Stiffness Optimization of Composite Wings with Aeroelastic Constraints

Dillinger, Johannes; Klimmek, Thomas; Abdalla, Mostafa; Gürdal, Zafer

doi:10.2514/1.c032084

Cited by 111 publications

(60 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…shown that the application of aeroelastic tailoring may lead to significant overall aircraft performance improvements in terms of reduced weight, reduced drag, reduced aerodynamic gust loads and higher flutter/divergence instability airspeeds [4][5][6][7][8][9], by concurrently optimizing the structural and aerodynamic behaviors. The improvement in wing static and/or dynamic behavior in different airflows is typically achieved by adjusting the wing stiffness and the coupling between wing bending and torsion deformations.…”

Section: Introductionmentioning

confidence: 99%

Aeroelastic Tailoring of a Representative Wing Box Using Tow-Steered Composites

et al. 2017

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Aeroelastic Tailoring of a Representative Wing Box Using Tow-Steered Composites

et al. 2017

View full text Add to dashboard Cite

“…Similar local panel-level analyses (within the context of a wing-level aeroelastic optimization procedure) are also used in Refs. [18] and [19]. These papers use a finite-strip method and a Rayleigh-Ritz method (assumed buckling modes), respectively.…”

Section: Panel Bucklingmentioning

confidence: 99%

Aeroelastic Tailoring of Transport Wings Including Transonic Flutter Constraints

Stanford

Wieseman

Jutte

2015

56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

View full text Add to dashboard Cite

Several minimum-mass optimization problems are solved to evaluate the effectiveness of a variety of novel tailoring schemes for subsonic transport wings. Aeroelastic stress and panel buckling constraints are imposed across several trimmed static maneuver loads, in addition to a transonic flutter margin constraint, captured with aerodynamic influence coefficient-based tools. Tailoring with metallic thickness variations, functionally graded materials, balanced or unbalanced composite laminates, curvilinear tow steering, and distributed trailing edge control effectors are all found to provide reductions in structural wing mass with varying degrees of success. The question as to whether this wing mass reduction will offset the increased manufacturing cost is left unresolved for each case.

show abstract

“…Following [26,27], each Article in Advance / STANFORD buckling analysis is conducted with a Rayleigh-Ritz method (assumed buckling modes). Both global buckling of a stiffened panel (bordered by ribs and spars) and local buckling in between each stiffener are computed, where simply supported boundary conditions are used for both scenarios.…”

Section: Panel Bucklingmentioning

confidence: 99%

Optimization of an Aeroservoelastic Wing with Distributed Multiple Control Surfaces

Stanford

2016

Journal of Aircraft

View full text Add to dashboard Cite

This paper considers the aeroelastic optimization of a subsonic transport wing box under a variety of static and dynamic aeroelastic constraints. Three types of design variables are used: structural variables (skin thickness, stiffener details), the quasi-steady deflection scheduling of a series of control surfaces distributed along the trailing edge for maneuver load alleviation and trim attainment, and the design details of a linear quadratic regulator controller (for flutter suppression), which commands oscillatory hinge moments into those same control surfaces. Optimization problems are solved where a closed-loop flutter constraint is forced to satisfy the required flight margin, and mass reduction benefits are realized by relaxing the open-loop flutter requirements.= aerodynamic influence coefficients E = elastic modulus, GPa F grav , F thrust = inertial and thrust force vectors g, ω = modal damping and frequency J = control cost K,K, K s , K = wing, panel, geometric, and reduced stiffness matrices K LQR = linear quadratic regulator feedback matrix KS σ , KS μ = stress and buckling failure functions L = semispan, m L α , L β , L δ , L γ , L p = downwash vectors L jig = jig shape downwash M = reduced mass matrix N = maneuver load factor P, Q = aerodynamic interpolation functions p = roll rate, rad∕s q = dynamic pressure, Pa Q, R = state and control weighting matrices q = design variable vector S L , S m , S p = pressure integration vectors U, U EAS = true, equivalent air speed, m∕s U LQR = linear quadratic regulator design speed, m∕s u ASE = input hinge moment vector W = total vehicle weight, N x, x A = modal amplitude and aerodynamic state vector x ASE = aeroservoelastic state vector x s , x a = structural solution vectors α = angle of attack, deg β = aileron deflection, deg γ i = lag roots γ s , γ a = control surface deflection vectors δ = elevator deflection, deg μ n , v n = panel buckling eigenpair ν = Poisson's ratio ρ = density, kg∕m 3 σ Y = yield stress, MPa

show abstract

Stiffness Optimization of Composite Wings with Aeroelastic Constraints

Cited by 111 publications

References 23 publications

Aeroelastic Tailoring of a Representative Wing Box Using Tow-Steered Composites

Aeroelastic Tailoring of a Representative Wing Box Using Tow-Steered Composites

Aeroelastic Tailoring of Transport Wings Including Transonic Flutter Constraints

Optimization of an Aeroservoelastic Wing with Distributed Multiple Control Surfaces

Contact Info

Product

Resources

About