Towards integrated multidisciplinary synthesis of actively controlled fiber composite wings

Livne, Eli; Schmit, L. A.; Friedmann, Peretz P.

doi:10.2514/3.45972

Cited by 64 publications

(17 citation statements)

References 77 publications

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“…The square of the RMS of the system outputs (the modes of the system) is computed as follows: (21) The state covariance matrix [X] of the closed-loop system is the solution of a Lyapunov equation in the following form [24] ( 22) where Q w is the intensity of white noise.…”

Section: Aeroelastic Control System Design For Glrmentioning

confidence: 99%

Aeroelastic Control of Smart Composite Plate with Delaminations

Nam¹,

Chattopadhyay²,

Kim³

2000

Journal of Intelligent Material Systems and Structures

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In this paper, aeroelastic performance of smart composite wing in the presence of delaminations is investigated. A control system is designed to enhance the dynamic stability of the delaminated composite wing. The refined higher order theory for analyzing an adaptive composite plate in the presence of delaminations is used. The theory accurately captures the transverse shear deformation through the thickness, which is important in anisotropic composites, particularly in the presence of discrete actuators and sensors and delaminations. The effects of delamination on the aeroelastic characteristics of composite plates are investigated. An active control system is designed to redistribute the loads and to minimize the effect of delamination. The pole placement technique is applied to design the closed loop system by utilizing piezoelectric actuators. Due to delamination, the significant changes in the natural frequencies of the lower modes are observed. This causes the reduction on the flutter speed of the delaminated plate model. The aeroelastic control results show that controller makes the delaminated plate model behave like a normal plate. The controller also reduces a significant amount of the root mean square values of the gust response due to gust.

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Section: Aeroelastic Control System Design For Glrmentioning

confidence: 99%

Aeroelastic Control of Smart Composite Plate with Delaminations

Nam¹,

Chattopadhyay²,

Kim³

2000

Journal of Intelligent Material Systems and Structures

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“…The small perturbation equations of motion for a wing structure have the following form Mass and stiffness matrices, [M] and [K], in equation ( I ) are obtained based on an equivalent plate modeling technique (Livne, 1990).…”

Section: Structural Modelingmentioning

confidence: 99%

Digital Adaptive Flutter Suppression and Simulation Using Approximate Transonic Aerodynamics

Pak¹,

Friedmann²,

Livne

1995

Journal of Vibration and Control

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A digital adaptive controller, using a trailing edge control surface, is introduced for the active flutter suppression of wings undergoing time varying flight conditions. The aeroservoelastic system is modeled in the time domain by a deterministic Auto Regressive Moving Average (ARMA) model together with a parameter estimator. Linear quadratic controller gains at each time step are obtained using an iterative Riccati solver. Aeroservoelastic transient response is obtained using Roger's approximation, state transition matrices, and an iterative time marching algorithm. Simulations of system performance for flights into the transonic speed regime are used to demonstrate suppression of flutter and divergence instabilities. NOMENCLATURE a; AR coefficient A~;(t)~ ith column of matrix [Ac(t)] [A,(s)] . generalized transonic aerodynamic influence coefficient matrix due to elastic wing motion [Ae(s)] generalized subsonic aerodynamic influence coefficient matrix due to elastic wing motion fA6(t)l generalized transonic aerodynamic influence coefficient vector due to control surface motion (si5(t)) generalized subsonic aerodynamic influence coefficient vector due to control surface motion [Ap], [Bp], [.Cp] aeroservoelastic system matrices . [A.,.], [B.I&dquo;]' [C,,.] structural system matrices [Ar], [Bar], [Blr], [Cr], [DorJ, [D,,] aerodynamic system matrices associated with elastic wing motion bi MA coefficient at East Carolina University on July 12, 2015 jvc.sagepub.com Downloaded from 364 [C] damping matrix {Glk k controller gain vector at time t = kTe ' *. [I] identity matrix J performance index for controller [K] ' stiffness matrix L}k k estimator gain vector at time t = kite m order of generalized displacement vector (q(t)) Moo free stream Mach number [M] mass matrix NG ' number of aerodynamic lag terms NM order of aeroelastic system matrices p 00 free stream pressure [P] steady state Riccati matrix [P]k Riccati matrix at time t = kTe qD ; free stream dynamic pressure (q(t) ) displacement vector {Q(t)} transonic aerodynamic force vector [Q] , weighting matrix for state vector {Xp h rw weighting factor for control surface deflection angle 8k '-(t) 1, ri unsteady component of generalized displacement vector, and its ith element R(t)} generalized transonic aerodynamic force vector R, generalized transonic aerodynamic force vector at steady statẽ Re(t)} generalized transonic aerodynamic force vector due to elastic wing ' motion {Re(t)} generalized subsonic aerodynamic force vector due to elastic wing motion . R.5 (t) generalized transonic aerodynamic force vector due to control surface motion (k5(t)) generalized subsonic aerodynamic force vector due to control surface motion s laplace variable Tu time step for iterative time-marching algorithm T, sampling time for parameter estimator Tbii(oc)) the ith diagonal element of matrix [T6(oo)] [Te(t)] transonic correction matrix due to elastic wing motion [T~(t)] ' transonic correction matrix due to control surface motion [U] eigen matrix of order n x m [V]k covariance matrix ...

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“…One of the first applications of MDO was aircraft wing design, where aerodynamics, structures, and controls are three strongly coupled disciplines [9,10,11,12,13,14,15,16]. Since then, the application of MDO has been extended to complete aircraft [17,18,19,20,21] and a wide range of other engineering systems, such as bridges [22], buildings [23,24], railway cars [25,26], microscopes [27], automobiles [28,29], ships [30,31], propellers [32,33], rotorcraft [34,35], wind turbines [36,37,38], and spacecraft [39,40].…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary Design Optimization: A Survey of Architectures

2013

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Multidisciplinary design optimization (MDO) is a field of research that studies the application of numerical optimization techniques to the design of engineering systems involving multiple disciplines or components. Since the inception of MDO, various methods (architectures) have been developed and applied to solve MDO problems. This paper provides a survey of all the architectures that have been presented in the literature so far. All architectures are explained in detail using a unified description that includes optimization problem statements, diagrams, and detailed algorithms. The diagrams show both data and process flow through the multidisciplinary system and computational elements, which facilitates the understanding of the various architectures, and how they relate to each other.A classification of the MDO architectures based on their problem formulations and decomposition strategies is also provided, and the benefits and drawbacks of the architectures are discussed from both a theoretical and experimental perspective. For each architecture, several applications to the solution of engineering design problems are cited. The result is a comprehensive but straightforward introduction to MDO for non-specialists, and a reference detailing all current MDO architectures for specialists.

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Towards integrated multidisciplinary synthesis of actively controlled fiber composite wings

Cited by 64 publications

References 77 publications

Aeroelastic Control of Smart Composite Plate with Delaminations

Aeroelastic Control of Smart Composite Plate with Delaminations

Digital Adaptive Flutter Suppression and Simulation Using Approximate Transonic Aerodynamics

Multidisciplinary Design Optimization: A Survey of Architectures

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