Farside Sledtests (Forman 2013)¶

Model validation information

Version	Date	Performed by	LS-Dyna
0.3.2	2022-01-30	Johan Iraeus	9.3.1
1.1.1	2024-05-22	Johan Iraeus	12.2.1
2.0.0	2024-10-28	Johan Iraeus	12.2.1

Added to VIVA+ Validation Catalog on : 2022-01-30

© 2019-2024, OpenVT Organization (OVTO)

Available openly under Creative Commons Attribution 4.0 International License

Experiment by Forman et al. (2013)¶

Summary:¶

The simulated outputs are compared to the references from PMHS tests reported by Forman et al., 2013

Forman, J. L., Lopez-Valdes, F., Lessley, D. J., Riley, P., Sochor, M., Heltzel, S., et al. (2013). Occupant kinematics and shoulder belt retention in far-side lateral and oblique collisions: a parametric study. Stapp Car Crash J 57, 343–385.

Experiment¶

Information on the subjects/specimens¶

|Subject #|Age|Mass|Statur|Sex|other comments| |---|--|---|---|---|---| |557|67|91|174|m| |551|67|83|171|m| |559|60|73|175|m|limited CPR performed at the time of death, but no rib fractures were observed on the pre-test CT| |591|44|86|182|m|possibly two pre-existing rib fractures| |587|70|91|180|m|multiple pre-existig rib fractures| |602|61|79|178|m |608|56|79|172|m|

Loading and Boundary Conditions¶

6 different configurations of farside sledtests were used in line with Pipkorn, B., Larsson, K.-J., Perez-Rapela, D., Markusic, C., Whitcomb, B., Ayyagari, M., et al. (2018). “Occupant Protection in Far‐Side Impacts,” in 2018 IRCOBI Conference Proceedings, ed. International Research Council on the Biomechanics of Injury (IRCOBI), 76–105.

Configuration	Delta v [km/h]	Impact Direction [°]	D-Ring position	Pre-tesioning	Pelvis blocked	PMHS ID	Test ID

1|34|60|Middle|Yes|No|591,602|S0124, S0135| 2|16|60|Middle|No|No|591,602,608|S0233,S0133,S0136| 3|16|60|Middle|Yes|No|591,602,608|S0123|S0134|S0137| 4|16|60|Back|Yes|Yes|587|S0129| 5|16|90|Forward|Yes|No|551,559|S0083,S0088| 6|34|90|Middle|Yes|No|559|S0091|

Positioning¶

Positionig happens within the first 350 ms of the simulations, which is considered by a time offset within the graphs. The VIVA+ 50M model was used in its original anthropometry (without scaling/morphing)

Other Notes for simulation¶

During the first 350 ms the simulation model is gravity settled, and tensioning the belt. During the first 300 ms global damping is applied.

In [3]:

# Your name 
name="Johan_Iraeus"

# LS-Dyna version (if you have not run the simulations on your own, fill in "example"):
dyna_executable_name="R12.2-86-g831c51f1f6"

# Overall number of CPUs
n_cpu = "32"

# Platform (from d3hsp file)
platform = "Intel-MPI 2018 Xeon64"

# OS Level (from d3hsp file)
os_level = "Linux CentOS 7.9 uom" 

# Do not change! Date is filled in automatically
date = datetime.date.today().strftime("%Y-%m-%d")

# Your name name="Johan_Iraeus" # LS-Dyna version (if you have not run the simulations on your own, fill in "example"): dyna_executable_name="R12.2-86-g831c51f1f6" # Overall number of CPUs n_cpu = "32" # Platform (from d3hsp file) platform = "Intel-MPI 2018 Xeon64" # OS Level (from d3hsp file) os_level = "Linux CentOS 7.9 uom" # Do not change! Date is filled in automatically date = datetime.date.today().strftime("%Y-%m-%d")

In [6]:

# write metadata
metadata={
  "date": date,
  "name": name,
  "n_cpu": n_cpu,
  "dyna_executable_name": dyna_executable_name,
  "platform": platform,
  "os_level": os_level
}

with open(os.path.join(processed_data_dir, simulation_meta_file_name), 'w+') as f:
  json.dump(metadata, f, indent=4)

# write metadata metadata={ "date": date, "name": name, "n_cpu": n_cpu, "dyna_executable_name": dyna_executable_name, "platform": platform, "os_level": os_level } with open(os.path.join(processed_data_dir, simulation_meta_file_name), 'w+') as f: json.dump(metadata, f, indent=4)

Results for different configurations¶

Configuration 1¶

In [14]:

hide-cell

simData_50M=simData_50M_Config1
Loadcase="Config1"

simData_50M=simData_50M_Config1 Loadcase="Config1"

In the figure below the energy balance for configuration 1 is shown

Belt Forces¶

Out[16]:

<matplotlib.legend.Legend at 0x28a022cd750>

Out[17]:

<matplotlib.legend.Legend at 0x28a7799d750>

Out[18]:

<matplotlib.legend.Legend at 0x28a0730b410>

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

Acromium¶

Out[19]:

<matplotlib.legend.Legend at 0x28a79018690>

Out[20]:

<matplotlib.legend.Legend at 0x28a77a9b1d0>

Head CoG¶

Out[21]:

<matplotlib.legend.Legend at 0x28a77ad70d0>

Pelvis¶

Out[22]:

<matplotlib.legend.Legend at 0x28a05623ed0>

Thoraic Spine (T1)¶

Out[23]:

<matplotlib.legend.Legend at 0x28a05662110>

Configuration 2¶

Energies¶

In the figure below the energy balance for configuration 2 is shown

Belt Forces¶

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

Configuration 3¶

Energies¶

In the figure below the energy balance for configuration 3 is shown

Belt Forces¶

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

Configuration 4¶

Energies¶

In the figure below the energy balance for configuration 4 is shown

Belt Forces¶

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

Configuration 5¶

Energies¶

In the figure below the energy balance for configuration 5 is shown

Belt Forces¶

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

Configuration 6¶

Energies¶

In the figure below the energy balance for configuration 6 is shown

Belt Forces¶

Kinematics¶

The figures below compares the predicted kinematics with teh kinematics from the PMHS tests, for the shoulder, pelvis, head and thoracic spine.

In [ ]: