Skip to main content
SpringerLink
Log in

Interactive design and variation of hull shapes: pros and cons of different CAD approaches

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

Modern paradigms to design complex engineering systems focus on interactive approaches able to simultaneously handle results from multidisciplinary applications. Keeping this in mind and considering that from a ship design perspective the definition of a hull shape is the first step of the whole design process, a reliable computer aided design model for shape representation and morphing becomes a key feature to navigate trough the design spiral. The proposed research focuses on the comparison of two different approaches to handle hull shape variations, namely the fully parametric model and a non-parametric method relying on the free form deformation technique. The comparison is developed considering both the easiness of building the shape of the reference model with its transformations laws and the resulting number of free design variables. The two methods are analyzed in the light of attainable hull shape variation in two design and modification test cases. The first is limited to the bulbous bow of a conventional hull while the second focuses on the whole surface of a fast, displacement, round-bilge hull.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Lamb, T., et al.: Ship Design and Construction, vol. 4. Society of Naval Architects and Marine Engineers, Jersey City (2004)

    Google Scholar 

  2. Larsson, L., Stern, F., Visonneau, M.: CFD in ship hydrodynamics—results of the Gothenburg 2010 workshop. In: MARINE 2011, IV International Conference on Computational Methods in Marine Engineering, pp. 237–259. Springer, Berlin (2013)

    Google Scholar 

  3. Liu, S., Papanikolaou, A.: On the prediction of the added resistance of large ships in representative seaways. Ships Offshore Struct. 12(5), 690–696 (2017)

    Google Scholar 

  4. Villa, D., Viviani, M., Gaggero, S., Vantorre, M., Eloot, K., Delefortrie, G.: CFD-based analyses for a slow speed manoeuvrability model. J. Mar. Sci. Technol. Jpn. 1–13 (2018)

  5. Rajendran, S., Soares, C.G.: Effect of slamming and green water on short-term distribution of vertical bending moment of a containership. In: Proceedings of the Fourth International Conference in Ocean Engineering (ICOE2018), Vol. 1, p. 333. Springer, Berlin (2019)

  6. Shigunov, V., Rathje, H., El Moctar, O.: Research towards goal based standards for container shipping. In: Contemporary Ideas on Ship Stability, pp. 679–687. Springer, Cham (2019)

    Google Scholar 

  7. Araki, M., Sadat-Hosseini, H., Sanada, Y., Umeda, N., Stern, F.: Improved maneuvering-based mathematical model for free-running ship motions in following waves using high-fidelity CFD results and system-identification technique. In: Contemporary Ideas on Ship Stability, pp. 91–115. Springer, Cham (2019)

    Google Scholar 

  8. Gaggero, S., Dubbioso, G., Villa, D., Muscari, R., Viviani, M.: Propeller modeling approaches for off-design operative conditions. Ocean Eng. 178, 283–305 (2019)

    Google Scholar 

  9. Vegh, J.M., Alonso, J.J., da Silva, I., Orra, C.R., Tarik, H.: Surrogate modeling to enable higher fidelity optimization. In: 18th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, pp. 36–61 (2017)

  10. Bonfiglio, L., Perdikaris, P., Vernengo, G., de Medeiros, J.S., Karniadakis, G.: Improving swath seakeeping performance using multi-fidelity Gaussian process and Bayesian optimization. J. Ship Res. 62(4), 223–240 (2018)

    Google Scholar 

  11. Pellegrini, R., Serani, A., Diez, M., Wackers, J., Queutey, P., Visonneau, M.: Adaptive sampling criteria for multi-fidelity metamodels in CFD-based shape optimization. In: ECCM-ECFD, Glasgow, Scotland (2018)

  12. Gaggero, S., Vernengo, G., Villa, D., Bonfiglio, L.: A reduced order approach for optimal design of efficient marine propellers. Ships Offshore Struct. 1–15 (2019)

  13. Gaggero, S., Coppede, A., Villa, D., Vernengo, G., Bonfiglio, L.: A data-driven probabilistic learning approach for the prediction of controllable pitch propellers performance. In: Proceedings of the 8th International Conference on Computational Methods in Marine Engineering, MARINE, pp. 1–12 (2019)

  14. Coraddu, A., Oneto, L., Ghio, A., Savio, S., Anguita, D., Figari, M.: Machine learning approaches for improving condition-based maintenance of naval propulsion plants. Proc. Inst. Mech. Eng. M J. Eng. Marit. Environ. 230(1), 136–153 (2016)

    Google Scholar 

  15. Diez-Olivan, A., Pagan, J.A., Sanz, R., Sierra, B.: Deep evolutionary modeling of condition monitoring data in marine propulsion systems. Soft Comput. 23(20), 9937–9953 (2019)

