Chance constrained business case of a three-engines hybrid aircraft

Sylvain Prigent (1, 2) , Mathieu Belleville (2) , Thierry Druot (2) , Aude Rondepierre (3) , Pierre Maréchal (1)

1 DMIA - Département de Mathématiques, Informatique, Automatique
2 Airbus Operation S.A.S.
3 IMT - Institut de Mathématiques de Toulouse UMR5219

The purpose of this article is to present a Chance Constrained Optimization of an unconventional configuration of hybrid-powered-aircraft. The Chance Constrained Methodology is applied using a method of uncertainty propagation based on error distribution moments. The hybrid configuration is compared to a conventional, pure thermodynamic one, both being designed according to the same set of requirements. Models are briefly described in the article, more details can be found in literature. We present the method of uncertainty propagation through the well-known Rosenbrock function and validate it in comparison to a Monte-Carlo method. Concerning the aircraft and engine models, the uncertainty brought by each design and simulation module has been assessed individually, according to its predictive performance versus existing database. The results show that under model uncertainties we would be able to reach an economically viable hybrid-powered-aircraft with a probability of 0.95 when energy and power management technologies become between 2 to 3 times better compared to today values. In addition, the efficiency of uncertainty propagation by the way of moments is really interesting in term of computation time. Improvements are currently done to make this method even more precise especially when the amount of uncertainty increases.

Mots clés: Uncertainty propagation; Optimization; Overall aircraft design; Hybrid aircraft

Domaines: Optimisation et contrôle [math.OC]

Sylvain Prigent, Mathieu Belleville, Thierry Druot, Aude Rondepierre, Pierre Maréchal. Chance constrained business case of a three-engines hybrid aircraft. 10th World Congress on Structural and Multidisciplinary Optimization, May 2013, Orlando, Florida, United States. Papers 5154 - 10 p. ⟨hal-00991343⟩