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Article

Assessment of Nanobag as a New Safety System in the Frontal Sled Test

1
New Technologies—Research Centre, Department of Biomechanical Human Body Models, University of West Bohemia, Univerzitní 8, 301 00 Pilsen, Czech Republic
2
EB Safety Systems, Bohunická 517/55, Horní Heršpice, 619 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(3), 989; https://doi.org/10.3390/app12030989
Submission received: 9 December 2021 / Revised: 12 January 2022 / Accepted: 15 January 2022 / Published: 19 January 2022
(This article belongs to the Special Issue Vehicle Crashworthiness and Lightweight Design)

Abstract

:
Objective: The future mobility challenges lead to considering new safety systems to protect vehicle passengers in non-standard and complex seating configurations. The objective of this study is to assess the performance of a brand new safety system called nanobag and to compare it to traditional airbag performance in the frontal sled test scenario. Methods: The nanobag technology is assessed in the frontal crash test scenario and compared with the standard airbag by numerical simulation. The previously identified material model is used to assemble the nanobag numerical model. The paper exploits an existing validated human body model to assess the performance of the nanobag safety system. Using both the new nanobag and the standard airbag, the sled test numerical simulations with the variation of human bodies were performed in 30 km/h and 50 km/h frontal impacts. Results: The sled test results for both the nanobag and the standard airbag based on injury criteria show a good and acceptable performance of the nanobag safety system compared to the traditional airbag. Conclusions: The results show that the nanobag system’s performance is comparable to the standard airbag’s, which means that, thanks to the design, the nanobag safety system has high potential and an extended application for multi-directional protection against impact.

1. Introduction

The vehicle safety is as old as mobility started. The improvement of vehicle occupants’ safety was approached in different ways, where all of them led to enforcement through vehicle manufacturers by embedding safety standards into legislation and policies. Whilst the history of safety belts dates back to the early 20th century, the airbag came more than 50 years later [1]. Although the active systems strongly support present vehicles’ safety, the passive systems still play a main role. Nowadays, the airbag is an inherent safety system in almost all road vehicles, protecting passengers not only in frontal and side impacts, but special airbag systems mitigate injury to other parts of the human body (knee, chest, etc.) that are also considered vulnerable and likely to be injured [2].
Current trends in the automotive industry bring new challenges for active and passive safety technology. Non-traditional seating configurations in autonomous vehicles and complex crash scenarios including multi-directional impacts are to be considered [3,4,5] in future automatic vehicles. The expected future scenarios will cover complex and highly unpredictable loading from various directions [6]. This study is testing a new restrain system that can find its benefit in these non-standard seating configurations, where standard airbags can lose their performance. However, nanobag technology needs to be tested and certified firstly in the standard seating position. Moreover, the main aim here is to assess the performance of the nanobag compared to the standard airbag, and it is certified for a standard seating configuration only. Thus, only the standard seating configuration and frontal impact are considered in this study.
The aim of this study is to assess a brand new concept of the interior safety system for front and back seats made of elastic ultralight materials [7,8]. The assessment concerns a virtual numerical simulation using validated models of both the occupant and the restrain system. The new supplemental restraint system called the nanobag concerns a foil folded in front of the passenger to serve as an airbag. The system is built in the way that it can be easily adapted for side impact. The main advantages of this new technology are in minimizing the volume of the folded airbag, decreasing the car weight, simplifying technology production and maintenance, the low cost of materials and the assembly and possible applicability to multidirectional accident scenarios with non-standard seating configurations, which is going to be a critical issue within new technologies of future mobility ecosystems.
For assessing the safety of future mobility, the traditional anthropometric testing devices (dummies) are not suitable due to their unidirectional bio-fidelity, which cannot address non-traditional seating [9]. The new state-of-the art ATDs (THOR) are still in the process of exploration for the automated vehicles crash scenario application [10]. Therefore, the paper utilised an existing validated human body model, Virthuman [11]).

2. Methods

The study adopts new safety system documentation for the nanobag [6] and the hybrid numerical virtual human body model Virthuman [11] in the standard frontal sled test scenario within the defined acceleration pulse simulating 30 km/h and 50 km/h collisions [11]. The computational approach using the validated numerical human model is used for assessing the performance of the new vehicle safety system in the frontal sled test with the identified material of the nanofoil. The nanobag concerns a thin foil (nanofoil) unrolled between side supports. The nanofoil is based on a linear low-density polyethylene (LLDPE), where the material properties were previously identified in both static and dynamic loading conditions [12]. The frontal scenario was chosen as the first step for assessing the performance of the nanobag system due to the ease of comparison to standard airbag performance. The entire numerical tests were performed under the ESI Virtual Performance Solution (VPS) package [13]. The injury risk of the most threatened body parts was monitored by the selected injury criteria [1].

2.1. Occupant Selection

The aim of the study was to compare the performance of both safety systems (the traditional airbag and the new nanobag) for a spectrum of the population by selecting the specific occupant anthropometry to be tested in the frontal sled test with the particular safety features. Authors chose the specific subjects based on the anthropometry of the Czech Republic population representing the average European population and selected the “local peaks” of the population only; see Figure 1.
Figure 1 shows the representation of the age groups in the Czech population. The peaks show the highest representation for 10, 28, 44, 54 and 62–66 years old (further referred to as YO) for both males (M) and females (F). As the comparison concerns frontal seat impact, the youngest group was selected to be in-line with the Czech Republic regulation for the front seat height limit, which is a passenger higher than 150 cm. Thus, the youngest age group was chosen in order to have the female higher than 150 cm (here 14–15 YO). Additionally, percentiles P50 and P95 for the males and P5 of the females from each age interval (in correspondence with the dummy size) were implemented. The particular intervals are defined from the Virthuman model and its internal scaling algorithm, where the age is defined in the intervals (not a single value) [14]. Thus, the specified age groups are selected based on Figure 1, but defined with respect to the Virthuman model scaling, where the ages are defined by the intervals. The list of the selected occupants are as follows, in Table 1:

2.2. Human Body Model

The study implements Virthuman as a hybrid model combining the advantages of the deformable elements based on finite element methods (FEM) coupled to the multibody structure (MBS) [11]. The deformable elements, representing the external shape of the human body, are connected via non-linear springs and dampers to the rigid segments. Such segments form an open tree structure based on the multibody principle. The particular rigid segments are connected via kinematic joints representing the real human joints (shoulder, elbow, knee, etc.) or breakable joints for the description of the bone fracture.
The Virthuman model is a fully scalable human body model, taking into account the gender, age, height (h) and weight (m) of the particular subject [14], where the wide set of a human anthropometric database [15] is the basis of the automatic scaling algorithm implemented in the model.
The Virthuman model was validated against a large set of validation tests. The full-body tests for various traffic scenarios [16,17,18] and body sizes [19,20] as well as detailed tests for the particular human body segments [11] were performed to ensure the biofidelity of the Virthuman model. This model is MBS-based using the deformable elements (virtual springs, dampers and kinematic joints with internal stiffness or breakable joints) to consider the deformability of the human body. It does not include internal structures and internal organs. However, the deformability and injury risk that could be assessed are taken into account through injury criteria coupled with the injury risk curves. The criteria are calculated based on the mechanical quantities, such as accelerations, velocities, forces, torques, etc.