    Google Scholar 

  16. Cipollini, F., Oneto, L., Coraddu, A., Murphy, A.J., Anguita, D.: Condition-based maintenance of naval propulsion systems: data analysis with minimal feedback. Reliab. Eng. Syst. Saf. 177, 12–23 (2018)

    Google Scholar 

  17. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019)

    MathSciNet  MATH  Google Scholar 

  18. Papanikolaou, A.: Holistic ship design optimization. Comput. Aided Des. 42(11), 1028–1044 (2010)

    Google Scholar 

  19. Boulougouris, E., Papanikolaou, A.: Risk-based design of naval combatants. Ocean Eng. 65, 49–61 (2013)

    Google Scholar 

  20. Vernengo, G., Rizzuto, E.: Ship synthesis model for the preliminary design of a fleet of compressed natural gas carriers. Ocean Eng. 89, 189–199 (2014)

    Google Scholar 

  21. Vernengo, G., Bonfiglio, L., Gaggero, S., Brizzolara, S.: Physics-based design by optimization of unconventional supercavitating hydrofoils. J. Ship Res. 60(4), 187–202 (2016)

    Google Scholar 

  22. Vernengo, G., Gaggero, T., Rizzuto, E.: Simulation based design of a fleet of ships under power and capacity variations. Appl. Ocean Res. 61, 1–15 (2016)

    Google Scholar 

  23. Royset, J.O., Bonfiglio, L., Vernengo, G., Brizzolara, S.: Risk-adaptive set-based design and applications to shaping a hydrofoil. J. Mech. Des. 139(10), 101403 (2017)

    Google Scholar 

  24. Diez, M., Volpi, S., Serani, A., Stern, F., Campana, E.F.: Simulation-based design optimization by sequential multi-criterion adaptive sampling and dynamic radial basis. Adv. Evol. Determ. Methods Des. Optim. Control Eng. Sci. 48, 213 (2018)

    Google Scholar 

  25. Ingrassia, T., Mancuso, A., Nigrelli, V., Tumino, D.: A multi-technique simultaneous approach for the design of a sailing yacht. Int. J. Interact. Des. Manuf. 11(1), 19–30 (2017)

    Google Scholar 

  26. Gaggero, T., Vernengo, G., Parodi, M., Rizzuto, E.: Logistics-based fleet design for complex transportation scenarios. Ships Offshore Struct. 13(7), 734–749 (2018)

    Google Scholar 

  27. Nikolopoulos, L., Boulougouris, E.: A Methodology for the Holistic, Simulation Driven Ship Design Optimization Under Uncertainty Methodology for the holistic, simulation driven ship design optimization under uncertainty. In: 13th International Marine Design Conference (2018)

  28. Ruggiero, V., Morace, F.: Methodology to study the comfort implementation for a new generation of hydrofoils. Int. J. Interact. Des. Manuf 13(1), 99–110 (2019)

    Google Scholar 

  29. Wei, X., Chang, H., Feng, B., Liu, Z.: Sensitivity analysis based on polynomial chaos expansions and its application in ship uncertainty-based design optimization. Math. Probl. Eng. 2019, 1–19 (2019)

    Google Scholar 

  30. Vernengo, G., Bonfiglio, L.: A computational framework to design optimally loaded supercavitating hydrofoils by differential evolution algorithm and a new viscous lifting line method. Adv. Eng. Softw. 133, 28–38 (2019)

    Google Scholar 

  31. Harries, S.: Parametric Design and Hydrodynamic Optimization of Ship Hull Forms. Mensch-und-Buch-Verlag, Berlin (1998)

    Google Scholar 

  32. Harries, S., Abt, C.: Parametric curve design applying fairness criteria. In: International Workshop on Creating Fair and Shape-Preserving Curves and Surfaces (1998)

  33. Nowacki, H., Liu, D., Lü, X.: Fairing bézier curves with constraints. Comput. Aided Geom. Des. 7(1–4), 43–55 (1990)

    MATH  Google Scholar 

  34. Biliotti, I., Brizzolara, S., Viviani, M., Vernengo, G., Ruscelli, D., Galliussi, M., Guadalupi, D., Manfredini, A.: Automatic parametric hull form optimization of fast naval vessels. In: Proceedings of the Eleventh International Conference on Fast Sea Transportation (FAST 2011) (2011)

  35. Han, S., Lee, Y.-S., Choi, Y.B.: Hydrodynamic hull form optimization using parametric models. J. Mar. Sci. Technol. 17(1), 1–17 (2012)

