IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v119y2017icp938-949.html
   My bibliography  Save this article

Optimization of HCCI (Homogeneous Charge Compression Ignition) engine combustion chamber walls temperature to achieve optimum IMEP using LHS and Nelder Mead algorithm

Author

Listed:
  • Mansoury, M.
  • Jafarmadar, S.
  • Talei, M.
  • Lashkarpour, S.M.

Abstract

Homogeneous Charge Compression Ignition (HCCI) engines produce power by using of auto ignition mechanism. In comparison to other conventional engines these types of engines have better efficiency and less pollution. Since, the temperature of combustion chamber walls is one of the important parameters for auto ignition and combustion characters; this work firstly, simulated multi-dimensional combustion in HCCI engines with Iso-butane as fuel by using of detailed kinetic chemical mechanism. After validating of results by existent experimental data, optimization of three parameters namely temperature of walls of piston, liner and head by means of Latin Hypercube Sampling method (LHS) and Nelder-Mead optimization algorithm was performed to reach to the maximum Indicated mean effective pressure (Imep). Finally, with comparison of effective parameters of optimized engine to those of original engine, it was found that by keeping the other operational parameters of engine such as fuel consumption at a fixed value, quantity of Imep has increased by 8.2%.

Suggested Citation

  • Mansoury, M. & Jafarmadar, S. & Talei, M. & Lashkarpour, S.M., 2017. "Optimization of HCCI (Homogeneous Charge Compression Ignition) engine combustion chamber walls temperature to achieve optimum IMEP using LHS and Nelder Mead algorithm," Energy, Elsevier, vol. 119(C), pages 938-949.
  • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:938-949
    DOI: 10.1016/j.energy.2016.11.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216316516
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2016.11.047?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. Henry Kaiser & Kern Dickman, 1962. "Sample and population score matrices and sample correlation matrices from an arbitrary population correlation matrix," Psychometrika, Springer;The Psychometric Society, vol. 27(2), pages 179-182, June.
    2. Park, Jungsoo & Lee, Kyo Seung & Kim, Min Su & Jung, Dohoy, 2014. "Numerical analysis of a dual-fueled CI (compression ignition) engine using Latin hypercube sampling and multi-objective Pareto optimization," Energy, Elsevier, vol. 70(C), pages 278-287.
    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. Taghavifar, Hadi & Nemati, Arash & Walther, Jens Honore, 2019. "Combustion and exergy analysis of multi-component diesel-DME-methanol blends in HCCI engine," Energy, Elsevier, vol. 187(C).
    2. Pachiannan, Tamilselvan & Zhong, Wenjun & Rajkumar, Sundararajan & He, Zhixia & Leng, Xianying & Wang, Qian, 2019. "A literature review of fuel effects on performance and emission characteristics of low-temperature combustion strategies," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Gharehghani, Ayat & Abbasi, Hamid Reza & Alizadeh, Pouria, 2021. "Application of machine learning tools for constrained multi-objective optimization of an HCCI engine," Energy, Elsevier, vol. 233(C).

    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. Lawrence Raffalovich & Glenn Deane & David Armstrong & Hui-Shien Tsao, 2008. "Model selection procedures in social research: Monte-Carlo simulation results," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1093-1114.
    2. Cho, Jungkeun & Park, Sangjun & Song, Soonho, 2019. "The effects of the air-fuel ratio on a stationary diesel engine under dual-fuel conditions and multi-objective optimization," Energy, Elsevier, vol. 187(C).
    3. Gary van Vuuren & Riaan de Jongh, 2017. "A comparison of risk aggregation estimates using copulas and Fleishman distributions," Applied Economics, Taylor & Francis Journals, vol. 49(17), pages 1715-1731, April.
    4. Jolan Wauters & Andy Keane & Joris Degroote, 2020. "Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization," Journal of Global Optimization, Springer, vol. 78(1), pages 137-160, September.
    5. Zhang, Shumei & Qiang, Jiaxi & Yang, Lin & Zhao, Xiaowei, 2016. "Prior-knowledge-independent equalization to improve battery uniformity with energy efficiency and time efficiency for lithium-ion battery," Energy, Elsevier, vol. 94(C), pages 1-12.
    6. Neil Timm, 1970. "The estimation of variance-covariance and correlation matrices from incomplete data," Psychometrika, Springer;The Psychometric Society, vol. 35(4), pages 417-437, December.
    7. Jolan Wauters & Andy Keane & Joris Degroote, 0. "Development of an adaptive infill criterion for constrained multi-objective asynchronous surrogate-based optimization," Journal of Global Optimization, Springer, vol. 0, pages 1-24.
    8. Bum Youl Park & Ki-Hyung Lee & Jungsoo Park, 2020. "Conceptual Approach on Feasible Hydrogen Contents for Retrofit of CNG to HCNG under Heavy-Duty Spark Ignition Engine at Low-to-Middle Speed Ranges," Energies, MDPI, Open Access Journal, vol. 13(15), pages 1-16, July.
    9. Lu, Hanan & Li, Qiushi & Pan, Tianyu, 2016. "Optimization of cantilevered stators in an industrial multistage compressor to improve efficiency," Energy, Elsevier, vol. 106(C), pages 590-601.
    10. Al-Subaihi, Ali A., 2004. "Simulating Correlated Multivariate Pseudorandom Numbers," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 9(i04).
    11. James Algina & Stephen F. Olejnik, 1982. "Multiple Group Time-Series Design," Evaluation Review, , vol. 6(2), pages 203-232, April.
    12. Park, Sangjun & Cho, Jungkeun & Park, Jungsoo & Song, Soonho, 2017. "Numerical study of the performance and NOx emission of a diesel-methanol dual-fuel engine using multi-objective Pareto optimization," Energy, Elsevier, vol. 124(C), pages 272-283.
    13. Heecheong Yoo & Bum Youl Park & Honghyun Cho & Jungsoo Park, 2019. "Performance Optimization of a Diesel Engine with a Two-Stage Turbocharging System and Dual-Loop EGR Using Multi-Objective Pareto Optimization Based on Diesel Cycle Simulation," Energies, MDPI, Open Access Journal, vol. 12(22), pages 1-26, November.
    14. Lan, Qi & Bai, Yun & Fan, Liyun & Gu, Yuanqi & Wen, Liming & Yang, Li, 2020. "Investigation on fuel injection quantity of low-speed diesel engine fuel system based on response surface prediction model," Energy, Elsevier, vol. 211(C).
    15. Todd Headrick & Shlomo Sawilowsky, 1999. "Simulating correlated multivariate nonnormal distributions: Extending the fleishman power method," Psychometrika, Springer;The Psychometric Society, vol. 64(1), pages 25-35, March.
    16. Beasley, T. Mark & Zumbo, Bruno D., 2003. "Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs," Computational Statistics & Data Analysis, Elsevier, vol. 42(4), pages 569-593, April.
    17. C. Vale & Vincent Maurelli, 1983. "Simulating multivariate nonnormal distributions," Psychometrika, Springer;The Psychometric Society, vol. 48(3), pages 465-471, September.
    18. Massimiliano Pastore & Luigi Lombardi, 2014. "The impact of faking on Cronbach’s alpha for dichotomous and ordered rating scores," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(3), pages 1191-1211, 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:119:y:2017:i:c:p:938-949. See general information about how to correct material in RePEc.

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

    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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.