Search

Your search keyword '"Poloni, Carlo"' showing total 13 results
Start Over Author "Poloni, Carlo" Publisher springer international publishing
13 results on '"Poloni, Carlo"'

1. Design Optimization Based on Multi-fidelity Metamodels

Author

Clarich, Alberto, Battaglia, Luca, Poloni, Carlo, Parussini, Lucia, Telib, Haysam, Scardigli, Angela, Oñate, Eugenio, Series Editor, Tuovinen, Tero, editor, Periaux, Jacques, editor, Knoerzer, Dietrich, editor, Bugeda, Gabriel, editor, and Pons-Prats, Jordi, editor

Published
2024
Full Text
View/download PDF

12. Multi-Objective Design Optimisation of an Airfoil with Geometrical Uncertainties Leveraging Multi-Fidelity Gaussian Process Regression

Author

Péter Zénó Korondi, Mariapia Marchi, Lucia Parussini, Domenico Quagliarella, Carlo Poloni, UTOPIAE network, Korondi, PETER ZENO, Marchi, MARIAPIA CORRADA, Parussini, Lucia, Quagliarella, Domenico, and Poloni, Carlo

Subjects
multi-objective optimisation under uncertainty, airfoil design, surrogate assisted optimisation, Gaussian process regression
Abstract

This paper presents the multi-objective optimisation of the MH114 high-lift airfoil. We seek the set of Pareto optimal solutions that maximise the airfoil lift and minimise the drag. The lift and drag forces are considered uncertain due to geometrical uncertainties. The uncer- tainty quantification of the probabilistic aerodynamic force values re- quires a large number of samples. However, the prediction of the aero- dynamic forces is expensive due to the numerical solution of the Navier- Stokes equations. Therefore, a multi-fidelity surrogate assisted approach is employed to combine expensive RANS simulations with cheap poten- tial flow calculations. The multi-fidelity surrogate-based approach allows us to economically optimise the aerodynamic design of the airfoil under uncertainty.

Published
2021

13. Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization

Author

Carlos Poloni, Andrew Pike, Gianluca Badjan, Nadir Ince, Greiner, D., Galván, B., Periaux, J., Gauger, N., Giannakoglou, K., Winter, G., Badjan, Gianluca, Poloni, Carlo, Pike, Andrew, and Ince, Nadir

Subjects
Polynomial, Mathematical optimization, turbo-machinery, Artificial neural network, Computer science, neural network, neural networks, Set (abstract data type), surrogate models, Kriging, Component (UML), Turbomachinery, Radial basis function, surrogate model, Engineering design process
Abstract

Surrogate models are used to approximate complex problems in order to reduce the final cost of the design process. This study has evaluated the potential for employing surrogate modelling methods in turbo-machinery component design optimization. Specifically four types of surrogate models are assessed and compared, namely: neural networks, Radial Basis Function (RBF) Networks, polynomial models and Kriging models. Guidelines and automated setting procedures are proposed to set the surrogate models, which are applied to two turbo-machinery application case studies.

Published
2015

Catalog

Books, media, physical & digital resources
See catalog results