Year: 2025
Type: article
Abstract: —Wake turbulence poses a critical safety threat in aviation, especially during takeoff and landing. This project leverages machine learning to dynamically predict turbulence risk using real-time aircraft data, environmental conditions, and flight scenarios. A custom risk formula incorporates continuous aircraft weight categories, required separation time, and weather factors. Visualization is achieved using PyOpenGL and Tkinter, with data sourced from MSFS and FlightRadar24. Monte Carlo simulations across 12 aircraft pairs yielded 83.3% accuracy. The system offers real-time risk assessment and 3D simulation, enabling smarter air traffic control and paving the way for safer, adaptive wake turbulence mitigation in congested airspaces.
DOI: 10.55041/ijsrem49656
Author: Pavan Practoor
Institution: R.V. College of Engineering
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411151268