The approach to panel paintings is a task that requires a multidisciplinary approach based on experimental observation of the artwork and advanced techniques to make these data actually useful for the knowledge and preservation of the object. This study illustrates how a series of scientific observations and instrumental analyses can be used to construct a numerical simulation that allows a deeper understanding of the physical structure and behaviour of the object itself, namely to construct a hygro-mechanical predictive model (a "Digital-Twin") of Leonardo da Vinci's Mona Lisa panel. Based on specific request from the Louvre Museum, a group of experts with different and complementary skills cooperated and are still cooperating to construct a complete set of experimental observation and non-invasive tests; so, the integration of the collected data made the construction of the panel's Digital-Twin possible. This paper also specifically examines how the Digital-Twin can be used to compare two framing conditions of the panel; although the two experimental configurations are not inherently comparable, the comparison is made possible by the introduction of a technique of projection of the fields obtained as results of the two analyses, named the Projected Model Comparison (PMC), which has been developed specifically for this research.

Learning from Objects: the use of advanced numerical methods to exploit a complete set of information from experimental data, for the Mona Lisa’s Digital-Twin / Lorenzo, RIPARBELLI; Fabrice, BRÉMAND; Paolo, DIONISI-VICI; Jean-Christophe, DUPRE; Giacomo, GOLI; Franck, HESSER; Delphine, JULLIEN; Paola, MAZZANTI; Marco, TOGNI; Elisabeth, RAVAUD; Luca, UZIELLI; Joseph, GRIL. - In: IOP CONFERENCE SERIES: MATERIALS SCIENCE AND ENGINEERING. - ISSN 1757-899X. - ELETTRONICO. - 949:(2020), pp. 1-10. [10.1088/1757-899X/949/1/012089]

Learning from Objects: the use of advanced numerical methods to exploit a complete set of information from experimental data, for the Mona Lisa’s Digital-Twin

Lorenzo, RIPARBELLI
;
Giacomo, GOLI;Paola, MAZZANTI;Marco, TOGNI;Luca, UZIELLI;
2020

Abstract

The approach to panel paintings is a task that requires a multidisciplinary approach based on experimental observation of the artwork and advanced techniques to make these data actually useful for the knowledge and preservation of the object. This study illustrates how a series of scientific observations and instrumental analyses can be used to construct a numerical simulation that allows a deeper understanding of the physical structure and behaviour of the object itself, namely to construct a hygro-mechanical predictive model (a "Digital-Twin") of Leonardo da Vinci's Mona Lisa panel. Based on specific request from the Louvre Museum, a group of experts with different and complementary skills cooperated and are still cooperating to construct a complete set of experimental observation and non-invasive tests; so, the integration of the collected data made the construction of the panel's Digital-Twin possible. This paper also specifically examines how the Digital-Twin can be used to compare two framing conditions of the panel; although the two experimental configurations are not inherently comparable, the comparison is made possible by the introduction of a technique of projection of the fields obtained as results of the two analyses, named the Projected Model Comparison (PMC), which has been developed specifically for this research.
2020
949
1
10
Goal 11: Sustainable cities and communities
Lorenzo, RIPARBELLI; Fabrice, BRÉMAND; Paolo, DIONISI-VICI; Jean-Christophe, DUPRE; Giacomo, GOLI; Franck, HESSER; Delphine, JULLIEN; Paola, MAZZANTI; Marco, TOGNI; Elisabeth, RAVAUD; Luca, UZIELLI; Joseph, GRIL
File in questo prodotto:
File Dimensione Formato  
RIPARBELLI949_012089.pdf

accesso aperto

Descrizione: PDF post print
Tipologia: Pdf editoriale (Version of record)
Licenza: Creative commons
Dimensione 1.31 MB
Formato Adobe PDF
1.31 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1217488
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact