corrosim 1.5.1

A comprehensive desktop application with 8 professional modules: Tafel, Galvanic, EIS, Pitting, Inhibitor, Prediction, Comparison, and Data Import.

⚡ CorroSim - Professional Corrosion Analysis Platform

A comprehensive desktop application with 8 professional modules: Tafel, Galvanic, EIS, Pitting, Inhibitor, Prediction, Comparison, and Data Import.

✨ 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

🔬 EIS Impedance Spectroscopy (NEW in v1.4.0)

• Weighted CNLS fitting with 4 circuit models
• Auto-Select Best Model (R/RQ/RW/RWQ)
• Nyquist, Bode, Residuals plots
• Kramers-Kronig validation

🕳️ Pitting Corrosion Analyzer (NEW in v1.4.0)

• Cyclic polarization Epit/Erp detection
• Gumbel extreme value statistics
• ASTM G61 compliant analysis

🧪 Inhibitor Efficiency Calculator (NEW in v1.5.0)

• IE% from corrosion rates
• Langmuir & Freundlich isotherm fitting
• ΔG° adsorption thermodynamics
• Synergy parameter analysis

📸 Screenshots

🖥️ Main Application

⚡ Tafel Analysis

🔮 Lifetime Prediction

📊 Sample Comparison

🔬 EIS Impedance Spectroscopy

🕳️ Pitting Corrosion Analyzer

🧪 Inhibitor Efficiency Calculator

🚀 Installation

Prerequisites

• Python 3.8 or higher
• pip package manager

Option 1: Install from PyPI (Recommended)

pip install corrosim
corrosim

To upgrade to the latest version:

pip install --upgrade corrosim

Option 2: Install from GitHub

git clone https://github.com/khadev/CorroSim.git
cd CorroSim
pip install -r requirements.txt
python run.py

Option 3: Install as Development Package

git clone https://github.com/khadev/CorroSim.git
cd CorroSim
pip install -e .
corrosim

Option 4: Direct pip 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

5.🔗 Galvanic Corrosion Simulator (NEW in v1.1.0)

• 14 metals database from ASTM G82 galvanic series
• Mixed potential theory calculations
• Cathode/Anode area ratio effect
• Real-time galvanic series bar chart
• Severity classification per NACE SP0775
• Engineering recommendations

6. 🔬 EIS Analysis

1. Go to 🔬 EIS tab
2. Select circuit model or use Auto-Select
3. Click Generate Test Data or Import CSV
4. Click Run Weighted CNLS Fit
5. View Nyquist, Bode, Residuals plots

7. 🕳️ Pitting Analysis

1. Go to 🕳️ Pitting tab
2. Click Generate Test Data or Import CSV
3. Click Analyze Pitting
4. View Epit, Erp, hysteresis

8.🧪 Inhibitor Efficiency

1. Go to 🧪 Inhibitor tab
2. Enter CR values or use data table
3. Click Auto-Fit Best Isotherm
4. View IE%, ΔG°, synergy analysis

