Research Article
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Identification of Vibration for Balancing in Fehn Pollux Ship with ECO Flettner Rotor

Year 2024, Volume: 8 Issue: 1, 1 - 10, 31.03.2024

Chetan Parmar Elmar Wings Farzaneh Nourmohammadi

https://doi.org/10.30521/jes.1255518

Abstract

Flettner rotors are wind propulsion systems using the Magnus effect to generate thrust, thereby reduce fuel consumption and carbon emissions in the ships. However, rotor unbalance can cause excessive vibrations and energy loss, affecting the performance and stability of the system. There is a need to have a system onboard, which can predict the vibrations. The paper proposes a deep learning approach to predict the vibrations and unbalanced forces of a Flettner rotor based on the data of ECO Flettner rotor onboard the vessel MV Fehn pollux. The paper develops two methods to estimate the direction and magnitude of the unbalanced forces using the reading values of the strain gauges. The work also compares two recurrent neural network models, namely Long-short term memory and Gated Recurrent Unit, for vibration prediction and evaluates their performance using Mean Absolute Error and Root Mean Squared Error metrics. The results show that Long-short term memory model outperforms Gated Recurrent Unit model in prediction accuracy and can be implemented on the system onboard to monitor and prevent rotor unbalance. The paper also suggests some possible solutions for automatic self-balancing of the rotor and identifies some areas for future work.

Keywords

Anomaly detection Deep neural networks Flettner rotor Gated recurrent unit Moving median Long-short term memory Rotor balancing