2.3. Safety System

This paper implemented two supplemental restraint system (SRS) into the frontal sled test scenario—namely the standard airbag and a new system called the nanobag, together with the standard three points seat belt.

2.4. Airbag

The previously utilized model of the standard airbag (referred to as AB in figures and tables) was used for the simulations [11]. The undeployed airbag model is fixed to the steering wheel, and the deploying process runs within the simulation. The airbag is activated at the begging of the simulation for both simulations. The airbag starts the deploying process within the simulation, defined via the characteristics of the airbag in the VPS software. The airbag is modelled as a single chamber with all its features, such as inflating or leakage.

2.5. Nanobag System

The nanobag (later referred to as NB in figures and tables) system consists of two thin, layered curtains folded in front of the occupant. Such technology consists of an elastic wall, brackets, a gas generator and a controlling system; see Figure 2. They are arranged under the roof and deployed under sensor activation (similar to the standard airbag). The simplified geometry of the nanobag system was built based on the documentation provided by [6]. The study implements the previously identified linear low density polyethylene LLDPE nanofoil material [12]. Hynčík [12] performed the numerical optimization of material parameters to fit the performed experimental tests. Static and dynamical analysis were considered to model and validate the material behaviour for such loading. The nanobag is considered as a several layers of such LLDPE foil. The number of layers is one of the main parameters of the safety assessment of this technology, and it is to be tested.
For the assessment of the safety performance, the nanobag support frame is considered a rigid body, and the nanobag is considered to be deployed at the beginning of the simulation. The process of the deployment (unfolding) of the nanobag was not considered in this study. The inclination of the nanobag support to the cushion seat is 25° for the inner layer and 30° for the outer layer. The position of the seat and the nanobag was equal for all the configurations. In order to respect the adjusting of the seat and to model the experimental test correctly, the footrest was adjusted closely to the feet (the feet are in the close contact with the footrest); see Figure 3. The nanobag rigid frame is considered to be fixed to the car roof (into the reinforcement). However, since this system is still under development and not has been tested and certified, the particular design and method of fixing to the frame is not considered in this study.

2.6. Seat Belts

The model is seated as a driver and fastened with a seatbelt using a semi-automatic tool build implemented in the VPS software with the default material properties of the seat belt. For simplicity, the seat is considered rigid. In order to represent a more realistic case, the hands were considered to be in contact with the steering wheel [21]. The seat belt consists of the membrane elements (in the part of contact with the body) and bar elements (in the connection to a retractor, slipring and buckle, respectively). The material characteristics of these structures are defined (and previously validated in VPS software).

2.7. Sled Test Scenario

The standard frontal sled test scenario was used here [22]. The configuration based on the previous tests [11] consists of the seat fixed to the rigid frame, 3-point seat belt system and the particular elements of the passive safety (airbag or nanobag). The steering wheel was also included (to fix the airbag), and it is being modelled as rigid. The motion of the sled device was defined by the acceleration pulses corresponding to the velocities 30 km/h and 50 km/h [11] defined on the COG of a rigid seat. The sled tests were performed for two velocities using two safety systems (airbag and nanobag). The test matrix can be summarized as follows:
  • Two velocities (30 km/h and 50 km/h);
  • 15 occupant anthropometries (according to Table 1);
  • Two safety approaches (airbag and nanobag),
where the nanobag was tested for 4, 6, 8 and 10 layers of LLDPE forming the nanobag, i.e., 2 × 15 × 5 = 150 configurations in total. Figure 3 shows the particular initial configurations for the airbag and the nanobag in case of the P50 male, age range 25–30 YO, height equal to 178 cm and weight equal to 78 kg.

3. Results and Discussion

This study virtually assessed new safety technology, the nanobag, with respect to the airbag. This feature consists of the rigid frame and thin elastic foil. Such technology can help in the safety of the future cars (where the non-standard seating configurations are being considered—i.e., autonomous car). The identified material model of the LLDPE foil was used to develop the nanobag safety system, and its safety performance was assessed and compared to the traditional airbag by the numerical simulations using a previously validated biomechanical virtual human body model.
Since the most vulnerable human parts in the case of frontal crashes are the head and the neck, respectively, their injury indicators (criteria) are considered the main assessment criteria for the nanobag safety system. The head injury risk is evaluated with the head injury criteria (HIC) [1] and brain criteria (BrIC and UBrIC) for soft tissue injury criteria [23,24,25], and neck injury is tested via neck injury criteria (Nij) [1,26]. The head acceleration curves are filtered with the CFC 1000 filter, and HIC is calculated from such filtered curves. In order to assess the performance of the nanobag with respect to the traditional airbag, the additional probability of AIS injury for head and brain injury were also considered [1]. The UBrIC criterion for predicting brain injury (soft tissue) is based on the response of the second-order mechanical system, and relates rotational head kinematics to strain-based brain injury. It was developed based on the maximum principal strain (MPS) or cumulative strain damage measure (CSDM), and it can be evaluated with respect to these matrices. The Nij criterion considers the axial force and bending moment generated on the neck spine and plots them into corridors. These corridors are changing with the anthropometry of a particular passenger.
Consequently, the HIC and maximum Nij criteria result in a single scalar value that can be formulated with the injury risk. These values can be recalculated by utilizing the S-Shape curve to obtain the probability of a particular injury risk in terms of abbreviated injury scale (AIS) value. The contact force between the occupant and a particular SRS is also monitored, and the maximum values are presented.

