Morales Tirado, Elisa Optimal Energy-Driven Aircraft Design Under Uncertainty. [Tesi di dottorato]

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Item Type: Tesi di dottorato
Resource language: English
Title: Optimal Energy-Driven Aircraft Design Under Uncertainty
Creators:
CreatorsEmail
Morales Tirado, Elisaemoralest6@gmail.com
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Industriale
Dottorato: Ingegneria industriale
Ciclo di dottorato: 33
Coordinatore del Corso di dottorato:
nomeemail
Grassi, Michelemichele.grassi@unina.it
Tutor:
nomeemail
Tognaccini, RenatoUNSPECIFIED
Quagliarella, DomenicoUNSPECIFIED
Keywords: Optimization, Uncertainty Quantification, Aerodynamics
Settori scientifico-disciplinari del MIUR: Area 09 - Ingegneria industriale e dell'informazione > ING-IND/06 - Fluidodinamica
Date Deposited: 20 Apr 2021 07:05
Last Modified: 07 Jun 2023 10:18
URI: http://www.fedoa.unina.it/id/eprint/14135

Collection description

Aerodynamic shape design robust optimization is gaining popularity in the aeronautical industry as it provides optimal solutions that do not deteriorate excessively in the presence of uncertainties. Several approaches exist to quantify uncertainty and, the dissertation deals with the use of risk measures, particularly the Value at Risk (VaR) and the Conditional Value at Risk (CVaR). The calculation of these measures relies on the Empirical Cumulative Distribution Function (ECDF) construction. Estimating the ECDF with a Monte Carlo sampling can require many samples, especially if good accuracy is needed on the probability distribution tails. Furthermore, suppose the quantity of interest (QoI) requires a significant computational effort, as in this dissertation, where has to resort to Computational Fluid Dynamics (CFD) methods. In that case, it becomes imperative to introduce techniques that reduce the number of samples needed or speed up the QoI evaluations while maintaining the same accuracy. Therefore, this dissertation focuses on investigating methods for reducing the computational cost required to perform optimization under uncertainty. Here, two cooperating approaches are introduced: speeding up the CFD evaluations and approximating the statistical measures. Specifically, the CFD evaluation is sped up by employing a far-field approach, capable of providing better estimations of aerodynamic forces on coarse grids with respect to a classical near-field approach. The advantages and critical points of the implementation of this method are explored in viscous and inviscid test cases. On the other hand, the approximation of the statistical measure is performed by using the gradient-based method or a surrogate-based approach. Notably, the gradient-based method uses adjoint field solutions to reduce the time required to evaluate them through CFD drastically. Both methods are used to solve the shape optimization of the central section of a Blended Wing Body under uncertainty. Moreover, a multi-fidelity surrogate-based optimization is used for the robust design of a propeller blade. Finally, additional research work documented in this dissertation focuses on utilizing an optimization algorithm that mixes integer and continuous variables for the robust optimization of High Lift Devices.

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