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https://github.com/edgar-mendonca/shpb-analysis

Python based SHPB Experimental data analysis
https://github.com/edgar-mendonca/shpb-analysis

matplotlib numpy pandas python scipy

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Python based SHPB Experimental data analysis

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README 

        
# SHPB Analysis Tool












This Python script analyses Split Hopkinson Pressure Bar (SHPB) experimental data to calculate stress-strain curves and other relevant parameters. Follow the instructions below to use the script with your experimental data.



## Instructions



1. **Install Dependencies:** Make sure you have Python installed on your system. You can install the required Python packages by running the following command:



    ```

    pip install -r requirements.txt

    ```



2. **Prepare Experimental Data:**



    - Ensure that your experimental data is stored in a CSV or Excel file.

    - The file should contain columns for "Time", "Incident", "Reflected", and "Transmitted" voltages.

    - Voltage values should be in volts (V).

    - Specify the file name in the Python script (`analysis.py`) using the `filename` variable. For example:



    ```python

    filename = "path/to/your/data_file.csv"

    ```



3. **Customize Input Parameters:**



    - Open the Python script (`analysis.py`) in a text editor.

    - Update the input parameters according to your experiment setup. Parameters such as Young's modulus, density, initial length, and cross-sectional areas should be adjusted based on your experimental conditions.



4. **Run the Script:**



    - Run the script using the following command:



    ```

    python analysis.py

    ```



5. **View Plots and Analyze Results:**



    - After running the script, view the generated plots to analyze the results.

    - The script will generate plots for filtered voltage data, strain data, stress-strain curve, and true stress-strain curve.



## Example Usage



Here's an example of how to use the script with your experimental data:



1. Ensure that Python and the required dependencies are installed on your system.

2. Prepare your experimental data in a CSV or Excel file format.

3. Customize the input parameters in the `analysis.py` script based on your experiment setup.

4. Specify the file name of your experimental data in the script.

5. Run the script using `python analysis.py`.

6. Analyze the generated plots to interpret the results of your SHPB experiment.



## Explanation for Stress-Strain Calculation in SHPB Experiment



In the Split Hopkinson Pressure Bar (SHPB) experiment, the characteristic relations associated with one-dimensional elastic wave propagation in the bar provide the basis for calculating stress and strain in the specimen.



1. **Particle Velocity at Specimen/Input-Bar and Specimen/Output-Bar Interface:**

   - The particle velocity $` v_1(t) `$ at the specimen/input-bar interface is given by:

     $` v_1(t) = c_b(\varepsilon_I - \varepsilon_R) `$ 

   - Here, $` c_b = \sqrt{\frac{E_b}{\rho_b}} `$ represents the bar wave speed, with $` E_b `$ denoting the Young's modulus and $` \rho_b `$ the density of the bar material.

   - The particle velocity $` v_2(t) `$ at the specimen/output-bar interface is given by:

     $` v_2(t) = c_b \varepsilon_T `$ 



2. **Mean Axial Strain Rate in the Specimen:**

   - The mean axial strain rate $` \dot{e}_s `$ in the specimen is calculated as:

     $` \dot{e}_s = \frac{c_b}{l_0} (\varepsilon_I - \varepsilon_R - \varepsilon_T) = \frac{v_1 - v_2}{l_0} `$ 

   - Here, $` l_0 `$ represents the initial specimen length.



3. **Calculation of Bar Stresses and Normal Forces:**

   - The stresses and normal forces at the specimen/bar interfaces are computed as follows:

     - $` P_1 = E_b (\varepsilon_I + \varepsilon_R) A_b `$ at the specimen/input-bar interface.

     - $` P_2 = E_b \varepsilon_T A_b `$ at the specimen/output-bar interface.

   - Here, $` A_b `$ denotes the cross-sectional area of the bars.



4. **Mean Axial Stress in the Specimen:**

   - The mean axial stress $` \bar{S}_s(t) `$ in the specimen is given by:

     $` \bar{S}_s(t) = \frac{(P_1 + P_2)}{2} \left( \frac{1}{A_s} \right) `$ 

   - Here, $` A_s `$ represents the initial cross-sectional area of the specimen.



5. **Stress-Strain Relationship:**

   - Assuming stress equilibrium, uniaxial stress conditions in the specimen, and one-dimensional elastic stress wave propagation without dispersion in the bars, the nominal strain rate $` \dot{e}_s `$, nominal strain $` e_s `$, and nominal stress $` S_s `$ in the specimen are estimated using:

     -  $` \dot{e}_s(t) = \frac{2c_b}{l_0} \varepsilon_R(t) `$ 

     -  $` e_s(t) = \int_0^t \dot{e}_s(\tau) d\tau `$ 

     -  $` S_s(t) = \frac{E_b A_b}{A_s} \varepsilon_T(t) `$

    

6. **True Stress-Strain:**

   - True strain $\varepsilon_s(t)$ in the specimen is given by:

      - $\varepsilon_s(t) = -\ln(1 - e_s(t))$   


     Here, $e_s(t)$ represents the engineering strain in the specimen.



   - The true strain rate $\dot{\varepsilon}_s(t)$ in the specimen is calculated as:

      - $\dot{\varepsilon}_s(t) = \frac{\dot{e}_s(t)}{1 - e_s(t)}$



   - The true stress $\sigma_s(t)$ in the specimen is obtained as:

        - $\sigma_s(t) = S_s(t) \cdot (1 - e_s(t))$    


     Here, $S_s(t)$ represents the nominal stress in the specimen.



# SHPB Cropping Tool








1. **Data Preparation:**

   - Prepare your SHPB data in CSV format. Each CSV file should contain three columns: 'Time' and the respective voltage data (e.g., 'Incident' and  'Transmitted').



2. **Launching the Tool:**

   - Run the Python script `crop.py`.

   - The tool will display a graphical interface with multiple subplots.



3. **Selecting Regions:**

   - For Incident Voltage: Click and drag on the subplot (labelled 'Incident Voltage') to select a region of interest.

   - For Reflected Voltage: Click and drag on the subplot (labelled 'Reflected Voltage') to select a region of interest.

   - For Transmitted Voltage: Click and drag on the subplot (labelled 'Transmitted Voltage') to select a region of interest.



4. **Saving Selected Data:**

   - The selected regions will be displayed on their respective subplots.

   - The selected data will be saved automatically to CSV files in the same directory as the script.

     - Selected Incident Voltage data will be saved to `selected_incident.csv`.

     - Selected Reflected Voltage data will be saved to `selected_reflected.csv`.

     - Selected Transmitted Voltage data will be saved to `selected_transmitted.csv`.



### Reference



The theory and equations used in this project are based on the following sources:



- Ramesh, K.T. (2008). High Rates and Impact Experiments. In: Sharpe, W. (eds) *Springer Handbook of Experimental Solid Mechanics*. Springer Handbooks. Springer, Boston, MA. [DOI: 10.1007/978-0-387-30877-7_33](https://doi.org/10.1007/978-0-387-30877-7_33)

- Kolsky, H. (1963). Stress Waves in Solids. *United Kingdom: Dover Publications*.



## License



This project is licensed under the  Apache-2.0 License. - see the [LICENSE](LICENSE) file for details.