The laboratory develops new digital twinning and advanced simulation capabilities for application at all stages in the development of products and processes. Through an agile, flexible, and scalable data-driven approach employing cutting-edge hardware and software simulation technologies, researchers aim to develop more functional models and to advance this transformative and revolutionary technology by creating prototypes of increasing complexity. The goal is to support, across all Leonardo's business areas, the technological evolution of products and services that save time and money while offering greater efficiency, quality, and sustainability.
A digital twin is a virtual representation of a physical object, system, or process. It is created using real-time data, from physical or virtual sensors, processed through modelling and numerical simulation techniques to reproduce the appearance, behaviour, and performance of its real-world counterpart, and to simulate future events in its operation. This interaction not only reduces dependence on physical prototypes and speeds up development, but also increases safety, improves operational quality and flexibility, and significantly reduces risks and waste of materials.
Implementation of a digital twin is based on advanced physical and mathematical models requiring an immense amount of computation. For this purpose, the laboratory makes use of cutting-edge technologies - IoT (Internet of Things), ML (machine learning), AI (Artificial Intelligence), HPC (High Performance Computing), cloud computing, MBSE (Model-Based System Engineering) and CAE (Computer-Aided Engineering) - to manage every step in the simulation workflow.
Search Areas
We develop cutting-edge simulations of electromagnetic fields, fluid and thermal dynamics, and structural mechanics, creating accurate digital twins of complex physical systems and phenomena operating in the real world.
We study the creation of intelligent radar antennas with anti-stealth properties. The design and optimisation of these complex systems requires a multidisciplinary approach combining digital twinning technology with advanced computational electromagnetism, high-performance computing, and the design of innovative materials.
We study low and medium fidelity methods for computational fluid dynamics (CFD). The main goal is to explore and validate these computational approaches for studying and designing multi-rotor aircraft, such as electric vertical take-off and landing (eVTOL) aircraft, and for studying the hydrodynamic performance of underwater propellers.
We explore supervised and unsupervised machine learning frameworks. These frameworks, combined with digital twinning technologies, can provide highly accurate and efficient simulation tools of use for structuring real-time and long-term decision-making. Key fields of research include Physics-Informed Neural Networks (PINN), which harness knowledge of physical laws and neural operators for problems in computational mechanics and fluid dynamics, as well as machine-learning virtual sensors for predictive maintenance.
We develop and customise advanced simulation tools for computational mechanics applications. Using simulation techniques, such as the Finite Element Method (FEM) and proprietary open-source tools, this field of research aims to maximise the computational capabilities of the davinci-1 supercomputer in order to solve complex problems in computational mechanics and structural dynamics. The goal is not only to accelerate complex simulations, but also to make significant progress in predictive modelling and engineering solutions.
We implement advanced Computational Fluid Dynamics (CFD) methodologies to improve the accuracy and efficiency of aerodynamic analysis of aircraft and helicopters. Our work includes testing innovative CFD tools, conducting simulations with Scale-Resolving Simulations (SRS), harnessing the power of the davinci-1 supercomputer to conduct large, high-fidelity simulations, and integrating sophisticated AI/ML algorithms.
We work on the full-stack design and development of a digital twin web platform referred to as the Digital Flight Lab. This software leverages Model-Based System Engineering (MBSE) to allow users to perform multidisciplinary simulations for the improvement of systems engineering practices. It offers a dynamic and cooperative environment that digitises the entire aircraft design process and improves human/machine teaming and interoperability.
We develop advanced simulation and integration techniques in the Digital Flight Lab (DFL) platform, in a context of heterogeneous systems. Integration of very different models requires creation of specific interfaces (Functional Mock-up Interfaces - FMI) and simulation orchestrators based on the nature of the models themselves and adopting the Model-Based System Engineering (MBSE) approach, based on the Systems Modelling Language (SysML) standard. Development of these techniques is fundamental for verification and validation (V&V) using formal and semi-formal methods (Certification by Simulation).
We develop custom workflows for multidisciplinary optimisation in aerospace design, using high-fidelity simulations. Use of Reduced Order Models (ROM) permits creation of dynamic virtual representations of components and systems, accurately replicating aircraft conditions for real-time exploration. Integrating these simulations into virtual and augmented reality (VR/AR) environments provides immediate feedback on models and simulations, enabling specialists to understand complex physical phenomena through direct interaction with virtual models.
We harness the transformative potential of digital twins to support the preliminary design of state-of-the-art aircraft configurations. The digital twin acts as a virtual replica of the aircraft, providing real-time simulation models that accurately reflect the aircraft's performance and dynamic behaviour under various flight conditions. By leveraging multi-disciplinary design optimisation (MDO) techniques in the design process, multiple design objectives and constraints can be assessed simultaneously, exploring innovative solutions, and leading to more efficient, sustainable, and high-performance aircraft.