🏗️ Project Structure

corrosim/
├── corrosim/                    # Main package
│   ├── __init__.py              # Package initialization (v1.5.0)
│   ├── main.py                  # Application entry point + splash screen
│   ├── app.py                   # Main window controller (8 tabs)
│   ├── theme.py                 # UI styling (light + dark themes)
│   ├── database.py              # SQLite database operations (CRUD)
│   ├── tafel_engine.py          # Tafel polarization analysis engine
│   ├── splash_screen.py         # Professional splash screen widget
│   ├── engines/                 # Analysis engines
│   │   ├── __init__.py
│   │   ├── galvanic_engine.py   # Galvanic corrosion prediction (ASTM G82)
│   │   ├── eis_engine.py        # EIS impedance fitting (Weighted CNLS)
│   │   ├── pitting_engine.py    # Pitting corrosion analysis (ASTM G61)
│   │   └── inhibitor_engine.py  # Inhibitor efficiency + adsorption isotherms
│   ├── tabs/                    # UI Tab modules (MVC pattern)
│   │   ├── __init__.py
│   │   ├── import_tab.py        # Data import (Excel/CSV)
│   │   ├── tafel_tab.py         # Tafel analysis interface
│   │   ├── prediction_tab.py    # Lifetime prediction (3 models)
│   │   ├── comparison_tab.py    # Multi-sample comparison + export
│   │   ├── galvanic_tab.py      # Galvanic simulator (14 metals)
│   │   ├── eis_tab.py           # EIS spectroscopy (4 circuits)
│   │   ├── pitting_tab.py       # Pitting analyzer (cyclic polarization)
│   │   └── inhibitor_tab.py     # Inhibitor efficiency + synergy
│   └── utils/                   # Utility modules
│       ├── __init__.py
│       └── constants.py         # Physical/electrochemical constants
├── tests/                       # Test suite
│   ├── __init__.py
│   └── test_tafel.py            # Tafel engine validation (R^2 > 0.999)
├── screenshots/                 # Application screenshots
├── dist/                        # Built packages for PyPI
├── requirements.txt             # Python dependencies
├── setup.py                     # Package setup script (pip install)
├── run.py                       # Quick launcher (python run.py)
├── README.md                    # Full 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

Electrochemical Methods

1. Stern, M., & Geary, A. L. (1957). Electrochemical Polarization: A Theoretical Analysis of the Shape of Polarization Curves. 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. Mansfeld, F. (1973). Tafel Slopes and Corrosion Rates from Polarization Resistance Measurements. Corrosion, 29(10), 397-402.

EIS Spectroscopy

4. Barsoukov, E., & Macdonald, J. R. (2018). Impedance Spectroscopy: Theory, Experiment, and Applications (3rd ed.). Wiley.
5. Boukamp, B. A. (1995). A Linear Kronig-Kramers Transform Test for Immittance Data Validation. Journal of the Electrochemical Society, 142(6), 1885-1894.
6. Orazem, M. E., & Tribollet, B. (2017). Electrochemical Impedance Spectroscopy (2nd ed.). Wiley.

Galvanic Corrosion

7. ASTM G82-98(2021) - Standard Guide for Development and Use of a Galvanic Series for Predicting Galvanic Corrosion Performance.
8. Hack, H. P. (1988). Galvanic Corrosion. ASTM International.

Pitting Corrosion

9. ASTM G61-86(2018) - Standard Test Method for Conducting Cyclic Potentiodynamic Polarization Measurements for Localized Corrosion Susceptibility.
10. ASTM G46-21 - Standard Guide for Examination and Evaluation of Pitting Corrosion.
11. Gumbel, E. J. (1958). Statistics of Extremes. Columbia University Press.

Corrosion Inhibitors

12. Langmuir, I. (1918). The Adsorption of Gases on Plane Surfaces of Glass, Mica and Platinum. Journal of the American Chemical Society, 40(9), 1361-1403.
13. Freundlich, H. (1906). Über die Adsorption in Lösungen. Zeitschrift für Physikalische Chemie, 57(1), 385-470.
14. Aramaki, K., & Hackerman, N. (1969). Inhibition Mechanism of Medium-Sized Polymethyleneimine. Journal of the Electrochemical Society, 116(5), 568-574.

Corrosion Rate Calculation

15. ASTM G102-89(2015)e1 - Standard Practice for Calculation of Corrosion Rates and Related Information from Electrochemical Measurements.
16. NACE SP0775-2013 - Preparation, Installation, Analysis, and Interpretation of Corrosion Coupons in Oilfield Operations.

Atmospheric Corrosion

17. ISO 9223:2012 - Corrosion of Metals and Alloys — Corrosivity of Atmospheres — Classification, Determination and Estimation.

Software & Tools

18. PyQt6 Documentation. Riverbank Computing. https://www.riverbankcomputing.com/static/Docs/PyQt6/
19. Matplotlib: Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), 90-95.
20. SciPy: Virtanen, P., et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261-272.

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