Saving Jet Fuel

Sep 16, 2026 06:17 AM - 1 day ago 5

A Boeing 787-9 Dreamliner flying nonstop from Newark Liberty International Airport (EWR) to Leonardo da Vinci-Fiumicino Airport (FCO) could request $68K successful pitchy substance complete the 8.5-hour flight. Adjusting the formation way for upwind conditions could trim substance depletion and perchance prevention a fewer 1000 dollars.

Firms for illustration Jeppesen person offerings successful this space, but Scikit-decide, together pinch a narrow- and wide-body substance depletion exemplary built by a professor astatine the Delft University of Technology and upwind information from NOAA, connection an unfastened root solution.

Scikit-decide has been successful improvement for six years. It's a model for reinforcement learning, automated readying and scheduling. The task tin optimise formation paths, re-organise hose workforce schedules and cipher drone swarm paths.

OpenAP is an craft capacity exemplary and toolkit developed by Dr. Junzi Sun. Dr. Sun has a PhD successful aerial postulation guidance and, among galore different things, teaches a people connected the taxable arsenic a tenured adjunct professor astatine TU Delft successful the Netherlands.

Scikit-decide's optimal formation way solver tin beryllium configured to usage different substance depletion models. In this post, I'll comparison 2 formation paths flown utilizing the Airbus A320 and OpenAP's substance depletion model.

My Workstation

I'm utilizing 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 successful a spacious, full-sized Cooler Master HAF 700 machine case.

The strategy has 96 GB of DDR5 RAM clocked astatine 4,800 MT/s and a 5th-generation, Crucial T700 4 TB NVMe M.2 SSD which tin publication astatine speeds up to 12,400 MB/s. There is simply a heatsink connected the SSD to thief support its somesthesia down. This is my system's C drive.

The strategy is powered by a 1,200-watt, afloat modular Corsair Power Supply and is sat connected an ASRock X870E Nova 90 Motherboard.

I'm moving Ubuntu 24 LTS via Microsoft's Ubuntu for Windows connected Windows 11 Pro. In lawsuit you're wondering why I don't tally a Linux-based desktop arsenic my superior activity environment, I'm still utilizing an Nvidia GTX 1080 GPU which has amended driver support connected Windows and ArcGIS Pro only supports Windows natively.

Installing Prerequisites

I'll usage Python 3.12 on pinch jq successful this post.

$ sudo add-apt-repository ppa:deadsnakes/ppa $ sudo apt update $ sudo apt install \ jq \ python3-pip \ python3.12-venv

I'll group up a Python Virtual Environment and instal scikit-decide, on pinch the OpenAP unfastened craft capacity exemplary and OpenTop, a formation trajectory toolkit that was besides developed by Dr. Sun.

$ python3 -m venv ~/.flight_planning $ source ~/.flight_planning/bin/activate $ pip install \ 'scikit-decide[all]' \ 'openap[all]' \ opentop

The supra will request astatine slightest 8 GB of retention 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 usage DuckDB, on pinch its H3, JSON, Lindel, Parquet and Spatial extensions successful 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 group up DuckDB to load each installed hold each clip it launches.

.timer on .width 180 LOAD h3; LOAD lindel; LOAD json; LOAD parquet; LOAD spatial;

The maps successful this station were rendered pinch QGIS type 4.2.1. QGIS is simply a desktop exertion that runs connected Windows, macOS and Linux. The exertion has grown successful fame successful caller years and has ~22M exertion launches from users each astir the world each month.

The boundaries and spot names were originated from Natural Earth. Maritime Boundaries were originated from Marine Regions.

OpenAP's Aircraft Types

I'll first clone the OpenAP repository.

$ git clone https://github.com/junzis/openap

Excluding portion tests and inferior scripts, location are 3,369 lines of Python successful this package.

OpenAP's exemplary relies connected a ample number of datasets that are packaged pinch its codebase. These screen a wide assortment of aircraft. Below are the craft shaper 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 pax: max: 853 low: 410 high: 620 fuselage: length: 72.72 height: 8.41 width: 7.14 wing: area: 845 span: 79.75 mac: null sweep: 33.5 t/c: 0.08 flaps: type: single-slotted area: null bf/b: null lambda_f: 0.900 cf/c: 0.150 Sf/S: 0.150 cruise: height: 12800 mach: 0.85 range: 14800 engine: type: turbofan mount: wing number: 4 default: GP7270 options: A380-841: Trent 970-84 A380-842: Trent 972-84 A380-861: GP7270 drag: cd0: 0.016 k: 0.050 e: 0.855 gears: 0.012

These are its resistance coefficients.

$ cat openap/data/dragpolar/a388.yml

aircraft: Airbus A380-800 clean: cd0: 0.016 k: 0.050 e: 0.855 gears: 0.012 flaps: lambda_f: 0.900 cf/c: 0.150 Sf/S: 0.150

These are immoderate further properties.

$ echo "import pandas arsenic pd; print( pd.read_fwf('openap/data/wrap/a388.txt') .to_csv(index=False))" \ | python3 \ | ~/duckdb \ -c '.maxwidth 150' \ -c "SELECT * EXCLUDE(parameters), parameters: SPLIT(parameters, '|') FROM READ_CSV('/dev/stdin')"

