Reliability Based Multidisciplinary Optimization of Aeroelastic Systems with Structural and Aerodynamic Uncertainties

Nikbay, Melike; Fakkusoglu, Necati; KURU, Muhammet Nasıf

doi:10.2514/6.2010-9187

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…In flutter calculation procedure, the necessary design parameters of the wing are taken from CAD model constructed in CATIA V5 by Nikbay et. al 21 Natural frequency determination employs Euler-Bernoulli beam equations. Euler-Bernoulli solution was previously investigated for AGARD 445.6 wing by Kamakoti, 23 Kamakoti and Shyy, 24 Kamakoti et.…”

Section: B Flutter Analysis Of Goland Wingmentioning

confidence: 99%

Flutter Based Aeroelastic Optimization of an Aircraft Wing with Analytical Approach

Nikbay¹

2012

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials ConferenceSelf Cite

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We present an analytical flutter prediction methodology employing assumed mode technique for three dimensional wing and wing/store configurations. The flutter solution makes use of Lagrange formulation for aeroelastic modeling and Theodorsen function for aerodynamic load calculation. An in-house flutter code is developed and validated by using benchmark problems, next applied to Goland and AGARD 445.6 wing models. Flutter results in all cases are in excellent agreement with the reference data. The flutter code is further enhanced to enable aeroelastic optimization and uncertainty based flutter analysis of AGARD 445.6 wing/store configurations. Firstly, aeroelastic optimization study varying input parameters such as taper ratio, sweep angle, spanwise elasticity and shear modulus is performed to maximize flutter boundary of AGARD 445.6 wing and an optimum clean wing model is ascertained. Next, the structural effects of designated external masses are investigated for flutter of initial and optimized AGARD 445.6 wing models and optimum configurations of store placement are determined. Finally, structural randomness such as in spanwise elasticity and shear modulus of the wing are propagated through flutter analyses and this uncertainty quantification is applied to initial and optimum AGARD 445.6 clean wing models. Finally, for wing/store models, random parameters relevant to store masses, store load placements are added to material property uncertainties and similarly flutter boundary uncertainty is examined. In all analyses, uncertainty quantification is accomplished by Monte Carlo Simulation method with various Coefficient of Variation estimates.

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Section: B Flutter Analysis Of Goland Wingmentioning
confidence: 99%

Flutter Based Aeroelastic Optimization of an Aircraft Wing with Analytical Approach
Nikbay¹
2012
53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials ConferenceSelf Cite
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“…Analytical flutter solution methodology which is based on linear techniques is expected to provide good agreement with experiments. In flutter calculation procedure, the necessary design parameters for reference station of the wing are taken from CAD model constructed in CATIA V5 by Nikbay et al 51 and also determined from the known geometrical properties of the standard configuration. By using the geometric properties given in Table 4 with the location of elastic axis, and bending and torsional natural frequencies.…”

Section: A Validation Of Flutter Analysismentioning
confidence: 99%

Robust Aeroelastic Design Optimization of Wing/Store Configurations Based on Flutter Criteria
Nikbay
¹

