbocharged Dual and Miller cycles under different operating conditions," Energy, Elsevier, vol. 93(P1), pages 75-87.
    14. Deh Kiani, M. Kiani & Ghobadian, B. & Tavakoli, T. & Nikbakht, A.M. & Najafi, G., 2010. "Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends," Energy, Elsevier, vol. 35(1), pages 65-69.
    15. Al-Sarkhi, A. & Jaber, J.O. & Abu-Qudais, M. & Probert, S.D., 2006. "Effects of friction and temperature-dependent specific-heat of the working fluid on the performance of a Diesel-engine," Applied Energy, Elsevier, vol. 83(2), pages 153-165, February.
    16. Al-Sarkhi, A. & Jaber, J.O. & Probert, S.D., 2006. "Efficiency of a Miller engine," Applied Energy, Elsevier, vol. 83(4), pages 343-351, April.
    17. Gandhidasan, P. & Mohandes, M.A., 2011. "Artificial neural network analysis of liquid desiccant dehumidification system," Energy, Elsevier, vol. 36(2), pages 1180-1186.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gonca, Guven, 2017. "Exergetic and ecological performance analyses of a gas turbine system with two intercoolers and two re-heaters," Energy, Elsevier, vol. 124(C), pages 579-588.
    2. Shahriyar Abedinnezhad & Mohammad Hossein Ahmadi & Seyed Mohsen Pourkiaei & Fathollah Pourfayaz & Amir Mosavi & Michel Feidt & Shahaboddin Shamshirband, 2019. "Thermodynamic Assessment and Multi-Objective Optimization of Performance of Irreversible Dual-Miller Cycle," Energies, MDPI, vol. 12(20), pages 1-25, October.
    3. Gonca, Guven, 2016. "Comparative performance analyses of irreversible OMCE (Otto Miller cycle engine)-DiMCE (Diesel miller cycle engine)-DMCE (Dual Miller cycle engine)," Energy, Elsevier, vol. 109(C), pages 152-159.
    4. Redel-Macías, M.D. & Hervás-Martínez, C. & Gutiérrez, P.A. & Pinzi, S. & Cubero-Atienza, A.J. & Dorado, M.P., 2018. "Computational models to predict noise emissions of a diesel engine fueled with saturated and monounsaturated fatty acid methyl esters," Energy, Elsevier, vol. 144(C), pages 110-119.
    5. Sun, Wenqiang & Wang, Zihao & Wang, Qiang, 2020. "Hybrid event-, mechanism- and data-driven prediction of blast furnace gas generation," Energy, Elsevier, vol. 199(C).
    6. Gonca, Guven & Sahin, Bahri & Parlak, Adnan & Ayhan, Vezir & Cesur, Idris & Koksal, Sakip, 2017. "Investigation of the effects of the steam injection method (SIM) on the performance and emission formation of a turbocharged and Miller cycle diesel engine (MCDE)," Energy, Elsevier, vol. 119(C), pages 926-937.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rashidi, M.M. & Ali, M. & Freidoonimehr, N. & Nazari, F., 2013. "Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm," Energy, Elsevier, vol. 55(C), pages 497-510.
    2. Zhao, Jinxing, 2017. "Research and application of over-expansion cycle (Atkinson and Miller) engines – A review," Applied Energy, Elsevier, vol. 185(P1), pages 300-319.
    3. Rossi, Francesco & Velázquez, David, 2015. "A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant," Energy, Elsevier, vol. 89(C), pages 528-544.
    4. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    5. Rashidi, M.M. & Galanis, N. & Nazari, F. & Basiri Parsa, A. & Shamekhi, L., 2011. "Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network," Energy, Elsevier, vol. 36(9), pages 5728-5740.
    6. Lin, Jiann-Chang & Hou, Shuhn-Shyurng, 2007. "Influence of heat loss on the performance of an air-standard Atkinson cycle," Applied Energy, Elsevier, vol. 84(9), pages 904-920, September.
