Research Article
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A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions

Year 2023, Volume: 12 Issue: 3, 370 - 379, 28.09.2023
https://doi.org/10.33714/masteb.1338476

Abstract

A mathematical model is created to obtain safe navigation for ships in regular head waves in this study. To validate the suggested model, firstly, the added resistances are calculated for two different ships using empirical formulas in the mathematical model. Secondly, the turning test simulations are performed for calm water and in waves with various wave amplitudes. After these validation studies, the path following simulation of the ship to the target destinations is performed in both waves and calm water for the determined course. It is assumed that regular head waves affect the ship as an external disturbance. The wavelengths and wave amplitudes are changed systematically to understand their effect during the path following simulations. When the ratio of wavelength to ship length, λ/Lpp, is nearly 1.0, the path following simulation times increase. Moreover, when the value of wave amplitude increases, so does the simulation time.

References

  • Abdel-latif, S., Abdel-geliel, M. & Zakzouk E. E. (2013). Simulation of ship maneuvering behavior based on the modular mathematical model. International Conference on Aerospace Sciences and Aviation Technology, 15(Aerospace Sciences & Aviation Technology), Egypt, pp. 1-14. https://doi.org/10.21608/asat.2013.22111
  • Akbar, A., Aasen, A., Msakni, M. K., Fagerholt, K., Lindstad, E. & Meisel, F. (2021). An economic analysis of introducing autonomous ships in a short‐sea liner shipping network. International Transactions in Operational Research, 28(4), 1740-1764. https://doi.org/10.1111/itor.12788
  • Aksu, E., & Köse, E. (2017). Evaluation of mathematical models for tankers’ maneuvering motions. Journal of ETA Maritime Science, 5(1), 95-109. https://dx.doi.org/10.5505/jems.2017.52523
  • Beji, S. (2020). Formulation of wave and current forces acting on a body and resistance of ships. Ocean Engineering, 218, 108121. https://doi.org/10.1016/j.oceaneng.2020.108121
  • Budak, G. (2023). The effect of controller selection in collision avoidance system on fuel consumption for unmanned surface vessel: a case study. Ships and Offshore Structures, 18(2), 315-323. https://doi.org/10.1080/17445302.2022.2157131
  • Çakıcı, F., Kahramanoglu, E., & Alkan, A. D. (2017). Numerical prediction of vertical ship motions and added resistance. Transactions of the Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, 159(Part A4), 393-402. https://doi.org/10.3940/rina.ijme.2017.a4.450
  • Chen, Y. Y., Ellis-Tiew, M. Z., Chen, W. C., & Wang, C. Z. (2021). Fuzzy risk evaluation and collision avoidance control of unmanned surface vessels. Applied Sciences, 11(14), 6338. https://doi.org/10.3390/app11146338
  • Degre, T., & Lefevre, X. (1981). A collision avoidance system. Journal of Navigation, 34(2), 294-302. https://doi.org/10.1017/S0373463300021408
  • Fang, M. -C., Luo, J. -H., & Lee, M. -L. (2005). A nonlinear mathematical model for ship turning circle simulation in waves. Journal of Ship Research, 49(2), 69-79. https://doi.org/10.5957/jsr.2005.49.2.69
  • Fossen, T. (2002). Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics.
  • He, Y., Li, Z., Mou, J., Hu, W., Li, L., & Wang, B. (2021). Collision-avoidance path planning for multi-ship encounters considering ship manoeuvrability and COLREGs. Transportation Safety and Environment, 3(2), 1-11. https://doi.org/10.1093/tse/tdab004
