Potential Formulation for Aeroelastic Constraint Analysis in a Conceptual Design Environment

Baets, Peter Wilfried Gaston De; Mavris, Dimitri N.

doi:10.2514/6.2002-1295

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…An MDO environment at this stage can speed up the process and it can also consider more disciplines than standard procedures to bring more information to support decisions. From the best of the authors' knowledge, there are several works admitting MDO methodology at conceptual and preliminary design [2,3,9,16,24,26], although none of them include aeroelastic characteristics as relevant information to those design stages. Caixeta Jr. and Marques [4,5] have included aeroelastic characteristics through neural networks for the particular optimal design of flexible wings.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Caixeta

Marques

2018

J Braz. Soc. Mech. Sci. Eng.

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The multidisciplinary design optimization (MDO) is an important concept that focuses on bringing together disciplines involved with machine design. It is possible to use MDO in any stage of an aircraft design, that is in the conceptual, preliminary, or detailed design, as long as the numerical models are fitted to each of these stages. This work describes the development of a multidisciplinary design optimization applied to flexible aircraft wings, with respect to structural and aeroelastic characteristics. As tools for the aircraft designer, the numerical models must be fairly accurate and fast. Therefore, metamodels for the critical flutter speed prediction of aircraft wings were considered, thereby reducing significantly the computational cost of the optimization. For this purpose, artificial neural networks (NN) metamodeling is evaluated, based on their inherent properties in dealing with complex mappings. The NN metamodel is prepared using an aeroelastic code based on finite-element model coupled with linear potential aerodynamics. Results of the metamodel performance are presented, from where one can note that the NN is well suited for flutter prediction. Multiobjective optimization (MOO) using the genetic algorithm (GA) based on non-dominance approach is considered. The objectives were the maximization of critical flutter speed and minimization of structural mass. One case study is presented to evaluate the performance of the MOO, revealing that overall optimization process actually achieves the search for the Pareto frontier.

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Section: Introductionmentioning

confidence: 99%

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Caixeta

Marques

2018

J Braz. Soc. Mech. Sci. Eng.

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“…Chen [5] proposed a complex wing box system and studied empirical mode decomposition (EMD) by using the Hilbert-Huang transform (HHT). Baets [6,7] investigated the sensitivity of the flutter speed, divergence speed and modal response when varying the composite skin lay-up, fiber orientation, and the root flexibility of the model. Ovesy [8] concluded that optimum lay-up configuration had great dependency to the buckling modal shape of the wing box.…”

Section: Introductionmentioning

confidence: 99%

Natural characteristics analysis of aircraft wing box based on finite element method and measured data

Zhang¹,

Ling²,

Xu³

et al. 2018

Vib. proced.

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Compared with other mechanical products, aircraft structures have more rigorous requirements on flying performance, safety, reliability and service life. Based on the finite element method (FEM), the key component of the wing box model is explored in this paper, which provides a reference for the structure design and manufacture of aircraft wing box. The three-dimensional point cloud data of components are obtained by optical measurement systems, the deviation analysis between the point cloud model and the nominal model is carried out as a prerequisite, and then the natural characteristics of the model is analyzed. The results show that 99.15 % of the measured points have deviations within 0.38 mm, which verifies the accuracy of the nominal model. The first six modes are all bending modal shape, and the larger amplitude region mainly occurs in the wing ribs, which means its bending strength should be improved for structure design. Besides, the sixth-mode simultaneously result in front spar, stringer and rib bending vibration.

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“…In previous BLISS studies 3,8,9 , polynomial RS models have been used. The response surface methodology 10 was originally developed in experimental sciences, but it has become popular to fit computer simulation results.…”

Section: Choice Of Surrogate Model For Blissmentioning

confidence: 99%

Flexible Approximation Model Approach for Bi-Level Integrated System Synthesis

Kim¹,

Ragon²,

Soremekun³

et al. 2004

10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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Bi-Level Integrated System Synthesis (BLISS) is an approach that allows design problems to be naturally decomposed into a set of subsystem optimizations and a single system optimization. In the BLISS approach, approximate mathematical models are used to transfer information from the subsystem optimizations to the system optimization. Accurate approximation models are therefore critical to the success of the BLISS procedure. In this paper, new capabilities that are being developed to generate accurate approximation models for BLISS procedure will be described. The benefits of using flexible approximation models such as Kriging will be demonstrated in terms of convergence characteristics and computational cost. An approach of dealing with cases where subsystem optimization cannot find a feasible design will be investigated by using the new flexible approximation models for the violated local constraints.

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Potential Formulation for Aeroelastic Constraint Analysis in a Conceptual Design Environment

Cited by 4 publications

References 22 publications

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Multiobjective optimization of an aircraft wing design with respect to structural and aeroelastic characteristics using neural network metamodel

Natural characteristics analysis of aircraft wing box based on finite element method and measured data

Flexible Approximation Model Approach for Bi-Level Integrated System Synthesis

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