Abstract

Crashworthiness should be regarded as a key requirement in the design of eVTOL aircraft, yet its inclusion in early design stages is challenging due to the need for rapid iteration and the complexity of large design domains. To address this issue, this work introduces an Abaqus/Explicit framework based on computationally efficient Finite Element/Multi-Body models of ATDs, seats, and the airframe, enabling fast parametric assessment of crash performance. Within this framework, a homogenized battery pack model, developed through a dedicated campaign of mechanical abuse experiments on cylindrical lithium-ion cells, is implemented to enable realistic yet efficient system-level crash simulations.

The framework is applied to a benchmark scenario, an 8 m/s vertical impact on rigid ground, considering multiple input parameters and performance metrics related to occupant survivability, structural loading, and battery pack deceleration. The adopted battery modeling strategy enables consistent evaluation of its mechanical response in full-scale simulations while preserving computational efficiency. In this context, the safety of lithium-ion batteries is numerically assessed under a variety of crash conditions, with particular attention to potential hazards associated with internal short circuits.

Gaussian process regression is employed for variance-based sensitivity analysis and to guide design space exploration. Results demonstrate the framework’s capability to identify design trends and capture interactions among energy-absorbing subsystems, providing a solid basis for informed and efficient design decisions in the early phases of eVTOL aircraft development.

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Speaker

Edoardo Novembre graduated in 2021 in Aerospace Engineering from Politecnico di Milano and is currently a third-year PhD candidate at the Department of Aerospace Science and Technology. Following the completion of his Master’s thesis, Edoardo worked as a research fellow, developing finite element models of carbon/carbon and ceramic matrix composites for structural-scale simulations.
Edoardo's PhD research focuses on the early-stage assessment of eVTOL aircraft crashworthiness through the integration of finite element and multibody modeling techniques.
His work also involves the application of supervised machine learning methods, particularly Gaussian Process Regression, to generalize simulation results, as well as the development of a homogenized Li-ion battery model based on mechanical abuse testing.
 

@EN 
PhD Candidate, Politecnico di Milano