Analysis of chaotic behavior in the rotational speed of internal combustion engines using Python. This repository includes scripts for calculating Lyapunov exponents, correlation dimensions, and other chaos-related metrics Overview This repository contains the Python code and data used to analyze the chaotic behavior in the rotational speed (RPM) of internal combustion engines. The analysis is based on the research article "Chaotic Behavior in the Rotational Speed of Internal Combustion Engines" by Lomakin and co-authors.
Features Lyapunov Exponents Calculation: Estimate the sensitivity to initial conditions, a key indicator of chaos. Correlation Dimension: Measure the fractal dimension of the system, reflecting its complexity. Phase Space Reconstruction: Visualize the system's dynamics in a reconstructed phase space. Sample Entropy: Determine the complexity and predictability of the RPM data. Power Spectral Density (PSD) Analysis: Analyze the distribution of power across frequencies in the RPM signal. Average Mutual Information (AMI): Identify the optimal time delay for phase space reconstruction. Requirements To run the analysis, you need the following Python libraries: numpy pandas matplotlib scipy nolds joblib tqdm You can install the required packages using pip: " bash pip install numpy pandas matplotlib scipy nolds joblib tqdm Usage Clone the repository: " " bash git clone https://github.com/yourusername/chaotic-engine-rpm-analysis.git cd chaotic-engine-rpm-analysis Run the main analysis script: " " bash python chaos_analysis_v1_3.py View the output: " The results, including plots and calculated values (e.g., Lyapunov Exponents, Correlation Dimensions), will be saved in the repository directory. Data Files The following data files are required to run the analysis:
550.txt 1000.txt 2000.txt 3100.txt high.txt low.txt Ensure these files are in the same directory as the script before running it.
Contribution Feel free to fork this repository and contribute by submitting pull requests. If you encounter any issues, please open an issue on GitHub.
License This project is licensed under the MIT License - see the LICENSE file for details.
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