┌──────────────────────┬────────────────┬───────────────────────────────────────┬────────┬────────┬─────────┬─────────┬──────────────────────────────┐ │ adaptable │ formation shape │ sanction │ opt │ min │ max │ exemplary │ parameters │ │ varchar │ varchar │ varchar │ double │ double │ double │ varchar │ varchar[] │ ├──────────────────────┼────────────────┼───────────────────────────────────────┼────────┼────────┼─────────┼─────────┼──────────────────────────────┤ │ to_v_lof │ takeoff │ Liftoff velocity │ 89.9 │ 75.4 │ 104.4 │ norm │ [89.93, 10.07] │ │ to_d_tof │ takeoff │ Takeoff region │ 2.56 │ 1.35 │ 3.78 │ norm │ [2.56, 0.74] │ │ to_acc_tof │ takeoff │ Mean takeoff accelaration │ 1.35 │ 1.04 │ 1.66 │ norm │ [1.35, 0.19] │ │ ic_va_avg │ initial_climb │ Mean airspeed │ 88.0 │ 80.0 │ 96.0 │ norm │ [88.15, 5.64] │ │ ic_vs_avg │ initial_climb │ Mean vertical complaint │ 5.65 │ 4.4 │ 8.94 │ gamma │ [4.76, 3.22, 0.65] │ │ cl_d_range │ climb │ Climb scope │ 296.0 │ 200.0 │ 446.0 │ beta │ [3.23, 5.18, 179.46, 335.24] │ │ cl_v_cas_const │ climb │ Constant CAS │ 163.0 │ 155.0 │ 170.0 │ norm │ [163.39, 4.51] │ │ cl_v_mach_const │ climb │ Constant Mach │ 0.84 │ 0.8 │ 0.86 │ beta │ [12.23, 5.32, 0.72, 0.17] │ │ cl_h_cas_const │ climb │ Constant CAS crossover altitude │ 3.3 │ 1.3 │ 5.3 │ norm │ [3.29, 1.24] │ │ cl_h_mach_const │ climb │ Constant Mach crossover altitude │ 8.9 │ 8.2 │ 9.7 │ norm │ [8.94, 0.47] │ │ cl_vs_avg_pre_cas │ climb │ Mean climb rate, pre-constant-CAS │ 7.85 │ 5.95 │ 9.75 │ norm │ [7.85, 1.16] │ │ cl_vs_avg_cas_const │ climb │ Mean climb rate, constant-CAS │ 7.51 │ 5.2 │ 9.82 │ norm │ [7.51, 1.40] │ │ cl_vs_avg_mach_const │ climb │ Mean climb rate, constant-Mach │ 5.56 │ 3.23 │ 7.91 │ norm │ [5.57, 1.42] │ │ cr_d_range │ cruise │ Cruise scope │ 4348.0 │ 892.0 │ 20565.0 │ gamma │ [2.81, 246.73, 2274.81] │ │ cr_v_cas_mean │ cruise │ Mean cruise CAS │ 136.0 │ 130.0 │ 145.0 │ beta │ [3.32, 5.27, 126.00, 29.75] │ │ cr_v_cas_max │ cruise │ Maximum cruise CAS │ 145.0 │ 134.0 │ 164.0 │ beta │ [2.02, 3.21, 130.38, 46.65] │ │ cr_v_mach_mean │ cruise │ Mean cruise Mach │ 0.84 │ 0.82 │ 0.86 │ norm │ [0.84, 0.01] │ │ cr_v_mach_max │ cruise │ Maximum cruise Mach │ 0.87 │ 0.85 │ 0.9 │ gamma │ [16.14, 0.80, 0.00] │ │ cr_h_init │ cruise │ Initial cruise altitude │ 11.55 │ 9.3 │ 12.23 │ beta │ [3.82, 1.66, 7.49, 5.01] │ │ cr_h_mean │ cruise │ Mean cruise altitude │ 11.73 │ 10.87 │ 12.28 │ beta │ [7.22, 3.92, 9.59, 3.14] │ │ cr_h_max │ cruise │ Maximum cruise altitude │ 12.06 │ 11.52 │ 12.6 │ norm │ [12.06, 0.33] │ │ de_d_range │ descent │ Descent scope │ 310.0 │ 238.0 │ 528.0 │ gamma │ [4.73, 213.47, 25.87] │ │ de_v_mach_const │ descent │ Constant Mach │ 0.83 │ 0.8 │ 0.87 │ norm │ [0.83, 0.02] │ │ de_v_cas_const │ descent │ Constant CAS │ 154.0 │ 142.0 │ 167.0 │ norm │ [154.84, 7.74] │ │ de_h_mach_const │ descent │ Constant Mach crossover altitude │ 10.1 │ 8.6 │ 11.5 │ norm │ [10.06, 0.88] │ │ de_h_cas_const │ descent │ Constant CAS crossover altitude │ 6.6 │ 3.9 │ 9.4 │ norm │ [6.64, 1.69] │ │ de_vs_avg_mach_const │ descent │ Mean descent rate, constant-Mach │ -6.06 │ -11.9 │ -2.97 │ beta │ [3.43, 2.08, -15.98, 14.36] │ │ de_vs_avg_cas_const │ descent │ Mean descent rate, constant-CAS │ -8.36 │ -11.74 │ -4.97 │ norm │ [-8.36, 2.06] │ │ de_vs_avg_after_cas │ descent │ Mean descent rate, after-constant-CAS │ -5.48 │ -6.93 │ -4.02 │ norm │ [-5.48, 0.88] │ │ fa_va_avg │ final_approach │ Mean airspeed │ 73.0 │ 68.0 │ 77.0 │ norm │ [73.28, 3.02] │ │ fa_vs_avg │ final_approach │ Mean vertical complaint │ -3.71 │ -4.13 │ -2.92 │ gamma │ [9.49, -4.74, 0.12] │ │ fa_agl │ final_approach │ Approach perspective │ 2.9 │ 2.42 │ 3.38 │ norm │ [2.90, 0.29] │ │ ld_v_app │ landing │ Touchdown velocity │ 70.0 │ 62.1 │ 78.0 │ norm │ [70.00, 5.52] │ │ ld_d_brk │ landing │ Braking region │ 2.26 │ 0.73 │ 3.8 │ norm │ [2.26, 0.93] │ │ ld_acc_brk │ landing │ Mean braking acceleration │ -1.01 │ -1.51 │ -0.52 │ norm │ [-1.01, 0.30] │ └──────────────────────┴────────────────┴───────────────────────────────────────┴────────┴────────┴─────────┴─────────┴──────────────────────────────┘

These are the craft type synonyms list.

$ ~/duckdb -c "FROM READ_CSV('/dev/stdin')" \ < openap/data/aircraft/_synonym.csv

┌─────────┬─────────┐ │ orig │ caller │ │ varchar │ varchar │ ├─────────┼─────────┤ │ a124 │ b744 │ │ a306 │ a332 │ │ a310 │ a318 │ │ at72 │ e145 │ │ at75 │ e145 │ │ at76 │ e145 │ │ b733 │ b734 │ │ b735 │ b734 │ │ b762 │ b763 │ │ b77l │ b77w │ │ c25a │ c550 │ │ c525 │ c550 │ │ c56x │ c550 │ │ crj2 │ e145 │ │ crj9 │ e75l │ │ e290 │ e190 │ │ glf5 │ glf6 │ │ gl5t │ glf6 │ │ lj45 │ glf6 │ │ md11 │ b773 │ │ pc24 │ c550 │ │ su95 │ e170 │ └─────────┴─────────┘

Aircraft Engines

Aircraft often person the action of astatine slightest 2 different engines to take from. There are 427 engines listed successful this package's dataset.

