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Hacker News front pageMark Litwintschik26 min readtutorialintermediate

Saving Jet Fuel

Read on marksblogg.comHN 138·76 comments

Summary

A step‑by‑step tutorial showing how to use the open‑source scikit‑decide framework together with the OpenAP aircraft performance model to compute fuel‑optimal flight trajectories. The post details the author’s high‑end workstation, installs Python 3.12, scikit‑decide, OpenAP, and DuckDB with several extensions, then explores OpenAP’s aircraft data (e.g., A380‑800 specs and drag polar) and demonst…

  • scikit‑decide can be configured with custom fuel consumption models (e.g., OpenAP) to solve flight‑path optimization problems.
  • OpenAP ships a large, YAML‑based aircraft database (Boeing, Airbus, Embraer, etc.) and detailed performance parameters that can be queried via Python or DuckDB.
  • The tutorial provides a reproducible environment: Python 3.12 virtualenv, required pip packages, DuckDB extensions (h3, lindel, json, parquet, spatial), and a Windows‑WSL Ubuntu setup.
  • Hardware specs (Ryzen 9 9950X, 96 GB DDR5, 4 TB NVMe) are listed, but the performance impact of the optimizer is not quantified in the visible portion.

Fuel consumption is a major cost driver for airlines. Open‑source tools like scikit‑decide and OpenAP make it feasible to prototype data‑driven trajectory optimization without proprietary software, potentially enabling research, custom airline scheduling, or academic studies on fuel‑saving strategi…

5/10
optimizationpythonaircraftflight planningscikit decideopenap

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