3.1. 30 km/h Results

Head acceleration results in the time dependence curve of its COG versus time. The plot on Figure 4 shows the limits (maximum and minimum values) of all the configurations as well as their mean values. This plot shows that all the cases have a similar curve shape, and there is not a one curve, which would behave in very different manner.
In order to assess the effect of the airbag and nanobag for various anthropometry, respectively, the plot of body mass index (BMI) vs. head injury criterion (HIC) are depicted. The BMI pairs the height and weight of the subject, and both are considered to be assessed for the safety effect; see Figure 5. All the curves report increasing the HIC value for higher a BMI for both safety systems. The approximation curve is the lowest in the case of the airbag, followed by the 4-layer nanobag. With increasing numbers of layers, the HIC values tend to increase (more layers, stiffer obstacle and higher head acceleration). However, all values are still within the acceptable region of head injury risk of <1000 [27]. The best protection is predicted for a standard airbag for a BMI up to 29. After this limit, the HIC predicts lower values for the nanobag with 4 layers.
Neck injury risk is tested by means of the Nij criterion, which plots the axial compression force depending on the bending moment into the corridor (function of the antopometry). If the curves are inside the corridor, the risk of neck injury is within the safety limits, and no serious injury should appear. The Nij criterion curves lay inside the defined corridors in all tested scenarios for the acceleration pulse of 30 km/h.
The full results are displayed in Table 2, where the HIC value, the Nij max value, the probability of AIS injury 2 for the head and 2–5 for the neck, BrIC, UBrIC criteria and the maximum contact force Fc between the passenger and the SRS (airbag and nanobag), respectively, are displayed. The last three lines (79–81) in the Table 2 show the maximum, minimum and averages of the particular values.
Particular subjects (VH) are represented as males (Ma) and females (Fa) of a given age (a) and percentile (p). Airbag (AB) and nanobag NBn of n layers are compared.
The maximum value of the HIC is 214 (M, P95, 54 YO, 184 cm, 96 kg for a 6-layer nanobag), and its minimum is 91 (M, P50, 28 YO, 178 cm, 76 kg, airbag). The results of the crash scenario with no safety features (seat belts only) for the average occupant (M P50, 28 YO, 178 cm, 76 kg) are also provided in Table 2 (line 78) to get a referenced value for no SRS configuration. The maximum probability of AIS 2+ for the head and neck are 6.7% and 14.7%, respectively. The criteria for soft tissue brain injury (BrIC and UBrIC) do not have specific thresholds to distinguish the particular injury risk. However, they can be used to assess the performance of the nanobag, comparing to a traditional airbag. BrIC results with a minimum value of 0.4 (F P05, 44 YO, 154 cm, 53 kg—airbag) and with a maximum of 0.9 (M P95, 64 YO, 180 cm, 95 kg—airbag). Generally, the BrIC gives slightly higher values for the particular anthropometry in configuration with the airbag, but the range of these values is not very large. Criterion UBrIC has its minimum MPS value 0.14 (F P05, 28 YO, 158 cm, 53 kg—nanobag 10 layers; F P05, 44 YO, 154 cm, 53 kg—airbag and nanobag 10 layers; F P05, 54 YO, 154 cm, 55 kg—airbag) and the minimum CSDM value 0.2 (F P05, 44 YO, 154 cm, 53 kg—airbag, F P05, 54 YO, 154 cm, 55 kg—airbag), while the maximum MPS value is 0.33 (M P95, 64 YO, 180 cm, 95 kg—airbag), and the maximum in CSDM is 0.45 (M P95, 64 YO, 180 cm, 95 kg—airbag). The UBrIC criterion results in very similar values for the specific anthropometry; however, the maximal values for MPS and CSDM are predicted for the airbag. The minimum contact force is 0.1 kN (M P50, 28 YO, 178 cm, 76 kg—airbag) and the maximum value 2.3 kN (M P95, 54 YO, 184 cm, 96 kg—airbag). The minimum value is for the average male (the size of P50 dummy) with the airbag SRS. Such results confirmed that the passive safety technologies (seat belts and airbag) are optimized for this anthropometry. The Table 3 summarizes the minimum, maximum and average values for the particular criteria, together with the anthropometry and SRS.
The results suggest that head and neck injury risks for all safety measures are in the acceptable range and predicts a similar safety performance of the nanobag and airbag in this specific crash configuration. The majority of the max values occurs for the airbag. However, the airbag SRS also offers a majority of the minimal values.

