Dimension scores are derived from public data and fields; weighted into the composite. Reference only.
OpenAP is an open-source aircraft performance library for air transport research. Since 2019, it has been used for aircraft performance modeling, trajectory generation, fuel and emissions analysis, and related scenarios. Its manual emphasizes serving researchers, developers, and aviation professionals, with authors from aviation and aerospace engineering research teams at TU Delft.
Based on the collected content, OpenAP covers a fairly complete aviation performance workflow: the prop module can query aircraft and engine parameters; Thrust, Drag, FuelFlow, and Emission handle thrust, drag, fuel flow, and emissions respectively. It also includes flight phase identification, trajectory generation, aerodynamic and geographic tools, and contrail-related modules. The extended Trajectory Optimizer Package supports fuel-optimal, wind-optimal, and 3D/4D cost-grid-based trajectory optimization, allowing weather, climate-sensitive regions, contrails, and other spatially or temporally dependent costs to be incorporated into the objective function.
The documentation examples are mainly in Python and are often used together with numpy, pandas, matplotlib, and cartopy. Performance classes support optional backends: in addition to the default NumPy backend, CasADi or JAX can be used, which is useful for numerical computing, optimization, and automatic differentiation workflows. The project provides an API Reference and command-line interface sections, and clearly lists parameters, units, and return values—for example, speed in kt, altitude in ft, mass in kg, thrust/drag in N, and fuel flow in kg/s.
The text explicitly describes OpenAP as an open-source initiative, and no commercial pricing, paid plans, payment methods, or enterprise support information was found. It can therefore be considered suitable for low-cost research and prototype development. However, if an enterprise requires an SLA, compliance audits, or dedicated technical support, the existing text does not demonstrate that such commercial services are available.
Its strengths are deep domain coverage, rich documentation examples, a clearly structured API, and support for complex trajectory optimization and multiple computational backends. Its limitations are that the tool is highly specialized, requiring users to have background knowledge in aircraft performance, unit systems, and optimization modeling. Large-scale 3D/4D grid interpolation may be time-consuming, and the documentation also suggests using caching. It is best suited for aviation research, sustainable aviation, flight trajectory optimization, and emissions analysis teams.
The collected text does not provide information about access from mainland China, mirrors, payment, or alternatives, so its accessibility status is unknown. If online documentation access is unstable, users may prefer using the source code repository, Python package caches, or an internal documentation mirror.
⚠ This review is compiled from public sources and does not constitute a purchase recommendation. Verify all facts on the vendor's official site. Verify on openap.dev official site.
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