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Hull-form stochastic optimization via computational-cost reduction methods

Abstract

The paper shows how cost-reduction methods can be synergistically combined to enable high-fidelity hull-form optimization under stochastic conditions. Specifically, a multi-objective hull-form optimization is presented, where (a) physics-informed design-space dimensionality reduction, (b) adaptive metamodeling, (c) uncertainty quantification (UQ) methods, and (d) global multi-objective algorithm are efficiently and effectively combined to achieve high-fidelity simulation-based design optimization (SBDO) solutions. The application pertains to the multi-objective optimization for resistance and seakeeping (operational efficiency and effectiveness) of a destroyer-type vessel. Two hierarchical multi-objective SBDO problems are presented, with a level of complexity decreasing from the most general (stochastic sea state, heading, and speed) to the least general (deterministic regular wave, at fixed sea state, heading, and speed). Design-space dimensionality reduction is based on a generalized Karhunen-Loève expansion of the shape modification vector combined with low-fidelity-based physical variables. A multi-objective deterministic particle swarm optimization algorithm is applied to a stochastic radial-basis-function metamodel that provides objective predictions. UQ methods include Gaussian quadrature and metamodel-based importance sampling. Numerical simulations are based on unsteady Reynolds-averaged Navier–Stokes and potential flow solvers. The paper shows and discusses the joint effort of computational-cost reduction methods in enabling high-fidelity SBDO, providing guidelines for future research directions in this area.

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Acknowledgements

The work is supported by the US Department of the Navy Office of Naval Research Global, NICOP grant N62909-15-1-2016, administered by Dr. Salahuddin Ahmed, Dr. Elena McCarthy, and Dr. Woei-Min Lin, and by the Italian Flagship Project RITMARE, funded by the Italian Ministry of Education. The research is performed within NATO STO Task Group AVT-252 ”Stochastic Design Optimization for Naval and Aero Military Vehicles”.

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Correspondence to Andrea Serani.

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Serani, A., Stern, F., Campana, E.F. et al. Hull-form stochastic optimization via computational-cost reduction methods. Engineering with Computers 38 (Suppl 3), 2245–2269 (2022). https://doi.org/10.1007/s00366-021-01375-x

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  • DOI: https://doi.org/10.1007/s00366-021-01375-x

Keywords

  • Simulation-based design optimization
  • Stochastic optimization
  • Reliability-based robust design optimization
  • Physics-informed design-space dimensionality reduction
  • Adaptive metamodeling
  • Uncertainty quantification
  • Global multi-objective optimization
  • Computational fluid dynamics