Nonlinear Aeroelastic Model Research Articles

Design of a High-Altitude Long-Endurance Solar-Powered Unmanned Air Vehicle for Multi-Payload and Operations

Several researches are being carried out at the Politecnico di Torino with the aim of designing a high altitude very-long endurance/unmanned air vehicle (HAVE/UAV). Being able to fly in the stratosphere (15-20 km) and with an endurance of about 4 months offers an advantage and possibility that is presently not available with conventional aircraft or satellites. A computer program has been developed to design the platform. The change in solar radiation over a period of a year, the altitude, masses, and efficiencies of the solar and fuel cells, as well as the aerodynamic, structural, flight mechanics, and aeroelastic performances have all been taken into account. Extensive use has been made of high modulus graphite/epoxy when designing the structure in order to minimize the airframe weight, but also to guarantee the required stiffness and aeroelastic performance. A blended wing body (BWB) configuration has been selected for solar HAVE aircraft multi payload and operation (SHAMPO) with eight brushless electric motors, as the result of a preliminary design. The BWB solution has been designed according to the conventional procedures and airworthiness regulations. It seems to be the best compromise between performance, available surfaces for solar cells and volume for multi-payload purposes, compared to conventional design. Several profiles and wing plans have been analysed and optimized to achieve the best efficiency using the Xfoil and Vsaero computational fluid dynamics (CFD) software. A finiteelement method and a classical theoretical analysis was carried out using the Msc/Patran/Nastran code to predict the static and aeroelastic behaviour of the SHAMPO. Aeroelastic analysis has been performed starting with a classical linear flutter analysis and considering an undeformed equilibrium condition. Classical linear flutter speed show as the airworthiness requirements has been achieved in the case of SHAMPO configuration. A preliminary non-linear aeroelastic model is introduced in the design process in order to deal with specific phenomena correlated with high static structural deflections occurring during standard flight conditions. Important flutter speed reduction (i.e. up to 42 per cent in special cases) are possible including such kind of phenomena.

Structural health monitoring based on sensitivity vector fields and attractor morphing

The dynamic responses of a thermo-shielding panel forced by unsteady aerodynamic loads and a classical Duffing oscillator are investigated to detect structural damage. A nonlinear aeroelastic model is obtained for the panel by using third-order piston theory to model the unsteady supersonic flow, which interacts with the panel. To identify damage, we analyse the morphology (deformation and movement) of the attractor of the dynamics of the aeroelastic system and the Duffing oscillator. Damages of various locations, extents and levels are shown to be revealed by the attractor-based analysis. For the panel, the type of damage considered is a local reduction in the bending stiffness. For the Duffing oscillator, variations in the linear and nonlinear stiffnesses and damping are considered as damage. Present studies of such problems are based on linear theories. In contrast, the presented approach using nonlinear dynamics has the potential of enhancing accuracy and sensitivity of detection.

Spectral and multiresolution Wiener expansions of oscillatory stochastic processes

Wiener chaos expansions are being evaluated for the representation of stochastic variability in the response of nonlinear aeroelastic systems, which often exhibit limit cycles. Preliminary studies with a simple nonlinear aeroelastic computational model have shown that the standard non-intrusive Wiener–Hermite expansion fails to maintain time accuracy as the simulation evolves. Wiener–Hermite expansions faithfully reproduce the short-term characteristics of the process but consistently lose energy after several mean periods of oscillation. This energy loss remains even for very high-order expansions. To uncover the cause of this energy loss and to explore potential remedies, the more elementary problem of a sinusoid with random frequency is used herein to simulate the periodic response of an uncertain system. As time progresses, coefficients of the higher order terms in both the Wiener–Hermite and Wiener–Legendre expansions successively gain and lose dominance over the lower-order coefficients in a manner that causes any fixed-order expansion in terms of global basis functions to fail over a simulation time of sufficient duration. This characteristic behavior is attributed to the continually increasing frequency of the process in the random dimension. The recently developed Wiener–Haar expansion is found to almost entirely eliminate the loss of energy at large times, both for the sinusoidal process and for the response of a two degree-of-freedom nonlinear system, which is examined as a prelude to the stochastic simulation of aeroelastic limit cycles. It is also found that Mallat's pyramid algorithm is more efficient and accurate for evaluating Wiener–Haar expansion coefficients than Monte Carlo simulation or numerical quadrature.

Nonlinear Statistical Approach for Aeroelastic Response Prediction

Aging aircraft and combat aircraft that carry heavy external stores potentially face problems arising from nonlinearities in structure. An expert data mining system is proposed that is capable of predicting the asymptotic behavior of an aeroelastic system with structural nonlinearities represented by polynomial restoring forces or freeplay models. The input is represented only by a limited set of transient data. The output provides a long-term nonlinear aeroelastic response, and the prediction is made when certain rule-based reasoning conditions are satisfied. An attractive feature of this new approach is that no information about the system parameters is needed. In the prediction module, we propose two methods, based on nonlinear time series models and the unscented Kalman filter. To our knowledge, these approaches have not been reported so far for predicting the long-term nonlinear aeroelastic responses. Compared with the classical extended Kalman filter, the unscented filter does not require differentiability and can be applied to nonlinear aeroelastic models with freeplay and hysteresis. The performances of the expert data mining system are demonstrated for simulated data and wind-tunnel experimental aeroelastic data resulting from a two-degree-of-freedom airfoil oscillating in pitch and plunge.