    Google Scholar 

  36. Brenner, M., Zagkas, V., Harries, S., Stein, T.: Optimization using viscous flow computations for retrofitting ships in operation. In: Proceedings of the 5th International Conference on Computational Methods in Marine Engineering, MARINE (2013)

  37. Brizzolara, S., Vernengo, G., Pasquinucci, C.A., Harries, S.: Significance of parametric hull form definition on hydrodynamic performance optimization. In: 6th International Conference on Computation Methods in Marine Engineering (Marine 2015) (2015)

  38. Pellegrini, R., Serani, A., Harries, S., Diez, M.: Multi-objective hull-form optimization of a SWATH configuration via design-space dimensionality reduction, multifidelity metamodels, and swarm intelligence. In: Proc. of 7th International Conference on Computational Methods in Marine Engineering (MARINE 2017), Nantes, France (2017)

  39. Vernengo, G., Brizzolara, S.: Numerical investigation on the hydrodynamic performance of fast swaths with optimum canted struts arrangements. Appl. Ocean Res. 63, 76–89 (2017)

    Google Scholar 

  40. Koelman, H.J., Horváth, I., Aalbers, A.: Hybrid representation of the shape of ship hulls. Int. Shipbuild. Prog. 48(3), 247–269 (2001)

    Google Scholar 

  41. Peri, D., Campana, E.F.: Multidisciplinary design optimization of a naval surface combatant. J. Ship Res. 47(1), 1–12 (2003)

    Google Scholar 

  42. Pérez-Arribas, F., Suárez-Suárez, J.A., Fernández-Jambrina, L.: Automatic surface modelling of a ship hull. Comput. Aided Des. 38(6), 584–594 (2006)

    Google Scholar 

  43. Kim, H., Yang, C.: A new surface modification approach for CFD-based hull form optimization. J. Hydrodyn. Ser. B 22(5), 520–525 (2010)

    MathSciNet  Google Scholar 

  44. Perez, F., Clemente, J.A.: Constrained design of simple ship hulls with b-spline surfaces. Comput. Aided Des. 43(12), 1829–1840 (2011)

    Google Scholar 

  45. Ceruti, A., Liverani, A., Caligiana, G.: Fairing with neighbourhood lod filtering to upgrade interactively b-spline into class-a curve. Int. J. Interact. Des. Manuf. 8(2), 67–75 (2014)

    Google Scholar 

  46. Choi, H.J.: Hull-form optimization of a container ship based on bell-shaped modification function. Int. J. Nav. Architect. Ocean Eng. 7(3), 478–489 (2015)

    Google Scholar 

  47. Coppedé, A., Vernengo, G., Villa, D.: A combined approach based on subdivision surface and free form deformation for smart ship hull form design and variation. Ships Offshore Struct. 13(7), 769–778 (2018)

    Google Scholar 

  48. Fischer, X., Nadeau, J.-P.: Interactive design: then and now. In: Research in Interactive Design, Vol. 3, pp. 1–5. Springer, Paris (2011)

    Google Scholar 

  49. Sobek II, D.K., Ward, A.C., Liker, J.K.: Toyota’s principles of set-based concurrent engineering. MIT Sloan Manag. Rev. 40(2), 67 (1999)

    Google Scholar 

  50. Nahm, Y.-E., Ishikawa, H.: Representing and aggregating engineering quantities with preference structure for set-based concurrent engineering. Concurr. Eng. 13(2), 123–133 (2005)

    Google Scholar 

  51. Singer, D.J., Doerry, N., Buckley, M.E.: What is set-based design? Nav. Eng. J. 121(4), 31–43 (2009)

    Google Scholar 

  52. Sederberg, T.W., Parry, S.R.: Free-form deformation of solid geometric models. ACM SIGGRAPH Comput. Graph. 20(4), 151–160 (1986)

    Google Scholar 

  53. Brenner, M., Harries, S., Kröger, J., Rung, T.: Parametric-adjoint approach for the efficient optimization of flow-exposed geometries. In: 6th International Conference on Computational Methods in Marine Engineering (2015)

  54. Lackenby, H.: On the systematic geometrical variation of ship forms. Trans. INA 92, 289–315 (1950)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electric, Electronic, Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa, Via Montallegro 1, 16145, Genoa, Italy

    Giuliano Vernengo, Diego Villa, Stefano Gaggero & Michele Viviani

Authors
  1. Giuliano Vernengo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Diego Villa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefano Gaggero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michele Viviani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Giuliano Vernengo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vernengo, G., Villa, D., Gaggero, S. et al. Interactive design and variation of hull shapes: pros and cons of different CAD approaches. Int J Interact Des Manuf 14, 103–114 (2020). https://doi.org/10.1007/s12008-019-00613-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-019-00613-3

Keywords

Search

Navigation