🌀 Research-Grade Cylindrical Filter Simulator

A high-fidelity simulator for cylindrical air filtration systems, designed for research applications, performance optimization, and publication-quality output.

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✨ Features

• ✅ Multi-physics simulation of airflow, pressure drop, dust loading, and filtration efficiency
• 📚 Filter media database with real-world materials like cellulose, nanofiber composites, and PTFE membranes
• 📈 Dust loading simulations to evaluate long-term performance degradation
• 🔍 Efficiency modeling with Brownian diffusion, interception, impaction
• 🧠 Geometry optimization for pressure drop and target efficiency
• 🖼️ Publication-ready plots using matplotlib
• 📝 PDF report generation with styled headers/footers (via ReportLab)
• 💾 Export results in JSON or CSV for research archiving

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📦 Requirements

Install dependencies using pip:

pip install matplotlib scipy pandas reportlab

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🚀 Quick Start (Colab-Compatible)

To run the simulator:

python filter_simulator.py

🧪 Example: Interactive Simulation

The script will prompt for:

• Outer radius (mm)
• Inner radius (mm)
• Filter height (mm)
• Mass flow rate (kg/s)
• Temperature (°C)
• Pressure (Pa)
• Relative Humidity (%)
• Material selection (from database)

Example input:

Outer radius: 125
Inner radius: 50
Height: 300
Mass flow rate: 0.083
Temperature: 20
Pressure: 101325
Relative Humidity: 50
Material index: 2

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📊 Output

• PDF Report: filter_report.pdf
• JSON Data: filter_results.json
• On-screen plots: Efficiency curves, pressure drop trends, velocity profiles

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🧵 Filter Media Database

Name	Key	Features
Standard Cellulose	cellulose_standard	Basic cost-effective paper media
Polyester + PTFE	polyester_ptfe	High-performance membrane filtration
Nanofiber Composite	nanofiber_composite	Ultra-fine fiber matrix, high capture
Glass Microfiber HEPA	glass_microfiber	HEPA-grade media with highest η₀

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🧮 Simulation Capabilities

Feature	Description
Velocity Field	Radial flow profile using continuity equation
Pressure Drop	Darcy–Forchheimer model under dust loading
Efficiency	Combined model of Brownian, interception, and impaction
Quality Factor	Q = -ln(Penetration) / ΔP
Optimization	Auto-tune R_out and H to balance pressure and efficiency

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📁 File Structure

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├── filter_simulator.py       # 🔬 Main simulation engine
├── filter_report.pdf         # 📝 Auto-generated simulation report
├── filter_results.json       # 💾 Exported results (JSON)
└── README.md                 # 📘 Project documentation

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📄 License

MIT License © 2025
Developed by Advanced Filter Research Lab
Author: Anish Nilesh Rane

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📬 Contact

For academic collaborations, suggestions, or bug reports:

📧 Email: anish.rane@researchlab.org
🌐 GitHub: github.com/anishrane

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🧪 Coming Soon

• GUI using ipywidgets for Google Colab
• Particle size distribution integration
• Experimental data import/export support

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Built for precision. Optimized for publication.