Saving Jet Fuel
Tutorial optimizes flight paths to cut jet fuel costs using open-source Scikit-decide planning framework and OpenAP aircraft performance models.
A technical walkthrough demonstrates wind-aware flight path optimization using Scikit-decide, an open-source framework for reinforcement learning and automated planning, paired with OpenAP fuel-consumption models built by Dr. Junzi Sun at TU Delft and NOAA wind data. A Boeing 787-9 flying EWR to FCO can require roughly $68K in fuel, and adjusted routing could save thousands. The post uses Python 3.12, DuckDB with spatial extensions, and QGIS for map rendering.
- Scikit-decide combines reinforcement learning, automated planning and scheduling
- OpenAP models cover 37 aircraft from Boeing, Airbus, Embraer, Gulfstream, Cessna
- Wind-aware routing could save thousands of dollars per long-haul flight
- Workflow uses Python, DuckDB spatial extensions and QGIS for analysis
From the original article
15 min read at tech.marksblogg.comA Boeing 787-9 Dreamliner flying nonstop from Newark Liberty International Airport (EWR) to Leonardo da Vinci-Fiumicino Airport (FCO) could need $68K in jet fuel over the 8. 5-hour flight. Adjusting the flight path for wind conditions could reduce fuel consumption and possibly save a few thousand dollars.
By marklit · tech.marksblogg.com
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