Emilio Di Lorenzo

Dr. Emilio Di Lorenzo


Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

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

Representative Publications

View All Publications
17 May 2016 in Structural Health Monitoring
Verified
Journal Article
Closed access
ELEmilio Di Lorenzo
Emilio Di Lorenzo
GPGiuseppe Petrone
Giuseppe Petrone
SMSimone Manzato
Simone Manzato
BPBart Peeters
Bart Peeters
WDWim Desmet
Wim Desmet
FMFrancesco Marulo
Francesco Marulo
In this paper a vibration-based approach to identify a crack in a wind turbine blade is described and demonstrated numerically and experimentally. Operational modal analysis has been performed before and after a buckling test, and vibration data, gathered from some accelerometers placed along the blade, was used to monitor the integrity of the structure, since the modal parameters are directly influenced by the physical properties of the structures. Additionally a numerical prediction has been done both with a full-scale model and with a one-dimensional model. The results show that this approach is able to estimate successfully the presence of damage and a good numerical and experimental correlation has been found. Finally, some considerations regarding the rotation of the blade in the undamaged and damaged cases have been done.

15 April 2024 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
ATAndré Tavares
André Tavares
ELEmilio Di Lorenzo
Emilio Di Lorenzo
BCBram Cornelis
Bram Cornelis
BPBart Peeters
Bart Peeters
WDWim Desmet
Wim Desmet
KGKonstantinos Gryllias
Konstantinos Gryllias

16 March 2024 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
SVS. Vettori
S. Vettori
ELEmilio Di Lorenzo
Emilio Di Lorenzo
BPB. Peeters
B. Peeters
ECE. Chatzi
E. Chatzi

24 December 2018 in Journal of Physics: Conference Series
Verified
Conference Paper
Open access
PDF file
ELEmilio Di Lorenzo
Emilio Di Lorenzo
PLP Lava
P Lava
RBR Balcaen
R Balcaen
SMS Manzato
S Manzato
BPB Peeters
B Peeters
Digital Image Correlation (DIC) is a non-contact full-field image analysis technique which allows to retrieve strains and displacements in three dimensions at the surface of any type of material and under arbitrary loading. In recent years, high-speed and high-resolution cameras have been developed for static as well as for dynamic applications. As consequence, the application fields for DIC have broadened and it has proven to be a flexible and very accurate measurement solution for deformation analysis and material characterization. Nevertheless, nowadays DIC is often used in a qualitative manner rather than as a metrological tool. This is especially due to the time-consuming task related to the post-processing of the images. When compared to other vibration testing techniques, full-field approaches (such as DIC) allow a greater flexibility by providing a very dense number of experimental data over a single measurement. Another advantage is related to the fact that the geometry is automatically extracted from the images. In this paper, the possibility to combine global acceleration measurements on a small component with local full-field standard machine vision quasi-static camera measurements is investigated. In particular, the regularization properties of DIC and their impact on modal analysis will be studied in detail. Strains and displacements could be used in a second stage for modal analysis purpose in order to characterize the dynamic behaviour of the specimen in a certain frequency range. Different approaches could be used for combining together the data obtained during the tests. The most obvious approach would be the alignment of the time histories based on reference signals for Frequency Response Functions (FRFs) calculation prior to perform any further processing. Unfortunately this is not always possible because of synchronization issues. An alternative possibility, in case broadband random excitation is used, requires to process time data into auto and crosspowers and identify the modal parameters by using Operational Modal Analysis (OMA).

24 September 2020
Verified
Book Chapter
Closed access
PDF file
ELEmilio Di Lorenzo
Emilio Di Lorenzo
DMDavide Mastrodicasa
Davide Mastrodicasa
LWLukas Wittevrongel
Lukas Wittevrongel
PLPascal Lava
Pascal Lava
BPBart Peeters
Bart Peeters

21 April 2025 in Mechanical Systems and Signal Processing
Verified
Journal Article
Closed access
ATAndré Tavares
André Tavares
ELEmilio Di Lorenzo
Emilio Di Lorenzo
BCBram Cornelis
Bram Cornelis
SMSimone Manzato
Simone Manzato
BPBart Peeters
Bart Peeters
KGKonstantinos Gryllias
Konstantinos Gryllias