ASCENT: Aerospace System for Chemical Emissions & Numerical Tracking

An AI-powered tool for predicting and visualizing atmospheric chemical composition changes from rocket launches.

Overview

ASCENT helps predict and analyze the environmental impact of rocket launches by modeling the dispersion of key pollutants (CO2, NOx, and Al2O3) based on launch parameters and weather conditions.

Features

• Real-time launch impact simulation
• Dynamic pollutant dispersion visualization
• Weather condition integration
• Multiple rocket and fuel type support
• Interactive heatmap generation
• Time-series predictions up to 48 hours
• Adjustable launch parameters:
  • Payload mass
  • Launch coordinates
  • Rocket type
  • Fuel type
  • Simulation duration

Installation

git clone https://github.com/notvasub/ascent.git
cd ascent
pip install -r requirements.txt

Usage

Run the Streamlit application:

streamlit run src/app.py

Train/retrain the model with new data:

python src/retrain.py

Project Structure

• src/app.py: Main Streamlit interface
• src/model.py: Machine learning model implementation
• src/data_collection.py: Data handling utilities
• src/weather_integration.py: Weather data integration
• models/: Saved model files
• data/: Holds the training data

License

This project is licensed under the MIT License - see the LICENSE file for details.