Saving Jet Fuel Saving Jet Fuel A 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
By Coderz Club · 2026-09-16 · Tags: tech, html
Saving Jet Fuel
Saving Jet Fuel A 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. Firms like Jeppesen have offerings in this space, but Scikit-decide, together with a narrow- and wide-body fuel consumption model built by a professor at the Delft University of Technology and wind data from NOAA, offer an open source solution. Scikit-decide has been in development for six years. It's a framework for reinforcement learning, automated planning and scheduling. The project can optimise flight paths, re-organise airline workforce schedules and calculate drone swarm paths. OpenAP is an aircraft performance model and toolkit developed by Dr. Junzi Sun. Dr. Sun has a PhD in air traffic management and, among many other things, teaches a course on the subject as a tenured assistant professor at TU Delft in the Netherlands. Scikit-decide's optimal flight path solver can be configured to use different fuel consumption models. In this post, I'll compare two flight paths flown using the Airbus A320 and OpenAP's fuel consumption model. My Workstation I'm using a 5.7 GHz AMD Ryzen 9 9950X CPU. It has 16 cores and 32 threads and 1.2 MB of L1, 16 MB of L2 and 64 MB of L3 cache. It has a liquid cooler attached and is housed in a spacious, full-sized Cooler Master HAF 700 computer case. The system has 96 GB of DDR5 RAM clocked at 4,800 MT/s and a 5th-generation, Crucial T700 4 TB NVMe M.2 SSD which can read at speeds up to 12,400 MB/s. There is a heatsink on the SSD to help keep its temperature down. This is my system's C drive. The system is powered by a 1,200-watt, fully modular Corsair Power Supply and is sat on an ASRock X870E Nova 90 Motherboard. I'm running Ubuntu 24 LTS via Microsoft's Ubuntu for Windows on Windows 11 Pro. In case you're wondering why I don't run a Linux-based desktop as my primary work environment, I'm still using an Nvidia GTX 1080 GPU which has better driver support on Windows and ArcGIS Pro only supports Windows natively. Installing Prerequisites I'll use Python 3.12 along with jq in this post. $ sudo add-apt-repository ppa:deadsnakes/ppa $ sudo apt update $ sudo apt install \ jq \ python3-pip \ python3.12-venv I'll set up a Python Virtual Environment and install scikit-decide, along with the OpenAP open aircraft performance model and OpenTop, a flight trajectory toolkit that was also developed by Dr. Sun. $ python3 -m venv ~/.flight_planning $ source ~/.flight_planning/bin/activate $ pip install \ scikit-decide[all] \ openap[all] \ opentop The above will need at least 8 GB of storage capacity. These are the packages that were installed. $ pip install pipdeptree $ pipdeptree -d0 lz4==4.4.5 openevolve==0.3.2 opentop==2.6.0 pip==24.0 pipdeptree==4.2.5 plado==0.1.6 pygeodesy==26.9.9 pygrib==2.1.8 pyRDDLGym-gurobi==0.2 pyRDDLGym-jax==3.1 pyRDDLGym-rl==0.2 pytz==2026.3.post1 ray==2.37.0 rddlrepository==2.2 sb3_contrib==2.3.0 scikit-decide==1.1.1 scikit-image==0.26.0 tensorboardX==2.6.5 torch-geometric==2.8.0.post1 typer==0.27.2 unified-planning==1.2.0 up-enhsp==0.0.27 up_fast_downward==0.5.2 up-pyperplan==1.1.0 z3-solver==5.1.0.0 I'll use DuckDB, along with its H3, JSON, Lindel, Parquet and Spatial extensions in this post. $ cd ~ $ wget -c https://github.com/duckdb/duckdb/releases/download/v1.5.4/duckdb_cli-linux-amd64.zip $ unzip -j duckdb_cli-linux-amd64.zip $ chmod +x duckdb $ ~/duckdb INSTALL h3 FROM community; INSTALL lindel FROM community; INSTALL json; INSTALL parquet; INSTALL spatial; I'll set up DuckDB to load every installed extension each time it launches. $ vi ~/.duckdbrc .timer on .width 180 LOAD h3; LOAD lindel; LOAD json; LOAD parquet; LOAD spatial; The maps in this post were rendered with QGIS version 4.2.1. QGIS is a desktop application that runs on Windows, macOS and Linux. The application has grown in popularity in recent years and has ~22M application launches from users all around the world each month. The boundaries and place names were sourced from Natural Earth. Maritime Boundaries were sourced from Marine Regions. OpenAP's Aircraft Types I'll first clone the OpenAP repository. $ git clone https://github.com/junzis/openap Excluding unit tests and utility scripts, there are 3,369 lines of Python in this package. OpenAP's model relies on a large number of datasets that are packaged with its codebase. These cover a wide variety of aircraft. Below are the aircraft manufacturer counts. $ grep -ho aircraft: .*[a-z] \ openap/data/aircraft/*.yml \ | cut -d -f2 \ | sort \ | uniq -c \ | sort -rn 17 Boeing 13 Airbus 5 Embraer 1 Gulfstream 1 Cessna These are the properties for the Airbus A380-800. $ cat openap/data/aircraft/a388.yml aircraft: Airbus A380-800 mtow: 560000 mlw: 386000 oew: 277000 mfc: 320000 vmo: 340 mmo: 0.89 ceiling: 13100 pa