  • Hirdaris, S., Bai, W., Dessi, D., Ergin, A., Gu, X., Hermundstad, O., Huijsmans, R., Iijima, K., Nielsen, U., Parunov, J., Fonseca, N., Papanikolaou, A., Argyriadis, K., & Incecik, A. (2014). Loads for use in the design of ships and offshore structures. Ocean Engineering, 78, 131–174. https://doi.org/10.1016/j.oceaneng.2013.09.012
  • Jin, Y., Yiew, L., Zheng, Y., Magee, A., Duffy, J., & Chai, S. (2021). Dynamic manoeuvres of KCS with CFD free-running computation and system-based modelling. Ocean Engineering, 241, 110043. https://doi.org/10.1016/j.oceaneng.2021.110043
  • Kafalı, M., & Aksu, E. (2022). A multi-objective optimization model for determining the performance of a sailboat. Journal of ETA Maritime Science, 10(3), 177-184. https://doi.org/10.4274/jems.2022.24392
  • Kobayashi, H., Blok, J., Barr, R., Kim, Y. S., & Nowicki, J. (2002). Specialist committee on Esso Osaka: Final report and recommendations to the 23rd ITTC. 23rd International Towing Tank Conference, pp. 8-14.
  • Kurt, I., & Aymelek, M. (2022). Operational and economic advantages of autonomous ships and their perceived impacts on port operations. Maritime Economics & Logistics 24(2), 302-326. https://doi.org/10.1057/s41278-022-00213-1
  • Lang, X., & Mao, W. (2020). A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements. Ocean Engineering, 209, 107494. https://doi.org/10.1016/j.oceaneng.2020.107494
  • Lee, J. H., Seo, M. G., Park, D. M., Yang, K. K., Kim, K. H., & Kim, Y. (2013). Study on the effects of hull form on added resistance. Proceedings of the 12th International Symposium on Practical Design of Ships and Other Floating Structures. Korea, pp. 329-337.
  • Li, G., & Zhang, X. (2022). Research on the influence of wind, waves, and tidal current on ship turning ability based on Norrbin Model. Ocean Engineering, 259, 111875. https://doi.org/10.1016/j.oceaneng.2022.111875
  • Liu, L., Zhang, L., Zhang, S., & Cao, S. (2020) Multi-UUV cooperative dynamic maneuver decision-making algorithm using intuitionistic fuzzy game theory. Complexity, 2020, 2815258. https://doi.org/10.1155/2020/2815258
  • Liu, S., & Papanikolaou, A. (2016). Fast approach to the estimation of the added resistance of ships in head waves. Ocean Engineering, 112, 211-225. https://doi.org/10.1016/j.oceaneng.2015.12.022
  • Liu, S., & Papanikolaou, A. (2017). Approximation of the added resistance of ships with small draft or in ballast condition by empirical formula. Proceedings of the IMechE Part M: Journal of Engineering for the Maritime Environment, 223(1), 27-40. https://doi.org/10.1177/1475090217710099
  • Lyu, H. G., & Yin, Y. (2017). Ship’s trajectory planning for collision avoidance at sea based on modified artificial potential field. 2nd International Conference on Robotics and Automation Engineering, (Icrae) Shanghai, pp. 351-357.
  • Mohamed-Seghir, M., Kula, K., & Kouzou, A. (2021). Artificial intelligence-based methods for decision support to avoid collisions at sea. Electronics, 19(10), 2360. https://doi.org/10.3390/electronics10192360
  • Moreira, L., Fossen, T. I., & Soares, C. G. (2007) Path following control system for a tanker ship model. Ocean Engineering, 34, (14,15), 2074-2085. https://doi.org/10.1016/j.oceaneng.2007.02.005
  • Ogawa, A., & Kasai, H. (1978). On the mathematical model of maneuvering motion of ships. International Shipbuild Progress, 25, 306–319.
