Figure 3 - uploaded by Jóna Marín Ólafsdóttir
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2: Force, length and velocity relationships in a Hill-type muscle model. Left: Normalised active (solid line) and passive (dotted line) force as a function of normalised muscle length. Right: Normalised force as a function of normalised shortening/lengthening velocity.
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Fatalities and injuries to car occupants in motor vehicle crashes continue to be a
serious global socio-economic issue. Advanced safety systems that provide improved
occupant protection and crash mitigation have the potential to reduce this burden.
For the development and virtual assessment of these systems, numerical human body
models (HBMs) that...
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Background
Low-velocity motor vehicle crashes often lead to severe and chronic neck disorders also referred to as whiplash-associated disorders (WAD). The etiology of WAD is still not fully understood. Many studies using a real or simulated collision scenario have focused on rear-end collisions, whereas the kinematics and muscular responses during...
Citations
... Since many active FE HBMs focus on a specific crash scenario, early models used open-loop control with pre-defined muscle activation patterns that were calculated offline from experimental EMG or by solving an application-specific optimization problem [46]. Closed-loop reflex control was later introduced to improve the response of the arms [89,127], and the neck [86] during crash simulations. ...
Significant trends in the vehicle industry are autonomous driving, micromobility, electrification and the increased use of shared mobility solutions. These new vehicle automation and mobility classes lead to a larger number of occupant positions, interiors and load directions. As safety systems interact with and protect occupants, it is essential to place the human, with its variability and vulnerability, at the center of the design and operation of these systems. Digital human body models (HBMs) can help meet these requirements and are therefore increasingly being integrated into the development of new vehicle models. This contribution provides an overview of current HBMs and their applications in vehicle safety in different driving modes. The authors briefly introduce the underlying mathematical methods and present a selection of HBMs to the reader. An overview table with guideline values for simulation times, common applications and available variants of the models is provided. To provide insight into the broad application of HBMs, the authors present three case studies in the field of vehicle safety: (i) in-crash finite element simulations and injuries of riders on a motorcycle; (ii) scenario-based assessment of the active pre-crash behavior of occupants with the Madymo multibody HBM; (iii) prediction of human behavior in a take-over scenario using the EMMA model.
... The SAFER HBM, Figure 2, has active musculature implemented using Hill-type beam muscle elements controlled by feedback controllers [19]. The most recent update of the active muscle control includes an omnidirectional lumbar and cervical spine controller [20], which enables modelling of both car driver and passenger reflexive and postural responses in pre-crash scenarios. ...
The objective of this ACEA funded study was to determine the effect of different pedestrian autonomous emergency braking (P-AEB) systems on the collision speeds of real world pedestrian accidents originating from three different accident databases. The precrash phases of real world passenger car to pedestrian frontal accidents from the in-depth accident databases were investigated using different pre-crash simulation tools. Collision parameters were compared between the original real-world cases and cases with treatment conditions. For treatment simulations, the car was equipped with a virtual generic P-AEB system, triggered at a time to collision (TTC)≤ 1 s. The range of the generic sensor was 80 m and the opening angle was varied between 60°, 90° and 120°. For the braking system, two different brake gradients (24.5 m/s³ and 35 m/s³) were modelled with different decelerations of 0.8 g and 1.1 g. Accidents from the Austrian in-depth accident database CEDATU (n=50), the German GIDAS (n=1084) and Swedish V_PAD (n=68) were used for the baseline. The effect of using different data samples was compared to the effect of assuming different generic AEB system parameters. The best performing P-AEB system (120°, innovative brake system) avoided 42% of the CEDATU cases, while the baseline P-AEB system (60°, standard brake system) avoided 18%. The best performing AEB System was able to avoid 79.4% of the V_PAD sample. The baseline P-AEB avoided in V_PAD at least 66.2% compared to GIDAS with 39.5%. The lower the mean collision speed of the sample, the higher was the benefit of the P-AEB system, as a higher percentage of cases can be avoided. The study shows that system parameters and the selection of accidents can greatly affect the outcome in prospective traffic safety analyses. As a significant reduction of collision speeds was seen in all three data sources, the study highlights the need for a combined vehicle safety assessment instead of a separate evaluation of active and passive pedestrian safety measures.
... • The controlling muscles activation was adopted from Östh et al. [13] and Olafsdottir. [14] 12. Goldberg ...
Summary
A previously validated open-source head-neck model, VIVA Open HBM [1-3], was enhanced by the addition of muscle activation. The previous model contained 129 beam elements on both the left and right side of the neck. Each element had Hill 3 elements (*MAT_MUSCLE_156) [4,5] definitions for material model. However, these muscles were defined as passive muscles model without any active tensile forces.
The first goal of this study was to implement the LS-DYNA PID (Proportional Integral Derivative) feedback control mechanism [5,6] on Finite Element (FE) models of cervical muscles, using the enhancements introduced in LS-DYNA version 9.2 [5]. The second goal was to calibrate the PID control gains by conducting a parameter identification using LS-OPT [7] with published-volunteer data in rear impact collisions [8-9] being used as reference performance data.
