import math def calculate_pressure_drop_bingham_plastic(yield_stress, viscosity, pipeline_length, pipeline_diameter, flow_rate): """ Calculate pressure drop for Bingham plastic slurry flow through a pipeline. Parameters: - yield_stress (float): Yield stress (τ₀) of the Bingham plastic fluid (in pascals, Pa). - viscosity (float): Viscosity (μ) of the Bingham plastic fluid (in pascal-seconds, Pa·s). - pipeline_length (float): Length of the pipeline (in meters, m). - pipeline_diameter (float): Diameter of the pipeline (in meters, m). - flow_rate (float): Flow rate (Q) of the fluid (in cubic meters per second, m³/s). Returns: - pressure_drop (float): Pressure drop (ΔP) in the pipeline (in pascals, Pa). - friction_factor (float): Friction factor (f) for the pipeline flow. - reynolds_number (float): Reynolds number (Re) for the pipeline flow. - head_loss (float): Head loss (He) in the pipeline (in meters, m). - wall_shear_stress (float): Wall shear stress (Tw) in the pipeline (in pascals, Pa). """ # Calculate Reynolds number (Re) for pipe flow Reynolds_number = (4 * flow_rate) / (math.pi * pipeline_diameter * viscosity) # Calculate friction factor (f) for pipe flow using the Darcy-Weisbach equation if Reynolds_number < 2000: # Laminar flow f = 16 / Reynolds_number else: # Turbulent flow (use an appropriate turbulent flow friction factor correlation) # For example, you can use the Colebrook-White equation, but it requires iterative solution: # Define initial values f_guess = 0.02 # Initial guess for f roughness = 0.0001 # Pipe roughness (in meters) # Perform iterative calculation for f using Colebrook-White equation while True: f_inv = -2.0 * math.log10((roughness / (3.7 * pipeline_diameter)) + (2.51 / (Reynolds_number * math.sqrt(f_guess)))) f_new = 1.0 / f_inv**2 if abs(f_new - f_guess) < 0.00001: # Check for convergence break f_guess = f_new f = f_new # Calculate head loss (He) in the pipeline head_loss = f * (pipeline_length / pipeline_diameter) * (flow_rate**2) / (2 * 9.81) # Calculate wall shear stress (Tw) in the pipeline wall_shear_stress = viscosity * flow_rate / (math.pi * (pipeline_diameter / 2)**2) # Calculate pressure drop due to yield stress pressure_drop_yield_stress = yield_stress * (pipeline_length / pipeline_diameter) # Total pressure drop is the sum of friction and yield stress components pressure_drop_total = pressure_drop_yield_stress + (9.81 * head_loss) return pressure_drop_total, f, Reynolds_number, head_loss, wall_shear_stress # Input parameters yield_stress = 6.00 # Example yield stress in Pa viscosity = 0.8 # Example viscosity in Pa·s pipeline_length = 1 # Example pipeline length in meters pipeline_diameter = 0.2 # Example pipeline diameter in meters flow_rate = 0.06 # Example flow rate in m³/s # Calculate pressure drop and related parameters pressure_drop, friction_factor, reynolds_number, head_loss, wall_shear_stress = calculate_pressure_drop_bingham_plastic( yield_stress, viscosity, pipeline_length, pipeline_diameter, flow_rate) # Print the results print(f"Total Pressure Drop for Bingham Plastic Slurry Flow: {pressure_drop} Pa") print(f"Friction Factor (f): {friction_factor}") print(f"Reynolds Number (Re): {reynolds_number}") print(f"Head Loss (He): {head_loss} meters") print(f"Wall Shear Stress (Tw): {wall_shear_stress} Pa")
Write, Run & Share Python code online using OneCompiler's Python online compiler for free. It's one of the robust, feature-rich online compilers for python language, supporting both the versions which are Python 3 and Python 2.7. Getting started with the OneCompiler's Python editor is easy and fast. The editor shows sample boilerplate code when you choose language as Python or Python2 and start coding.
OneCompiler's python online editor supports stdin and users can give inputs to programs using the STDIN textbox under the I/O tab. Following is a sample python program which takes name as input and print your name with hello.
import sys
name = sys.stdin.readline()
print("Hello "+ name)
Python is a very popular general-purpose programming language which was created by Guido van Rossum, and released in 1991. It is very popular for web development and you can build almost anything like mobile apps, web apps, tools, data analytics, machine learning etc. It is designed to be simple and easy like english language. It's is highly productive and efficient making it a very popular language.
When ever you want to perform a set of operations based on a condition IF-ELSE is used.
if conditional-expression
#code
elif conditional-expression
#code
else:
#code
Indentation is very important in Python, make sure the indentation is followed correctly
For loop is used to iterate over arrays(list, tuple, set, dictionary) or strings.
mylist=("Iphone","Pixel","Samsung")
for i in mylist:
print(i)
While is also used to iterate a set of statements based on a condition. Usually while is preferred when number of iterations are not known in advance.
while condition
#code
There are four types of collections in Python.
List is a collection which is ordered and can be changed. Lists are specified in square brackets.
mylist=["iPhone","Pixel","Samsung"]
print(mylist)
Tuple is a collection which is ordered and can not be changed. Tuples are specified in round brackets.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
Below throws an error if you assign another value to tuple again.
myTuple=("iPhone","Pixel","Samsung")
print(myTuple)
myTuple[1]="onePlus"
print(myTuple)
Set is a collection which is unordered and unindexed. Sets are specified in curly brackets.
myset = {"iPhone","Pixel","Samsung"}
print(myset)
Dictionary is a collection of key value pairs which is unordered, can be changed, and indexed. They are written in curly brackets with key - value pairs.
mydict = {
"brand" :"iPhone",
"model": "iPhone 11"
}
print(mydict)
Following are the libraries supported by OneCompiler's Python compiler
Name | Description |
---|---|
NumPy | NumPy python library helps users to work on arrays with ease |
SciPy | SciPy is a scientific computation library which depends on NumPy for convenient and fast N-dimensional array manipulation |
SKLearn/Scikit-learn | Scikit-learn or Scikit-learn is the most useful library for machine learning in Python |
Pandas | Pandas is the most efficient Python library for data manipulation and analysis |
DOcplex | DOcplex is IBM Decision Optimization CPLEX Modeling for Python, is a library composed of Mathematical Programming Modeling and Constraint Programming Modeling |