  • Ozdemir, Y., & Barlas, B. (2017). Numerical study of ship motions and added resistance in regular incident waves of KVLCC2 model. International Journal of Naval Architecture and Ocean Engineering, 9, 149-159. https://doi.org/10.1016/j.ijnaoe.2016.09.001
  • Park, D. -M. Kim, Y., Seo, M. -G., & Lee, J. (2016). Study on added resistance of a tanker in head waves at different drafts. Ocean Engineering, 111, 569-581. https://doi.org/10.1016/j.oceaneng.2015.11.026
  • Perera, L. P., Carvalho, J. P., & Soares, C. G. (2010). Bayesian network based sequential collision avoidance action execution for an ocean navigational system. IFAC Proceedings, 43(20), 266-271.
  • Praczyk, T. (2015). Neural anti-collision system for autonomous surface vehicle. Neurocomputing, 149, 559–572. https://doi.org/10.1016/j.neucom.2014.08.018
  • Sadat-Hosseini, H., Wu, P., Carrica, P., Kim, H., Toda, Y., & Stern, F. (2013). CFD verification and validation of added resistance and motions of KVLCC2 with fixed and free surge in short and long head waves. Ocean Engineering, 59, 240–273. https://doi.org/10.1016/j.oceaneng.2012.12.016
  • Suo, Y., Chen, W., Claramunt, C., & Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20, 5133. https://doi.org/10.3390/s20185133
  • Szelangiewicz, T., Wiśniewski, B., & Żelazny, K. (2014). The influence of wind, wave and loading condition on total resistance and speed of the vessel. Polish Maritime Research, 21(3), 61-67. https://doi.org/10.2478/pomr-2014-0031
  • Tsou, M. C., & Hsueh, C. K. (2010). The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology, 18(5), 746-756. https://doi.org/10.51400/2709-6998.1929
  • Tsujimoto, M., Shibata, K., & Kuroda, M. (2008). A practical correction method for added resistance in waves. Journal of the Japan Society of Naval Architects and Ocean Engineers, 8, 177-184. https://doi.org/10.2534/jjasnaoe.8.177
  • Wang, C., & Fu, Y. (2020). Ship trajectory prediction based on attention in bidirectional recurrent neural networks. 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China, pp. 529–533.
  • Xie, Z., Falzarano, J. M., & Wang, H. (2020). A framework of numerically evaluating a maneuvering vessel in waves. Journal of Marine Science and Engineering, 8(6), 392. https://doi.org/10.3390/jmse8060392
  • Yasukawa, H., & Sakuno, R. (2019). Application of the mmg method for the prediction of steady sailing condition and course stability of a ship under external disturbances. Journal of Marine Science and Technology, 25, 196–220. https://doi.org/10.1007/s00773-019-00641-4
  • Yasukawa, H., & Yoshimura, Y. (2015). Introduction of mmg standard method for ship maneuvering predictions. Journal of Marine Science and Technology, 20, 37–52. https://doi.org/10.1007/s00773-014-0293-y
  • Zaccone, R., & Martelli, M. (2020). A collision avoidance algorithm for ship guidance applications. Journal of Marine Engineering & Technology, 19, 62-75. https://doi.org/10.1080/20464177.2019.1685836
  • Zhao, W., Wang, Y., Zhang, Z., & Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357. https://doi.org/10.3390/jmse9040357
  • Zhou, Z., Zhang, Y., & Wang, S. A. (2021). Coordination system between decision making and controlling for autonomous collision avoidance of large intelligent ships. Journal of Marine Science and Engineering, 9(11), 1202. https://doi.org/10.3390/jmse9111202
Year 2023, Volume: 12 Issue: 3, 370 - 379, 28.09.2023
https://doi.org/10.33714/masteb.1338476