$ wc -l openap/data/engine/engines.csv # 427

These are the specifications for the Trent 970-84.

$ echo "FROM 'openap/data/engine/engines.csv' WHERE sanction = 'Trent 970-84' LIMIT 1" \ | ~/duckdb -json \ | jq -S .

[ { "bpr": 8.45, "cruise_alt": null, "cruise_mach": null, "cruise_sfc": null, "cruise_thrust": null, "ei_co_app": 1.16, "ei_co_co": 0.31, "ei_co_idl": 13.38, "ei_co_to": 0.32, "ei_hc_app": 0.08, "ei_hc_co": 0.12, "ei_hc_idl": 0.04, "ei_hc_to": 0.02, "ei_nox_app": 12.09, "ei_nox_co": 29.42, "ei_nox_idl": 5.44, "ei_nox_to": 38.29, "ff_app": 0.72, "ff_co": 2.157, "ff_idl": 0.255, "ff_to": 2.605, "fuel_lto": 965.0, "manufacturer": "Rolls-Royce plc", "max_thrust": 338700.0, "name": "Trent 970-84", "pr": 38.0, "type": "TF", "uid": "18RR081" } ]

These are the motor shaper counts.

CREATE OR REPLACE TABLE a AS FROM 'openap/data/engine/engines.csv'; SELECT COUNT(*), manufacturer FROM a GROUP BY 2 ORDER BY 1 DESC;

┌──────────────┬────────────────────────────┐ │ count_star() │ shaper │ │ int64 │ varchar │ ├──────────────┼────────────────────────────┤ │ 108 │ GE Aircraft Engines │ │ 94 │ CFM International │ │ 85 │ Pratt & Whitney │ │ 62 │ Rolls-Royce plc │ │ 13 │ International Aero Engines │ │ 12 │ Pratt & Whitney Canada │ │ 11 │ Rolls-Royce Corporation │ │ 8 │ Rolls-Royce Deutschland │ │ 8 │ Honeywell │ │ 7 │ Aviadvigatel │ │ 5 │ Textron Lycoming │ │ 4 │ KKBM │ │ 3 │ IVCHENKO PROGRESS ZMBK │ │ 2 │ PowerJet S.A. │ │ 2 │ Allied Signal │ │ 1 │ Engine Alliance │ │ 1 │ Garret AiResearch │ └──────────────┴────────────────────────────┘

These are the engine-type counts for Turbofan (TF), Mixed-flow Turbofan (MTF), Turboprop (TP) and Piston (PS) engines successful this dataset.

SELECT COUNT(*), type FROM a GROUP BY 2 ORDER BY 1 DESC;

┌──────────────┬─────────┐ │ count_star() │ type │ │ int64 │ varchar │ ├──────────────┼─────────┤ │ 322 │ TF │ │ 98 │ MTF │ │ 5 │ TP │ │ 1 │ PS │ └──────────────┴─────────┘

This is the motor database classed by their maximum thrust.

SELECT manufacturer, name, type, max_thrust FROM a ORDER BY 4 DESC LIMIT 25;

┌─────────────────────┬───────────────┬─────────┬────────────┐ │ shaper │ sanction │ type │ max_thrust │ │ varchar │ varchar │ varchar │ double │ ├─────────────────────┼───────────────┼─────────┼────────────┤ │ GE Aircraft Engines │ GE90-115B │ TF │ 513900.0 │ │ GE Aircraft Engines │ GE90-113B │ TF │ 504900.0 │ │ GE Aircraft Engines │ GE90-110B1 │ TF │ 492600.0 │ │ Rolls-Royce plc │ Trent XWB-97 │ TF │ 436748.0 │ │ GE Aircraft Engines │ GE90-94B │ TF │ 430920.0 │ │ GE Aircraft Engines │ GE90-92B │ TF │ 426720.0 │ │ GE Aircraft Engines │ GE90-90B │ TF │ 419250.0 │ │ Rolls-Royce plc │ Trent 895 │ TF │ 413050.0 │ │ Rolls-Royce plc │ Trent 892 │ TF │ 411480.0 │ │ Pratt & Whitney │ PW4090 │ TF │ 408300.0 │ │ GE Aircraft Engines │ GE90-85B │ TF │ 397210.0 │ │ Rolls-Royce plc │ Trent 884 │ TF │ 390100.0 │ │ Pratt & Whitney │ PW4084D │ TF │ 385900.0 │ │ Rolls-Royce plc │ Trent XWB-84 │ TF │ 379000.0 │ │ Pratt & Whitney │ PW4084 │ TF │ 369600.0 │ │ GE Aircraft Engines │ GE90-77B │ TF │ 366750.0 │ │ Rolls-Royce plc │ Trent 1000-R3 │ TF │ 363900.0 │ │ GE Aircraft Engines │ GE90-76B │ TF │ 363220.0 │ │ Rolls-Royce plc │ Trent 877 │ TF │ 361640.0 │ │ Rolls-Royce plc │ Trent 1000-M3 │ TF │ 358100.0 │ │ Rolls-Royce plc │ Trent 1000-N3 │ TF │ 358100.0 │ │ Pratt & Whitney │ PW4077D │ TF │ 355700.0 │ │ Rolls-Royce plc │ Trent XWB-79B │ TF │ 355200.0 │ │ Rolls-Royce plc │ Trent XWB-79 │ TF │ 355200.0 │ │ Rolls-Royce plc │ Trent 970B-84 │ TF │ 352900.0 │ └─────────────────────┴───────────────┴─────────┴────────────┘

These are the substance exemplary defaults and overrides.

