A symbolic LFT approach for robust flutter analysis of high-order models

Iannelli, Andrea; Marcos, Andrés; Bombardieri, Rocco; Cavallaro, Rauno

doi:10.23919/ecc.2019.8796122

Cited by 1 publication

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…13 shows the upper bounds of µ obtained in the first four iterations (ITER#) of Algorithm 2. The lower bound at ITER 1 was shown in [15] and is close to the corresponding upper bound curve in the range of frequencies relative to the highest peak.…”

Section: Robust Flutter Analysis Of the Cw Case Studysupporting

confidence: 64%

“…The article, which extends preliminary results published in [15], is structured as follows. Section 2 provides the essential background of the work.…”

Section: Introductionmentioning

confidence: 91%

“…red , is obtained by leveraging distinctive features of this approach, namely the modal-oriented aspect and the availability of a symbolic expression for the dependence of the structural matrices on the uncertainties. Further details on this order reduction can be found in [15] and are omitted here for brevity.…”

Section: Robust Flutter Analysis Of the Cw Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Linear Fractional Transformation co-modeling of high-order aeroelastic systems for robust flutter analysis

Iannelli

Marcos

Bombardieri

et al. 2020

European Journal of Control

Self Cite

View full text Add to dashboard Cite

This article presents a new paradigm for robust flutter modeling and analysis of high-order uncertain and linear aeroelastic systems. The fundamental idea is to couple the state-of-art in robust worst-case analysis (Linear Fractional Transformation modeling and µ analysis) with the state-of-practice in aeroelasticity (fluidstructure-interaction solvers). The issue with the latter is that, although capable of providing different levels of fidelity, they are less efficient in coping with the analysis of systems subject to uncertainties. In fact, while they have the advantage of capturing directly the physical uncertainty, the analyses can only be applied to a defined parameter combination, and due to their computational cost, it is usually only possible to consider a limited set of cases. To tackle this lack of robustness, in recent works the application of analytic worst-case methods has been proposed, but the intimately related problem of constructing accurate uncertain models has not been fully addressed. In this article, a co-modeling framework is presented that leverages the main features of both fluid-structure interaction solvers and robust control-based methods. The key idea is to combine these two typically distinct steps in a single one, enabling in this way to obtain an uncertainty description which is flexible and reconciles the physical sources of uncertainty with the uncertain parameters used in the LFT model. An exemplification of the developed framework on an unconventional aircraft configuration is provided. Results show its potential to provide valuable physical insights into the problem when analyzing complex systems.

show abstract