@article {TezzeleSalmoiraghiMolaRozza2018,
	title = {Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems},
	journal = {Advanced Modeling and Simulation in Engineering Sciences},
	volume = {5},
	number = {1},
	year = {2018},
	month = {Sep},
	pages = {25},
	abstract = {<p>We present the results of the first application in the naval architecture field of a methodology based on active subspaces properties for parameters space reduction. The physical problem considered is the one of the simulation of the hydrodynamic flow past the hull of a ship advancing in calm water. Such problem is extremely relevant at the preliminary stages of the ship design, when several flow simulations are typically carried out by the engineers to assess the dependence of the hull total resistance on the geometrical parameters of the hull, and others related with flows and hull properties. Given the high number of geometric and physical parameters which might affect the total ship drag, the main idea of this work is to employ the active subspaces properties to identify possible lower dimensional structures in the parameter space. Thus, a fully automated procedure has been implemented to produce several small shape perturbations of an original hull CAD geometry, in order to exploit the resulting shapes to run high fidelity flow simulations with different structural and physical parameters as well, and then collect data for the active subspaces analysis. The free form deformation procedure used to morph the hull shapes, the high fidelity solver based on potential flow theory with fully nonlinear free surface treatment, and the active subspaces analysis tool employed in this work have all been developed and integrated within SISSA mathLab as open source tools. The contribution will also discuss several details of the implementation of such tools, as well as the results of their application to the selected target engineering problem.</p>},
	doi = {10.1186/s40323-018-0118-3},
	author = {Marco Tezzele and Filippo Salmoiraghi and Andrea Mola and Gianluigi Rozza}
}
