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Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions

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Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences

Part of the book series: Computational Methods in Applied Sciences ((COMPUTMETHODS,volume 48))

Abstract

The paper presents a global method for simulation-based design optimization (SBDO) which combines a dynamic radial basis function (DRBF) surrogate model with a sequential multi-criterion adaptive sampling (MCAS) technique. Starting from an initial training set, groups of new samples are sequentially selected aiming at both the improvement of the surrogate model global accuracy and the reduction of the objective function. The objective prediction and the associated uncertainty provided by the DRBF model are used by a multi-objective particle swarm optimization algorithm to identify Pareto-optimal solutions. These are used by the MCAS technique, which selects new samples by down-sampling the Pareto front, allowing for a parallel infill of an arbitrary number of points at each iteration. The method is applied to a set of 28 unconstrained global optimization test problems and a six-variable SBDO of the DTMB 5415 hull-form in calm water, based on potential flow simulations. Results show the effectiveness of the method in reducing the computational cost of the SBDO, providing the background for further developments and application to more complex ship hydrodynamic problems.

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References

  1. Bassanini, P., Bulgarelli, U., Campana, E., Lalli, F.: The wave resistance problem in a boundary integral formulation. Surv. Math. Ind. 4, 151–194 (1994)

    MathSciNet  MATH  Google Scholar 

  2. Booker, A.J., Dennis Jr., J., Frank, P.D., Serafini, D.B., Torczon, V., Trosset, M.W.: A rigorous framework for optimization of expensive functions by surrogates. Struct. Optim. 17(1), 1–13 (1999). https://doi.org/10.1007/BF01197708

    Article  Google Scholar 

  3. Campana, E.F., Diez, M., Iemma, U., Liuzzi, G., Lucidi, S., Rinaldi, F., Serani, A.: Derivative-free global ship design optimization using global/local hybridization of the DIRECT algorithm. Optim. Eng. 17(1), 127–156 (2015)

    Article  MathSciNet  Google Scholar 

  4. Campana, E.F., Liuzzi, G., Lucidi, S., Peri, D., Piccialli, A., Pinto, A.: New global optimization methods for ship design problems. Optim. Eng. 10(4), 533–555 (2009). https://doi.org/10.1007/s11081-009-9085-3

    Article  MATH  Google Scholar 

  5. Chen, X., Diez, M., Kandasamy, M., Zhang, Z., Campana, E.F., Stern, F.: High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm. Eng. Optim. 47(8), 473–494 (2015)

    Article  Google Scholar 

  6. Clerc, M.: Stagnation analysis in particle swarm optimization or what happens when nothing happens (2006). http://clerc.maurice.free.fr/pso

  7. Cox, D.D., John, S.: SDO: a statistical method for global optimization. In: Multidisciplinary Design Optimization: State of the Art, pp. 315–329 (1997)

    Google Scholar 

  8. Dawson, C.W.: A practical computer method for solving ship-wave problems. In: Proceedings of the 2nd International Conference on Numerical Ship Hydrodynamics, pp. 30–38. Berkeley (1977)

    Google Scholar 

  9. Diez, M., Campana, E.F., Stern, F.: Design-space dimensionality reduction in shape optimization by Karhunen-Loève expansion. Comput. Methods Appl. Mech. Eng. 283, 1525–1544 (2015)

    Article  Google Scholar 

  10. Diez, M., Chen, X., Campana, E.F., Stern, F.: Reliability-based robust design optimization for ships in real ocean environment. In: Proceedings of 12th International Conference on Fast Sea Transportation, FAST2013, Amsterdam, The Netherlands (2013)

    Google Scholar 

  11. Diez, M., Peri, D.: Robust optimization for ship conceptual design. Ocean Eng. 37(11), 966–977 (2010)

    Article  Google Scholar 

  12. Diez, M., Serani, A., Campana, E.F., Goren, O., Sarioz, K., Danisman, D.B., Grigoropoulos, G., Aloniati, E., Visonneau, M., Queutey, P., Stern, F.: Multi-objective hydrodynamic optimization of the DTMB 5415 for resistance and seakeeping. In: Proceedings of the 13th International Conference on Fast Sea Transportation, FAST 2015. Washington, D.C., USA (2015)

    Google Scholar 

  13. Grigoropoulos, G., Campana, E., Diez, M., Serani, A., Goren, O., Sarioz, K., Danisman, D., Visonneau, M., Queutey, P., Abdel-Maksoud, M., et al.: Mission-based hull form and propeller optimization of a transom stern destroyer for best performance in the sea environment. In: Michel Visonneau, P.Q., Touzé, D.L. (eds.) VII International Conference on Computer Methods in Marine Engineering, pp. 83–94 (2017)

    Google Scholar 

  14. Huang, D., Allen, T., Notz, W., Zeng, N.: Global optimization of stochastic black-box systems via sequential Kriging meta-models. J. Global Optim. 34(3), 441–466. https://doi.org/10.1007/s10898-005-2454-3, http://dx.doi.org/10.1007/s10898-005-2454-3

    Article  MathSciNet  Google Scholar 

  15. Jin, R., Du, X., Chen, W.: The use of metamodeling techniques for optimization under uncertainty. Struct. Multidiscip. Optim. 25(2), 99–116 (2003)

    Article  Google Scholar 

  16. Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)

    Article  MathSciNet  Google Scholar 

  17. Kandasamy, M., Wu, P.C., Zalek, S., Karr, D., Bartlett, S., Nguyen, L., Stern, F.: CFD based hydrodynamic optimization and structural analysis of the hybrid ship hull. SNAME (2014)

    Google Scholar 

  18. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE Conference on Neural Networks, IV, Piscataway, NJ, pp. 1942–1948 (1995)