3.2. 50 km/h Results

The head acceleration curves (corridors of max and min values and average curve) are plotted at the Figure 6. There is a significant peak in the acceleration at about the time of 700 ms, with the peak value being about 135 g. These are curves of Male, P95 percentile, 54 YO, 184 cm, 96 kg. Such values are higher than the rest of the curves; however, the HIC criterion are still lower than critical threshold of 1000 (here the max value is 702 for the 10 layers nanobag). The peak is caused by the internal structure of the Virthuman, where the neck vertebrae reaches their defined physiological limit and stops the head’s forward motion.
The curves of BMI vs. HIC have a similar trend to the 30 km/h pulse. However, there is an interesting effect of the higher impact velocity. The approximation curve (mean value) does not significantly increase with the increasing number of LLDPE layers; see Figure 7. The best protection of the head is achieved by the application of the four-layer nanobag. These results suggest that in a higher impact velocity, the difference between the standard airbag and nanobag is lower than in a slow velocity impact.
The Nij curves are significantly higher than in case of 30 km/h; however, they still lay inside the corridors. There is only one configuration where the curves cross the corridors (M, P95, 54 YO, 184 cm, 95 kg); see Figure 8, where also the results of Male, P50, 28 YO, 178 cm, 76 kg (standard dummy size) are plotted as a reference.
The full results table are displayed in Table 4, where the HIC value, the Nij max value, the probability of AIS injury 2 for the head and 2–5 for the neck, BrIC, UBrIC criteria and the maximum contact force Fc between passenger and SRS (airbag and nanobag), respectively, are displayed. The last three lines (79–81) in the Table 4 show the maximum, minimum and average of the particular values.
Particular subjects (VH) are represented as males (M) and females (F) of a given percentile (P). Airbag (AB) and nanobag NBn of n layers are compared.
The maximum value of the HIC is 702 (M, P95, 54 YO, 184 cm, 96 kg; 10 layers nanobag); its minium is 377 (M, P05, 54 YO, 154 cm, 55 kg, nanobag 4) and the average value is 454 (safety value of HIC). The maximal probability of AIS 2+ for head and neck are 35.5% and 19%, respectively. These results suggest the safety region of injury risk for all safety features. The results also indicate a significant increase of head injury risk with a higher impact velocity (AIS2+ for head: 6.73% and 35.5% for 30 km/h and 50 km/h pulse, respective), while the neck injury risk increases in-significantly (AIS2+ for neck: 14.66% and 17.7% for 30 km/h and 50 km/h pulse, respective). The BrIC criterium results with the minimum value 0.5 (F P05, 64 YO, 154 cm, 60 kg—airbag) and with the maximum 1.09 (M P50, 64 YO, 180 cm, 95 kg—airbag). Generally, the BrIC gives similar values for each particular anthropometry in all configurations. Criterion UBrIC has its minimum MPS value at 0.17 (F P05, 14 YO, 153 cm, 41 kg—nanobag 10 layers; F P05, 64 YO, 154 cm, 60 kg—airbag) and the minimum CSDM value of 0.24 (F P05, 64 YO, 154 cm, 60 kg—airbag), while the maximum MPS value is 0.4 (M P50, 64 YO, 172 cm, 83 kg—airbag) and the maximum in CSDM is 0.55 (M P50, 64 YO, 172 cm, 83 kg—airbag). The UBrIC criterion results in very similar values for the specific anthropometry; however, the maximal values for MPS and CSDM are predicted for the airbag. The minimum contact force is 0.12 kN (M P50, 28 YO, 178 cm, 76 kg—airbag) and maximum value 3.69 kN (M P95, 64 YO, 180 cm, 95 kg—airbag). The minimum value is predicted for the average male (the size of P50 dummy) with the airbag SRS. Such results confirmed that the passive safety measures (seat belts and airbag) are optimized for this anthropometry. Similarly, to the 30 km/h pulse, the results of the crash scenario with no safety features (seat belts only) for the average occupant (M, P50, 28 YO, 178 cm, 76 kg) are also provided. Table 5 summarizes the minimum, maximum and average values for all particular criteria, together with the anthropometry and SRS.
The results of 50 km/h impacting velocities also predicted the assessment of the nanobag with the safety region of the head and neck injury risk. Moreover, it also gave similar safety measures to the nanobag and airbag in this specific crash configuration. Most of the extremes values were also predicted for the airbag (maximum and minimum) in the higher velocity impact.

3.3. Summary of Results

The results of a frontal sled test exhibited a similar performance for the standard airbag and for the nanobag. In the impacting velocity of 30 km/h, all the HIC values lied within the minor injury risk (max. value was 214—nanobag). The maximal HIC of all the airbag scenarios was 164—which was in the same anthropometry as the total max value of 214 (M, P95, 54 years, 184 cm, 96 kg—NB6). The highest probability of head injury of AIS2+ was 6.7%. The highest neck injury risks were 14.6% (AIS2+), 6.0 (AIS3+), 8.3 (AIS4+) and 2.9 (AIS5+). AIS 2+ injury was classified as a moderate injury, and in case of the neck it was considered a minor laceration of vertebrae or dislocation without fracture.
The scenario with the higher impact velocity (50 km/h) resulted in a higher injury risk for the head and neck, respectively. The maximal HIC value was 702 (acceptable injury) (M, P95, 54 years, 184 cm, 96 kg—NB10), while the average was 520 (minor injury—good condition of survivability for human) [27]. The maximal HIC of all the airbag scenarios was 595 (M, P50, 44 years, 174 cm, 79 kg). The highest probability of head injury of AIS2+ was 35.5%. The highest neck injury risks were 17.7% (AIS2+), 8.6 (AIS3+), 10.2 (AIS4+) and 3.6 (AIS5+). All the values of injury risk lied within the safety region. The results suggest that increasing of the impact velocity affects more the head then the neck injury risk: head AIS2+: 6.7 (30 km/h) and 35.5 (50 km/h); and neck AIS2+: 14.7 (30 km/h) and 17.7 (50 km/h). The brain soft tissue injury criteria (BrIC and UBrIC) and maximum contact force had similar results for all particular scenarios (both impacting velocities and all configurations).

4. Conclusions

This paper tested the safety assessment of a new safety system called nanobag. Such a system consists of thin polymer foil protecting the head in the similar way to a traditional airbag. It is a brand new system, and it is not certified yet. This paper compares its protective effect in the frontal test and compares it to standard airbag performance. The benefits of the nanobag solution lie in the minimization of the volume of the folded nanobag, the minimization of the weight, the simplicity of the technology production and the low cost of material inputs, including the assembly and maintenance. The system can be easily adapted for nonstandard seating positions (tightly connected with the autonomous vehicles), where the side, oblique or rear direction of the impact are to be occurred. The performance of the standard airbag is assumed to lose its benefit in such configurations. The numerical simulations with the application of human body models as a surrogate for the occupant could lead to faster progress in the field of safety measure optimization for a diverse society. The nanobag safety system shows comparable performance to the traditional airbag solution in frontal direction impacts. For the tested populations (five age groups and 5th female, 50th percentile male and 94th percentile male) the assessment of the nanobag is comparable to the airbag. Most of the monitored criterion gave the maximum values (weak protection) for the airbag, but the airbag also predicts the minimal values in the same criterion (for different anthropometries). Such results show a similar protective effect for both safety systems (traditional airbag and new nanobag) for a frontal impact. The maximal HIC criterion results are below the critical safety limit of 1000 (214 for 30 km/h and 702 for 50 km/h), which is considered a safety region. However, the key point of the calculated results are comparable to the safety assessment of the nanobag system compared to the traditional airbag in the two velocities for frontal crashes.
It gives another possible safety solution for car developers and enables them to build future cars safer. The suggested technology does not try to replace the airbag; it only suggests a new technical solution, which, of course, could have some pros and cons, especially that a prototype of the mechanical system based on a nanobag must be tested in a real full-scale setup to be certified. Moreover, the technology of adjusting the system to real vehicles is also not fully developed. However, the safety effect is expected to be beneficial and comparable to the traditional airbag. Together with other safety technologies, the annanobag can find some benefits for future cars.

