We we inspired by the costly loss of nearly 40 Starlink satellites in 2022 which showed a critical blind spot in our ability to predict atmospheric drag. This sparked our core idea of trying to find a way to both detect and help predict potential satellite fallout. We learned early on that this was a significant challenge, as the key public data point for drag is noisy and difficult to draw conclusions from. However, we spent time working through this challenge and eventually finished our app on time! To build this we used a combination of Python, Go, and Typescript. We used Python and Go to extract and work with the data and used Typescript to build our UI.
Summary of our solution:
We built Density Flare, a real-time system using NASA and external APIs to calculate density multipliers and ballistic coefficients of LEO satellites with high accuracy.
Key achievements:
• Created a "fingerprint" database for over 5,000 satellites, currently in orbit, by calibrating their drag properties during calm space weather.
• Engineered a real-time system to calculate a density multiplier, showing exactly how the atmosphere swells during a solar storm.
• Enabled high-accuracy prediction of orbital drag, providing a critical tool for preventing satellite re-entry.
• Visualized the live data and risk assessments on a dashboard built with React and TypeScript.
Built With
- go
- python
- railway
- react
- typescript
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