$ ~/duckdb -c "FROM READ_CSV('/dev/stdin')" \ < openap/data/fuel/fuel_models.csv

┌──────────┬─────────────┬────────────────────┬────────────────────┬────────────────────┐ │ typecode │ engine_type │ c1 │ c2 │ c3 │ │ varchar │ varchar │ double │ double │ double │ ├──────────┼─────────────┼────────────────────┼────────────────────┼────────────────────┤ │ A318 │ CFM56-5B9/3 │ 0.7769784596099123 │ 1.765377288174942 │ 2.5349134936316693 │ │ A319 │ V2524-A5 │ 0.8694169413032631 │ 1.9542690629047836 │ 2.5028187026860103 │ │ A320 │ CFM56-5B4/P │ 1.0453208160586924 │ 2.3633720747416573 │ 1.2378127479131922 │ │ A321 │ V2533-A5 │ 1.3979999999999444 │ 2.054028451829268 │ 1.0008941993511127 │ │ A332 │ Trent 772 │ 2.886430057340283 │ 1.0960397632560752 │ 2.3772585567580293 │ │ A333 │ Trent 772 │ 3.1199999999999997 │ 1.0365152289922772 │ 1.950599421257047 │ │ B737 │ CFM56-7B26 │ 1.0237419750954273 │ 1.4670109921175798 │ 3.2566140275646456 │ │ B738 │ CFM56-7B26E │ 1.075484518912494 │ 1.8777303165419037 │ 1.8895522140156369 │ │ B739 │ CFM56-7B27E │ 1.3079999999999998 │ 1.5986016771932572 │ 1.2789091908108752 │ │ CRJ9 │ CF34-8C5 │ 0.6437136288905128 │ 1.9690234662778772 │ 1.4375859706162741 │ │ E170 │ CF34-8E5 │ 0.6341784688704629 │ 2.778729428440142 │ 1.0149695061665696 │ │ E190 │ CF34-10E5 │ 0.8339999999998783 │ 2.3343013671118475 │ 0.4847704716061958 │ │ E195 │ CF34-10E5A1 │ 0.911999999999993 │ 1.929664699695295 │ 0.8452746256489131 │ │ E75L │ CF34-8E5 │ 0.6340709359225759 │ 2.614653287356019 │ 0.8714282723568036 │ │ default │ default │ 0.937564901246902 │ 1.9767611682280135 │ 1.3954794843472482 │ └──────────┴─────────────┴────────────────────┴────────────────────┴────────────────────┘

Airports & Navigation

There are almost 14K airdrome locations and codes shipped pinch this package.

$ wc -l openap/data/nav/airports.csv # 13796 $ ~/duckdb -c "FROM READ_CSV('/dev/stdin') WHERE state = 'CA' ORDER BY lat LIMIT 20" \ < openap/data/nav/airports.csv

┌─────────┬──────────┬───────────┬───────┬─────────┬───────────────────────────────┬────────────────┐ │ icao │ lat │ lon │ alt │ state │ sanction │ location │ │ varchar │ double │ double │ int64 │ varchar │ varchar │ varchar │ ├─────────┼──────────┼───────────┼───────┼─────────┼───────────────────────────────┼────────────────┤ │ CYQG │ 42.27334 │ -82.97056 │ 622 │ CA │ Windsor │ Windsor │ │ CYQS │ 42.77202 │ -81.11923 │ 778 │ CA │ St Thomas Muni │ St. Thomas │ │ CYZR │ 43.00444 │ -82.31528 │ 594 │ CA │ Sarnia - Chris Hadfield │ Sarnia │ │ CYXU │ 43.04211 │ -81.1598 │ 912 │ CA │ London │ London │ │ CYFD │ 43.12389 │ -80.34667 │ 815 │ CA │ Brantford │ Brant │ │ CYHM │ 43.18056 │ -79.95306 │ 780 │ CA │ John C Munro Hamilton Intl │ Ancaster │ │ CYSN │ 43.18792 │ -79.1786 │ 321 │ CA │ Niagara District │ St. Catharines │ │ CYCE │ 43.28306 │ -81.51806 │ 824 │ CA │ Huron Airpark │ South Huron │ │ CYSA │ 43.41087 │ -80.93994 │ 1215 │ CA │ Stratford Municipal │ Stratford │ │ CZBA │ 43.445 │ -79.85472 │ 602 │ CA │ Burlington Airpark │ Burlington │ │ CYKF │ 43.45694 │ -80.39056 │ 1054 │ CA │ Waterloo │ Cambridge │ │ CYTZ │ 43.62747 │ -79.40336 │ 251 │ CA │ Toronto City Centre │ Toronto │ │ CYYZ │ 43.66073 │ -79.62394 │ 568 │ CA │ Toronto Lester B Pearson Intl │ Etobicoke │ │ CYZD │ 43.74972 │ -79.47417 │ 652 │ CA │ Downsview │ Concord │ │ CYGD │ 43.77111 │ -81.71639 │ 712 │ CA │ Goderich │ Goderich │ │ CYQI │ 43.8175 │ -66.0975 │ 141 │ CA │ Yarmouth │ Yarmouth │ │ CYKZ │ 43.86444 │ -79.37334 │ 650 │ CA │ Buttonville Muni │ Richmond Hill │ │ CYOO │ 43.92444 │ -78.90389 │ 459 │ CA │ Oshawa │ Oshawa │ │ CYTR │ 44.10889 │ -77.54222 │ 283 │ CA │ Trenton │ Quinte West │ │ CYGK │ 44.21833 │ -76.60083 │ 305 │ CA │ Kingston │ Kingston │ └─────────┴──────────┴───────────┴───────┴─────────┴───────────────────────────────┴────────────────┘

These airports are located crossed 236 different countries.

SELECT COUNT(DISTINCT country) FROM READ_CSV('openap/data/nav/airports.csv');

These are the astir represented countries successful the airports dataset.

SELECT COUNT(*), country FROM READ_CSV('openap/data/nav/airports.csv') GROUP BY 2 ORDER BY 1 DESC LIMIT 20;

┌──────────────┬─────────┐ │ count_star() │ state │ │ int64 │ varchar │ ├──────────────┼─────────┤ │ 2849 │ BR │ │ 2459 │ US │ │ 2062 │ AU │ │ 441 │ FR │ │ 345 │ CA │ │ 325 │ DE │ │ 258 │ GB │ │ 234 │ ID │ │ 176 │ NA │ │ 168 │ VE │ │ 156 │ RU │ │ 148 │ IN │ │ 143 │ AR │ │ 139 │ SE │ │ 126 │ JP │ │ 118 │ IT │ │ 106 │ NZ │ │ 99 │ CZ │ │ 98 │ BO │ │ 97 │ ZA │ └──────────────┴─────────┘

These are a fewer of the navigation waypoints.