Author Contributions

Conceptualization, J.Š., T.B. and L.H.; methodology, J.Š., T.B., L.H. and A.H.; software, J.Š.; validation, J.Š., T.B. and L.H.; formal analysis, J.Š., T.B. and L.H.; investigation, A.H.; resources, J.Š. and L.H.; data curation, J.Š., T.B. and L.H.; writing—original draft preparation, J.Š.; writing—review and editing, T.B., L.H. and A.H.; visualization, J.Š.; supervision, L.H.; project administration, L.H.; funding acquisition, L.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by European Regional Development Fund, Project “Application of Modern Technologies in Medicine and Industry” (No. CZ.02.1.01/0.0/0.0/17_048/0007280), by the internal grant project “Computer modelling and monitoring of human body used for medicine” (No. SGS-2019-002) and by the John H. and Amy Bowles Lawrence Foundation.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

This work is supported by the European Regional Development Fund, Project “Application of Modern Technologies in Medicine and Industry” (No. CZ.02.1.01/0.0/0.0/17_048/0007280), by the internal grant project “Computer modelling and monitoring of human body used for medicine” (No. SGS-2019-002) and by the John H. and Amy Bowles Lawrence Foundation.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Schmitt, K.U.; Niederer, P.F.; Walz, F.; Muser, M.H. Trauma Biomechanics: Introduction to Accidental Injury; Springer Science & Business Media: New York, NY, USA, 2013; ISBN 978-3-642-53920-6. [Google Scholar]
  2. Nie, B.; Gan, S.; Chen, W.; Zhou, Q. Seating preferences in highly automated vehicles and occupant safety awareness: A national survey of chinese perceptions. Traffic Inj. Prev. 2020, 21, 247–253. [Google Scholar] [CrossRef] [PubMed]
  3. Jorlöv, S.; Bohman, K.; Larsson, A. Seating Positions and Activities in Highly Automated Cars—A Qualitative Study of Future Automated Driving Scenarios. In Proceedings of the International Research Council on the Biomechanics of Injury (IRCOBI) Conference, Antwerp, Belgium, 13–15 September 2017. [Google Scholar]
  4. Talimian, A.; Vychytil, J. Numerical study of frontal collision effects on an occupant’s safety, in autonomous vehicles, with non-standard seating configurations. Acta Polytech. Hung. 2021, 18, 127–140. [Google Scholar] [CrossRef]
  5. WHO. Global Status Report on the Road Safety. 2018. Available online: https://www.who.int/publications/i/item/9789241565684 (accessed on 14 January 2022).
  6. Nie, B.; Crandall, J.R.; Panzer, M.B. Computational investigation of the effects of knee airbag design on the interaction with occupant lower extremity in frontal and oblique impacts. Traffic Inj. Prev. 2017, 18, 207–215. [Google Scholar] [CrossRef] [PubMed]
  7. Hanuliak, A. Safety Restraint System for Motor Vehicles. Patent Number WO2018/219371, 6 December 2018. [Google Scholar]
  8. Špička, J.; Hynčík, L.; Hanuliak, A.; Kovář, L. Virtual assessment of advanced safety systems for new mobility modes. In Proceedings of the 16th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2019), New York, NY, USA, 14–16 August 2019. [Google Scholar]
  9. Scataglini, S.; Gunther, P. DHM and Posturography; Academic Press: Cambridge, MA, USA, 2019; ISBN 978-0-12-816713-7. [Google Scholar]
  10. Wang, J.Z.; Li, J.; Pallavajhala, K.; Zhou, Z.; Hu, J.; Boyle, K.; Reed, M. THOR 50M Suitability for Automated Vehicle Crashworthiness. In Proceedings of the ESV Conference, Eindhoven, The Netherlands, 10–13 June 2019. Paper No. 19-0162. [Google Scholar]
  11. Vychytil, J.; Maňas, J.; Čechová, H.; Špirk, S.; Hynčík, L.; Kovář, L. Scalable Multi-Purpose Virtual Human Model for Future Safety Assessment; SAE Technical Paper No. 2014-01-0534; SAE: Detroit, MI, USA, 2014. [Google Scholar] [CrossRef]
  12. Hynčík, L.; Kochová, P.; Špička, J.; Bońkowski, T.; Cimrman, R.; Kaňáková, S.; Kottner, R.; Pašek, M. Identification of LLDPE Constitutive Material Model for Energy Absorption in Impact Applications. Polymers 2021, 13, 1537. [Google Scholar] [CrossRef]
  13. ESI Group. Available online: https://www.esi-group.com/products/virtual-performance-solution (accessed on 9 October 2021).
  14. Hynčík, L.; Čechová, H.; Kovář, L.; Bláha, P. On Scaling Virtual Human Models; SAE Technical Paper No. 2013-01-0074; SAE: Detroit, MI, USA, 2013. [Google Scholar] [CrossRef]
  15. Bláha, P. Antropometrie Československé Populace od 6 do 55 Let; Ústř. štáb Čs. Spartakiády 1985: Praha, Czech Republic, 1987. [Google Scholar]
  16. Bońkowski, T.; Hyncik, L.; Lv, W. Numerical Assessment of Motorcyclist Accident. J. Soc. Automot. Eng. Malays. 2019, 3, 210–217. [Google Scholar]
  17. Lindstedt, L.; Vychytil, J.; Dziewwonski, T.; Hynčík, L. Numerical tests of the virtual human model response under dynamic load conditions defined in federal aviation regulation part 23.562 and 25.562—Preliminary study. Arch. Mech. Eng. 2016, LXIII, 511–530. [Google Scholar] [CrossRef] [Green Version]
  18. Lv, W.; Hynčík, L.; Bonkowski, T. Rider Stature Influence to Injury Risk in Motorcycle Rear Impact to Car; SAE Technical Paper 2019-01-1436; SAE: Detroit, MI, USA, 2019. [Google Scholar] [CrossRef]