References

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  • [2] Seifert, J. A review of the Magnus effect in aeronautics. Progress in Aerospace Sciences 2012; 55: 17-45. DOI: 10.1016/j.paerosci.2012.07.001
  • [3] Morisseau, KC. Marine application of magnus effect devices. Naval Engineers Journal. 1986; 98(5): 83-84. DOI: 10.1111/j.1559-3584.1986.tb01741.x
  • [4] Nuttall, P, Kaitu’u, J. The Magnus Effect and the Flettner Rotor: Potential Application for Future Oceanic Shipping. The Journal of Pacific Studies 2016; 36(2):161-182.
  • [5] De Marco, A, Mancini, S, Pensa, C, Calise, G, De Luca, F. Flettner Rotor Concept for Marine Applications: A Systematic Study. International Journal of Rotating Machinery. 2016; 2016: 1-12. DOI: 10.1155/2016/3458750
  • [6] Bishop, RED, Gladwell, GML. The Vibration and Balancing of an Unbalanced Flexible Rotor. Journal of Mechanical Engineering Science 1959; 1(1): 66-77. DOI: 10.1243/jmes_jour_1959_001_010_02
  • [7] Cho S, Jeon K, Kim C-W. Vibration analysis of electric motors considering rotating rotor structure using flexible multibody dynamics-electromagnetic-structural vibration coupled analysis. Journal of Computational Design and Engineering. 2023 Apr;10(2):578-588. DOI:10.1093/jcde/qwad012.
  • [8] Kwon CS, Yeon SM, Kim YC, Kim YG, Kim YH, Kang HJ. A parametric study for a flettner rotor in standalone condition using CFD. Int J Nav Archit Ocean Eng. 2022;14:100493. doi:10.1016/j.ijnaoe.2022.100493.
  • [9] Zhang Y, Li H, Wang Z, Zhang J, Wang Y. Study on the calculation methods of vibration characteristics of the rotor with initial bending. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA). Takamatsu, Japan: IEEE; 2017. p. 1120-1125. DOI: 10.1109/ICMA.2017.8015913
  • [10] Wang Z, Wang W, Jiang Z, Yu D. New adaptive method for extrating fundamental vibration component in the process of rotor startup and shutdown. In: 2020 IEEE International Conference on Power Electronics Smart Grid (PELSG). Nanjing, China: IEEE; 2020. p. 1-6. DOI:10.1109/ICECE51594.2020.9353030
  • [11] Müller, M, Götting, M, Peetz, T, Vahs, M, Wings, E. An Intelligent Assistance System for Controlling Wind-Assisted Ship Propulsion Systems. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN); 22-25 July 2019: IEEE, pp. 19315748. DOI : 10.1109/INDIN41052.2019.8972271
  • [12] Bordogna, G, Muggiasca, S, Giappino, S, Belloli, M, Keuning, JA, Huijsmans, RHM. The effects of the aerodynamic interaction on the performance of two Flettner rotors. Journal of Wind Engineering and Industrial Aerodynamics 2020; 196:104024. DOI: 10.1016/j.jweia.2019.104024
  • [13] Lu, R, Ringsberg, JW. Ship energy performance study of three wind-assisted ship propulsion technologies including a parametric study of the Flettner rotor technology. Ships and Offshore Structures 2019; 15(3):249-258. DOI: 10.1080/17445302.2019.1612544
  • [14] Gunter EJ., Jackson C. Balancing of Rigid and Flexible Rotors. Handbook of Rotor Dynamics, New York, USA, McGraw-Hill, 1992.
  • [15] Li, L, Cao, S, Li, J, Nie, R, Hou, L. Review of rotor balancing methods. Machines 202; 9(5): 89. DOI: 10.3390/machines9050089
  • [16] Allwright G. Commercial Wind Propulsion Solutions: Putting the Sail Back into Sailing. Trends and Challenges in Maritime Energy Management. 2018:433-43. DOI: 10.1007/978-3-319-74576-3_30
  • [17] Nuttall P, Vahs M, Morshead J, Newell A. The case for field trialing and technology/knowledge transfer of emerging low carbon maritime technologies to Pacific Island countries. Nova Science Publishers.
  • [18] Fayyad, U, Piatetsky-Shapiro, G, Smyth, P. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 1996; 39(11): 27-34. DOI: 10.1145/240455.240464
  • [19] Choi, K, Yi, J, Park, C, Yoon, S. Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 2021; 9: 120043-65. DOI: 10.1109/access.2021.3107975
  • [20] Nourmohammadi, F, Jumabayev, A, Wings, E. Anomaly Detection in the Time Series Data from Fehn Pollux Ship with ECO Flettner Rotor. In 19th International Conference on Industrial Informatics (INDIN); 21-23 July 2021: IEEE, pp. 1-6. DOI: 10.1109/INDIN45523.2021.9557422
  • [21] Teng M. Anomaly detection on time series. In: 2010 IEEE International Conference on Progress in Informatics and Computing; 10-12 December 2010: IEEE, pp. 603-608. DOI: 10.1109/PIC.2010.5687485
  • [22] Siami-Namini S, Tavakoli N, Namin AS. The performance of LSTM and BiLSTM in forecasting time series. In: IEEE International Conference on Big Data (Big Data); 09-12 December 2019: IEEE, pp. 3285-3292. DOI: 10.1109/bigdata47090.2019.9005997
  • [23] Wen S., Wang Y., Tang Y, Xu Y, P. Li and Zhao T, "Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System," in IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 5266-5275, Sept. 2019, DOI: 10.1109/TII.2019.2910416.
  • [24] Fu, R, Zhang, Z, Li, L. Using LSTM and GRU neural network methods for traffic flow prediction. 2016 YAC 31st Youth Academic Annual Conference of Chinese Association of Automation; 1-13 November 2016: IEEE, pp. 16579568, DOI: 10.1109/yac.2016.7804912
  • [25] Dey, R, Salem, FM. Gate-variants of Gated Recurrent Unit (GRU) neural networks. 2017 MWSCAS IEEE 60th International Midwest Symposium on Circuits and Systems; 06-09 August 2017: IEEE, pp. 17214663, DOI: 10.1109/mwscas.2017.8053243
  • [26] Almalaq, A, Edwards, G. A review of deep learning methods applied on load forecasting. In: 2017 ICMLA 16th IEEE international conference on machine learning and applications; 18-21 December 2017: IEEE pp. 511-516. DOI: 10.1109/ICMLA.2017.0-110

