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<p>In the last decade, novel advancements in electric motors have renewed the potential for developing innovative VTOL aircraft. While their potential is clear, significant engineering challenges remain. This study aimed to test different advanced modeling and simulation techniques, including physics-based and data-driven approaches. This work is part of the MODEL-SI project, funded by Horizon Europe and managed by the European Union Aviation Safety Agency (EASA). A previous paper reviewed applicable methods for developing an eVTOL Digital Twin (DT), while in this work, a comprehensive Flight Simulation Model (FSM) was built. Given the non-trivial physical complexity involved, a flexible and modular implementation approach was adopted to accommodate various possible techniques. Conventional physicsbased methodologies were primarily employed, while certain modules were developed using state-of-the-art machine learning (ML) techniques, namely, Co-Kriging and the Bayesian Neural Network with Transfer Learning (BNN-TL). An initial assessment between the two methods demonstrated the superior capabilities of BNN-TL in handling complex problems. Ultimately, the final FSM can perform static and dynamic analyses, such as aircraft trim, transition simulation, and aeroelastic analyses.</p>

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Flight simulation model of a multi-fidelity digital twin of an eVTOL drone

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Andrea Pedrioli,
  • Andrea Vaiuso,
  • Noé Pedrazzini,
  • Oier Coretti,
  • Elena Garcia Sanchez,
  • Laurent Pinsard,
  • Joana Gomes,
  • Pierluigi Capone,
  • Marcello Righi

摘要

In the last decade, novel advancements in electric motors have renewed the potential for developing innovative VTOL aircraft. While their potential is clear, significant engineering challenges remain. This study aimed to test different advanced modeling and simulation techniques, including physics-based and data-driven approaches. This work is part of the MODEL-SI project, funded by Horizon Europe and managed by the European Union Aviation Safety Agency (EASA). A previous paper reviewed applicable methods for developing an eVTOL Digital Twin (DT), while in this work, a comprehensive Flight Simulation Model (FSM) was built. Given the non-trivial physical complexity involved, a flexible and modular implementation approach was adopted to accommodate various possible techniques. Conventional physicsbased methodologies were primarily employed, while certain modules were developed using state-of-the-art machine learning (ML) techniques, namely, Co-Kriging and the Bayesian Neural Network with Transfer Learning (BNN-TL). An initial assessment between the two methods demonstrated the superior capabilities of BNN-TL in handling complex problems. Ultimately, the final FSM can perform static and dynamic analyses, such as aircraft trim, transition simulation, and aeroelastic analyses.