Rocket Motor Static Test Data Analysis - PRD 🚀

An advanced application developed for Potiguar Rocket Design (PRD) to analyze and visualize data from static rocket motor tests. This tool transforms raw load cell data into actionable engineering insights using modern data science libraries and Artificial Intelligence.

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✨ Key Features

• Multi-Format Upload: Supports .csv, .txt, and .wsv raw data files.
• Smart Burn Detection: Automatically identifies the ignition and burnout points using thrust thresholding.
• Interactive Visualizations: High-fidelity thrust-time curves powered by Plotly.
• Automated Engineering Metrics:
  • Maximum & Average Thrust (N)
  • Total Impulse (Ns)
  • Burn Time (s)
  • Time to Peak (s)
• AI-Powered Technical Reports: Integrates with Google Gemini 2.0 Flash to provide concise technical analysis of motor efficiency.
• Comparative Analysis: Compare multiple motor tests side-by-side in a single unified chart.
• Export Ready: Generate and download high-quality PNG tables of test statistics.

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🛠️ Technologies Used

• Streamlit: Interactive web interface.
• Plotly: Dynamic and interactive charting.
• NumPy & Pandas: Data processing and numerical analysis.
• Google GenAI (Gemini API): Intelligent technical reporting.
• Python-dotenv: Secure environment variable management.

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🖥️ Usage Guide

1. File Upload

Upload one or more files containing your test data. The app will process each file individually and offer a comparison if multiple files are uploaded.

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2. Data Calibration & Results

The system automatically applies calibration factors and filters noise. You will see an interactive graph and a summary table for each motor.

3. Comparative View

When analyzing multiple motors, a consolidated chart at the bottom allows for direct performance comparison.

Bulding AI analisisys...

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📦 Installation & Setup

1. Clone the repository:

   git clone https://github.com/Sonryu/prd_data_analysis.git
   cd prd_data_analysis

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Environment Variables: Create a .env file in the root directory and add your Gemini API Key (optional):

   GOOGLE_API_KEY=your_key_here

5. Run the application:

   streamlit run app.py

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📄 License

Copyright (c) 2026 Ramon Watson de Lima Vilar. This project is licensed under the MIT License. See the LICENSE file for full details.