  19. Hynčík, L.; Maňas, J.; Špička, J.; Špirk, S.; Kovář, L. Development of 6 Years Old Child Virtual Model by Automatic Scaling; SAE Technical Paper 2014-01-2028; SAE: Detroit, MI, USA, 2014. [Google Scholar] [CrossRef]
  20. Hynčík, L.; Špička, J.; Maňas, J.; Vychytil, J. Stature Based Approach towards Vehicle Safety (No. 2015-26-0209); SAE Technical Paper; SAE: Detroit, MI, USA, 2015. [Google Scholar] [CrossRef]
  21. Happee, R.; De Vlugt, E.; Schouten, A.C. Posture maintenance of the human upper extremity; identification of intrinsic and reflex based contributions. SAE Int. J. Passeng. Cars—Mech. Syst. 2008, 1, 1125–1135. [Google Scholar] [CrossRef]
  22. Vezin, P.; Garnier, K.; Bermond, F.; Verriest, J.P. Comparison of Hybrid III, Thor-alfa and PMHS response in frontal sled tests. Stapp Car Crash J. 2002, 46, 1–26. [Google Scholar] [CrossRef] [PubMed]
  23. Gabler, L.F.; Crandall, J.R.; Panzer, M.B. Development of a metric for predicting brain strain responses using head kinematics. Ann. Biomed. Eng. 2018, 46, 972–985. [Google Scholar] [CrossRef] [PubMed]
  24. Gabler, L.F.; Crandall, J.R.; Panzer, M.B. Development of a second-order system for rapid estimation of maximum brain strain. Ann. Biomed. Eng. 2019, 47, 1971–1981. [Google Scholar] [CrossRef] [PubMed]
  25. Sahoo, D.; Coulongeat, F.; Fuerst, F.; Marini, G. Comparison of Head Injury Criteria Based on Real-World Accident Simulations under Visual Performance Solution (VPS). In Proceedings of the IRCOBI Conference, Online, 8–10 September 2020. [Google Scholar]
  26. Wheeler, J.B.; Smith, T.; Siegmund, G.P.; Brault, J.R.; King, D.J. Validation of the neck injury criterion (NIC) using kinematic and clinical results from human subjects in rear-end collisions. In Proceedings of the International IRCOBI Conference on the Biomechanics of Impacts, Gothenburg, Sweden, 16–18 September 1998; pp. 335–348. [Google Scholar]
  27. Hobbs, C.A.; McDonough, P.J. Development of the European new car assessment programme (Euro NCAP). Regulation 1998, 44, 2439–2453. [Google Scholar]
Figure 1. Population of the Czech Republic in 2019 (source: Czech Statistical Office).
Figure 1. Population of the Czech Republic in 2019 (source: Czech Statistical Office).
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Figure 2. Nanobag system.
Figure 2. Nanobag system.
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Figure 3. The sled test initial configurations. The airbag (left) and the nanobag (right).
Figure 3. The sled test initial configurations. The airbag (left) and the nanobag (right).
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Figure 4. Head COG acceleration for 30 km/h pulse.
Figure 4. Head COG acceleration for 30 km/h pulse.
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Figure 5. BMI vs. HIC plots for 30 km/h pulse.
Figure 5. BMI vs. HIC plots for 30 km/h pulse.
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Figure 6. Head COG acceleration for 50 km/h pulse.
Figure 6. Head COG acceleration for 50 km/h pulse.
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Figure 7. BMI vs. HIC plots for 50 km/h pulse.
Figure 7. BMI vs. HIC plots for 50 km/h pulse.
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Figure 8. Nij criterion for average Male, P50, 28 YO, 178 cm, 76 kg (left) and Male, P95, 54 YO, 184 cm, 95 kg (right).
Figure 8. Nij criterion for average Male, P50, 28 YO, 178 cm, 76 kg (left) and Male, P95, 54 YO, 184 cm, 95 kg (right).
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Table 1. Selected occupant anthropometries of Virthuman model.
Table 1. Selected occupant anthropometries of Virthuman model.
AgeGenderPh [cm]m [kg]
14–15M5017057
M9518070
F5152.641.4
25–30M5017876
M9518794
F515852.5
35–45M5017479
M95185.596.3
F5154.352.5
45–55M50172.378.5
M95184.496
F5154.255
55–65M50171.983
M95180.295
F5153.560
Table 2. Results of 30 km/h impact pulse.
Table 2. Results of 30 km/h impact pulse.
VHm [kg]h [cm]BMISRSHICNijHeadNeckBrICUBrICFc [kN]
AIS2+AIS2+AIS3+AIS4+AIS5+MPSCSDM
M14 p505717020AB1260.131.6913.084.917.362.520.570.190.281.37
5717020NB41130.0711.2212.244.366.862.340.500.170.240.46
5717020NB61250.0611.6712.124.286.792.310.490.170.240.64
5717020NB81350.0612.0612.124.286.792.310.480.160.230.73
5717020NB101450.0512.5511.994.206.712.280.470.160.220.79
M14 p957018022AB950.0990.6912.624.607.082.420.640.230.321.41
7018022NB41100.0571.1112.064.256.752.300.500.170.240.49
7018022NB61280.051.7811.984.196.702.280.530.180.260.64
7018022NB81350.0452.1011.914.156.662.270.510.180.250.76
7018022NB101430.0392.4611.844.116.622.250.500.170.240.83
F14 p054115318AB1080.231.0614.455.888.182.820.500.170.240.90
4115318NB41260.131.6913.054.897.342.510.490.160.230.39
4115318NB61380.112.2012.794.727.182.450.480.160.230.48
4115318NB81450.0972.5612.594.587.062.410.470.150.220.54
4115318NB101530.0862.9212.444.496.982.380.460.150.220.63
M28 p507617824AB910.140.5913.225.017.442.550.630.220.310.10
7617824NB41000.090.8112.494.527.002.390.510.170.240.45
7617824NB61080.0771.0612.334.426.912.360.500.170.240.62
7617824NB81160.0691.3312.224.356.842.330.490.160.240.74