Abstract

References

  • Abdel-latif, S., Abdel-geliel, M. & Zakzouk E. E. (2013). Simulation of ship maneuvering behavior based on the modular mathematical model. International Conference on Aerospace Sciences and Aviation Technology, 15(Aerospace Sciences & Aviation Technology), Egypt, pp. 1-14. https://doi.org/10.21608/asat.2013.22111
  • Akbar, A., Aasen, A., Msakni, M. K., Fagerholt, K., Lindstad, E. & Meisel, F. (2021). An economic analysis of introducing autonomous ships in a short‐sea liner shipping network. International Transactions in Operational Research, 28(4), 1740-1764. https://doi.org/10.1111/itor.12788
  • Aksu, E., & Köse, E. (2017). Evaluation of mathematical models for tankers’ maneuvering motions. Journal of ETA Maritime Science, 5(1), 95-109. https://dx.doi.org/10.5505/jems.2017.52523
  • Beji, S. (2020). Formulation of wave and current forces acting on a body and resistance of ships. Ocean Engineering, 218, 108121. https://doi.org/10.1016/j.oceaneng.2020.108121
  • Budak, G. (2023). The effect of controller selection in collision avoidance system on fuel consumption for unmanned surface vessel: a case study. Ships and Offshore Structures, 18(2), 315-323. https://doi.org/10.1080/17445302.2022.2157131
  • Çakıcı, F., Kahramanoglu, E., & Alkan, A. D. (2017). Numerical prediction of vertical ship motions and added resistance. Transactions of the Royal Institution of Naval Architects Part A: International Journal of Maritime Engineering, 159(Part A4), 393-402. https://doi.org/10.3940/rina.ijme.2017.a4.450
  • Chen, Y. Y., Ellis-Tiew, M. Z., Chen, W. C., & Wang, C. Z. (2021). Fuzzy risk evaluation and collision avoidance control of unmanned surface vessels. Applied Sciences, 11(14), 6338. https://doi.org/10.3390/app11146338
  • Degre, T., & Lefevre, X. (1981). A collision avoidance system. Journal of Navigation, 34(2), 294-302. https://doi.org/10.1017/S0373463300021408
  • Fang, M. -C., Luo, J. -H., & Lee, M. -L. (2005). A nonlinear mathematical model for ship turning circle simulation in waves. Journal of Ship Research, 49(2), 69-79. https://doi.org/10.5957/jsr.2005.49.2.69
  • Fossen, T. (2002). Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics.
  • He, Y., Li, Z., Mou, J., Hu, W., Li, L., & Wang, B. (2021). Collision-avoidance path planning for multi-ship encounters considering ship manoeuvrability and COLREGs. Transportation Safety and Environment, 3(2), 1-11. https://doi.org/10.1093/tse/tdab004
  • Hirdaris, S., Bai, W., Dessi, D., Ergin, A., Gu, X., Hermundstad, O., Huijsmans, R., Iijima, K., Nielsen, U., Parunov, J., Fonseca, N., Papanikolaou, A., Argyriadis, K., & Incecik, A. (2014). Loads for use in the design of ships and offshore structures. Ocean Engineering, 78, 131–174. https://doi.org/10.1016/j.oceaneng.2013.09.012
  • Jin, Y., Yiew, L., Zheng, Y., Magee, A., Duffy, J., & Chai, S. (2021). Dynamic manoeuvres of KCS with CFD free-running computation and system-based modelling. Ocean Engineering, 241, 110043. https://doi.org/10.1016/j.oceaneng.2021.110043
  • Kafalı, M., & Aksu, E. (2022). A multi-objective optimization model for determining the performance of a sailboat. Journal of ETA Maritime Science, 10(3), 177-184. https://doi.org/10.4274/jems.2022.24392
  • Kobayashi, H., Blok, J., Barr, R., Kim, Y. S., & Nowicki, J. (2002). Specialist committee on Esso Osaka: Final report and recommendations to the 23rd ITTC. 23rd International Towing Tank Conference, pp. 8-14.
  • Kurt, I., & Aymelek, M. (2022). Operational and economic advantages of autonomous ships and their perceived impacts on port operations. Maritime Economics & Logistics 24(2), 302-326. https://doi.org/10.1057/s41278-022-00213-1
  • Lang, X., & Mao, W. (2020). A semi-empirical model for ship speed loss prediction at head sea and its validation by full-scale measurements. Ocean Engineering, 209, 107494. https://doi.org/10.1016/j.oceaneng.2020.107494
  • Lee, J. H., Seo, M. G., Park, D. M., Yang, K. K., Kim, K. H., & Kim, Y. (2013). Study on the effects of hull form on added resistance. Proceedings of the 12th International Symposium on Practical Design of Ships and Other Floating Structures. Korea, pp. 329-337.
  • Li, G., & Zhang, X. (2022). Research on the influence of wind, waves, and tidal current on ship turning ability based on Norrbin Model. Ocean Engineering, 259, 111875. https://doi.org/10.1016/j.oceaneng.2022.111875
  • Liu, L., Zhang, L., Zhang, S., & Cao, S. (2020) Multi-UUV cooperative dynamic maneuver decision-making algorithm using intuitionistic fuzzy game theory. Complexity, 2020, 2815258. https://doi.org/10.1155/2020/2815258
  • Liu, S., & Papanikolaou, A. (2016). Fast approach to the estimation of the added resistance of ships in head waves. Ocean Engineering, 112, 211-225. https://doi.org/10.1016/j.oceaneng.2015.12.022