2012
12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis And
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We present a robust aeroelastic design optimization study for three dimensional wing/ store configurations by employing an analytical flutter analysis. The flutter solution uses Lagrange equations with energy terms where structural displacements are represented by assumed mode technique and aerodynamic loads are calculated by Theodorsen function. An in-house flutter code is developed parametrically in terms of design variables such as taper ratio, sweep angle, elastic axis location and material properties, and validated by using available reference data for well-known aeroelastic benchmark problems which are the Goland and AGARD 445.6 wing models. The flutter code is coupled with an optimization software to perform deterministic design optimization of the AGARD 445.6 clean wing and then further developed to enable flutter analysis and optimization of wing/store configurations. The aeroelastic modeling counts for both structural and aerodynamic effects of external masses while couple of different optimization strategies are applied to determine optimum store locations and wing design to maximize flutter speed. Finally, robustness criterion is implemented into aeroelastic optimization in the presence of structural uncertainties. These uncertainties are propagated by Monte Carlo Simulation (MCS) while 2 nd order Polynomial Chaos Expansion (PCE) is used through MCS to reduce the computational time. NomenclatureA Cross-sectional area, [m 2 ] aNon-dimensional length between elastic axis and center of mass a s Non-dimensional length between elastic axis of the store and mass center of the wing bHalf chord length, [m] b R Half chord length of reference station, [m] ea Elastic axis length from leading edge for clean wing ea i Elastic axis length from leading edge for external masses (i= 1 to n s ) E y Elasticity modulus in spanwise direction, [M P a] EI Bending stiffness, [N m 2 ] (EI) wing Bending stiffness of clean wing, [N m 2 ] (EI) store Bending stiffness of store, [N m 2 ] g Damping term g w Damping term for bending motion g θ Damping term for torsional motion G y Shear modulus in spanwise direction, [M P a] GJ Torsional stiffness, [N m 2 ] (GJ) wing Torsonal stiffness of clean wing, [N m 2 ] (GJ) store Torsional stiffness of store, [N m 2 ] I sz Total moment of inertia of store, [kgm 2 ] * Associate Professor,AIAA 2012-5455 þÿ C o p y r i g h t © 2 0 1 2 b y P 1 n a r A c a r . P u b l i s h e d b y t h e A m e r i c a n I n s t i t u t e o f A e r o n a u t i c s a n d A s t r o n a u t i c s , I n c . , w i t h p e r m i s s i o n . Downloaded by KUNGLIGA TEKNISKA HOGSKOLEN KTH on July 30, 2015 | http://arc.aiaa.org |

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“…Similarly, Nikbay and Kuru develop a reliability-based MDO framework called MpCCI that couples high order commercial solvers for aeroelastic systems. 11 However, uncertainty propagation using high order computational frameworks can be computationally prohibitive, and there has been research to decrease the computational burden. Zou and Mahadevan developed an efficient reliability based design optimization (RBDO) method that allows for direct reliability assessments rather than low order approximations.…”

Section: Introductionmentioning
confidence: 99%

Preliminary Wing Weight Estimation Under Probabilistic Loads for a Transport Aircraft
Corman
¹
,
Rancourt
²
,
Lee
³

et al. 2014
55th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
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In preliminary design of aircraft wings, a common practice is to set target loads and target weights as governing parameters for more detailed design phases. These values must be reliable in the presence of uncertainty because overly conservative target estimates could result in over-design of the wing while non-conservative estimates could lead to costly latephase redesign to satisfy constraints. The process of confidently identifying these targets is typically very computationally expensive because a large multivariate space of flight scenarios and vehicle configurations must be investigated. Therefore, a method has been implemented to reduce the computational expense of reliability-based aero-structural preliminary design. To efficiently identify critical loading conditions for structural sizing, this method adaptively samples the discretized multivariate space with a kriging-based sequential routine. Each new sample point is identified by an expected improvement function based on the probabilistic distributions of internal structural loads. These distributions are determined by propagating uncertainty using Monte Carlo simulation through an aerostructural analysis code. This process is applied to the wing of a reference aircraft derived from the NASA Common Research Model, and the effect of reliability indices is shown on preliminary sizing and weight estimation.

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Reliability Based Multidisciplinary Optimization of Aeroelastic Systems with Structural and Aerodynamic Uncertainties

Cited by 6 publications

References 23 publications

Flutter Based Aeroelastic Optimization of an Aircraft Wing with Analytical Approach

Flutter Based Aeroelastic Optimization of an Aircraft Wing with Analytical Approach

Robust Aeroelastic Design Optimization of Wing/Store Configurations Based on Flutter Criteria

Preliminary Wing Weight Estimation Under Probabilistic Loads for a Transport Aircraft

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