Discretization effects on flutter aspects and control of wing/store configurations

Abstract

Two discretization approaches are considered for the prediction of the flutter characteristics of the Goland+ wing with a store. In one approach, referred to as an uncoupled-mode approach, the common notion that neglects the store when generating the basis functions is considered. This approach results in uncoupled mode shapes of the bending and torsion motions. In the second approach, referred to as semi-coupled-mode approach, coupling between the bending and torsion motions due to the shear force and moment exerted by the store at the tip of the wing are taken into account. It is found that the two approaches yield different instability parameters and characteristics. Through comparison with the exact solution, it is found that the semi-coupled-mode approach correctly predicts the natural frequencies of the wing/store system and the speed and type of the instability, whereas the uncoupled-mode approach fails to predict the natural frequencies of the structure, overestimates the instability speed, and fails to predict the type of instability. The usefulness of a physics-based discretization approach used to assess control schemes and effects of gains on the flutter speed and type of instability is demonstrated.

Normal form representation of the aeroelastic response of the Goland wing

“…Thus, to calculate the α's, we start with an initial guess the value given from the discretization approach and iterate on it using the Newton-Raphson method until Det(M) < , where is a small specified number. Further details of the computation of the flutter speed based from the exact solution are provided in [13]. Table 2 presents the flutter speed obtained using the exact and discretization approaches.…”

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

“…Mazidi and Fazelzadeh 11 examined the effects of a powered engine at different positions along a swept wing by comparing its aeroelastic characteristics with the original wing. Nayfeh et al 12 investigated the flutter behavior of the Goland wing model and an external store by introducing a linear analysis methodology. Eastep et al 13 determined the flutter and limit-cycle oscillation (LCO) characteristics of a wing/store configuration in transonic regime by performing a linear flutter analysis in MSC/NASTRAN with p-k method.…”

Adaptive nonlinear optimal control for active suppression of airfoil flutter via a novel neural-network-based controller

“…In terms of control algorithms for suppressing LCOs various methods apply (Chen et al., 2009; Keyser et al., 2017; Saaed et al., 2017). Due to the time-varying nature and nonlinear characteristics of an aeroelastic system (Bichiou et al., 2016; Nayfeh et al., 2012; Vasconcellos et al., 2016) and the increasing demand on a wider operation range beyond the flutter boundary, advanced methods in adaptive, nonlinear, and robust control have received more attention in recent ASAF studies, although conventional frequency-domain analysis remains a useful tool for control synthesis (Schmidt, 2016). These advanced methods include but are not limited to: online linear-quadratic regulator (Pak et al., 1995); optimal control synthesized via time-domain finite elements method (Fazelzadeh et al., 2014); self-tuning regulator (Viswamurthy and Ganguli, 2008); linear-parameter-varying techniques (Chen et al., 2012; Prime et al., 2010); feedback linearization (Platanitis and Strganac, 2004; Strganac et al., 2000); model reference adaptive control (Ko et al., 2002); back-stepping-based adaptive output feedback control (Singh and Wang, 2002); robust output feedback control (Zhang and Behal, 2016); modular adaptive control (Rao et al., 2006; Singh and Brenner, 2003); modified filtered-X least-mean-square control (Carnahan and Richards, 2008); L1 adaptive control (Lee and Singh, 2013); sliding-mode control (Luo et al., 2016; Wang et al., 2015); finite-time H∞ adaptive fault-tolerant control (Gao and Cai, 2016; Gao et al., 2016); and neural-network(NN)-based adaptive control (Brillante and Mannarino, 2016; Gujjula et al., 2005; Wang et al., 2011), etc.…”