Riccardo Lombardi

Dr. Riccardo Lombardi


Research Focus & Metrics

Academic Career Length

7Years (2020-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
11 May 2023 in Aerospace
Verified
Journal Article
Open access
PDF file
David MassegurDavid Massegur
David Massegur
DCDeclan Clifford
Declan Clifford
Andrea Da RonchAndrea Da Ronch
Andrea Da Ronch
RLRiccardo Lombardi
Riccardo Lombardi
MPMarco Panzeri
Marco Panzeri
Determining the aero-icing characteristics is key for safety assurance in aviation, but it may be a computationally expensive task. This work presents a framework for the development of low-dimensional models for application to aerofoil icing. The framework builds on: an adaptive sampling strategy to identify the local, nonlinear features across the icing envelope for continuous intermittent icing; a classic technique based on Proper Orthogonal Decomposition, and a modern Neural Network architecture. The extreme diversity in simulated ice shapes, from smooth and streamlined to rugged and irregular shapes, motivated the use of an unsupervised classification of the ice shapes. This allowed deploying the Proper Orthogonal Decomposition locally within each sub-region, sensibly improving the prediction accuracy over the global model. On the other hand, the Neural Network architecture and the convolutional auto-encoder were found insensitive to the complexity in ice shapes. A strong correlation was found to exist between the ice shape, resulting ice mass and aerodynamic performance of the iced aerofoil, both in terms of the average and variance. On average, rime ice causes a loss of maximum lift coefficient of 21.5% compared to a clean aerofoil, and the average ice thickness is 0.9% of the aerofoil chord. For glaze ice, the average loss of maximum lift coefficient is 46.5% and the average ice thickness is 2.1%. Glaze ice was also found to have three times more surface coverage than rime ice.

3 April 2020 in Applied Sciences
Verified
Journal Article
Open access
PDF file
Venanzio GiannellaVenanzio Giannella
Venanzio Giannella
RLRiccardo Lombardi
Riccardo Lombardi
MPMatteo Maria Pisani
Matteo Maria Pisani
Luigi FedericoLuigi Federico
Luigi Federico
Mattia BarbarinoMattia Barbarino
Mattia Barbarino
roberto citarellaroberto citarella
roberto citarella
In this work, a novel optimization framework, based on a Multi-Disciplinary Optimization (MDO) procedure, applied to the vibro-acoustic Finite Element Method (FEM) model of an aircraft fuselage mock-up, is proposed. The MDO procedure, based on an Efficient Global Optimization (EGO)-like approach, is implemented to characterize acoustic sources that replicate the sound pressure field generated by the engines on the fuselage. A realistic sound pressure field, evaluated by aeroacoustic simulations, was considered as the reference acoustic load, whereas two equivalent sound fields, displayed by two different arrays of microphones and generated by the same configuration of monopoles, were calculated by the proposed vibro-acoustic FEM-MDO procedure. The proposed FEM-MDO framework enables to set up ground experimental tests on aircraft components, useful to replicate their vibro-acoustic performances as if tested in flight. More in general, such a procedure can also be used as a reference tool to design simplified tests starting from more complex ones.