  • Liu, S., & Papanikolaou, A. (2017). Approximation of the added resistance of ships with small draft or in ballast condition by empirical formula. Proceedings of the IMechE Part M: Journal of Engineering for the Maritime Environment, 223(1), 27-40. https://doi.org/10.1177/1475090217710099
  • Lyu, H. G., & Yin, Y. (2017). Ship’s trajectory planning for collision avoidance at sea based on modified artificial potential field. 2nd International Conference on Robotics and Automation Engineering, (Icrae) Shanghai, pp. 351-357.
  • Mohamed-Seghir, M., Kula, K., & Kouzou, A. (2021). Artificial intelligence-based methods for decision support to avoid collisions at sea. Electronics, 19(10), 2360. https://doi.org/10.3390/electronics10192360
  • Moreira, L., Fossen, T. I., & Soares, C. G. (2007) Path following control system for a tanker ship model. Ocean Engineering, 34, (14,15), 2074-2085. https://doi.org/10.1016/j.oceaneng.2007.02.005
  • Ogawa, A., & Kasai, H. (1978). On the mathematical model of maneuvering motion of ships. International Shipbuild Progress, 25, 306–319.
  • Ozdemir, Y., & Barlas, B. (2017). Numerical study of ship motions and added resistance in regular incident waves of KVLCC2 model. International Journal of Naval Architecture and Ocean Engineering, 9, 149-159. https://doi.org/10.1016/j.ijnaoe.2016.09.001
  • Park, D. -M. Kim, Y., Seo, M. -G., & Lee, J. (2016). Study on added resistance of a tanker in head waves at different drafts. Ocean Engineering, 111, 569-581. https://doi.org/10.1016/j.oceaneng.2015.11.026
  • Perera, L. P., Carvalho, J. P., & Soares, C. G. (2010). Bayesian network based sequential collision avoidance action execution for an ocean navigational system. IFAC Proceedings, 43(20), 266-271.
  • Praczyk, T. (2015). Neural anti-collision system for autonomous surface vehicle. Neurocomputing, 149, 559–572. https://doi.org/10.1016/j.neucom.2014.08.018
  • Sadat-Hosseini, H., Wu, P., Carrica, P., Kim, H., Toda, Y., & Stern, F. (2013). CFD verification and validation of added resistance and motions of KVLCC2 with fixed and free surge in short and long head waves. Ocean Engineering, 59, 240–273. https://doi.org/10.1016/j.oceaneng.2012.12.016
  • Suo, Y., Chen, W., Claramunt, C., & Yang, S. (2020). A ship trajectory prediction framework based on a recurrent neural network. Sensors, 20, 5133. https://doi.org/10.3390/s20185133
  • Szelangiewicz, T., Wiśniewski, B., & Żelazny, K. (2014). The influence of wind, wave and loading condition on total resistance and speed of the vessel. Polish Maritime Research, 21(3), 61-67. https://doi.org/10.2478/pomr-2014-0031
  • Tsou, M. C., & Hsueh, C. K. (2010). The study of ship collision avoidance route planning by ant colony algorithm. Journal of Marine Science and Technology, 18(5), 746-756. https://doi.org/10.51400/2709-6998.1929
  • Tsujimoto, M., Shibata, K., & Kuroda, M. (2008). A practical correction method for added resistance in waves. Journal of the Japan Society of Naval Architects and Ocean Engineers, 8, 177-184. https://doi.org/10.2534/jjasnaoe.8.177
  • Wang, C., & Fu, Y. (2020). Ship trajectory prediction based on attention in bidirectional recurrent neural networks. 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT), Shenyang, China, pp. 529–533.
  • Xie, Z., Falzarano, J. M., & Wang, H. (2020). A framework of numerically evaluating a maneuvering vessel in waves. Journal of Marine Science and Engineering, 8(6), 392. https://doi.org/10.3390/jmse8060392
  • Yasukawa, H., & Sakuno, R. (2019). Application of the mmg method for the prediction of steady sailing condition and course stability of a ship under external disturbances. Journal of Marine Science and Technology, 25, 196–220. https://doi.org/10.1007/s00773-019-00641-4
  • Yasukawa, H., & Yoshimura, Y. (2015). Introduction of mmg standard method for ship maneuvering predictions. Journal of Marine Science and Technology, 20, 37–52. https://doi.org/10.1007/s00773-014-0293-y
  • Zaccone, R., & Martelli, M. (2020). A collision avoidance algorithm for ship guidance applications. Journal of Marine Engineering & Technology, 19, 62-75. https://doi.org/10.1080/20464177.2019.1685836
  • Zhao, W., Wang, Y., Zhang, Z., & Wang, H. (2021). Multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm. Journal of Marine Science and Engineering, 9(4), 357. https://doi.org/10.3390/jmse9040357
  • Zhou, Z., Zhang, Y., & Wang, S. A. (2021). Coordination system between decision making and controlling for autonomous collision avoidance of large intelligent ships. Journal of Marine Science and Engineering, 9(11), 1202. https://doi.org/10.3390/jmse9111202
There are 42 citations in total.