7617824NB101240.0661.6212.184.326.822.320.490.160.230.81
M28 p959418727AB950.110.6812.824.737.202.460.730.260.362.16
9418727NB41120.0691.2012.234.356.852.330.510.170.250.54
9418727NB61270.0551.7512.044.236.742.290.520.180.250.71
9418727NB81410.0492.3711.974.196.702.280.520.180.250.86
9418727NB101530.0452.9311.924.166.672.270.500.170.251.00
F28 p055315821AB1250.211.6714.175.688.022.750.470.160.221.17
5315821NB41370.132.1812.964.837.292.490.480.160.230.47
5315821NB61520.0952.9012.564.567.052.400.460.150.220.67
5315821NB81640.0773.5312.324.416.902.350.450.150.210.80
5315821NB101730.0724.0512.264.376.872.340.430.140.210.88
M44 p507917426AB1170.161.3613.385.127.542.580.720.260.361.64
7917426NB41250.0871.6612.464.506.992.380.570.200.280.47
7917426NB61400.0682.3312.214.346.842.330.620.220.310.64
7917426NB81510.0582.8612.084.266.762.300.630.230.310.84
7917426NB101620.0493.4411.974.196.702.280.640.230.320.94
M44 p959618628AB1440.142.4813.184.987.422.540.710.240.342.16
9618628NB41140.0871.2412.454.496.982.380.650.230.320.51
9618628NB61320.0781.9612.344.426.912.360.560.190.270.68
9618628NB81410.0632.3612.154.306.802.320.550.190.270.83
9618628NB101450.0592.5512.104.276.772.310.710.260.360.97
F44 p055315422AB1160.241.3414.545.948.242.830.420.140.201.45
5315422NB41370.122.1912.904.797.252.480.480.160.230.50
5315422NB61520.0892.8912.484.517.002.390.460.150.220.64
5315422NB81620.0873.4512.464.506.982.380.440.150.210.73
5315422NB101720.0863.9712.444.496.982.380.440.140.210.84
M54 p507917227AB1190.151.4113.285.057.482.560.750.270.381.77
7917227NB41440.0832.4712.404.466.952.370.670.240.340.53
7917227NB61500.0752.8112.304.406.892.350.670.240.330.71
7917227NB81720.0513.9711.994.206.712.280.790.290.400.87
7917227NB101840.054.7311.984.196.702.280.820.310.421.02
M54 p959618428AB1640.173.5213.565.247.652.620.610.210.302.30
9618428NB41470.0732.6612.284.386.882.340.570.200.280.53
9618428NB62140.0736.7312.274.386.872.340.510.170.250.77
9618428NB81800.0584.4612.094.266.772.300.600.210.300.87
9618428NB101930.0565.3412.064.256.752.300.570.200.290.99
F54 p055515423AB1220.241.5614.666.038.312.860.420.140.201.59
5515423NB41240.131.6113.044.897.342.510.560.190.270.47
5515423NB61470.12.6512.624.617.082.420.520.180.250.67
5515423NB81590.0863.2812.444.496.982.380.520.180.250.77
5515423NB101660.083.6812.364.446.932.360.440.140.210.86
M64 p508317228AB1240.171.6213.645.297.692.640.870.320.441.77
8317228NB41500.0992.7812.614.607.082.420.630.220.310.54
8317228NB61700.0773.8712.334.426.912.360.720.260.360.74
8317228NB81850.0634.8112.154.306.802.320.770.290.390.90
8317228NB101990.0635.7112.154.306.802.320.830.310.420.98
M64 p959518029AB1260.151.6813.265.037.472.550.900.330.452.11
9518029NB41360.0872.1012.464.506.982.380.830.310.420.53
9518029NB61700.0663.8812.184.326.822.320.800.300.410.71
9518029NB81680.0533.7812.024.226.732.290.690.250.350.85
9518029NB101930.0555.3312.044.236.742.290.700.260.351.00
F64 p056015425AB1200.241.4814.585.988.262.840.440.150.211.74
6015425NB41500.162.8013.415.147.562.590.540.180.250.47
6015425NB61670.143.7413.124.947.382.530.510.170.240.61
6015425NB81790.124.4412.924.807.262.480.490.160.230.71
6015425NB101870.114.8912.754.697.162.450.480.160.230.86
M28 p507617824--840.120.4512.904.797.252.480.540.180.26
Max 214.000.246.7314.666.038.312.860.900.330.452.30
Min 91.000.040.5911.844.116.622.250.420.140.200.10
Ø 144.150.102.6512.604.607.072.410.580.200.280.88
Table 3. Summary of the extreme values of the particular criteria for 30 km/h.
Table 3. Summary of the extreme values of the particular criteria for 30 km/h.
CriteriaMinMaxAverage
ValueAnthropometrySRSValueAnthropometrySRS
HIC91M P50, 28 YO, 178 cm, 76 kgAB214M P95, 54 YO, 184 cm, 96 kgNB6144
Nij0.039M P95, 14 YO, 180 cm, 70 kgNB100.24F P05, 64 YO, 150 cm, 60 kgAB0.1
BrIC0.4F P05, 44 YO, 154 cm, 53 kgAB0.9M P95, 64 YO, 180 cm, 95 kgAB0.58
UBrIC—MPS0.14F P05, 28 YO, 158 cm, 53 kgNB100.33M P95, 64 YO, 180 cm, 95 kgAB0.2
F P05, 44 YO, 154 cm, 53 kgAB & NB10
F P05, 54 YO, 154 cm, 55 kgAB
UBrIC—CSDM0.2F P05, 44 YO, 154 cm, 53 kgAB0.45M P95, 64 YO, 180 cm, 95 kgAB0.28
Fc [kN]0.1M P50, 28 YO, 178 cm, 76 kgAB2.3M P95, 54 YO, 184 cm, 96 kgAB0.88
Table 4. Results of 50 km/h impact pulse.
Table 4. Results of 50 km/h impact pulse.
VHm [kg]h [cm]BMISRSHICNijHeadNeckBrICUBrICFc [kN]
AIS2+AIS2+AIS3+AIS4+AIS5+MPSCSDM
M14 p505717020AB4930.2126.5914.135.657.992.750.720.240.351.00
5717020NB44090.1221.2612.874.777.232.470.600.200.290.67
5717020NB64280.1122.5712.704.667.132.440.580.190.280.95
5717020NB84560.0924.3612.524.547.022.400.600.200.291.16
5717020NB104760.0825.5912.344.426.922.360.610.210.301.36
M14 p957018022AB4990.1626.9013.405.137.552.590.960.350.482.49
7018022NB44600.1224.5812.844.757.212.460.620.200.300.67
7018022NB64950.1026.6712.594.597.072.410.580.190.280.89
7018022NB85410.0829.1412.394.466.952.370.710.250.351.13
7018022NB105750.0730.7812.294.396.892.350.790.290.401.32
F14 p054115318AB4230.3322.2416.027.099.153.170.570.190.281.99
4115318NB44190.2021.9614.075.607.952.730.610.200.290.56
4115318NB64400.1623.3413.485.187.602.600.570.190.270.79
4115318NB84760.1225.5912.864.767.222.470.540.180.260.96
