Uncertainty Quantification to Support Hybrid Electric Engine Robust Design

  • Stumpo, Leonardo (Polytechnic University of Turin)
  • Zanotti, Sara (Polytechnic University of Turin)
  • Antonazzo, Maria Carmela (Polytechnic University of Turin)
  • Babbini, Francesco (Avio Aero)
  • Russo, Michele (Avio Aero)
  • Ferrero, Andrea (Polytechnic University of Turin)
  • Masseni, Filippo (Polytechnic University of Turin)
  • Pastrone, Dario Giuseppe (Polytechnic University of Turin)
  • Amato, Filippo (Avio Aero)
  • Lopez Ruiz, Jesus (Avio Aero)

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This work explores a hybrid-electric propulsion architecture for short- and medium-range aircraft to accelerate aviation decarbonization. The configuration couples a gas turbine and a fuel cell–powered electrical subsystem, mechanically linked to a propeller via a gearbox. An integrated system model captures hybrid-cycle behavior, while Polynomial Chaos Expansion (PCE)–based uncertainty quantification (UQ) provides efficient, non-intrusive statistics on system performance under uncertainty. These insights feed a robust design optimization framework that identifies solutions maintaining strong performance across varied conditions. The integrated workflow improves early-stage decision making, highlights key variability drivers, supports requirement definition and trade-space exploration for next-generation of lower-emission aircraft.