Topology optimization of a C-clip

Material property: Young Modulus: 210GPa

Optimization Task:

Optimization task: Freeze load regions

Design Response: Strain Energy and Volume

Objective Function: Strain Energy

Constraint: Volume(30%)

Optimization is run for 25 cycles.

Topology Optimization of a rectangular plate:

update is coming

Sorting values by group or category using groupby:

Find the jupyter notebook here

The ductile-brittle transition temperature is the minimum temperature in which a given material has the ability to absorb a specific amount of energy without fracturing.

At low temperatures some metals that would be ductile at room temperature become brittle. This is known as a ductile to brittle transition.

The ductile to brittle transition temperature is strongly dependent on the composition of the metal. Steel is the most commonly used metal that shows this behavior.

For some steels, the transition temperature can be around 0°C, and in winter the temperature in some parts of the world can be below this. As a result, some steel structures are very likely to fail in winter.

This occurs mainly due to the atoms present in the material. As the temperature increases, the frequency and amplitude with which the atoms vibrate increases. This causes the atoms under stress to break bonds and form new ones with other atoms(slip). This slipping of atoms is seen as plastic deformation on the external surface of the material, a common feature of ductile materials. However, below the transition temperature, the frequency and amplitude of the vibrations decreases and the atoms just break the bonds but does not slip to a new place. As there is no slip, there is very little plastic deformation before fracture, commonly known as a brittle fracture.

Data Visualization-matplotlib.ipynb

numpy.ipynb

Exploring a pandas series

HOW DO I SELECT A PANDAS SERIES FROM A DATAFRAME.ipynb

Sorting a pandas DataFrame or Series.ipynb

changing datatypes of a pandas series.ipynb

applying multiple filter criteria to a pandas DataFrame.ipynb

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handling missing values in pandas.ipynb

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selecting specific rows and columns of pandas DataFrame.ipynb

using groupby in pandas.ipynb

using string methods in pandas.ipynb

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sorting values by group or category using groupby .ipynb

Try out this code below:

print("Enter a negetive number to terminate the program ")
while True:
     factorial=1
     x=int(input("Enter the number "))
if x<0:
    print("Negetive number has no factorial\n Thanks for trying")
    break
elif x==0:
    print(x,"!=",1)
else: 
    for i in range(1,x+1):
        factorial=factorial*i
    print(x,"!=",factorial)

While True, is a while loop that will loop forever as the evaluated condition is always met, always True.

Try this code below:

secret_code=521
while True:
x=int(input(“input a number= “))

if (x!=secret_code):
print (“This is not the right code”)
elif(x==secret_code):
print(“ok”)
break

It is a common structure seen in ship. These structures remain under a high stress and temperature. If only static analysis is done for this kind of structures, they might look safe as the structures don’t cross the yield stress limit over the time. But creep analysis shows the time and locations of the presence of creep strain for the same working conditions. Where safety is the first priority creep analysis data are valuable to the engineers

See the full analysis PDF