mechRedox

mechRedox is a research-oriented software suite for chemical kinetics modeling and Chemical Reactor Network (CRN) analysis, specifically focused on predicting NOx emissions in gas turbine combustion systems. The project implements and validates methodologies from several key research papers, focusing on syngas, ammonia, and hydrogen fuel blends.

Project Structure

The repository is organized into bibliography, implementation scripts, and an automated test suite:

• scripts/: The core implementation, divided into study groups:
  • groupC/C1/: Syngas RQL (Rich-Quench-Lean) NOx kinetics study based on Liu et al. (2018).
  • groupC/C2/: Ammonia (NH3) and Ammonia-Hydrogen (NH3/H2) kinetics study based on Bedick et al. (2022/2023).
• tests/: Automated testing suite:
  • unit/: Fast, independent tests for core validation logic.
  • integration/: End-to-end pipeline tests (requires Cantera).
• htmlcov/: Generated HTML coverage reports providing a visual overview of test quality.

Key Research Groups

Group C1: Syngas RQL NOx Kinetics

Based on the study by Liu et al. (2018), this module compares kinetic mechanisms (e.g., GRI-Mech 3.0 vs. SYN-Mech) for NOx prediction in coal-derived syngas RQL combustors.

• Methodology: 9-PSR rich zone model followed by a PFR lean zone.
• Analyses: Temperature sweeps, air flow split sensitivity, and residence time distribution.
• Validation: Scripts verify model outputs against literature values for 1908K and 1750K outlet temperatures.

Group C2: Ammonia & Ammonia-Hydrogen Kinetics

Based on studies by Bedick et al. (2022/2023) at NETL, this module evaluates ammonia-based fuel blends for gas turbine applications.

• Methodology: Laminar flame speed calculations and two-stage rich-lean CRN modeling.
• Analyses: Flame speed vs. equivalence ratio, H2 addition effects, and mechanism comparison (14 variants including GRI 3.0, Okafor, Otomo, and Glarborg).
• Validation: Verifies results against NETL reference data and Li & Li (2021) Chemkin results.

Testing & Quality Assurance

The project employs a rigorous testing strategy using pytest and coverage.py:

• Automated Validation: Core scientific logic is verified by unit tests in tests/unit/.
• Pipeline Testing: Integration tests in tests/integration/ ensure the full modeling pipelines (Cantera simulations) are functional.
• Coverage Reporting: Detailed HTML reports are generated to htmlcov/index.html to monitor test coverage across the scripts/ directory.

To run tests and generate a coverage report:

pytest --cov=scripts --cov-report=html

Technologies Used

• Python: Primary scripting language (3.9+).
• Cantera: Suite of tools for chemical kinetics, thermodynamics, and transport processes.
• NumPy & Pandas: Data processing and numerical analysis.
• Matplotlib: Visualization and figure generation.
• Pytest: Automated testing framework.

Getting Started

Prerequisites

Ensure the following are installed:

• Python 3.9+
• Cantera
• NumPy, Pandas, Matplotlib, Pytest, Pytest-cov

Execution

To run a full study pipeline:

1. Navigate to the study scripts directory (e.g., scripts/groupC/C1/scripts/).
2. Execute the master script: python run_all.py.
3. Review results in the outputs/ folder and logs in the logs/ folder.

Bibliography References

• Liu et al. (2018): "Kinetics Modeling on NOx Emissions of Gas Turbine Combustors for Syngas Applications".
• Bedick et al. (2022/2023): "A Modeling Study on Ammonia and Ammonia-Hydrogen Kinetics for Gas Turbine Engines".
• Fichet et al. (2010): "A reactor network model for predicting NOx emissions in gas turbines".
• Park et al. (2013): "Prediction of NOx and CO Emissions from an Industrial Lean-Premixed Gas Turbine Combustor...".
• Cameretti et al. (2007): "Cycle Optimization and Combustion Analysis in a Low-NOx Micro-Gas Turbine".