PSI - Issue 64

Nicola Nisticò et al. / Procedia Structural Integrity 64 (2024) 2230–2237 Author name / Structural Integrity Procedia 00 (2019) 000–000

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Digitalization refers to the process of discretizing specific geometric entities based on a predefined level of detail. The chosen level of detail depends on factors such as the scale of representation, visualization, and thematic content. For instance, in the case of paintings, digitalization can be utilized for virtual restoration, which includes tasks like crack detection and removal. Understanding how processes spanning multiple scales influence macroscopic material properties is crucial. Geometrical models play a pivotal role in predicting how objects respond to external forces and environmental factors. Methods like the Finite Element Method have emerged as reliable means of describing the inelastic behavior of materials. Among them microplane and discrete numerical models, such as discrete lattice models, are instrumental in studying fracture behavior in heterogeneous materials (Nisticò, 2024). Multiscale modeling and analysis are essential for comprehending materials and structures at various levels, including nano-, micro-, and meso-scales. These concepts find wide applicability across disciplines, including image recognition, where they contribute to both coarse-grained and fine-grained multi-scale approaches. At the nanoscale, atomistic simulation techniques like Molecular Dynamics are valuable tools for modeling various properties and gaining insights into material structures. These simulations offer a cost-effective and efficient alternative to experimental investigations, aiding researchers in exploring complex materials and their behaviors. Model error methodologies encompass standards that define procedures for evaluating discrepancies between dataset values (such as coordinates or pixel intensity) and reference values. Likelihood and uncertainty maps, as proposed by Pauly et al. (2004), offer informative representations of model errors. These maps provide valuable insights into the reliability and uncertainty associated with model predictions. The process of digitalization can be oriented to a Digital Twins that, originally introduced by Grieves (2014), comprises physical products in Real Space, virtual products in Virtual Space, and the interconnected information bridging the virtual and real products. The pioneering notion, first advanced by NASA, entails the creation of ultra realistic Digital Twins that simulate and mirror the life of their physical counterparts. This foundational methodology, aiming to integrate "multiphysics, multiscale, probabilistic simulation of an as-built vehicle or system that uses the best available physical models, sensor updates, fleet history, etc., to mirror the life of its corresponding flying twin" (Glaessgen and Stargel, 2012), facilitates the development of predictive systems with profound impacts on risk reduction, safety enhancement, and fostering a green economy. The pioneering representation reported in Figure 2, enclose most of the advanced today requirements that regard material, modelling, prediction and monitoring. Digital Twins metamorphoses a real object into virtual entities, deployable in museums, whether they physically exist or reside in the metaverse. This transition to the metaverse heralds opportunities for leveraging Physical Art Non Fungible Tokens (PANFTs) and blockchain technology to streamline information sharing, catalyse sustainable economic development, combat climate change, and bolster initiatives like the European Green New Deal (EC, 2023).

Fig. 2. Digital Twin representation: (a) failed fibers in a fiber reinforced composite material; (b) fiber optic strain sensing system; (c) atom modeled in a molecular simulation; (d) human brain model; (e) Bayesian update, in the time, of the failure probability; (f) standards and book to be included in the Digital Twin