Draft:GEMSEO
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| GEMSEO | |
|---|---|
| Developers | IRT Saint Exupéry and open-source contributors |
| Release | April 2021 |
| Stable release | 6.3.3
/ July 22, 2026 |
| Written in | Python |
| Operating system | Linux, Windows |
| Type | Technical computing |
| License | GNU LGPL v3.0 |
| Website | gemseo |
GEMSEO (Generic Engine for Multi-disciplinary Scenarios, Exploration and Optimization) is an open-source Python library for multidisciplinary design optimization (MDO), released under the GNU LGPL v3.0.[1] It automates the generation of multidisciplinary analysis and optimization processes from a declarative description of the problem and of the chosen MDO formulation.[2]
History
[edit]The library was started in 2015 within the MDO competence centre of
IRT Saint Exupéry under the name GEMS, and was first described at the 2018
AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference.[2]
It was renamed GEMSEO and released as open source in April 2021.[3]
The project is organised as a core package, gemseo, together with a set of
plugins prefixed gemseo-, such as gemseo-umdo for optimization under
uncertainty.[4]
Design
[edit]GEMSEO is built around MDO formulations, also known as MDO architectures.[5] Two abstractions are central to the library: the discipline, which may be an analytical expression, a wrapper around a black-box executable or a surrogate model, and is defined by its inputs, its outputs and its internal behaviour; and the scenario, which connects disciplines and configures the optimization algorithm.[6] The library is built on NumPy, SciPy, Matplotlib and NLopt,[6] and can wrap disciplines implemented in Python, MATLAB, Scilab, spreadsheets or external executables.[7]
From the disciplines, the design space, the objective and the constraints, GEMSEO generates the corresponding multidisciplinary process for the chosen MDO formulation, so that switching between formulations such as Multidisciplinary Feasible (MDF), Individual Discipline Feasible (IDF) or bi-level strategies requires changing a single parameter rather than restructuring the process.[7] Beyond optimization, it provides built-in optimization algorithms, design of experiments and sampling plans, and the automated generation of surrogate models,[8][7] as well as uncertainty quantification and machine learning regression.[3]
A 2023 comparative study of open-source MDAO frameworks by researchers at ISAE-SUPAERO examined GEMSEO alongside OpenMDAO and CoSApp, comparing their programming models and the effort required to switch between MDO formulations.[9]
Use in research
[edit]The 2021 update of the CFD Vision 2030 Roadmap, prepared for NASA, cites GEMSEO as a framework for the automatic generation of MDO processes covering distributed and multilevel formulations.[10]
GEMSEO has been used in academic and industrial aerospace design studies. Researchers at the German Aerospace Center and Technical University of Braunschweig built an automated surrogate model generation framework on it for rapid aeroelastic structural sizing in conceptual aircraft design.[8] At the University of Cagliari it was used for the surrogate-based optimization of vortex generators in a modern aero-engine fan, coupled with the Rolls-Royce Hydra CFD suite.[6] Researchers at the Polytechnic University of Milan and Airbus Defence and Space coupled it with the DUST aerodynamic solver to optimize a hybrid-electric regional aircraft configuration under flying-qualities constraints.[7] A 2025 doctoral thesis at ISAE-SUPAERO used it to build a wing and control-surface design framework including a manoeuvre load alleviation law.[11]
It also serves as the coupling layer of several open aircraft design environments: AeroMAPS, an ISAE-SUPAERO tool for assessing prospective air transport scenarios,[12] and TOPAZ, a framework for zero-emission aircraft and powerplant design developed at Charles III University of Madrid.[13] It has been applied at the Aerospace Systems Design Laboratory of Georgia Tech to couple aircraft performance, cost and industrial logistics in a study of aircraft design under uncertainty.[14]
See also
[edit]
References
[edit]- ↑ "GEMSEO". Retrieved 18 September 2026.
- 1 2 Gallard, François; Vanaret, Charlie; Guénot, Damien; Gachelin, Vincent; Lafage, Rémi; Pauwels, Benoît; Barjhoux, Pierre-Jean; Gazaix, Anne (January 2018). GEMS: A Python Library for Automation of Multidisciplinary Design Optimization Process Generation. 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. doi:10.2514/6.2018-0657. AIAA 2018-0657.