    Google Scholar 

  19. Kushner, H.J.: A new method of locating the maximum point of an arbitrary multipeak curve in the presence of noise. J. Fluids Eng. 86(1), 97–106 (1964)

    Google Scholar 

  20. Longo, J., Stern, F.: Uncertainty assessment for towing tank tests with example for surface combatant DTMB model 5415. J. Ship Res. 49(1), 55–68 (2005)

    Google Scholar 

  21. Olivieri, A., Pistani, F., Avanzini, A., Stern, F., Penna, R.: Towing tank experiments of resistance, sinkage and trim, boundary layer, wake, and free surface flow around a naval combatant INSEAN 2340 model. Technical report, DTIC Document (2001)

    Google Scholar 

  22. Pellegrini, R., Serani, A., Leotardi, C., Iemma, U., Campana, E.F., Diez, M.: Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization. Appl. Soft Comput. 58, 714–731 (2017)

    Article  Google Scholar 

  23. Schlichting, H., Gersten, K.: Boundary-Layer Theory. Springer, Berlin (2000)

    Book  Google Scholar 

  24. Serani, A., Diez, M.: Are random coefficients needed in particle swarm optimization for simulation-based ship design? In: Proceedings of the 7th International Conference on Computational Methods in Marine Engineering (MARINE 2017), pp. 48–59. Nantes, France (2017)

    Google Scholar 

  25. Serani, A., Fasano, G., Liuzzi, G., Lucidi, S., Iemma, U., Campana, E.F., Stern, F., Diez, M.: Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms. Appl. Ocean Res. 59, 115–128 (2016)

    Article  Google Scholar 

  26. Serani, A., Leotardi, C., Iemma, U., Campana, E.F., Fasano, G., Diez, M.: Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Appl. Soft Comput. 49, 313–334 (2016)

    Article  Google Scholar 

  27. Stern, F., Longo, J., Penna, R., Olivieri, A., Ratcliffe, T., Coleman, H.: International collaboration on benchmark CFD validation data for surface combatant DTMB model 5415. In: Proceedings of Twenty-Third Symposium on Naval Hydrodynamics (2001)

    Google Scholar 

  28. Tahara, Y., Peri, D., Campana, E.F., Stern, F.: Computational fluid dynamics-based multiobjective optimization of a surface combatant using a global optimization method. J. Mar. Sci. Technol. 13(2), 95–116 (2008)

    Article  Google Scholar 

  29. Volpi, S., Diez, M., Gaul, N.J., Song, H., Iemma, U., Choi, K.K., Campana, E.F., Stern, F.: Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification. Struct. Multidiscip. Optim. 51(2), 347–368 (2015). https://doi.org/10.1007/s00158-014-1128-5

    Article  Google Scholar 

  30. Watson, A.G., Barnes, R.J.: Infill sampling criteria to locate extremes. Math. Geol. 27(5), 589–608 (1995)

    Article  Google Scholar 

  31. Zhao, L., Choi, K.K., Lee, I., Gorsich, D.: Conservative surrogate model using weighted Kriging variance for sampling-based RBDO. J. Mech. Des. 135(9), 091,003 (2013)

    Article  Google Scholar 

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Acknowledgements

The work was performed within NATO RTO Task Group AVT-204 “Assess the Ability to Optimize Hull Forms of Sea Vehicles for Best Performance in a Sea Environment.” The authors are grateful to Dr Woei-Min Lin, Dr Ki-Han Kim, and Dr Salahuddin Ahmed of the US Office of Naval Research, for their support through NICOP grant N62909-15-1-2016 and grant N00014-14-1-0195. The authors are also grateful to the Italian Flagship Project RITMARE, coordinated by the Italian National Research Council and founded by the Italian Ministry of Education.

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Authors and Affiliations

  1. CNR-INSEAN, National Research Council-Marine Technology Research Institute, Via di Vallerano 139, 00128, Rome, Italy

    Matteo Diez, Andrea Serani & Emilio F. Campana

  2. IIHR–Hydroscience and Engineering, The University of Iowa 100 C. Maxwell Stanley Hydraulics Laboratory, Iowa City, 52242-1585, USA

    Silvia Volpi & Frederick Stern

Authors
  1. Matteo Diez
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  2. Silvia Volpi
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  3. Andrea Serani
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  4. Frederick Stern
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  5. Emilio F. Campana
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Corresponding author

Correspondence to Matteo Diez .

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Editors and Affiliations

  1. Aerospace Centre of Excellence, University of Strathclyde, Glasgow, United Kingdom

    Edmondo Minisci

  2. Aerospace Centre of Excellence, University of Strathclyde, Glasgow, United Kingdom

    Massimiliano Vasile

  3. CIMNE, Universitat Politècnica de Catalunya, Barcelona, Spain

    Jacques Periaux

  4. Chair for Scientific Computing, TU Kaiserslautern, Kaiserslautern, Germany

    Nicolas R. Gauger

  5. School of Mechanical Engineering, National Technical University of Athens, Athens, Greece

    Kyriakos C. Giannakoglou

  6. Fluid Mechanics Department, Multidisciplinary Analysis and Optimization Group, Italian Aerospace Research Center, Capua, Italy

    Domenico Quagliarella

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Diez, M., Volpi, S., Serani, A., Stern, F., Campana, E.F. (2019). Simulation-Based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions. In: Minisci, E., Vasile, M., Periaux, J., Gauger, N., Giannakoglou, K., Quagliarella, D. (eds) Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences. Computational Methods in Applied Sciences, vol 48. Springer, Cham. https://doi.org/10.1007/978-3-319-89988-6_13

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  • DOI: https://doi.org/10.1007/978-3-319-89988-6_13

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  • Print ISBN: 978-3-319-89986-2

  • Online ISBN: 978-3-319-89988-6

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