Quantile-based reliability-constrained optimization of a nonlinear absorber for passive aeroelastic control under aleatory uncertainty
Abstract
This paper presents a robust optimization methodology formulated as a quantile minimization problem, that incorporates reliability-based constraints and aleatory variability in both the design and environmental variables. An original methodology is proposed, relying on Kriging-based surrogate models constructed in a coupled input space and combined with a sparse adaptive sampling strategy. The optimization method is assessed on simple analytical functions to validate and illustrate the methodology. It is then applied to the robust design of a nonlinear absorber, used as a passive means to mitigate flutter instability and reduce the amplitude of limit-cycle oscillations in a two-dimensional airfoil exhibiting geometric nonlinearities. Numerical results demonstrate the effectiveness of the proposed strategy while requiring only a limited number of numerical solver evaluations. The developed framework is therefore particularly well suited for practical engineering applications, where computational resources are limited and uncertainties must be accounted for in the early design process, to develop more robust and reliable systems.
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BibTeX
@article{razaaly2026quantilebased,
title = {{Quantile-based reliability-constrained optimization of a nonlinear absorber for passive aeroelastic control under aleatory uncertainty}},
author = {Nassim Razaaly and Benjamin Chouvion},
journal = {Structural and Multidisciplinary Optimization},
year = {2026},
month = sep,
doi = {10.1007/s00158-026-04420-9},
url = {https://hal.science/hal-05772664v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Quantile-based reliability-constrained optimization of a nonlinear absorber for passive aeroelastic control under aleatory uncertainty
AU - Nassim Razaaly
AU - Benjamin Chouvion
PY - 2026
DA - 2026/09/30
JO - Structural and Multidisciplinary Optimization
DO - 10.1007/s00158-026-04420-9
UR - https://hal.science/hal-05772664v1
AB - This paper presents a robust optimization methodology formulated as a quantile minimization problem, that incorporates reliability-based constraints and aleatory variability in both the design and environmental variables. An original methodology is proposed, relying on Kriging-based surrogate models constructed in a coupled input space and combined with a sparse adaptive sampling strategy. The optimization method is assessed on simple analytical functions to validate and illustrate the methodology. It is then applied to the robust design of a nonlinear absorber, used as a passive means to mitigate flutter instability and reduce the amplitude of limit-cycle oscillations in a two-dimensional airfoil exhibiting geometric nonlinearities. Numerical results demonstrate the effectiveness of the proposed strategy while requiring only a limited number of numerical solver evaluations. The developed framework is therefore particularly well suited for practical engineering applications, where computational resources are limited and uncertainties must be accounted for in the early design process, to develop more robust and reliable systems.
SN - 1615-147X
ER -