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Developed at the University of Illinois Urbana-Champaign
Laboratory for Emerging Aircraft Design and Systems (LEADS)

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RCAIDE: Research Community Aerospace Interdisciplinary Design Environment

RCAIDE (pronounced "arcade") is an open-source Python platform for the modeling, analysis, and optimization of both conventional and unconventional aerospace systems. The successor to the globally recognized SUAVE code, RCAIDE builds on a robust foundation of validated engineering methods while extending its scope to address novel design paradigms including hybrid-electric propulsion, hydrogen-powered aircraft, urban air mobility vehicles, and supersonic transports.

RCAIDE's modular architecture supports multi-fidelity analysis, high-performance parallel computing, and a generalized energy-agnostic powertrain network that enforces thermodynamic closure across all powertrain domains. It is developed and maintained by the Laboratory for Emerging Aircraft Design and Systems (LEADS) at the University of Illinois Urbana-Champaign.

Key Features

Multidisciplinary Analysis

Discipline Methods
Geometry 2D & 3D parametric planform definition, OpenVSP & AVL interfaces
Atmosphere US Standard 1976 atmospheric model
Aerodynamics Vortex Lattice Method (VLM), viscous vortex panel method, SU2 CFD interface
Stability VLM-based perturbation approach; longitudinal and lateral-directional derivatives
Mass Properties FLOPS, Raymer, and physics-based weight methods; CG and MOI estimation
Powertrain Polytropic turbomachinery, BEMT/actuator-disk rotors, electric motors, fuel cells, heat exchangers, cryogenic tanks
Aeroacoustics Semi-empirical airframe and jet noise; physics-based rotor harmonic noise
Emissions Empirical emission indices; chemical reactor network (CRN) via Cantera
Performance Payload-range diagrams, V-n diagrams, takeoff/landing field length, stall speed
Optimization Gradient-based (SLSQP) and non-gradient methods via pyOptSparse

Propulsion Architectures Supported

  • Conventional turbofan, turbojet, turboprop
  • Fully electric (battery-powered rotors and ducted fans)
  • Series and parallel hybrid-electric
  • Hydrogen (gaseous and liquid cryogenic)
  • Internal combustion engine + propeller
  • Fuel cell systems

Mission Solver

RCAIDE employs a pseudospectral collocation method using Chebyshev polynomials for solving flight mechanics, departing from traditional time-marching techniques. A closed-loop powertrain network solver enforces energy balance across electrical, thermal, mechanical, pneumatic, and hydraulic domains at every mission point — eliminating the open-loop approximations that can mask design flaws in complex powertrains.

Validation

RCAIDE's methods are validated against experimental data and high-fidelity simulations. Aerodynamic predictions using the VLM and semi-empirical drag buildup achieve R² = 0.96 (C_L) and R² = 0.94 (C_D) against National Transonic Facility wind-tunnel data for the NASA Common Research Model. Vehicle-level validation includes payload-range comparisons for the Boeing 737-800, ATR 72-600, Boeing 787-8, and Concorde.

Full validation tables are available in the RCAIDE paper and the Verification & Validation subdirectory.

Installation

pip install RCAIDE-LEADS

We recommend installing within a virtual environment. See the full installation guide for environment setup, optional dependencies (OpenVSP, AVL, SU2), and platform-specific notes.

Documentation & Tutorials

Citing RCAIDE

If you use RCAIDE in your research, please cite:

Clarke, Matthew A., et al. "RCAIDE: A Multidisciplinary Analysis Toolbox for Aircraft Design and Flight Simulation." Aerospace Science and Technology (2026): 112328.

@article{clarke2026rcaide,
  title   = {RCAIDE: A Multidisciplinary Analysis Toolbox for Aircraft Design and Flight Simulation},
  author  = {Clarke, Matthew A. and others},
  journal = {Aerospace Science and Technology},
  pages   = {112328},
  year    = {2026}
}

Contributing

Contributions are welcome. Please read the contributing guidelines before submitting a pull request.

Contact

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