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Computational investigation of a large containership propulsion engine operation at slow steaming conditions

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  • Guan, Cong
  • Theotokatos, Gerasimos
  • Zhou, Peilin
  • Chen, Hui

Abstract

In this article, the operation of a large containership main engine was investigated with emphasis at slow steaming conditions. A cycle mean value approach implemented in the MATLAB/Simulink environment was adopted to simulate the two-stroke marine diesel engine due to the fact that it combines simplicity with adequate prediction accuracy. For accurately representing the compressor performance when the engine operates at low loads, the extension of the compressor map at the low rotational speed region was carried out based on a non-dimensional parameters method incorporating a novel way of calculating the compressor isentropic efficiency. The compressor map extension method results were validated using a corrected similarity laws approach. The engine steady state operation for various loads was simulated and the predicted engine performance parameters were validated using shop trial measurements. Furthermore, the engine transient operation in the load region below 50% was studied and the simulation results including the compressor operating points trajectory are presented and discussed. Based on the obtained results, the influence of the activation/deactivation of the installed electric driven blowers and the turbocharger cut-out on the engine operation was analysed.

Suggested Citation

  • Guan, Cong & Theotokatos, Gerasimos & Zhou, Peilin & Chen, Hui, 2014. "Computational investigation of a large containership propulsion engine operation at slow steaming conditions," Applied Energy, Elsevier, vol. 130(C), pages 370-383.
  • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:370-383
    DOI: 10.1016/j.apenergy.2014.05.063
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    1. Cortés, O. & Urquiza, G. & Hernández, J.A., 2009. "Optimization of operating conditions for compressor performance by means of neural network inverse," Applied Energy, Elsevier, vol. 86(11), pages 2487-2493, November.
    2. Molina, S. & Guardiola, C. & Martín, J. & García-Sarmiento, D., 2014. "Development of a control-oriented model to optimise fuel consumption and NOX emissions in a DI Diesel engine," Applied Energy, Elsevier, vol. 119(C), pages 405-416.
    3. Theo E Notteboom, 2006. "The Time Factor in Liner Shipping Services," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 8(1), pages 19-39, March.
    4. Yu, Youhong & Chen, Lingen & Sun, Fengrui & Wu, Chih, 2007. "Neural-network based analysis and prediction of a compressor's characteristic performance map," Applied Energy, Elsevier, vol. 84(1), pages 48-55, January.
    5. Ghorbanian, K. & Gholamrezaei, M., 2009. "An artificial neural network approach to compressor performance prediction," Applied Energy, Elsevier, vol. 86(7-8), pages 1210-1221, July.
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    6. Cong Guan & Gerasimos Theotokatos & Hui Chen, 2015. "Analysis of Two Stroke Marine Diesel Engine Operation Including Turbocharger Cut-Out by Using a Zero-Dimensional Model," Energies, MDPI, vol. 8(6), pages 1-27, June.
    7. 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).
    8. Geertsma, R.D. & Visser, K. & Negenborn, R.R., 2018. "Adaptive pitch control for ships with diesel mechanical and hybrid propulsion," Applied Energy, Elsevier, vol. 228(C), pages 2490-2509.
    9. Geertsma, R.D. & Negenborn, R.R. & Visser, K. & Loonstijn, M.A. & Hopman, J.J., 2017. "Pitch control for ships with diesel mechanical and hybrid propulsion: Modelling, validation and performance quantification," Applied Energy, Elsevier, vol. 206(C), pages 1609-1631.
    10. Sakellaridis, Nikolaos F. & Raptotasios, Spyridon I. & Antonopoulos, Antonis K. & Mavropoulos, Georgios C. & Hountalas, Dimitrios T., 2015. "Development and validation of a new turbocharger simulation methodology for marine two stroke diesel engine modelling and diagnostic applications," Energy, Elsevier, vol. 91(C), pages 952-966.
    11. Wang, Yuhua & Wang, Guiyong & Yao, Guozhong & Shen, Qianqiao & Yu, Xuan & He, Shuchao, 2023. "Combining GA-SVM and NSGA-Ⅲ multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine," Energy, Elsevier, vol. 278(PA).
    12. Ling-Chin, Janie & Roskilly, Anthony P., 2016. "Investigating the implications of a new-build hybrid power system for Roll-on/Roll-off cargo ships from a sustainability perspective – A life cycle assessment case study," Applied Energy, Elsevier, vol. 181(C), pages 416-434.
    13. Baldi, Francesco & Theotokatos, Gerasimos & Andersson, Karin, 2015. "Development of a combined mean value–zero dimensional model and application for a large marine four-stroke Diesel engine simulation," Applied Energy, Elsevier, vol. 154(C), pages 402-415.
    14. Olympia Nisiforou & Louisa Marie Shakou & Afroditi Magou & Alexandros G. Charalambides, 2022. "A Roadmap towards the Decarbonization of Shipping: A Participatory Approach in Cyprus," Sustainability, MDPI, vol. 14(4), pages 1-27, February.
    15. Larsen, Ulrik & Wronski, Jorrit & Andreasen, Jesper Graa & Baldi, Francesco & Pierobon, Leonardo, 2017. "Expansion of organic Rankine cycle working fluid in a cylinder of a low-speed two-stroke ship engine," Energy, Elsevier, vol. 119(C), pages 1212-1220.
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