Evaluation and Uncertainty Quantification of Bifurcation Diagram: Landing Gear, a Case Study

Tartaruga, Irene; Cooper, Jonathan E.; Löwenberg, Mark; Sartor, Pia N; Lemmens, Yves

doi:10.7712/120215.4307.512

Cited by 8 publications

(18 citation statements)

References 17 publications

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“…To this end techniques already adopted by the author 20,22,23 to perform sensitivity analysis (SA) and uncertainty quantification (UQ) have been adopted. Sobol indexes are used for performing SA and SVD/metamodelling based techniques for the uncertainty propagation.…”

Section: Methodsmentioning

confidence: 99%

“…These are needed to train surrogate models adopted in the SVD based methodology. [18][19][20][21][22] Using the SVD/metamodelling based methodology can reduce by 95% the time consumption due to the investigation over the parameters space to look for the set of nominal values F * for which the stated tangency occurs. Knowing F * , the interval (x j,low , x j,upp ) of interest for each jth design parameter can be defined (eq.…”

Section: Second Phase: Data Collectionmentioning

confidence: 99%

“…[15][16][17] It is an iterative procedure that aims to robustly minimize the probability of failure and to provide an understanding of the acceptable range of uncertainties exploiting geometrical considerations to distribute the points to be investigated in the considered parameter space, and SVD/metamodelling based techniques. [18][19][20][21][22] The optimization strategy is conceived in order to solve problems that are very time demanding and for which it is difficult (and expensive) to determine derivatives and to identify and define the optimum set of parameters. The reduction in computational time is both in terms of number of cases to be analysed directly through experiments or run of numerical models and in the lack of a need to compute gradients.…”

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confidence: 99%

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Optimization of Aerospace Structures under Uncertainty using an Iterative Distribution Evolutionary Algorithm

Tartaruga¹,

Lowenberg

Cooper³

et al. 2017

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. An Iterative Differential Evolutionary Algorithm is proposed for the optimized identification of sets of parameter values for a system whose analysis is very time demanding and for which it is difficult to identify and define the optimum set. The algorithm is an evolutionary technique and reduces the number of evaluations required for both numerical and experimental systems thanks to an iterative procedure. It exploits geometrical considerations to distribute the points to be investigated in the considered parameter space, and SVD/metamodeling based techniques to further decrease the computational burden. The developed strategy aims to minimize the probability of failure, guaranteeing a reliable optimum, providing an understanding of the acceptable range of uncertainties and keeping robustness. The approach is validated considering the optimization of landing gear designs minimizing the probability of occurrence of shimmy phenomena during ground manoeuvres. Nomenclature B, Bmtrack of the main assembly and distance between the nose and the axis of the main assembly d direction of interest F * set of tangency parameters F opt optimum nominal parameter value g limit state function L Lift LoI locus of interest P maxi maximum range of variation for the ith considered parameters T ol , T ol p tolerances adopted in the algorithm W vertical load acting on the landing gear structure W weight of the aircraft x vector of design parameters * Marie Curie early stage PhD researcher, ALPES Project,

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Section: Methodsmentioning

confidence: 99%

Section: Second Phase: Data Collectionmentioning

confidence: 99%

mentioning

confidence: 99%

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Optimization of Aerospace Structures under Uncertainty using an Iterative Distribution Evolutionary Algorithm

Tartaruga¹,

Lowenberg

Cooper³

et al. 2017

58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…The SVD can then be applied for feature extraction [2][3][4][5][6][7]. The matrix defined for the flutter dynamic pressure has as many rows as the number of parameter variations (e.g.…”

Section: S4t Model and Parameter Uncertaintymentioning

confidence: 99%

“…The authors have considered an iterative procedure shown in Figure 4, where the number of singular values is increased, and so the percentage %, and the rank reduction updated until the desired maximum error is met. The Mean Average Percentage Error (MAPE) [2][3][4][5][6][7] is considered here with the average being taken in terms of all the considered training points. Once the sampling plane for the training has been defined and the rank reduction selected with the procedure presented above, k T ∈ ℝ × is considered as a basis which is assumed not to change throughout the design space.…”

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confidence: 99%

Flutter Uncertainty Quantification for the S4T Model

Tartaruga¹,

Cooper²,

Γεωργίου

et al. 2017

55th AIAA Aerospace Sciences Meeting

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General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms The application of uncertainty analysis, including probabilistic and non-probabilistic approaches, to the prediction of the dynamic pressure at which flutter occurs is considered for the S4T Semi-Span Super-Sonic Transport wind-tunnel model. A SVD based reduced order modelling approach, along with the Polynomial Chaos Expansion and Fuzzy Analysis methods for uncertainty quantification, are described and used to explore the effects that variations in the structural mass, resembling variations in the fuel load, have on the linear flutter analyses for a range of subsonic and supersonic speeds. All the approaches are able to efficiently quantify the effects of structural uncertainty on the aeroelastic behavior.

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