# Open source flight planning can save real fuel > A hands-on test shows how Scikit-decide, OpenAP, NOAA winds, and OpenTop can search smarter flight paths. - Title: Open source flight planning can save real fuel · Marksblogg - Summary: A hands-on test shows how Scikit-decide, OpenAP, NOAA winds, and OpenTop can search smarter flight paths. Open flight planning tools can model aircraft… - Keywords: aviation, flight planning, open source, fuel, optimization, technology, startups, Open, source, flight, planning, save - Source: Marksblogg — https://tech.marksblogg.com/scikit-decide-openap-optimal-flight-planning.html - Author: Mark Litwintschik - Read time: 3 min - Topics: aviation, flight planning, open source, fuel, optimization, technology, startups ## Fuel is the prize A nonstop Boeing 787-9 from Newark to Rome could burn about $68K in jet fuel. A better wind-aware route might save a few thousand dollars on one flight. > could need $68K in jet fuel ## Open tools compete Commercial flight planning exists, but the test uses Scikit-decide, OpenAP aircraft models, and NOAA wind data to build an open source path finder. > offer an open source solution ## Scikit-decide plans paths Scikit-decide has spent six years growing into a toolkit for reinforcement learning, planning, and scheduling, including flight paths and drone swarms. > Scikit-decide has been in development for six years. ## OpenAP models aircraft OpenAP, built by TU Delft's Dr. Junzi Sun, supplies aircraft performance and fuel behavior. The post pairs it with Scikit-decide's flight path solver. ## The setup is heavy The workstation is a high-end Ryzen 9 desktop with 96 GB of RAM, a fast 4 TB NVMe SSD, and Ubuntu 24 running through WSL on Windows 11 Pro. ## Python does the work The environment uses Python 3.12, scikit-decide, OpenAP, and OpenTop. The install pulls a large stack of packages and needs at least 8 GB of disk space. > at least 8 GB of storage capacity ## DuckDB handles data DuckDB is set up with H3, JSON, Lindel, Parquet, and Spatial extensions. QGIS renders the route and wind maps using Natural Earth and Marine Regions data. ## Aircraft data is packed in OpenAP ships thousands of lines of Python plus aircraft datasets. Its aircraft files cover Boeing, Airbus, Embraer, Gulfstream, and Cessna models. ## The A380 shows detail The Airbus A380 file includes weight limits, wing geometry, cruise range, engine options, drag coefficients, and phase behavior from takeoff to landing. ## Engines get modeled too OpenAP includes 427 engines, with fuel flow, emissions indexes, thrust, bypass ratio, and manufacturer data. Turbofans dominate the dataset. > There are 427 engines listed ## Fuel formulas vary Fuel model rows define coefficients by aircraft type and engine. The A320, B737, A332, E190, and other types each get tuned values, with a default fallback. ## Navigation data is global The package ships almost 14,000 airports across 236 countries, plus navigation fixes and aids. That gives route search real geographic anchors. > almost 14K airport locations and codes ## Toulouse to Berlin The first Scikit-decide run searches an A320 path from Toulouse to Berlin using May 1, 2026 NOAA weather, A* search, and fuel as the cost target. ## Weather enters the graph If recent NOAA GRB2 files are missing, Scikit-decide downloads them. Each 143 to 144 MB file covers global weather for one forecast time. > Each file has data covering the entire planet. ## Berlin route solved fast A* finds a 19-step route in 0.28 seconds. The rollout reports about 7,666 seconds of flight time and 5,855 kg of fuel. > Goal reached after 19 steps! ## Warsaw changes the answer For Toulouse to Warsaw, the initial target altitude is higher. That lets the A320 climb sooner, fly more directly, and solve a 14-step route. ## Farther can burn less Warsaw is 500 km farther than Berlin, yet the modeled path arrives almost 45 minutes faster and uses only 76% of the Berlin flight's fuel. > only needed 76% of the fuel ## OpenTop compares jets OpenTop adds trajectory planning with a grid cost file. An Amsterdam to Frankfurt test compares an A320 and B737 against a fuel-and-grid objective. ## Small differences matter On Amsterdam to Frankfurt, the A320 burns 1,625.6 kg in 35.8 minutes. The B737 burns 1,622.1 kg, flies higher, and takes 39.2 minutes. > fuel burn: 1622.1 kg ## Key takeaway Open flight planning tools can model aircraft, weather, routes, and fuel closely enough to compare real choices before a plane flies.