49
Views
0
CrossRef citations to date
0
Altmetric
Articles

The construction of a neural network proxy model for ship hull design based on multi-fidelity datasets and the parameter freezing strategy

, ORCID Icon, &
Pages 270-280 | Received 24 Jul 2022, Accepted 10 Mar 2024, Published online: 18 Mar 2024

References

  • Alfonsi G. 2009. Reynolds-averaged Navier–Stokes equations for turbulence modeling. Appl Mech Rev. 62(4):040802. doi:10.1115/1.3124648.
  • Anderson JD, Wendt J. 1995. Computational fluid dynamics. Vol. 206. Springer.
  • Ao Y., Duan H., Li S. 2024. An integrated-hull design assisted by artificial intelligence-aided design method. Computers & Structures. 297:107320. doi:10.1016/j.compstruc.2024.107320.
  • Ao Y, Li Y, Gong J, Li S. 2021. An artificial intelligence-aided design (AIAD) of ship hull structures. J Ocean Eng Sci. 8(1):15–32.
  • Ao Y, Li Y, Gong J, Li S. 2022. Artificial intelligence design for ship structures: a variant multiple-input neural network based ship resistance prediction. J Mech Des. 144(9):091707.
  • Bakhtiari M, Ghassemi H. 2020. CFD data based neural network functions for predicting hydrodynamic performance of a low-pitch marine cycloidal propeller. Appl Ocean Res. 94:101981. doi: 10.1016/j.apor.2019.101981.
  • Buhmann MD. 2000. Radial basis functions. Acta Numer. 9:1–38. doi: 10.1017/S0962492900000015.
  • Chorin AJ. 1997. A numerical method for solving incompressible viscous flow problems. J Comput Phys. 135:118–125. doi: 10.1006/jcph.1997.5716.
  • Claveria O, Monte E, Torra S. 2015. Research Institute of Applied Economics. 2015:1–28.
  • D'Agostino D, Serani A, Stern F, Diez M. 2021. Recurrent-type neural networks for real-time short-term prediction of ship motions in high sea state. arXiv preprint arXiv:2105.13102.
  • Dawson C. 1977. A practical computer method for solving ship-wave problems. In: Proceedings of second international conference on numerical ship hydrodynamics. p. 30–38.
  • Dick S. 2019. Artificial Intelligence. Harvard Data Science Review. 1(1). https://doi.org/10.1162/99608f92.92fe150c.
  • Eymard R, Gallouët T, Herbin R. 2000. Finite volume methods. Handb Numer Anal. 7:713–1018.
  • Forti D, Rozza G. 2014. Efficient geometrical parametrisation techniques of interfaces for reduced-order modelling: application to fluid–structure interaction coupling problems. Int J Comut Fluid Dyn. 28:158–169. doi: 10.1080/10618562.2014.932352.
  • He K, Zhang X, Ren S, Sun J. 2015. Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision. p. 1026–1034.
  • Hess JL, Smith A. 1967. Calculation of potential flow about arbitrary bodies. Prog Aerosp Sci. 8:1–138. doi: 10.1016/0376-0421(67)90003-6.
  • Hirt CW, Nichols BD. 1981. Volume of fluid (VOF) method for the dynamics of free boundaries. J Comput Phys. 39:201–225. doi: 10.1016/0021-9991(81)90145-5.
  • Huang CJ, Kuo PH. 2019. Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access. 7:74822–74834. doi: 10.1109/Access.6287639.
  • Kazemi H, Doustdar MM, Najafi A, Nowruzi H, Ameri MJ. 2021. Hydrodynamic performance prediction of stepped planing craft using CFD and ANNs. J Mar Sci Appl. 20:67–84. doi: 10.1007/s11804-020-00182-y.
  • Li Y, Gong J, Ma Q, Yan S. 2018. Effects of the terms associated with ϕzz in free surface condition on the attitudes and resistance of different ships. Eng Anal Bound Elem. 95:266–285. doi: 10.1016/j.enganabound.2018.08.006.
  • Mittendorf M, Nielsen UD, Bingham HB. 2022. Data-driven prediction of added-wave resistance on ships in oblique waves comparison between tree-based ensemble methods and artificial neural networks. Appl Ocean Res. 118:102964. doi: 10.1016/j.apor.2021.102964.
  • Nagelkerke NJ. 1991. A note on a general definition of the coefficient of determination. biometrika. 78:691–692. doi: 10.1093/biomet/78.3.691.
  • Panda JP, Warrior H. 2022. Data-driven prediction of complex flow field over an axisymmetric body of revolution using machine learning. J Offsh Mech Arct Eng. 144:060903. doi: 10.1115/1.4055280.
  • Peng H, Ni S, Qiu W. 2014. Wave pattern and resistance prediction for ships of full form. Ocean Eng. 87:162–173. doi: 10.1016/j.oceaneng.2014.06.004.
  • Prpić-Oršić J, Valčić M, Čarija Z. 2020. A hybrid wind load estimation method for container ship based on computational fluid dynamics and neural networks. J Mar Sci Eng. 8:539. doi: 10.3390/jmse8070539.
  • Siemens Digital Industries Software. 2021. Simcenter STAR-CCM+ User Guide, version 2021.1. 
  • Silva KM, Maki KJ. 2022. Data-driven system identification of 6-DoF ship motion in waves with neural networks. Appl Ocean Res. 125:103222. doi: 10.1016/j.apor.2022.103222.
  • Sun Z, Sun Ly, Xu Lx, Hu Yl, Zhang Gy, Zong Z. 2023. A CFD-based data-driven reduced order modeling method for damaged ship motion in waves. J Mar Sci Eng. 11:686. doi: 10.3390/jmse11040686.
  • Willmott CJ, Matsuura K. 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res. 30:79–82. doi: 10.3354/cr030079.
  • Zhang RZ. 2010. Verification and validation for RANS simulation of KCS container ship without/with propeller. J Hydrodyn. 22:889–896. doi: 10.1016/S1001-6058(10)60055-8.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.