    7. Antonio Lecuona & José I. Nogueira & Antonio Famiglietti, 2021. "Open Dual Cycle with Composition Change and Limited Pressure for Prediction of Miller Engines Performance and Its Turbine Temperature," Energies, MDPI, vol. 14(10), pages 1-25, May.
    8. Gonca, Guven & Sahin, Bahri & Parlak, Adnan & Ayhan, Vezir & Cesur, Idris & Koksal, Sakip, 2017. "Investigation of the effects of the steam injection method (SIM) on the performance and emission formation of a turbocharged and Miller cycle diesel engine (MCDE)," Energy, Elsevier, vol. 119(C), pages 926-937.
    9. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng & Qu, Shuan, 2015. "Comparative analysis and evaluation of turbocharged Dual and Miller cycles under different operating conditions," Energy, Elsevier, vol. 93(P1), pages 75-87.
    10. Gonca, Guven & Dobrucali, Erinc, 2016. "Theoretical and experimental study on the performance of a diesel engine fueled with diesel–biodiesel blends," Renewable Energy, Elsevier, vol. 93(C), pages 658-666.
    11. Zhu, Sipeng & Gu, Yuncheng & Yuan, Hao & Ma, Zetai & Deng, Kangyao, 2020. "Thermodynamic analysis of the turbocharged marine two-stroke engine cycle with different scavenging air control technologies," Energy, Elsevier, vol. 191(C).
    12. San, J.-Y., 2010. "Second-law performance of heat exchangers for waste heat recovery," Energy, Elsevier, vol. 35(5), pages 1936-1945.
    13. Gonca, Guven, 2016. "Comparative performance analyses of irreversible OMCE (Otto Miller cycle engine)-DiMCE (Diesel miller cycle engine)-DMCE (Dual Miller cycle engine)," Energy, Elsevier, vol. 109(C), pages 152-159.
    14. Gonca, Guven & Sahin, Bahri & Ust, Yasin, 2013. "Performance maps for an air-standard irreversible Dual–Miller cycle (DMC) with late inlet valve closing (LIVC) version," Energy, Elsevier, vol. 54(C), pages 285-290.
    15. Gonca, Guven, 2017. "Exergetic and ecological performance analyses of a gas turbine system with two intercoolers and two re-heaters," Energy, Elsevier, vol. 124(C), pages 579-588.
    16. Mehrpooya, Mehdi & Moftakhari Sharifzadeh, Mohammad Mehdi & Rosen, Marc A., 2015. "Optimum design and exergy analysis of a novel cryogenic air separation process with LNG (liquefied natural gas) cold energy utilization," Energy, Elsevier, vol. 90(P2), pages 2047-2069.
    17. Mikulandrić, Robert & Lončar, Dražen & Cvetinović, Dejan & Spiridon, Gabriel, 2013. "Improvement of existing coal fired thermal power plants performance by control systems modifications," Energy, Elsevier, vol. 57(C), pages 55-65.
    18. Rashidi, M.M. & Aghagoli, A. & Raoofi, R., 2017. "Thermodynamic analysis of the ejector refrigeration cycle using the artificial neural network," Energy, Elsevier, vol. 129(C), pages 201-215.
    19. Shuangshuang Shi & Yanlin Ge & Lingen Chen & Huijun Feng, 2021. "Performance Optimizations with Single-, Bi-, Tri-, and Quadru-Objective for Irreversible Atkinson Cycle with Nonlinear Variation of Working Fluid’s Specific Heat," Energies, MDPI, vol. 14(14), pages 1-23, July.
    20. Fredrik Skaug Fadnes & Reyhaneh Banihabib & Mohsen Assadi, 2023. "Using Artificial Neural Networks to Gather Intelligence on a Fully Operational Heat Pump System in an Existing Building Cluster," Energies, MDPI, vol. 16(9), pages 1-33, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:94:y:2016:i:c:p:100-109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.