$ echo "import pandas arsenic pd; print( pd.read_fwf('openap/data/nav/fix.dat', skiprows=3, header=None, encoding='unicode_escape') .to_csv(index=False))" \ | python3 \ | ~/duckdb \ -c '.maxwidth 150' \ -c "FROM READ_CSV('/dev/stdin') WHERE column0 BETWEEN 57 AND 59 AND column1 BETWEEN 21 AND 27 LIMIT 20"

┌───────────┬───────────┬─────────┐ │ column0 │ column1 │ column2 │ │ double │ double │ varchar │ ├───────────┼───────────┼─────────┤ │ 57.133196 │ 23.888414 │ ALISA │ │ 57.105833 │ 25.254167 │ AMOLI │ │ 58.416389 │ 24.478333 │ ANAMA │ │ 58.412778 │ 22.521667 │ EIKLA │ │ 58.506944 │ 25.715278 │ EKLON │ │ 58.626667 │ 21.766111 │ EVERI │ │ 57.278611 │ 25.050556 │ GEKLI │ │ 58.9425 │ 25.576944 │ GONOS │ │ 58.053333 │ 26.762778 │ KANEP │ │ 58.331944 │ 22.221111 │ KARLA │ │ 58.7225 │ 24.586944 │ KEMET │ │ 58.725753 │ 26.736943 │ KOLEV │ │ 58.708056 │ 22.845833 │ KUKET │ │ 58.931111 │ 24.661944 │ KUNUX │ │ 58.176667 │ 26.93 │ KUUST │ │ 58.441667 │ 26.451667 │ LAEVA │ │ 58.553333 │ 25.934444 │ LALSI │ │ 57.336944 │ 22.636944 │ LAPSA │ │ 57.774167 │ 22.104444 │ LATEG │ │ 58.180278 │ 25.779167 │ LATKA │ └───────────┴───────────┴─────────┘

These are a fewer of the navigation aids.

$ wc -l openap/data/nav/nav.dat # 26775 $ echo "import pandas arsenic pd; print( pd.read_fwf('openap/data/nav/nav.dat', skiprows=3, header=None, encoding='unicode_escape') .to_csv(index=False))" \ | python3 \ | ~/duckdb \ -c '.maxwidth 150' \ -c "SELECT * EXCLUDE(column9) FROM READ_CSV('/dev/stdin') WHERE column1 BETWEEN 57 AND 59 AND column2 BETWEEN 21 AND 27 LIMIT 20"

┌─────────┬───────────┬───────────┬─────────┬─────────┬─────────┬─────────┬─────────┬────────────────────┐ │ column0 │ column1 │ column2 │ column3 │ column4 │ column5 │ column6 │ column7 │ column8 │ │ int64 │ double │ double │ varchar │ double │ double │ double │ varchar │ varchar │ ├─────────┼───────────┼───────────┼─────────┼─────────┼─────────┼─────────┼─────────┼────────────────────┤ │ 2 │ 58.957117 │ 22.872158 │ 0 │ 317.0 │ 80.0 │ 0.0 │ OZ │ KARDLA NDB │ │ 2 │ 58.270722 │ 22.508778 │ 0 │ 350.0 │ 80.0 │ 0.0 │ WA │ KURESSAARE NDB │ │ 2 │ 58.490806 │ 24.571556 │ 0 │ 425.0 │ 80.0 │ 0.0 │ RC │ PARNU NDB │ │ 2 │ 58.435833 │ 24.495861 │ 0 │ 376.0 │ 25.0 │ 0.0 │ R │ PARNU NDB │ │ 2 │ 58.308583 │ 26.768417 │ 0 │ 397.0 │ 80.0 │ 0.0 │ UM │ TARTU NDB │ │ 3 │ 58.228333 │ 22.515361 │ 39 │ 240.0 │ 50.0 │ 3.0 │ KRS │ KURESSAARE VOR-DME │ │ 3 │ 58.416583 │ 24.465972 │ 58 │ 590.0 │ 25.0 │ 6.0 │ PRN │ PARNU VOR-DME │ │ 3 │ 57.366944 │ 21.556222 │ 0 │ 360.0 │ 130.0 │ 5.3 │ VNT │ VENTSPILS VOR-DME │ │ 3 │ 58.655889 │ 25.574778 │ 227 │ 490.0 │ 80.0 │ 5.0 │ VI │ VOHMA VOR-DME │ │ 1 │ 58.228333 │ 22.515361 │ 3 │ 124.0 │ 5.0 │ 0.0 │ KR │ KURESSAARE VOR-DME │ │ 1 │ 58.416583 │ 24.465972 │ 5 │ 159.0 │ 2.0 │ 0.0 │ PR │ PARNU VOR-DME │ │ 1 │ 57.366944 │ 21.556222 │ NULL │ 136.0 │ 13.0 │ 0.0 │ VN │ VENTSPILS VOR-DME │ │ 1 │ 58.655889 │ 25.574778 │ 22 │ 149.0 │ 8.0 │ 0.0 │ VI │ VOHMA VOR-DME │ │ 1 │ 58.992083 │ 22.830972 │ 3 │ 176.0 │ 2.0 │ 0.0 │ KR │ KARDLA DME │ └─────────┴───────────┴───────────┴─────────┴─────────┴─────────┴─────────┴─────────┴────────────────────┘

Toulouse to Berlin

Below, I'll find an optimal formation way from Toulouse-Blagnac Airport (LFBO / TLS) to Berlin Brandenburg Airport (EDDB / BER).

import numpy as np from openap.aero import cas2mach, ft, kts from openap.extra.nav import airport from pygeodesy.ellipsoidalVincenty import LatLon from skdecide.hub.domain\ .flight_planning\ .aircraft_performance\ .bean.aircraft_state \ import AircraftState from skdecide.hub.domain\ .flight_planning\ .aircraft_performance\ .performance.performance_model_enum \ import PerformanceModelEnum from skdecide.hub.domain\ .flight_planning\ .aircraft_performance\ .performance.phase_enum \ import PhaseEnum from skdecide.hub.domain\ .flight_planning\ .aircraft_performance\ .performance.rating_enum \ import RatingEnum from skdecide.hub.domain\ .flight_planning\ .domain \ import FlightPlanningDomain, \ WeatherDate from skdecide.hub.domain\ .flight_planning\ .flightplanning_utils \ import plot_network_adapted from skdecide.hub.solver.astar import Astar

The heuristic parameter tin beryllium either "time", "distance", "lazy_fuel", "lazy_time", aliases None. If thing is passed, A* will usage a Dijkstra-like hunt algorithm.

