Answer:
Explanation:
Thermostatic expansion valve is mainly a throttling device commonly used in air conditioning systems and refrigerators.
It is an automatic valve that maintains proper flow of refrigerant in the evaporator according to the load inside the evaporator. When the load in the evaporator is higher the valve opens and allows the increase in flow of refrigerant and when the load reduces the valve closes a bit and reduces the flow of refrigerant. This process leads to higher efficiency of compressor as well as the whole refrigeration system. Thus TEV works to reduce the pressure of refrigerant from higher condenser pressure to the lower evaporator pressure. It also keeps the evaporator active.
Explanation:
Styrene is a vinyl monomer in which there is a carbon carbon double bond.
The polymerization of the styrene, which is initiated by using a free radical which reacts with the styrene and the compound thus forms react again and again to form polystyrene (PS).
The equation is shown below as:
⇒ ![\begin{matrix}&C_6H_5 \\&|\\ -[-H_2C & -CH-]-_n\end{matrix}](https://tex.z-dn.net/?f=%5Cbegin%7Bmatrix%7D%26C_6H_5%20%5C%5C%26%7C%5C%5C%20-%5B-H_2C%20%26%20-CH-%5D-_n%5Cend%7Bmatrix%7D)
Explanation:
150 divide by 150 and that how you do the is you what to divide together 15/ 150 you welcome have a good day is you need something else
Answer:
import numpy as np
import time
def matrixMul(m1,m2):
if m1.shape[1] == m2.shape[0]:
t1 = time.time()
r1 = np.zeros((m1.shape[0],m2.shape[1]))
for i in range(m1.shape[0]):
for j in range(m2.shape[1]):
r1[i,j] = (m1[i]*m2.transpose()[j]).sum()
t2 = time.time()
print("Native implementation: ",r1)
print("Time: ",t2-t1)
t1 = time.time()
r2 = m1.dot(m2)
t2 = time.time()
print("\nEfficient implementation: ",r2)
print("Time: ",t2-t1)
else:
print("Wrong dimensions!")
Explanation:
We define a function (matrixMul) that receive two arrays representing the two matrices to be multiplied, then we verify is the dimensions are appropriated for matrix multiplication if so we proceed with the native implementation consisting of two for-loops and prints the result of the operation and the execution time, then we proceed with the efficient implementation using .dot method then we return the result with the operation time. As you can see from the image the execution time is appreciable just for large matrices, in such a case the execution time of the efficient implementation can be 1000 times faster than the native implementation.