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Ensuring numerical stability of wave propagation by tuning model parameters using genetic algorithms and response surface methods
Roselli, R.A.R.; Vernengo, G.; Altomare, C.; Brizzolara, S.; Bonfiglio, L.; Guercio, R. (2018). Ensuring numerical stability of wave propagation by tuning model parameters using genetic algorithms and response surface methods. Environ. Model. Softw. 103: 62-73. https://hdl.handle.net/10.1016/j.envsoft.2018.02.003
In: Environmental Modelling & Software. Elsevier: Oxford. ISSN 1364-8152; e-ISSN 1873-6726, meer
Peer reviewed article  

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Trefwoord
    Marien
Author keywords
    Smoothed particle hydrodynamics (SPH); Wave propagation; Non-dominatedsorting genetic algorithm (NSGA-II); Response surface method (RSM);Parameter identification

Auteurs  Top 
  • Roselli, R.A.R.
  • Vernengo, G.
  • Altomare, C., meer
  • Brizzolara, S.
  • Bonfiglio, L.
  • Guercio, R.

Abstract
    The effectiveness of a Metamodel-Embedded Evolution Framework for model parameter identification of a Smoothed Particles Hydrodynamic (SPH) solver, called DualSPHysics, is demonstrated when applied to the generation and propagation of progressive ocean waves. DualSPHysics is an open-source code that provides GP-GPU acceleration, allowing for highly refined simulations. The automatic optimization framework combines the global-convergence capabilities of a Multi-Objective Genetic Algorithm (MOGA) with Response Surface Method (RSM) based on a Kriging approximation. The proposed Metamodel-Embedded Evolutionary framework is used to find the set of SPH model parameters that ensures an accurate reproduction of a 2nd order Stokes wave propagating in a numeric flume tank. In order to demonstrate the consistency of the obtained results, the optimum set of parameters found by the framework is finally used to reproduce other 2nd and 3rd order Stokes waves propagating over the same flume tank.

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