origin = "LFPG" destination = "LFBO" aircraft = "A320" weather_date = WeatherDate(day=1, month=5, year=2026) heuristic = "lazy_fuel" cost_function = "fuel" acState = AircraftState( model_type="A320", performance_model_type=PerformanceModelEnum.OPENAP, gw_kg=80_000, zp_ft=10_000, mach=cas2mach(250 * kts, h=10_000 * ft), phase=PhaseEnum.CLIMB, rating_level=RatingEnum.MCL, cg=0.3) domain_factory = lambda: FlightPlanningDomain( aircraft_state=acState, mach_cruise=0.78, mach_climb=0.7, mach_descent=0.65, nb_forward_points=20, nb_lateral_points=10, nb_climb_descent_steps=5, flight_levels_ft=list(np.arange(30_000, 38_000 + 2_000, 2_000)), graph_width="medium", origin=LatLon(43.629444, 1.363056), destination="EDDB", objective=cost_function, heuristic_name=heuristic, weather_date=weather_date) domain = domain_factory()

When the supra runs, if upwind information hasn't been fetched from NOAA and if the day of the formation is wrong the past six months, GRB2 files will beryllium downloaded.

$ du -hs ~/skdecide_data/weather/grib/nowcast/*/*.grb2

144M /home/mark/skdecide_data/weather/grib/nowcast/20260501/gfs_4_20260501_0000_000.grb2 144M /home/mark/skdecide_data/weather/grib/nowcast/20260501/gfs_4_20260501_0600_000.grb2 143M /home/mark/skdecide_data/weather/grib/nowcast/20260501/gfs_4_20260501_1200_000.grb2 143M /home/mark/skdecide_data/weather/grib/nowcast/20260501/gfs_4_20260501_1800_000.grb2

Each record has information covering the full planet. These are the contents of gfs_4_20260501_1800_000.grb2 rendered connected a globe successful QGIS.

Flight Planning

This is the solver's altitude and geographical hunt space.

plot_network_adapted( graph=domain.network, p0=LatLon(43.629444, 1.363056), p1=LatLon( airport("EDDB")["lat"], airport("EDDB")["lon"], airport("EDDB")["alt"] * ft))

Flight Planning

This is the optimal formation way according to the solver.

solver = Astar( domain_factory=domain_factory, heuristic=lambda d, s: d.heuristic(s), parallel=False) solver.solve()

A* vanished to lick from authorities ... successful 0.28 seconds

domain.custom_rollout(solver=solver, make_img=True)

Flight Planning

Goal reached aft 19 steps!

({'time': 7666.281474928903, 'fuel': 5855.093906205222}, None)

I'll format each of the formation plan's steps truthful they're easier to read.

domain.observation.trajectory.to_csv('TLS-BER.csv', index=None)

SELECT phase: UPPER(phase), time_: ts::INT, alt: alt::INT, mass: mass::INT, mach: ROUND(mach, 2), cas: cas::INT, fuel: fuel::INT, geom: ST_POINT(lon, lat) FROM 'TLS-BER.csv' ORDER BY ts;

┌─────────┬───────┬───────┬───────┬────────┬───────┬───────┬────────────────────────────────────────────────┐ │ shape │ time_ │ alt │ wide │ mach │ cas │ substance │ geom │ │ varchar │ int32 │ int32 │ int32 │ double │ int32 │ int32 │ geometry │ ├─────────┼───────┼───────┼───────┼────────┼───────┼───────┼────────────────────────────────────────────────┤ │ CLIMB │ 28800 │ 0 │ 80000 │ 0.45 │ 154 │ 0 │ POINT (1.363056 43.629444) │ │ CLIMB │ 29183 │ 12000 │ 79402 │ 0.7 │ 194 │ 598 │ POINT (1.3614644301412264 44.431571122861556) │ │ CLIMB │ 29767 │ 18000 │ 78717 │ 0.7 │ 173 │ 685 │ POINT (0.8028803270337778 45.54329961845038) │ │ CLIMB │ 30367 │ 24000 │ 78104 │ 0.7 │ 154 │ 2 │ POINT (0.22308186850312028 46.64860136322122) │ │ CLIMB │ 30369 │ 24000 │ 78102 │ 0.7 │ 154 │ 2 │ POINT (0.2213600223765711 46.65181397470445) │ │ CLIMB │ 30691 │ 30000 │ 77808 │ 0.7 │ 135 │ 294 │ POINT (0.781910701674247 47.146798229437145) │ │ CRUISE │ 31222 │ 30000 │ 77331 │ 0.78 │ 152 │ 477 │ POINT (0.18057649919536045 48.254448220756515) │ │ CRUISE │ 31515 │ 30000 │ 77070 │ 0.78 │ 152 │ 262 │ POINT (0.7571242215277763 48.74964861278412) │ │ CRUISE │ 31805 │ 30000 │ 76812 │ 0.78 │ 152 │ 258 │ POINT (1.3446668642885302 49.24219441171162) │ │ CRUISE │ 32093 │ 30000 │ 76555 │ 0.78 │ 152 │ 256 │ POINT (1.943586016571517 49.732000774167815) │ │ CRUISE │ 32382 │ 30000 │ 76298 │ 0.78 │ 152 │ 257 │ POINT (2.554285524033502 50.218985517421046) │ │ CRUISE │ 32672 │ 30000 │ 76041 │ 0.78 │ 152 │ 257 │ POINT (3.1771982015706532 50.70307279893911) │ │ CRUISE │ 32961 │ 30000 │ 75786 │ 0.78 │ 152 │ 256 │ POINT (3.812797488711077 51.18419969012497) │ │ CRUISE │ 33252 │ 30000 │ 75529 │ 0.78 │ 152 │ 257 │ POINT (4.461619033936127 51.66232853895605) │ │ CRUISE │ 33547 │ 32000 │ 75270 │ 0.78 │ 146 │ 259 │ POINT (5.1243038676232 52.13747186609129) │ │ CRUISE │ 34103 │ 30000 │ 74794 │ 0.78 │ 152 │ 476 │ POINT (7.006628090474678 51.93831668841654) │ │ DESCENT │ 34458 │ 24031 │ 74503 │ 0.65 │ 142 │ 290 │ POINT (7.7011825929472675 52.40044729715865) │ │ DESCENT │ 35058 │ 18063 │ 73995 │ 0.65 │ 160 │ 51 │ POINT (9.423199233939213 52.1821111144618) │ │ DESCENT │ 35114 │ 18063 │ 73944 │ 0.65 │ 160 │ 51 │ POINT (9.580613666598857 52.160789987133924) │ │ DESCENT │ 35714 │ 12094 │ 73388 │ 0.65 │ 179 │ 13 │ POINT (11.398384342445423 51.895274115132494) │ │ DESCENT │ 35727 │ 12094 │ 73375 │ 0.65 │ 179 │ 13 │ POINT (11.435080901553494 51.88959204464927) │ │ DESCENT │ 36063 │ 6126 │ 73023 │ 0.65 │ 199 │ 352 │ POINT (12.17769156482302 52.32776873134525) │ │ DESCENT │ 36466 │ 48 │ 72534 │ 0.65 │ 221 │ 488 │ POINT (13.48503 52.36769) │ └─────────┴───────┴───────┴───────┴────────┴───────┴───────┴────────────────────────────────────────────────┘

I'll export the formation scheme to Parquet and render it connected apical of the ground-level upwind information successful QGIS.

