corrosim 1.1.0

Professional Corrosion Analysis Platform

⚡ CorroSim - Professional Corrosion Analysis Platform

A comprehensive desktop application for electrochemical corrosion analysis, featuring Tafel polarization fitting, lifetime prediction, and multi-sample comparison.

✨ Features

🔬 Tafel Polarization Analysis

• Automatic detection of current units (A, mA, μA)
• Proper Tafel region identification (80-200 mV from Ecorr)
• Verified Butler-Volmer kinetics fitting
• Real-time visualization with Matplotlib
• Export plots as PNG or PDF

🔮 Lifetime Prediction

• Linear, Power Law, and Exponential degradation models
• Confidence band visualization
• Service life estimation with threshold detection
• Interactive parameter adjustment

📊 Sample Comparison

• Multi-sample database with SQLite storage
• Search and filter functionality
• Color-coded corrosion rate indicators
• Export to Excel for further analysis

📁 Data Import

• Support for Excel (.xlsx, .xls) and CSV files
• Automatic column detection
• Data preview with statistics
• Unit specification options

🎨 Professional UI

• Modern, responsive interface with PyQt6
• Custom splash screen with progress indicator
• Consistent styling with professional color theme
• Sidebar navigation with active state indicators

📸 Screenshots

🖥️ Main Application

⚡ Tafel Analysis

📁 Data Import

🔮 Lifetime Prediction

📊 Sample Comparison

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Quick Install

Clone the repository

git clone https://github.com/khadev/corrosim.git cd corrosim

Install dependencies

pip install -r requirements.txt

Run the application

python run.py

Install as Package

pip install -e . corrosim

Direct Install from GitHub

pip install git+https://github.com/khadev/corrosim.git corrosim

📦 Dependencies

txt PyQt6>=6.5.0 matplotlib>=3.7.0 numpy>=1.24.0 pandas>=2.0.0 scipy>=1.10.0 openpyxl>=3.1.0

🧪 Running Tests

python tests/test_tafel.py

Expected output:

✓ Test data generation passed ✓ All accuracy checks passed!

📖 Usage Guide

1. Import Data

1. Click 📁 Import in the sidebar
2. Browse and select your Excel/CSV file
3. Click Load Preview to verify data
4. Enter sample name and select test type
5. Specify current units (or use Auto-detect)
6. Click Import to Database

2. Tafel Analysis

1. Navigate to ⚡ Tafel tab
2. Click Load Data to retrieve imported data
3. Verify current units and electrode area
4. Click ⚡ Run Tafel Analysis
5. View results: Ecorr, Icorr, Corrosion Rate, Tafel slopes
6. Export plot as PNG or PDF

3. Lifetime Prediction

1. Go to 🔮 Prediction tab
2. Select degradation model (Linear/Power Law/Exponential)
3. Set initial corrosion rate and failure threshold
4. Set analysis period
5. Click 🔮 Calculate Prediction
6. View predicted lifetime and degradation curve

4. Compare Samples

1. Open 📊 Compare tab
2. Search and filter samples by name
3. View all analysis results in sortable table
4. Color-coded CR values: Green (low), Yellow (medium), Red (high)
5. Export data to Excel

🏗️ Project Structure

corrosim/
├── corrosim/                    # Main package
│   ├── __init__.py              # Package initialization
│   ├── main.py                  # Application entry point
│   ├── app.py                   # Main window controller
│   ├── theme.py                 # UI styling and theme
│   ├── database.py              # SQLite database operations
│   ├── tafel_engine.py          # Tafel analysis algorithms
│   ├── splash_screen.py         # Splash screen widget
│   ├── tabs/                    # Tab modules
│   │   ├── __init__.py
│   │   ├── import_tab.py        # Data import interface
│   │   ├── tafel_tab.py         # Tafel analysis interface
│   │   ├── prediction_tab.py    # Lifetime prediction
│   │   └── comparison_tab.py    # Sample comparison
│   └── utils/                   # Utility modules
│       ├── __init__.py
│       └── constants.py         # Physical constants
├── tests/                       # Test suite
│   ├── __init__.py
│   └── test_tafel.py            # Tafel engine validation
├── screenshots/                 # Application screenshots
├── dist/                        # Built packages (PyPI)
├── requirements.txt             # Python dependencies
├── setup.py                     # Package setup script
├── run.py                       # Quick launcher
├── README.md                    # Documentation
├── LICENSE                      # MIT License
└── .gitignore                   # Git ignore rules

🔬 Algorithm Verification

The Tafel engine has been validated using synthetic Butler-Volmer data:

Parameter	Expected	Recovered	Error
Ecorr	-0.500 V	-0.502 V	0.4%
Icorr	10.0 μA/cm²	9.5 μA/cm²	5.0%
βa	120 mV/dec	118.1 mV/dec	1.6%
βc	120 mV/dec	118.4 mV/dec	1.3%
R²	—	0.9997	—

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add feature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

👤 Author

Khaled Oukil

• GitHub: @khadev
• Email: [e-mail]

📚 References

1. Stern, M., & Geary, A. L. (1957). Electrochemical Polarization. Journal of the Electrochemical Society, 104(1), 56-63.
2. Tafel, J. (1905). Über die Polarisation bei kathodischer Wasserstoffentwicklung. Zeitschrift für Physikalische Chemie, 50(1), 641-712.
3. ASTM G102-89(2015) - Standard Practice for Calculation of Corrosion Rates from Electrochemical Measurements.

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