Year 2024, Volume: 8 Issue: 1, 1 - 10, 31.03.2024

Chetan Parmar Elmar Wings Farzaneh Nourmohammadi

https://doi.org/10.30521/jes.1255518

Abstract

References

  • [1] Talluri, L, Nalianda, DK, Giuliani, E. Techno economic and environmental assessment of Flettner rotors for marine propulsion. Ocean Engineering 2018; 154: 1-15. DOI: 10.1016/j.oceaneng.2018.02.020
  • [2] Seifert, J. A review of the Magnus effect in aeronautics. Progress in Aerospace Sciences 2012; 55: 17-45. DOI: 10.1016/j.paerosci.2012.07.001
  • [3] Morisseau, KC. Marine application of magnus effect devices. Naval Engineers Journal. 1986; 98(5): 83-84. DOI: 10.1111/j.1559-3584.1986.tb01741.x
  • [4] Nuttall, P, Kaitu’u, J. The Magnus Effect and the Flettner Rotor: Potential Application for Future Oceanic Shipping. The Journal of Pacific Studies 2016; 36(2):161-182.
  • [5] De Marco, A, Mancini, S, Pensa, C, Calise, G, De Luca, F. Flettner Rotor Concept for Marine Applications: A Systematic Study. International Journal of Rotating Machinery. 2016; 2016: 1-12. DOI: 10.1155/2016/3458750
  • [6] Bishop, RED, Gladwell, GML. The Vibration and Balancing of an Unbalanced Flexible Rotor. Journal of Mechanical Engineering Science 1959; 1(1): 66-77. DOI: 10.1243/jmes_jour_1959_001_010_02
  • [7] Cho S, Jeon K, Kim C-W. Vibration analysis of electric motors considering rotating rotor structure using flexible multibody dynamics-electromagnetic-structural vibration coupled analysis. Journal of Computational Design and Engineering. 2023 Apr;10(2):578-588. DOI:10.1093/jcde/qwad012.
  • [8] Kwon CS, Yeon SM, Kim YC, Kim YG, Kim YH, Kang HJ. A parametric study for a flettner rotor in standalone condition using CFD. Int J Nav Archit Ocean Eng. 2022;14:100493. doi:10.1016/j.ijnaoe.2022.100493.
  • [9] Zhang Y, Li H, Wang Z, Zhang J, Wang Y. Study on the calculation methods of vibration characteristics of the rotor with initial bending. In: 2017 IEEE International Conference on Mechatronics and Automation (ICMA). Takamatsu, Japan: IEEE; 2017. p. 1120-1125. DOI: 10.1109/ICMA.2017.8015913
  • [10] Wang Z, Wang W, Jiang Z, Yu D. New adaptive method for extrating fundamental vibration component in the process of rotor startup and shutdown. In: 2020 IEEE International Conference on Power Electronics Smart Grid (PELSG). Nanjing, China: IEEE; 2020. p. 1-6. DOI:10.1109/ICECE51594.2020.9353030
  • [11] Müller, M, Götting, M, Peetz, T, Vahs, M, Wings, E. An Intelligent Assistance System for Controlling Wind-Assisted Ship Propulsion Systems. In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN); 22-25 July 2019: IEEE, pp. 19315748. DOI : 10.1109/INDIN41052.2019.8972271
  • [12] Bordogna, G, Muggiasca, S, Giappino, S, Belloli, M, Keuning, JA, Huijsmans, RHM. The effects of the aerodynamic interaction on the performance of two Flettner rotors. Journal of Wind Engineering and Industrial Aerodynamics 2020; 196:104024. DOI: 10.1016/j.jweia.2019.104024
  • [13] Lu, R, Ringsberg, JW. Ship energy performance study of three wind-assisted ship propulsion technologies including a parametric study of the Flettner rotor technology. Ships and Offshore Structures 2019; 15(3):249-258. DOI: 10.1080/17445302.2019.1612544
  • [14] Gunter EJ., Jackson C. Balancing of Rigid and Flexible Rotors. Handbook of Rotor Dynamics, New York, USA, McGraw-Hill, 1992.