Details

Primary Language English
Subjects Ship Manoeuvring and Control
Journal Section Research Article
Authors

Gökhan Budak 0000-0002-4043-9304

Publication Date September 28, 2023
Submission Date August 6, 2023
Acceptance Date August 27, 2023
Published in Issue Year 2023 Volume: 12 Issue: 3

Cite

APA Budak, G. (2023). A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions. Marine Science and Technology Bulletin, 12(3), 370-379. https://doi.org/10.33714/masteb.1338476
AMA Budak G. A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions. Mar. Sci. Tech. Bull. September 2023;12(3):370-379. doi:10.33714/masteb.1338476
Chicago Budak, Gökhan. “A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions”. Marine Science and Technology Bulletin 12, no. 3 (September 2023): 370-79. https://doi.org/10.33714/masteb.1338476.
EndNote Budak G (September 1, 2023) A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions. Marine Science and Technology Bulletin 12 3 370–379.
IEEE G. Budak, “A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions”, Mar. Sci. Tech. Bull., vol. 12, no. 3, pp. 370–379, 2023, doi: 10.33714/masteb.1338476.
ISNAD Budak, Gökhan. “A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions”. Marine Science and Technology Bulletin 12/3 (September 2023), 370-379. https://doi.org/10.33714/masteb.1338476.
JAMA Budak G. A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions. Mar. Sci. Tech. Bull. 2023;12:370–379.
MLA Budak, Gökhan. “A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions”. Marine Science and Technology Bulletin, vol. 12, no. 3, 2023, pp. 370-9, doi:10.33714/masteb.1338476.
Vancouver Budak G. A Study on Safe Navigation Towards Intelligent Shipping Considering Sea Conditions. Mar. Sci. Tech. Bull. 2023;12(3):370-9.

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