COPY ( SELECT * EXCLUDE(lon, lat), geometry: ST_POINT(lon, lat) FROM 'TLS-BER.csv' ORDER BY ts ) TO 'TLS-BER.parquet' ( FORMAT 'PARQUET', CODEC 'ZSTD', COMPRESSION_LEVEL 22, ROW_GROUP_SIZE 15000);

Flight Planning

Toulouse to Warsaw

Below, I'll find an optimal formation way from Toulouse-Blagnac Airport (LFBO / TLS) to Warsaw Chopin Airport (EPWA / WAW).

The first target altitude will beryllium overmuch higher than successful the erstwhile example. The consequence is simply a formation that is capable to return a overmuch much nonstop route.

acState = AircraftState( model_type="A320", performance_model_type=PerformanceModelEnum.OPENAP, gw_kg=80_000, zp_ft=18000.0, mach=cas2mach(250 * kts, h=10_000 * ft), phase=PhaseEnum.CLIMB, rating_level=RatingEnum.MCL, cg=0.3, x_graph=5, y_graph=5, z_graph=10) domain_factory = lambda: FlightPlanningDomain( aircraft_state=acState, mach_cruise=0.78, mach_climb=0.7, mach_descent=0.65, nb_forward_points=20, nb_lateral_points=10, nb_climb_descent_steps=5, flight_levels_ft=list(np.arange(30_000, 38_000 + 2_000, 2_000)), graph_width="medium", origin=LatLon(43.629444, 1.363056), destination="EPWA", objective=cost_function, heuristic_name=heuristic, weather_date=weather_date) domain = domain_factory() solver = Astar( domain_factory=domain_factory, heuristic=lambda d, s: d.heuristic(s), parallel=False) solver.solve()

A* vanished to lick from authorities ... successful 29.45 seconds.

domain.custom_rollout(solver=solver, make_img=True)

Flight Planning

Goal reached aft 14 steps!

({'time': 6153.660431613251, 'fuel': 5600.171145693044}, None)

Warsaw is 500 KM further distant from Toulouse than Berlin. But the faster climb to cruising altitude nether the fixed upwind conditions meant the craft could return a much nonstop route. It made it to Warsaw almost 45 minutes faster and only needed 76% of the substance that the Berlin formation needed.

These are the steps successful the supra formation plan.

domain.observation.trajectory.to_csv('TLS-WAW.csv', index=None)

SELECT phase: UPPER(phase), time_: ts::INT, alt: alt::INT, mass: mass::INT, mach: ROUND(mach, 2), cas: cas::INT, fuel: fuel::INT, geom: ST_POINT(lon, lat) FROM 'TLS-WAW.csv' ORDER BY ts;

┌─────────┬───────┬───────┬───────┬────────┬───────┬───────┬───────────────────────────────────────────────┐ │ shape │ time_ │ alt │ wide │ mach │ cas │ substance │ geom │ │ varchar │ int32 │ int32 │ int32 │ double │ int32 │ int32 │ geometry │ ├─────────┼───────┼───────┼───────┼────────┼───────┼───────┼───────────────────────────────────────────────┤ │ CLIMB │ 28800 │ 30000 │ 80000 │ 0.45 │ 85 │ 0 │ POINT (6.321780309765473 45.78957852956533) │ │ CRUISE │ 29196 │ 32000 │ 79639 │ 0.78 │ 146 │ 361 │ POINT (7.274006698354081 46.275553205520794) │ │ CRUISE │ 29604 │ 34000 │ 79276 │ 0.78 │ 139 │ 363 │ POINT (8.243146568075773 46.75332543362598) │ │ CRUISE │ 30018 │ 36000 │ 78915 │ 0.78 │ 133 │ 361 │ POINT (9.229481862337197 47.22261290149568) │ │ CRUISE │ 30436 │ 38000 │ 78555 │ 0.78 │ 127 │ 360 │ POINT (10.23327610333648 47.68312559281413) │ │ CRUISE │ 30861 │ 38000 │ 78191 │ 0.78 │ 127 │ 365 │ POINT (11.25477083484032 48.13456566890824) │ │ CRUISE │ 31297 │ 36000 │ 77819 │ 0.78 │ 133 │ 371 │ POINT (12.29418112454121 48.57662718213212) │ │ CRUISE │ 31732 │ 34000 │ 77449 │ 0.78 │ 139 │ 370 │ POINT (13.351689351409924 49.00899540355703) │ │ CRUISE │ 32161 │ 32000 │ 77082 │ 0.78 │ 146 │ 367 │ POINT (14.427435465032865 49.431345252175966) │ │ CRUISE │ 32589 │ 30000 │ 76710 │ 0.78 │ 152 │ 372 │ POINT (15.521498988740307 49.843337488922174) │ │ DESCENT │ 33101 │ 24066 │ 76280 │ 0.65 │ 142 │ 429 │ POINT (16.633858546975723 50.2446086742223) │ │ DESCENT │ 33589 │ 18131 │ 75861 │ 0.65 │ 160 │ 420 │ POINT (17.764276668345886 50.63474030548783) │ │ DESCENT │ 34046 │ 12197 │ 75433 │ 0.65 │ 179 │ 428 │ POINT (18.91184798374883 51.01313503252613) │ │ DESCENT │ 34472 │ 6262 │ 74984 │ 0.65 │ 199 │ 449 │ POINT (20.071843186874247 51.378167839584854) │ │ DESCENT │ 34954 │ 100 │ 74400 │ 0.65 │ 221 │ 584 │ POINT (20.94663 52.17147) │ └─────────┴───────┴───────┴───────┴────────┴───────┴───────┴───────────────────────────────────────────────┘

Airbus A320 vs Boeing 737

OpenTop tin beryllium paired pinch OpenAP and utilized to fig retired formation trajectories betwixt 2 airports.