  • [15] Li, L, Cao, S, Li, J, Nie, R, Hou, L. Review of rotor balancing methods. Machines 202; 9(5): 89. DOI: 10.3390/machines9050089
  • [16] Allwright G. Commercial Wind Propulsion Solutions: Putting the Sail Back into Sailing. Trends and Challenges in Maritime Energy Management. 2018:433-43. DOI: 10.1007/978-3-319-74576-3_30
  • [17] Nuttall P, Vahs M, Morshead J, Newell A. The case for field trialing and technology/knowledge transfer of emerging low carbon maritime technologies to Pacific Island countries. Nova Science Publishers.
  • [18] Fayyad, U, Piatetsky-Shapiro, G, Smyth, P. The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM 1996; 39(11): 27-34. DOI: 10.1145/240455.240464
  • [19] Choi, K, Yi, J, Park, C, Yoon, S. Deep learning for anomaly detection in time-series data: review, analysis, and guidelines. IEEE Access 2021; 9: 120043-65. DOI: 10.1109/access.2021.3107975
  • [20] Nourmohammadi, F, Jumabayev, A, Wings, E. Anomaly Detection in the Time Series Data from Fehn Pollux Ship with ECO Flettner Rotor. In 19th International Conference on Industrial Informatics (INDIN); 21-23 July 2021: IEEE, pp. 1-6. DOI: 10.1109/INDIN45523.2021.9557422
  • [21] Teng M. Anomaly detection on time series. In: 2010 IEEE International Conference on Progress in Informatics and Computing; 10-12 December 2010: IEEE, pp. 603-608. DOI: 10.1109/PIC.2010.5687485
  • [22] Siami-Namini S, Tavakoli N, Namin AS. The performance of LSTM and BiLSTM in forecasting time series. In: IEEE International Conference on Big Data (Big Data); 09-12 December 2019: IEEE, pp. 3285-3292. DOI: 10.1109/bigdata47090.2019.9005997
  • [23] Wen S., Wang Y., Tang Y, Xu Y, P. Li and Zhao T, "Real-Time Identification of Power Fluctuations Based on LSTM Recurrent Neural Network: A Case Study on Singapore Power System," in IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 5266-5275, Sept. 2019, DOI: 10.1109/TII.2019.2910416.
  • [24] Fu, R, Zhang, Z, Li, L. Using LSTM and GRU neural network methods for traffic flow prediction. 2016 YAC 31st Youth Academic Annual Conference of Chinese Association of Automation; 1-13 November 2016: IEEE, pp. 16579568, DOI: 10.1109/yac.2016.7804912
  • [25] Dey, R, Salem, FM. Gate-variants of Gated Recurrent Unit (GRU) neural networks. 2017 MWSCAS IEEE 60th International Midwest Symposium on Circuits and Systems; 06-09 August 2017: IEEE, pp. 17214663, DOI: 10.1109/mwscas.2017.8053243
  • [26] Almalaq, A, Edwards, G. A review of deep learning methods applied on load forecasting. In: 2017 ICMLA 16th IEEE international conference on machine learning and applications; 18-21 December 2017: IEEE pp. 511-516. DOI: 10.1109/ICMLA.2017.0-110
There are 26 citations in total.

Details

Primary Language English
Subjects Energy
Journal Section Research Articles
Authors

Chetan Parmar This is me 0009-0001-7973-5223

Elmar Wings 0000-0001-9532-5163

Farzaneh Nourmohammadi This is me 0009-0003-5121-835X

Early Pub Date January 13, 2024
Publication Date March 31, 2024
Acceptance Date December 1, 2023
Published in Issue Year 2024 Volume: 8 Issue: 1

Cite

Vancouver Parmar C, Wings E, Nourmohammadi F. Identification of Vibration for Balancing in Fehn Pollux Ship with ECO Flettner Rotor. JES. 2024;8(1):1-10.
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