To calculate and visualize the Number Mean Diameter (NMD) using the Discrete Method in Population Balance Model (PBM) within ANSYS Fluent, follow these steps:

1. Overview of Number Mean Diameter (NMD)

The Number Mean Diameter, Dn, is defined as:

Dn=∑i(nidi)/∑i​(ni)​​Dn=∑i(nidi)/∑i​(ni)​​

Where:

• ni: Number of particles in each size class (discretized size range).
• di: Diameter corresponding to the size class iii.

In Fluent's PBM, the size distribution is described discretely, so you can compute Dn​ from the particle size classes and their respective populations.

2. Steps in ANSYS Fluent

Step 1: Enable Population Balance Model (PBM)

Step 2: Post-Processing Variables

Step 3: Visualization in Fluent

• Use Contours or XY Plots to visualize number distribution or derived metrics.
• Plot a size distribution histogram by bin using Fluent's in-built tools or export the data for external visualization.

3. Using External Tools for Better Visualization

4. Tutorials and Resources

Tutorials Available

• ANSYS Fluent tutorials (built-in):Look for "Population Balance Model" or "Particle Size Distribution Analysis." These are often available within the Fluent Documentation and Tutorial Guides.

External Resources

• ANSYS Customer Portal: Search for PBM and NMD-focused examples.
• YouTube Channels: Many experts provide walkthroughs of PBM in Fluent.
• Papers and case studies on PBM modeling in Fluent.

Here’s an example Python script to calculate and visualize the Number Mean Diameter (NMD) from exported Fluent data:

Script for NMD Calculation and Visualization

Assumptions:

• You have exported the particle size distribution data from Fluent into a CSV file.
• The CSV file contains two columns:bin_diameter: Mean diameter of each size bin (di​). bin_count: Number of particles in each size bin (ni).
• Pay attention to the code:
• import pandas as pd
• import matplotlib.pyplot as plt
• # Load data from Fluent CSV file
• # Replace 'fluent_data.csv' with your file's name
• data = pd.read_csv("fluent_data.csv")
• # Ensure the file has two columns: 'bin_diameter' and 'bin_count'
• # Rename columns if necessary
• data.columns = ['bin_diameter', 'bin_count']
• # Calculate Number Mean Diameter (NMD)
• numerator = (data['bin_diameter'] * data['bin_count']).sum()
• denominator = data['bin_count'].sum()
• number_mean_diameter = numerator / denominator
• print(f"Number Mean Diameter (NMD): {number_mean_diameter:.2f} μm")
• # Visualization of Particle Size Distribution
• plt.figure(figsize=(8, 5))
• plt.bar(data['bin_diameter'], data['bin_count'], width=0.5, color='skyblue', edgecolor='black')
• plt.axvline(number_mean_diameter, color='red', linestyle='--', label=f'NMD = {number_mean_diameter:.2f} μm')
• plt.xlabel("Bin Diameter (μm)")
• plt.ylabel("Particle Count")
• plt.title("Particle Size Distribution")
• plt.legend()
• plt.grid(alpha=0.3)
• plt.show()

Thanks for your elaborate reply and explanation. I will try your steps.