@article {2021,
	title = {An efficient computational framework for naval shape design and optimization problems by means of data-driven reduced order modeling techniques},
	journal = {Bolletino dell Unione Matematica Italiana},
	volume = {14},
	year = {2021},
	pages = {211-230},
	abstract = {<p>This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive{\textemdash}especially dealing with complex industrial geometries{\textemdash}we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.</p>},
	doi = {10.1007/s40574-020-00263-4},
	author = {Nicola Demo and Giulio Ortali and Gianluca Gustin and Gianluigi Rozza and Gianpiero Lavini}
}
@conference {demo2018shape,
	title = {Shape Optimization by means of Proper Orthogonal Decomposition and Dynamic Mode Decomposition},
	booktitle = {Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship \& Maritime Research},
	year = {2018},
	publisher = {IOS Press},
	organization = {IOS Press},
	chapter = {212},
	address = {Trieste, Italy},
	abstract = {Shape optimization is a challenging task in many engineering fields, since the numerical solutions of parametric system may be computationally expensive. This work presents a novel optimization procedure based on reduced order modeling, applied to a naval hull design problem. The advantage introduced by this method is that the solution for a specific parameter can be expressed as the combination of few numerical solutions computed at properly chosen parametric points. The reduced model is built using the proper orthogonal decomposition with interpolation (PODI) method. We use the free form deformation (FFD) for an automated perturbation of the shape, and the finite volume method to simulate the multiphase incompressible flow around the deformed hulls. Further computational reduction is done by the dynamic mode decomposition (DMD) technique: from few high dimensional snapshots, the system evolution is reconstructed and the final state of the simulation is faithfully approximated. Finally the global optimization algorithm iterates over the reduced space: the approximated drag and lift coefficients are projected to the hull surface, hence the resistance is evaluated for the new hulls until the convergence to the optimal shape is achieved. We will present the results obtained applying the described procedure to a typical Fincantieri cruise ship.},
	doi = {10.3233/978-1-61499-870-9-212},
	url = {http://ebooks.iospress.nl/publication/49229},
	author = {Nicola Demo and Marco Tezzele and Gianluca Gustin and Gianpiero Lavini and Gianluigi Rozza}
}