4115318NB105080.0827.4312.394.466.952.370.520.170.251.15
M28 p507617824AB4520.2324.1014.495.918.212.821.020.370.510.12
7617824NB44960.1726.7713.645.307.692.640.630.210.310.79
7617824NB65190.1628.0213.405.137.552.580.590.200.291.02
7617824NB85410.1429.1813.204.997.432.540.580.200.281.27
7617824NB105620.1330.1912.974.837.292.490.620.220.311.51
M28 p959418727AB5100.1927.5113.925.507.872.700.910.330.452.41
9418727NB45410.1829.1413.745.377.752.660.840.280.401.00
9418727NB65050.1627.2413.425.147.562.590.930.320.460.99
9418727NB84990.1426.9113.104.927.372.520.640.220.321.44
9418727NB105170.1327.9112.984.857.302.500.720.250.361.70
F28 p055315821AB5610.4430.1517.778.5710.243.570.810.260.422.42
5315821NB45710.2730.5915.076.358.562.950.710.230.350.63
5315821NB66000.2231.8914.315.788.102.790.670.220.340.93
5315821NB86200.1832.6913.675.327.712.640.650.210.331.17
5315821NB106430.1633.5813.425.147.562.590.640.210.321.38
M44 p507917426AB5950.2431.7014.545.958.242.840.930.330.472.79
7917426NB44750.1525.5313.365.107.532.580.650.220.320.70
7917426NB65060.1327.3313.094.927.362.520.620.210.301.00
7917426NB85450.1129.3612.744.687.152.440.670.240.331.29
7917426NB105770.0930.8812.454.496.982.380.750.270.371.57
M44 p959618628AB5670.2430.4314.585.988.272.840.890.310.443.34
9618628NB46240.1732.8713.645.307.702.640.800.260.390.82
9618628NB65000.1426.9813.114.937.382.520.640.210.311.09
9618628NB86190.1732.6813.525.217.622.610.770.260.371.30
9618628NB106360.1633.3213.465.177.582.600.760.260.371.55
F44 p055315422AB4530.3924.1416.957.869.723.380.630.220.312.73
5315422NB44480.2623.8514.826.168.412.900.640.210.310.63
5315422NB64760.2025.6114.045.587.942.730.610.200.290.85
5315422NB85130.1627.7013.465.177.582.600.580.190.281.09
5315422NB105420.1429.2213.104.937.372.520.570.190.281.27
M54 p507917227AB5310.2328.6614.495.918.212.820.790.280.403.35
7917227NB44760.1625.5813.395.127.542.580.680.230.330.70
7917227NB65160.1427.8613.154.967.402.530.620.210.301.00
7917227NB85450.1229.3912.854.767.222.470.710.250.351.29
7917227NB105710.1030.6112.594.587.062.410.820.300.411.53
M54 p959618428AB5630.2530.2214.806.148.402.890.860.310.433.62
9618428NB45190.2028.0514.065.597.952.730.850.280.410.68
9618428NB66760.1934.7113.905.487.852.700.880.290.420.98
9618428NB87040.1835.5513.755.377.762.660.850.280.401.00
9618428NB107030.1935.5313.875.467.832.690.830.280.401.14
F54 p055515423AB4440.3823.6016.807.749.633.350.560.190.272.81
5515423NB43770.2419.0114.535.948.232.830.640.210.310.61
5515423NB64500.2023.9714.055.597.942.730.570.190.270.89
5515423NB84560.1524.3813.295.057.482.560.580.200.281.09
5515423NB104870.1326.2612.984.847.302.490.630.220.311.30
M64 p508317228AB4970.2526.7914.816.158.402.891.090.400.552.59
8317228NB45380.1829.0413.695.337.722.650.580.190.290.83
8317228NB65920.1631.5313.475.187.602.600.650.210.320.94
8317228NB86350.1633.2713.415.147.562.590.750.240.391.21
8317228NB106240.1332.8713.014.867.322.500.700.250.351.44
M64 p959518029AB6290.2233.0614.305.778.092.781.040.380.533.69
9518029NB45380.1429.0413.134.947.392.530.730.240.360.88
9518029NB65150.1127.8012.784.717.182.450.820.290.411.10
9518029NB86480.1333.7712.964.837.292.490.640.210.321.50
9518029NB105630.1030.2312.594.597.072.410.930.340.471.67
F64 p056015425AB4050.3620.9816.497.489.443.280.500.170.242.53
6015425NB44740.2425.4314.545.948.242.830.680.220.330.63
6015425NB64990.1926.9413.905.487.852.690.640.210.300.88
6015425NB85240.1728.2913.545.237.642.620.630.210.311.16
6015425NB105390.1629.0513.445.167.582.600.640.210.321.37
M28 p507617824--5390.1729.0713.655.307.702.640.740.250.35
Max 7020.4435.5017.778.5710.243.571.090.400.553.69
Min 3770.0719.0212.294.396.892.350.500.170.240.12
Ø 4540.0924.1912.524.547.022.400.600.200.291.16
Table 5. Summary of the extreme values of the particular criteria for 50 km/h.
Table 5. Summary of the extreme values of the particular criteria for 50 km/h.
CriteriaMinMaxAverage
ValueAnthropometrySRSValueAnthropometrySRS
HIC377F P05, 54 YO, 154 cm, 55 kgNB4702M P95, 54 YO, 184 cm, 96 kgNB10454
Nij0.07M P95, 14 YO, 180 cm, 70 kgNB100.44F P05, 28 YO, 158 cm, 53 kgAB0.09
BrIC0.5F P05, 64 YO, 154 cm, 60 kgAB1.09M P50, 64 YO, 172 cm, 83 kgAB0.6
UBrIC—MPS0.17F P05, 14 YO, 153 cm, 41 kgNB100.4M P50, 64 YO, 172 cm, 83 kgAB0.2
F P05, 64 YO, 154 cm, 60 kgAB
UBrIC—CSDM0.24F P05, 64 YO, 154 cm, 60 kgAB0.55M P50, 64 YO, 172 cm, 83 kgAB0.29
Fc [kN]0.12M P50, 28 YO, 178 cm, 76 kgAB3.69M P95, 64 YO, 180 cm, 95 kgAB1.16
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Špička, J.; Bońkowski, T.; Hynčík, L.; Hanuliak, A. Assessment of Nanobag as a New Safety System in the Frontal Sled Test. Appl. Sci. 2022, 12, 989. https://doi.org/10.3390/app12030989

AMA Style

Špička J, Bońkowski T, Hynčík L, Hanuliak A. Assessment of Nanobag as a New Safety System in the Frontal Sled Test. Applied Sciences. 2022; 12(3):989. https://doi.org/10.3390/app12030989

Chicago/Turabian Style

Špička, Jan, Tomasz Bońkowski, Luděk Hynčík, and Alojz Hanuliak. 2022. "Assessment of Nanobag as a New Safety System in the Frontal Sled Test" Applied Sciences 12, no. 3: 989. https://doi.org/10.3390/app12030989

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