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Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters

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  • Cheng, Xianda
  • Zheng, Haoran
  • Dong, Wei
  • Yang, Xuesen

Abstract

The performance of marine intercooled cycle gas turbines (ICGTs) is affected by atmospheric and sea conditions. Gas turbine operators have to rely on complicated and unfriendly simulation models to predict the performance of ICGTs under different ambient conditions. Aiming at this problem, this paper introduces a novelty fast prediction method based on similarity theory, which can help gas turbine operators realize performance parameters prediction of ICGTs on the spot. For this purpose, the similarity theory is firstly extended to ICGTs. The similarity parameters corresponding to seawater flow rate, glycol solution flow rate, and seawater temperature are derived using Buckingham's Pi Theorem. On this basis, the performance prediction formula of ICGTs is developed. The second-order and dissimilar effects of ICGTs are fully considered in this formula to improve the prediction accuracy. The values of the unknown coefficients in the formula can be obtained by fitting from a small amount of test data. Finally, the high-fidelity ICGT simulation model and the actual ambient conditions verify the proposed method. The results show that the proposed method has good practicability and accuracy, which provides a new approach to predicting marine ICGT performance.

Suggested Citation

  • Cheng, Xianda & Zheng, Haoran & Dong, Wei & Yang, Xuesen, 2023. "Performance prediction of marine intercooled cycle gas turbine based on expanded similarity parameters," Energy, Elsevier, vol. 265(C).
  • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032881
    DOI: 10.1016/j.energy.2022.126402
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    1. He, Feifei & Zhou, Jianzhong & Feng, Zhong-kai & Liu, Guangbiao & Yang, Yuqi, 2019. "A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm," Applied Energy, Elsevier, vol. 237(C), pages 103-116.
    2. Lee, Jae Hong & Kim, Tong Seop & Kim, Eui-hwan, 2017. "Prediction of power generation capacity of a gas turbine combined cycle cogeneration plant," Energy, Elsevier, vol. 124(C), pages 187-197.
    3. Picón-Núñez, Martín & Rumbo-Arias, Jamel E., 2021. "Improving thermal energy recovery systems using welded plate heat exchangers," Energy, Elsevier, vol. 235(C).
    4. Polverino, Pierpaolo & Bove, Giovanni & Sorrentino, Marco & Pianese, Cesare & Beretta, Davide, 2019. "Advancements on scaling-up simulation of Proton Exchange Membrane Fuel Cells impedance through Buckingham Pi theorem," Applied Energy, Elsevier, vol. 249(C), pages 245-252.
    5. Lee, Jong Jun & Kang, Do Won & Kim, Tong Seop, 2011. "Development of a gas turbine performance analysis program and its application," Energy, Elsevier, vol. 36(8), pages 5274-5285.
    6. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    7. Abou Elmaaty, Talal M. & Kabeel, A.E. & Mahgoub, M., 2017. "Corrugated plate heat exchanger review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 852-860.
    8. Gu, Chun-wei & Wang, Hao & Ji, Xing-xing & Li, Xue-song, 2016. "Development and application of a thermodynamic-cycle performance analysis method of a three-shaft gas turbine," Energy, Elsevier, vol. 112(C), pages 307-321.
    9. González-Díaz, Abigail & Alcaráz-Calderón, Agustín M. & González-Díaz, Maria Ortencia & Méndez-Aranda, Ángel & Lucquiaud, Mathieu & González-Santaló, Jose Miguel, 2017. "Effect of the ambient conditions on gas turbine combined cycle power plants with post-combustion CO2 capture," Energy, Elsevier, vol. 134(C), pages 221-233.
    10. Sanaye, Sepehr & Hajabdollahi, Hassan, 2010. "Thermal-economic multi-objective optimization of plate fin heat exchanger using genetic algorithm," Applied Energy, Elsevier, vol. 87(6), pages 1893-1902, June.
    11. Kumari, Anupam & Sanjay,, 2015. "Investigation of parameters affecting exergy and emission performance of basic and intercooled gas turbine cycles," Energy, Elsevier, vol. 90(P1), pages 525-536.
    12. Song, Yin & Gu, Chun-wei & Ji, Xing-xing, 2015. "Development and validation of a full-range performance analysis model for a three-spool gas turbine with turbine cooling," Energy, Elsevier, vol. 89(C), pages 545-557.
    13. Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "A new transient performance adaptation method for an aero gas turbine engine," Energy, Elsevier, vol. 193(C).
    Full references (including those not matched with items on IDEAS)

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