To activate these muscles, the closed-loop control strategy (reflexive feedback control function) was applied. To develop this strategy, the LS-DYNA PID Control function (PIDCTL) which can be defined inside the *DEFINE_CURVE_FUNCTION keyword was utilized. The method of controlling muscles activation with reflexive feedback control was adopted from Östh, et.al [10] and Olafsdottir [11].
To mimic the human body’s vestibular system, the coordinates of two nodes (Head Center of Gravity node and T1 node) and a reference node were sampled at specific times reference and used to define the controller vector. An angle was calculated between these two vectors. The error angle between the current measured value and referenced time value was calculated. This error signal then was delayed, mimicking the human’s neural delay. To model this neural delay, the DELAY function inside *DEFINE_CURVE_FUNCTION was applied. The PID controllers were given the delayed-error signal from the previous calculation and used to compute a control signal with an objective of zero error.
A calibration study was conducted to identify reasonable gain values of the controller so that the head displacements of the model in X and Z direction match within ± 1 Standard Deviation (SD) head displacements of the volunteer data.
The simulation results had shown that the LS-DYNA PIDCTL and DELAY function were successfully utilized for controlling the model muscle’s activation.
Keywords: PID control, feedback control, cervical muscles, finite element, human body model.
References
1. Östh, J., Vazquez, M. M., Svensson, M. Y., Linder, A., & Brolin, K. (2016). Development of a 50th percentile female human body model. 2016 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, 573–575.
2. Östh, J., Vazquez, M. M., Linder, A., Svensson, M. Y., & Brolin, K. (2017). The VIVA Open HBM Finite Element 50th Percentile Female Occupant Model: Whole Body Model Development and Kinematic Validation. 2017 IRCOBI Conference Proceedings - International Research Council on the Biomechanics of Injury, 443-466.
3. Östh, J., Mendoza-Vazquez, M., Sato, F., Svensson, M. Y., Linder, A., & Brolin, K. (2017). A female head–neck model for rear impact simulations. Journal of Biomechanics, 51, 49–56. https://doi.org/10.1016/j.jbiomech.2016.11.066
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7. Stander, N., Roux, W., Goel, T., Eggleston, T., & Craig, K. (2010). LS-OPT ® User’s Manual - A Design Optimization and Probabilistic Analysis Tool.
8. Ono, K., Ejima, S., Suzuki, Y., Kaneoka, K., Fukushima, M., & Ujihashi, S. (2006). Prediction of Neck Injury Risk Based on the Analysis of Localized Cervical Vertebral Motion of Human Volunteers During Low-Speed Rear Impacts. IRCOBI Conference Proceedings, 103–113.
9. Sato, F., Nakajima, T., Ono, K., & Svensson, M. (2014). Dynamic Cervical Vertebral Motion of Female and Male Volunteers and Analysis of its Interaction with Head/Neck/Torso Behavior during Low-Speed Rear. IRCOBI Conference Proceedings, 227–249.
10. Östh, J., Brolin, K., & Happee, R. (2012). Active muscle response using feedback control of a finite element human arm model. Computer Methods in Biomechanics and Biomedical Engineering, 15(4), 347–361. https://doi.org/10.1080/10255842.2010.535523
11. Ólafsdóttir, J. M. (2017) Muscle Responses in Dynamic Events- Volunteer Experiments And Numerical Modelling For The Advancement Of Human Body Models For Vehicle Safety Assessment, Ph.D Thesis In Machine And Vehicle Systems, Chalmers University of Technology.
Acknowledgement:
This study was funded by the Swedish Governmental Agency for Innovation Systems (VINNOVA). The simulations were performed on resources at Chalmers Centre for Computational Science and Engineering (C3SE) provided by the Swedish National Infrastructure for Computing (SNIC) and carried out at Vehicle and Traffic Safety Research Centre at Chalmers (SAFER). The authors would like to thank the project members: Astrid Linder, Mats Svensson, Lotta Jacobson, Anders Kullgren and Anders Flögard.
In contrast to conventional hard actuators, soft actuators offer many vivid advantages, such as improved flexibility, adaptability, and reconfigurability, which are intrinsic to living systems. These properties make them particularly promising for different applications, including soft electronics, surgery, drug delivery, artificial organs, or prosthesis. The additional degree of freedom for soft actuatoric devices can be provided through the use of intelligent materials, which are able to change their structure, macroscopic properties, and shape under the influence of external signals. The use of such intelligent materials allows a substantial reduction of a device's size, which enables a number of applications that cannot be realized by externally powered systems. This review aims to provide an overview of the properties of intelligent synthetic and living/natural materials used for the fabrication of soft robotic devices. We discuss basic physical/chemical properties of the main kinds of materials (elastomers, gels, shape memory polymers and gels, liquid crystalline elastomers, semicrystalline ferroelectric polymers, gels and hydrogels, other swelling polymers, materials with volume change during melting/crystallization, materials with tunable mechanical properties, and living and naturally derived materials), how they are related to actuation and soft robotic application, and effects of micro/macro structures on shape transformation, fabrication methods, and we highlight selected applications.