Saving Jet Fuel A 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. Firms like Jeppesen have offerings in this space, but Scikit-decide, together with a narrow- and wide-body fuel consumption model built by a professor at the Delft University of Technology and wind data from NOAA, offer an open source solution. Scikit-decide has been in development for six years. It's a framework for reinforcement learning, automated planning and scheduling. The project can optimise flight paths, re-organise airline workforce schedules and calculate drone swarm paths. OpenAP is an aircraft performance model and toolkit developed by Dr. Junzi Sun. Dr. Sun has a PhD in air traffic management and, among many other things, teaches a course on the subject as a tenured assistant professor at TU Delft in the Netherlands. Scikit-decide's optimal flight path solver can be configured to use different fuel consumption models. In this post, I'll compare two flight paths flown using the Airbus A320 and OpenAP's fuel consumption model. My Workstation I'm using a 5.7 GHz AMD Ryzen 9 9950X CPU. It has 16 cores and 32 threads and 1.2 MB of L1, 16 MB of L2 and 64 MB of L3 cache. It has a liquid cooler attached and is housed in a spacious, full-sized Cooler Master HAF 700 computer case. The system has 96 GB of DDR5 RAM clocked at 4,800 MT/s and a 5th-generation, Crucial T700 4 TB NVMe M.2 SSD which can read at speeds up to 12,400 MB/s. There is a heatsink on the SSD to help keep its temperature down. This is my system's C drive. The system is powered by a 1,200-watt, fully modular Corsair Power Supply and is sat on an ASRock X870E Nova 90 Motherboard. I'm running Ubuntu 24 LTS via Microsoft's Ubuntu for Windows on Windows 11 Pro. In case you're wondering why I don't run a Linux-based desktop as my primary work environment, I'm still using an Nvidia GTX 1080 GPU which has better driver support on Windows and ArcGIS Pro only supports Windows natively. Installing Prerequisites I'll use Python 3.12 along with jq in this post. $ sudo add-apt-repository ppa:deadsnakes/ppa $ sudo apt update $ sudo apt install \ jq \ python3-pip \ python3.12-venv I'll set up a Python Virtual Environment and install scikit-decide, along with the OpenAP open aircraft performance model and OpenTop, a flight trajectory toolkit that was also developed by Dr. Sun. $ python3 -m venv ~/.flight_planning $ source ~/.flight_planning/bin/activate $ pip install \ scikit-decide[all] \ openap[all] \ opentop The above will need at least 8 GB of storage capacity. These are the packages that were installed. $ pip install pipdeptree $ pipdeptree -d0 lz4==4.4.5 openevolve==0.3.2 opentop==2.6.0 pip==24.0 pipdeptree==4.2.5 plado==0.1.6 pygeodesy==26.9.9 pygrib==2.1.8 pyRDDLGym-gurobi==0.2 pyRDDLGym-jax==3.1 pyRDDLGym-rl==0.2 pytz==2026.3.post1 ray==2.37.0 rddlrepository==2.2 sb3_contrib==2.3.0 scikit-decide==1.1.1 scikit-image==0.26.0 tensorboardX==2.6.5 torch-geometric==2.8.0.post1 typer==0.27.2 unified-planning==1.2.0 up-enhsp==0.0.27 up_fast_downward==0.5.2 up-pyperplan==1.1.0 z3-solver==5.1.0.0 I'll use DuckDB, along with its H3, JSON, Lindel, Parquet and Spatial extensions in this post. $ cd ~ $ wget -c https://github.com/duckdb/duckdb/releases/download/v1.5.4/duckdb_cli-linux-amd64.zip $ unzip -j duckdb_cli-linux-amd64.zip $ chmod +x duckdb $ ~/duckdb INSTALL h3 FROM community; INSTALL lindel FROM community; INSTALL json; INSTALL parquet; INSTALL spatial; I'll set up DuckDB to load every installed extension each time it launches. $ vi ~/.duckdbrc .timer on .width 180 LOAD h3; LOAD lindel; LOAD json; LOAD parquet; LOAD spatial; The maps in this post were rendered with QGIS version 4.2.1. QGIS is a desktop application that runs on Windows, macOS and Linux. The application has grown in popularity in recent years and has ~22M application launches from users all around the world each month. The boundaries and place names were sourced from Natural Earth. Maritime Boundaries were sourced from Marine Regions. OpenAP's Aircraft Types I'll first clone the OpenAP repository. $ git clone https://github.com/junzis/openap Excluding unit tests and utility scripts, there are 3,369 lines of Python in this package. OpenAP's model relies on a large number of datasets that are packaged with its codebase. These cover a wide variety of aircraft. Below are the aircraft manufacturer counts. $ grep -ho aircraft: .*[a-z] \ openap/data/aircraft/*.yml \ | cut -d -f2 \ | sort \ | uniq -c \ | sort -rn 17 Boeing 13 Airbus 5 Embraer 1 Gulfstream 1 Cessna These are the properties for the Airbus A380-800. $ cat openap/data/aircraft/a388.yml aircraft: Airbus A380-800 mtow: 560000 mlw: 386000 oew: 277000 mfc: 320000 vmo: 340 mmo: 0.89 ceiling: 13100 pa