Pedestrian (Snedeker 2003)

Pedestrian (Snedeker 2003)#

Model validation information

Performed by: Nico Erlinger
Reviewed by: Corina Klug

Added to VIVA+ Validation Catalog on 2022-11-30 by Corina Klug

Version	Date	Performed by	LS-Dyna
	2022-11-30	Nico Erlinger	9.3.1
0.3.2	2023-11-23	Corina Klug	9.3.1
1.1.1	2024-05-22	Matej Kranjec	12.2.1

Available openly under under Creative Commons Attribution 4.0 International License

References#

Experiments by Snedeker et al. (2003)#

Snedeker, J.G., Muser, M.H., Walz, F.H., 2003. Assessment of pelvis and upper leg injury risk in car-pedestrian collisions: comparison of accident statistics, impactor tests and a human body finite element model. Stapp Car Crash J 47, 437–457. References

Snedeker, J.G., Muser, M.H., Walz, F.H., 2003. Assessment of pelvis and upper leg injury risk in car-pedestrian collisions: comparison of accident statistics, impactor tests and a human body finite element model. Stapp Car Crash J 47, 437–457. Snedeker, J.G., Walz, F.H., Muser, M.H., Schroeder, G., Mueller, T.L., Müller, R., 2006. Microstructural insight into pedestrian pelvic fracture as assessed by high-resolution computed tomography. Journal of Biomechanics 39, 2709–2713. https://doi.org/10.1016/j.jbiomech.2005.09.008.

VIVA+ validation#

Manuscript currently under preparation

Information on the subjects/specimens#

PMHS	Sex	Height [cm]	Weight [kg]	Age [yr]	Scale factor z (height)	Scale factor xand y (weight)
T1	f	160	50	52	0.99	0.89
T2	f	166	74	76	1.03	1.08
T3	m	177	75	32	1.01	0.98
T4	m	180	64	78	1.03	0.90
T5	m	172	60	76	0.98	0.89

Loading and Boundary Conditions#

The vehicle shapes are differing in the different loadcases, which is shown exemplary for loadcase T1 & T5 in the figure below.

The initial velocity of the vehicle and its deceleration is prescribed. Open Issue: Implement damping to adress vehicle front oscillations (visible in energy plots).

Experimental responses#

The experimental responses from the original papers were digitalised.

Simulation metadata#

# Your name 
name="Matej_Kranjec"

# LS-Dyna version (if you have not run the simulations on your own, fill in "example"):
dyna_executable_name="ls-dyna_mpp_s_R12.2.1_86_winx64_ifort170_msmpi"

# Overall number of CPUs
n_cpu = "40"

# Platform (from d3hsp file)
platform = "MS API x86_64"

# OS Level (from d3hsp file)
os_level = "Windows 11" 

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

# 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#

Trajectories#

../_images/3a76da71474b27f6c816112f2d20fbca5bf27b178ea8a921f088e4ac2477e632.png

../_images/c4afe217717caf1ac2e207c08bdca3b572cfaeb467f90194c49eda5993f3dad7.png

Femur and Tibia strains#

../_images/4877ac415fee12a85810d5a506883e229332897333acb1040e7213c286a42fe5.png

../_images/a8f22bab40200432722100735db778a318719cccb7ba061b960dd8bc9069cdcd.png

Energy time histories#

../_images/792b3f494f8b2178ace358a247247dce95e3be13d5d524af7be41ff6117585de.png

Plotting peak strains of tibia and femur on IRCs#

Show code cell source Hide code cell source

def find_peak_strains(title, x_data, y_data):
    list = []
    for i in simulation_list:
        processed_data_path = os.path.join(processed_data_dir, i).replace('\\', '/')
        simData = pd.read_csv(os.path.join(processed_data_path, dynasaur_output_file_name), 
                                    delimiter=';', na_values='-', header = [0,1,2,3])
        
        simData.fillna(0.0, inplace=True)
        sim_x_data = np.array(simData[x_data]).flatten()
        sim_y_data = np.array(simData[y_data]).flatten()
        
        peak_strain_ind = np.argmax(sim_y_data)
        peak_time = sim_x_data[peak_strain_ind]
        peak_strain = sim_y_data[peak_strain_ind]
        list.append([i, peak_time, peak_strain])

    list = np.reshape(list, (len(simulation_list), 3))
    return list
    
def plot_peak_strains_on_irc(x_label, filename_strains_irc, peak_strain_array):
    plotT1 = { "marker" :'D', "color" : 'black',}
    plotT2 = { "marker" :'D', "color" : 'red', }
    plotT3 = { "marker" :'D', "color" : 'darkorange',}
    plotT4 = { "marker" :'D', "color" : 'lime',}
    plotT5 = { "marker" :'D', "color" : 'blue',}

    sim_strain_data = peak_strain_array[:,2].astype(float)
    sim_name_data = peak_strain_array[:,0]
    df_strains_irc = pd.read_csv(filename_strains_irc, sep = ";")

    plt.figure(figsize=(7,5))
    weibull_fit = Fit_Weibull_2P(failures=list(df_strains_irc.PS99),show_probability_plot=False,print_results=False, CI=0.95, CI_type="time")
    
    weibull_fit.distribution.CDF(label='Fitted Distribution',color='steelblue')
    plt.xlabel(x_label, fontweight='semibold',fontsize=10)
    plt.ylabel("Probability",fontweight='semibold',fontsize=10)

    x_values = sim_strain_data.flatten()
    axs = plt.gca()
    line = axs.lines[0]
    IRC_distrib_x = line.get_xdata()
    IRC_distrib_y = line.get_ydata()

    probability_list = []
    for x in range(len(x_values)):
        smaller_values = np.where(IRC_distrib_x < x_values[x])
        bigger_values = np.where(IRC_distrib_x > x_values[x])
        
        ind_small = smaller_values[0][-1]
        ind_big = ind_small+1
        
        if ind_small == 199:
            y_value = 0.9999    
        else:
            x_interp_array = [IRC_distrib_x[ind_small],IRC_distrib_x[ind_big]]
            y_interp_array = [IRC_distrib_y[ind_small],IRC_distrib_y[ind_big]]
            y_value = np.interp(x_values[x],x_interp_array,y_interp_array)

        simPlot = locals()["plot" + sim_name_data[x]]
        plt.scatter(x_values[x], y_value,**simPlot,label=sim_name_data[x] + ' = ' + str(round(y_value*100,1)) + '%')
        plt.legend(loc=0)
        probability_list.append(y_value*100)
    return(probability_list)

	Simulation	Tibia fracture probability [%]	Experiment tibia fracture	Femur fracture probability [%]	Experiment femur fracture
0	T1	99.99	no	4.12596	no
1	T2	99.99	no	2.69578	no
2	T3	99.99	yes	2.70425	no
3	T4	99.99	yes	8.0442	no
4	T5	98.1308	yes	4.57149	no

../_images/ea6a921e67e45b25e3120453504e1db9685c7d11395b18c17e009f5f0903b58d.png

../_images/0a1a5d55f0fb5917ca8520c2b0384c8539622a1b159f8e9cbed5b99df51fa283.png

Head impact times#

	Simulation	First leg contact [ms]	First head contact [ms]	HIT [ms]
0	T1	4.0	124.0	120
1	T2	1.0	131.0	130
2	T3	2.0	146.0	144
3	T4	3.0	102.0	99
4	T5	3.0	99.0	96

../_images/904f3f131db65f0c50dbe66a3f260afbf6d6bda116947463c2849d910030053f.png