Its optimiser requires a grid costs file. I'll first download an illustration 142 MB NetCDF record provided by the project.

$ wget https://opendap.4tu.nl/thredds/fileServer/data2/djht/bea8a3fe-e34c-4598-9f94-c5a5c63348e5/1/contrail_original.nc

The costs record tin beryllium either successful Casadi aliases Parquet format. I worked from an example successful its documentation, which produced a 246 KB Casadi file.

import openap import pandas as pd from scipy.ndimage import gaussian_filter from opentop.tools import cached_interpolant_from_dataframe import xarray as xr ds = xr.open_dataset('contrail_original.nc')\ .sel(time='2015-12-18') level_pressure = [ 0.0000, 10.0000, 30.0000, 50.0000, 70.0000, 90.0787, 110.6606, 132.3968, 155.7909, 181.1544, 208.6494, 238.3258, 270.1530, 304.0465, 339.8891, 377.5467, 416.8789, 457.7442, 500.0000, 543.4970, 588.0685, 633.5144, 679.5799, 725.9285, 772.1102, 817.5241, 861.3757, 902.6287, 939.9520, 971.6610, 995.6532, 1009.3396] df = ( ds.to_dataframe() .reset_index() .assign(lev=lambda x: x.lev.astype(int)) .merge( pd.DataFrame(level_pressure, columns=["hPa"]).reset_index(names="lev"), on="lev", ) .assign(height=lambda x: openap.aero.h_isa(x.hPa * 100).round(-2)) .assign(longitude=lambda x: ((x.lon + 180) % 360 - 180)) .query("height<15000")) df_cost_world = df.rename( columns={ "lat": "latitude", "atr20_contrail": "cost", } )[["time", "latitude", "longitude", "hPa", "height", "cost"]] df_cost = df_cost_world.query( "-20<longitude<40 and 30<latitude<70 and time.dt.hour==12" ).sort_values(["height", "latitude", "longitude"]) cost = df_cost.cost.values.reshape( df_cost.height.nunique(), df_cost.latitude.nunique(), df_cost.longitude.nunique()) cost_ = gaussian_filter(cost, sigma=1, mode="nearest") df_cost = df_cost.assign(cost=cost_.flatten()) interpolant = cached_interpolant_from_dataframe( df_cost, "contrail.casadi", shape="bspline")

These are the first and past fewer bytes of its contents.

$ hexdump -C contrail.casadi | head

00000000 6a 68 70 6e 6e 61 67 69 69 65 61 68 61 61 61 61 |jhpnnagiieahaaaa| 00000010 64 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 |daaaaaaaaaaaaaaa| 00000020 61 61 66 61 65 67 61 61 6c 61 61 61 61 61 61 61 |aafaegaalaaaaaaa| 00000030 6a 65 6f 67 65 68 66 67 63 68 61 68 70 67 6d 67 |jeogehfgchahpgmg| 00000040 62 67 6f 67 65 68 68 61 61 61 61 61 61 61 63 67 |bgogehhaaaaaaacg| 00000050 64 68 61 68 6d 67 6a 67 6f 67 66 67 63 61 61 61 |dhahmgjgogfgcaaa| 00000060 61 61 61 61 6a 61 61 61 61 61 61 61 68 67 63 68 |aaaajaaaaaaahgch| 00000070 6a 67 65 67 70 66 64 67 70 67 64 68 65 68 61 61 |jgegpfdgpgdhehaa| 00000080 61 61 61 61 61 61 62 61 69 61 61 61 61 61 61 61 |aaaaaabaiaaaaaaa| 00000090 62 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 |baaaaaaaaaaaaaaa|

$ hexdump -C contrail.casadi | tail

0003d620 61 61 61 61 61 61 64 62 61 61 61 61 61 61 61 61 |aaaaaadbaaaaaaaa| 0003d630 61 61 61 61 61 61 67 62 61 61 61 61 61 61 61 61 |aaaaaagbaaaaaaaa| 0003d640 61 61 61 61 61 61 68 62 61 61 61 61 61 61 61 61 |aaaaaahbaaaaaaaa| 0003d650 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 |aaaaaaaaaaaaaaaa| 0003d660 61 61 61 61 61 61 62 61 61 61 61 61 61 61 61 61 |aaaaaabaaaaaaaaa| 0003d670 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 61 |aaaaaaaaaaaaaaaa| 0003d680 61 61 61 61 61 61 62 61 61 61 62 61 61 61 61 61 |aaaaaabaaabaaaaa| 0003d690 61 61 61 61 61 61 61 61 61 61 63 68 67 61 61 61 |aaaaaaaaaachgaaa| 0003d6a0 61 61 61 61 61 61 61 61 61 61 61 61 |aaaaaaaaaaaa| 0003d6ac

I noticed the contents are repetitive and compress well.

$ gzip -9 < contrail.casadi | wc -c

I'll get the metrics of an optimal formation betwixt Amsterdam's Schiphol (EHAM / AMS) and Frankfurt (EDDF / FRA) connected an Airbus A320.

$ opentop optimize \ EHAM EDDF \ -a A320 \ --phase all \ --obj "0.3*fuel+0.7*grid" \ --grid contrail.casadi

aircraft: A320 route: EHAM → EDDF phase: all objective: 0.3*fuel+0.7*grid m0: 0.85 max_iter: 1500 grid file: contrail.casadi success: True return_status: Solve_Succeeded iter_count: 179 wall time: 12.2 s objective: 4.8768e+02 fuel burn: 1625.6 kg max altitude: 19891 ft flight time: 35.8 min

I'll past do the aforesaid utilizing a Boeing 737.

$ opentop optimize \ EHAM EDDF \ -a B737 \ --phase all \ --obj "0.3*fuel+0.7*grid" \ --grid contrail.casadi

aircraft: B737 route: EHAM → EDDF phase: all objective: 0.3*fuel+0.7*grid m0: 0.85 max_iter: 1500 grid file: contrail.casadi success: True return_status: Solve_Succeeded iter_count: 141 wall time: 9.9 s objective: 4.8662e+02 fuel burn: 1622.1 kg max altitude: 21968 ft flight time: 39.2 min

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