Modeling small remotely piloted aircraft system to head impact for investigating craniocerebral response
Introduction
With the technological innovations in small remotely piloted aircraft system (sRPAS), also referred to as small unmanned aircraft system (sUAS), various applications including commercial and recreational usages have been observed (Chung et al., 2017). The sRPAS industry was worth $609 million in 2014 and it has been continuously growing with up to $4.8 billion expected in 2021 (Atwater, 2015). The rapid growth of sRPASs usage may also bring the risk to public safety because some of these machines are operated over people with the risk of impact human heads with speeds over 20 m/s (Olivares et al., 2019).
In recent years, the Federal Aviation Administration (FAA) supported a large project to understand sRPAS to human head impacts, presenting Alliance for System Safety of UAS through Research Excellence (ASSURE) report (Olivares et al., 2019). Extensive sRPAS to Hybrid III dummy tests have been conducted to evaluate head injury risk (Campolettano et al., 2017). Human head responses due to sRPAS impacts have been investigated using cadaveric subjects (Stark et al., 2019). Prior to the reported cadaveric data, while FE simulations of sRPAS to head impacts have been studied (Du et al., 2018), the validations against cadaveric data have yet to be conducted. Other efforts included estimating the injury severity by the function of drone mass and impact velocity (Civil Aviation Safety Authority, 2013), using a mathematical model to predict injury level (Li, 2018), and assessing the sRPAS-related injury based on blunt ballistic impact (Magister, 2010).
Combined cadaveric experiments and FE models can provide useful information in understanding head injuries. A skull linear fracture was observed from one quadcopter-type sRPAS to head impact (Stark et al., 2019). Understanding skull stresses under such an impact can provide insights into the injury mechanisms, for which FE head model has been helpful. A scalable child model was used to investigate the relationship between skull stress and skull fracture (Giordano et al., 2017) and it’s reported that von Mises stress can better predict skull fracture than kinematic-based injury measures (Li et al., 2015). One challenge that the cadaveric testing hasn’t addressed is to understand brain strains, partially because that in an experimental setting, either human brain-skull relative motion or brain strains need to be evaluated through either using high-speed X-ray (Hardy et al., 2001, Hardy et al., 2007) or sonomicrometry crystals (Alshareef et al., 2020). To this, validated 3D human head FE models can provide unique information on detailed brain strains during sRPAS to head impacts.
There are various human full-body models including the GHBMC (Global Human Body Model Consortium) model with validated head & neck components (Barker and Cronin, 2020, Mao et al., 2013) and THUMS (Total Human Model for Safety) model (Toyota Motor Corporation, 2018). Especially, the THUMS v4.02 has been improved with a detailed brain model that has been used for brain injury analysis (Atsumi et al., 2016, Fahlstedt et al., 2021, Miyazaki., xxxx, Sahoo., xxxx, Shi et al., 2020). These full-body FE models provide an opportunity to investigate skull stresses and brain strains.
The main objectives of this study were to develop a representative quadcopter-style FE model and to understand sRPAS to human impact-induced craniocerebral responses. Based on 17 validated impact simulations, skull von Mises stress and brain maximum principal strain (MPS) were analyzed. Additionally, the sensitivity studies on impact angles, impact directions, and impacted sRPAS components, which are difficult and expensive to conduct on cadaveric subjects, were investigated using the validated models.
Section snippets
Available cadaver data for validation
The sRPAS to human collision cadaveric test data are available through the detailed ASSURE report (Olivares et al., 2019). 17 quadcopter-style sRPAS related experimental data could be used for FE model validation with detailed head kinematics time histories measured on 3 cadaveric subjects. The experiments were conducted at different angles and impact locations, including 4 typical location settings as frontal 58°, lateral 0°, lateral 58° and top 90°.
sRPAS model development
A representative 1.2 kg quadcopter-style
Results
A total of 45 simulations including 17 cases for validation, 8 cases for impact degree sensitivity study, 16 cases for impact location sensitivity study, and 4 cases for arm-first impacts were calculated using LS-DYNA. Using 2 CPUs from the 8-core Xeon, it took approximately 20 h to solve 40-millisecond impact cases.
Discussion
To investigate human head responses during sRPAS to human impacts, we developed a detailed FE model of a representative quadcopter style sRPAS and validated the model with 17 sRPAS-to-human-head impact experiments. Model-predicted head linear acceleration and head rotational velocity agreed with data collected from cadaveric heads. Based on validated FE models, it was shown that the human head experienced very different stress and brain responses that were greatly affected by impact settings.
Conclusions
A representative quadcopter type sRPAS finite element model was developed and validated under 17 impact scenarios, with settings from lateral 0°, frontal 58°, lateral 58°, to top 90°. Overall, model-predicted head linear accelerations and rotational velocities agreed with measured data. High skull stresses and mild to moderate level of brain strains were observed from these impacts, while these stress/strain values varied greatly among different impact scenarios. Additional sensitivity analysis
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgement
The authors acknowledge the detailed material property and cadaveric test data collected and presented in the ASSURE report that made this study possible. The authors also acknowledge Dr. Mike Shkrum and Mr. Jose Martin for their inputs during the early stage of designing this project. The authors thank Mr. Carlos Ruella and Mr. Matthew Spanos from Transport Canada for their insights and technical advice during the course of this study.
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