2. Simulating Seive Analysis

2.1. How to use the tool?

1. Go to the Binder by clicking the rocket button (top-right of the page)

2. Execute the code cell

3. Change the values of different quantities in the box and click the run interact.

4. From the resulting figure, using your mouse and selecting points in the figure obtain d10 and d60.

5. Execute the second code-cell and provide d10, d60 and temperature date

6. Click the exectute button.

7. For re-simulations - changes the input values in the boxes and click the “run interact” button.

This tool can also be downloaded and run locally. For that download the deacy.ipynb file and execute the process in any editor (e.g., JUPYTER notebook, JUPYTER lab) that is able to read and execute this file-type.

The code may also be executed in the book page.

The codes are licensed under CC by 4.0 (use anyways, but acknowledge the original work)

The plot shown is interactive use the pointer and others tools in the graph to obtain d10 and d60 for the next step

# used Python library
import numpy as np # for calculation 
import matplotlib.pyplot as plt  # for plotting
import pandas as pd  # for data table
import ipywidgets as widgets # for widgets
#%matplotlib widget
import warnings; warnings.simplefilter('ignore')
from scipy.interpolate import interp1d,CubicSpline,UnivariateSpline,Rbf
from scipy import interpolate

#from scipy.interpolate import Rbf

print("Please provide the seive data in the boxes:  ")

def SA(mu, m1, m2, m3, m4, ml,perdat):
    dia = [6,2,0.6,0.2, 0.06, 0.01] # mm, diameter <0.06 (cup)= 0.01, >2 = 6
    mass = [mu, m1, m2, m3, m4, ml] # g, the residue in seive 
    Total_mass = np.sum(mass)  # add the mass column to get total mass
    retain_per = np.round(mass/Total_mass*100,3)   # retain percentage
    retain_per_cumsum = np.round(np.cumsum(retain_per),3) # get the cummulative sum of the reatined
    passing_per = np.round(100 - retain_per_cumsum, 3) # substract 100-cummsum to get passing %
    data = {"mesh diameter [mm]": dia, "residue in the sieve [g]": mass, "Σtotal": retain_per, "Σ/Σtotal": passing_per }

    df1= pd.DataFrame(data)
    df1 = df1.set_index("mesh diameter [mm]")
    print(df1)

    plt.rcParams['axes.linewidth']=2
    #plt.rcParams["axes.edgecolor"]='white'
    plt.rcParams['grid.linestyle']='--'
    plt.rcParams['grid.linewidth']=1
    x = np.append([20],dia) # adding data to extend over 6 mm dia
    y = np.append([100],passing_per) # adding 100% to plot
    
    
    y.sort()
    x.sort()
    interp_func = interp1d(y,x)
    
    ######## Other Interpolation Functions #############
    
    #interp_func=interpolate.splev(y,x, der=0)
    #interp_func = UnivariateSpline(y, x)
    #interp_func = CubicSpline(y, x)
    #interp_func = Rbf(y, x)
    
    Dd60 = interp_func(60)
    Dd10=interp_func(10)
    Ddx=interp_func(perdat)
    

    
    print('\n','\n')
    print("d60 =","%.2f" % Dd60)
    print("d10 =","%.2f" % Dd10)
    print ('d%d = %.2f' % (perdat, Ddx))
    

    
    
    fig,ax = plt.subplots(figsize=(15,10))
    fig.canvas.header_visible = False
    plt.semilogx(x, y, 'x-', color='red')  
    tics=x.tolist()

    ax.grid(which='major', color='k', alpha=0.7) 
    ax.grid(which='minor', color='k', alpha=0.3)
    ax.set_xticks(x);  
    ax.set_yticks(np.arange(0,110,10));
    plt.title('grain size distribution');
    plt.xlabel('grain size d [mm]');
    plt.ylabel('grain fraction < d ins % of total mass');

    plt.plot([0,Dd60,Dd60,Dd60],[60,60,0,60],ls='-',color='g');
    plt.plot([0,Dd10,Dd10,Dd10],[10,10,0,10],ls='-',color='r');
    plt.plot([0,Ddx,Ddx,Ddx],[perdat,perdat,0,perdat],ls='-',color='#8A2BE2')
    ax.set_xlim(0, 30)
    from matplotlib.ticker import StrMethodFormatter
    ax.xaxis.set_major_formatter(StrMethodFormatter('{x:0.2f}'))
    
    #return(Dd60,Dd10)
    
style = {'description_width': '200px'}    

Inter=widgets.interact_manual(SA, 
                       mu= widgets.FloatText(description="6 mm", style=style),
                       m1= widgets.FloatText(description="2 mm",style=style),
                       m2= widgets.FloatText(description="0.6 mm", style=style),
                       m3= widgets.FloatText(description="0.2 mm", style=style),
                       m4= widgets.FloatText(description="0.06 mm", style=style),
                       ml= widgets.FloatText(description="0.01 mm", style=style),
                       perdat= widgets.FloatText(description="Enter Fineness % ", style=style)     )

Please provide the seive data in the boxes:  

def SA2(d10, d60, t):
    U = d60/d10
    K_h =  0.0116*(0.7+0.03*t)*d10**2
    print("\n The coefficient of non-uniformity: {0:0.2f}".format(U), "\n")
    print("The Hydraulic Conductivity based on Hazen Formula: {0:0.2e} m/s".format(K_h))

style = {'description_width': '200px'}    

Inter=widgets.interact_manual(SA2, 
                       d10= widgets.FloatText(description="d10 (mm)", style=style),
                       d60= widgets.FloatText(description="d60 (mm)",style=style),
                       t= widgets.FloatText(description="Temperature (°C)", style=style))