Calculate divergence of velocity field 3d in python

When working with fluid dynamics or any other field that involves the study of velocity fields, it is often necessary to calculate the divergence of the velocity field. The divergence of a vector field measures the rate at which the field is expanding or contracting at a given point. In this article, we will explore three different ways to calculate the divergence of a 3D velocity field in Python.

Method 1: Using NumPy

NumPy is a powerful library for numerical computing in Python. It provides a wide range of mathematical functions and operations that can be used to solve complex problems efficiently. To calculate the divergence of a velocity field using NumPy, we can make use of the gradient function.

import numpy as np

def calculate_divergence(velocity_field):
    gradient = np.gradient(velocity_field)
    divergence = gradient[0] + gradient[1] + gradient[2]
    return divergence

# Example usage
velocity_field = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                          [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                          [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])

divergence = calculate_divergence(velocity_field)
print(divergence)

In this method, we first calculate the gradient of the velocity field using the np.gradient function. The gradient function returns the partial derivatives of the velocity field with respect to each dimension. We then sum up the partial derivatives along each dimension to obtain the divergence of the velocity field.

Method 2: Using SymPy

SymPy is a Python library for symbolic mathematics. It provides a wide range of mathematical functions and operations that can be used to solve symbolic equations and expressions. To calculate the divergence of a velocity field using SymPy, we can make use of the divergence function.

import sympy as sp

def calculate_divergence(velocity_field):
    x, y, z = sp.symbols('x y z')
    velocity_field = sp.Matrix(velocity_field)
    divergence = sp.div(velocity_field, (x, y, z))
    return divergence

# Example usage
velocity_field = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]

divergence = calculate_divergence(velocity_field)
print(divergence)

In this method, we first define symbolic variables for each dimension of the velocity field using the sp.symbols function. We then convert the velocity field into a SymPy matrix using the sp.Matrix function. Finally, we use the sp.div function to calculate the divergence of the velocity field with respect to each dimension.

Method 3: Using Finite Differences

If you prefer a more hands-on approach, you can calculate the divergence of a velocity field using finite differences. Finite differences is a numerical method for approximating derivatives by computing the difference between function values at neighboring points. To calculate the divergence using finite differences, we can use the central difference formula.

def calculate_divergence(velocity_field, h):
    divergence = np.zeros_like(velocity_field)
    for i in range(3):
        shift = np.zeros_like(velocity_field)
        shift[i] = h
        forward_diff = (np.roll(velocity_field, -1, axis=i) - velocity_field) / h
        backward_diff = (velocity_field - np.roll(velocity_field, 1, axis=i)) / h
        divergence += (forward_diff - backward_diff) / h
    return divergence

# Example usage
velocity_field = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]],
                          [[10, 11, 12], [13, 14, 15], [16, 17, 18]],
                          [[19, 20, 21], [22, 23, 24], [25, 26, 27]]])
h = 1

divergence = calculate_divergence(velocity_field, h)
print(divergence)

In this method, we first initialize an array to store the divergence of the velocity field. We then iterate over each dimension of the velocity field and compute the forward and backward differences using the np.roll function. Finally, we sum up the differences and divide by the step size h to obtain the divergence.

After exploring these three methods, it is clear that the first method using NumPy is the most efficient and concise way to calculate the divergence of a 3D velocity field in Python. It leverages the power of NumPy’s mathematical functions and operations to perform the calculations efficiently. Therefore, the first method is the recommended approach for calculating the divergence of a velocity field in Python.

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