Composite materials have the potential to significantly impact the environment in the future such as reduced energy usage, raw material reduction, economical and lightweight, reduction of waste during production, bio-based raw materials, longer lifespan, and reduced maintenance.
Analysis, design, and verification of composites is mostly done numerically via the finite element method and there is no implementation which can model the mechanics of composites by considering socio-economic and environmental variables which are more common in Lotka-Volterra models.
This project aims at developing transdisciplinary models which couples physico-mechanical models with socio-economic and environmental variables that are fundamental for the decision-making process in the industry for an optimized production and marketing of a product with no (or as little as possible) social impact and environmental impact on a global scale.
These interdisciplinary models cross the boundaries among different research fields.
The present transdisciplinary approach appears to be pioneering in the design of composite materials and structures as it drives the design problem from theoretical and experimental points of view. In fact, classical mechanical models are enriched by socio-economic variables such as CO2, KPIs, social footprint, exploitation, resource depletion, recyclability, public debt, and climate change. Therefore, novel constitutive equations which mathematically correlate mechanical with socio-economic variables, will be developed to provide a non-conventional innovative PDE model for basic research and industrial development.
The present model will be calibrated and verified on a pilot field project of a green and sustainable composite self-driving pleasure boat for sea monitoring (e.g. micro-plastics, pollution) and its digital twin driven by the present transdisciplinary model. The boat will be launched in the Adriatic sea close to the Ravenna Campus of the University of Bologna. The boat will be driven by an hybrid hydrogen power train with batteries and photovoltaic panels.