- 1 2 De Lozzo, Matthias; Laboulfie, Clément; Sapin, Olivier; Roussouly, Nicolas; Gallard, François; Aziz-Alaoui, Amine; Dechaume, Antoine; Gazaix, Anne (June 2025). Multi-disciplinary design optimization under uncertainty: the open source capabilities of GEMSEO. UNCECOMP 2025, 6th ECCOMAS Thematic Conference on Uncertainty Quantification in Computational Science and Engineering. Rhodes Island, Greece. doi:10.7712/120225.12360.21300.
- ↑ Espoeys, Romain; De Lozzo, Matthias; Béchet, Sylvain; Giret, Jean-Christophe; Gallard, François; Mancini, Simone; Klaproth, Tim (14 May 2026). "A Generic Tool for Multi-Fidelity MDO Under Uncertainty, with Application on Hybrid Electric Regional Aircraft". Engineering Proceedings. 133 (1): 135. doi:10.3390/engproc2026133135.
- ↑ Martins, Joaquim R. R. A.; Lambe, Andrew B. (2013). "Multidisciplinary Design Optimization: A Survey of Architectures". AIAA Journal. 51 (9): 2049–2075. Bibcode:2013AIAAJ..51.2049M. doi:10.2514/1.J051895.
- 1 2 3 Putzu, Roberto (April 2025). Multidisciplinary Optimization of Next-Generation Aero-Engine Fans (PhD thesis). Università degli Studi di Cagliari.
- 1 2 3 4 Granata, Daniele; Mancini, Simone; Mateo-Gabin, A.; Zanotti, Alex (2025). Enhancing a Distributed Electric Propulsion Configuration Aircraft Design with a Multidisciplinary Analysis and Optimization Approach. European Rotorcraft Forum. Paper ERF2025-019.
- 1 2 Golombek, H.; Bustamante, J.; Maierl, R.; Staack, I. (20 July 2026). "An automated surrogate model generation framework for rapid aeroelastic structural sizing optimizations in conceptual aircraft design". CEAS Aeronautical Journal. Bibcode:2026CEAAJ.tmp...80G. doi:10.1007/s13272-026-00996-6.
{{cite journal}}: CS1 maint: bibcode (link) - ↑ Di Giuseppe, Roberto; Delbecq, Scott; Budinger, Valérie; Pauvert, Vincent (July 2023). An exploratory study of open-source frameworks for MDAO. AeroBest 2023, II ECCOMAS Thematic Conference on Multidisciplinary Design Optimization of Aerospace Systems. Lisbon.
- ↑ Cary, Andrew W.; Chawner, John; Duque, Earl P.; Gropp, William; Kleb, William L.; Kolonay, Raymond M.; Nielsen, Eric; Smith, Brian (2021). CFD Vision 2030 Road Map: Progress and Perspectives. AIAA Aviation 2021 Forum. doi:10.2514/6.2021-2726.
- ↑ Muradas Odriozola, Daniel (24 November 2025). Développement d'un cadre de conception optimale multidisciplinaire de la voilure et ses gouvernes avec prise en compte d'une loi d'allègement de charges (PhD thesis) (in French). ISAE-SUPAERO, Université de Toulouse.
- ↑ Planès, Thomas; Delbecq, Scott; Salgas, Antoine (2023). "AeroMAPS: a framework for performing multidisciplinary assessment of prospective scenarios for air transport". Journal of Open Aviation Science. 1 (1). doi:10.59490/joas.2023.7147.
- ↑ Norczyk Simon, Pablo; Quiben Figueroa, Rodrigo; Cini, Alessandro; Cavallaro, Rauno (2025). Multidisciplinary Design and Optimization of H2-Powered Regional Aircraft Architectures. AIAA SCITECH 2025 Forum. doi:10.2514/6.2025-0360.
- ↑ Srinivasan, Naveen R.; Kallou, Eirini; Bagdatli, Burak; Mavris, Dimitri (2026). Uncertainty Propagation and Visualization of Aircraft Design, Economic, and Industrial Systems Using Design Space Exploration Methodology. AIAA SCITECH 2026 Forum. doi:10.2514/6.2026-1526.
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