Samson Cooper

An Experimental Case Study for Nonlinear Model Validation: Effect of Nonlinearities in an Aero-Engine Structure

Linear FE-models are commonly validated with measured data obtained from experimental test conducted under similar FE-simulated boundary conditions. However, measured data at higher or operational amplitudes of vibration often exhibit evidence of nonlinear characteristics. Research has proven that majority of the causes and sources of these nonlinearities are frequently local in nature while a large proportion of the structure can be represented using linear theory. This paper presents the experimental investigations conducted on an aircraft structure ranging from linear to nonlinear regime, the aim of the investigation was to understand the influence of connecting accessories or components to the proposed aircraft structure. Broadband, sine-sweeps and stepped-sine excitations were used to detect and characterise the nature of the nonlinear behaviour in the assembly.

Nonlinear Identification of an Aero-Engine Component Using Polynomial Nonlinear State Space Model

In non-linear structural dynamics, the identification of nonlinearity often requires prior knowledge or an initial assumption of the mathematical law (model) of the type of nonlinearity present in a system. However, applying such assumptions to large structures with several sources and types of nonlinearities can be difficult or practically impossible due to the individualistic nature of nonlinear systems. This paper presents the identification of an aerospace component using polynomial nonlinear state space models. As a first step, the best linear approximation (BLA), noise and nonlinear distortion levels are estimated over different amplitudes of excitation. Next, a linear state space model is estimated on the nonparametric BLA using the frequency domain subspace identification method. The nonlinear model is constructed using a set of multivariate polynomial terms in the state variables and the parameters are estimated through a nonlinear optimisation routine. The polynomial nonlinear state space models are tested and validated on measured data obtained from the experimental investigation of the Aero-Engine component.

Static load estimation using artificial neural network: Application on a wing rib

Abstract This paper presents a novel approach to predicting the static load on a large wing rib in the absence of load cells. A Finite Element model of the wing rib was designed and calibrated using measured data obtained from static experimental test. An Artificial Neural Network (ANN) model was developed to predict the static load applied on the wing rib, this was achieved by using random data and strain values obtained from the static test as input parameters. A number of two layer feed-forward networks were designed and trained in MATLAB using the back-propagation algorithm. The first set of Neural Networks (NN) were trained using random data as inputs, measured strain values were introduced as input into the already trained neural network to access the training algorithm and quantify the accuracy of the static load prediction produced by the trained NN. In addition, a procedure that combines ANN and FE modelling to create a hybrid inverse problem analysis and load monitoring tool is presented. The hybrid approach is based on using trained NN to estimate the applied load from a known FE structural response. Results obtained from this research proves that using an ANN to identify loads is feasible and a well-trained NN shows fast convergence and high degree of accuracy of 92% in the load identification process. Finally, additional trained network results showed that ANN as an inverse problem solver can be used to estimate the load applied on a structure once the load-response relationship has been identified.

Nonlinear Vibration Analysis of a Complex Aerospace Structure

Complex shaped aerodynamic structures such missiles are prone to exhibit some level of nonlinear phenomena due to their aerodynamically tailored design and application. Aside from the aerodynamic and aeroelastic challenges experienced by a missile, an important but fundamental challenge encountered by a deployable missile is the inevitable concentrated structural nonlinearities which are observed around the hinge of its fins. Due to the current design and manufacturing process, the hinge of the fin of a missile often consist of complex configurations, joints and other nonlinear features that leads to concentrated structural nonlinearities. Some of the nonlinearities encountered includes off sets, piecewise linear, bilinear nonlinearity, hysteresis, coulomb friction and damping nonlinearities. These nonlinearities are frequently triggered at large vibration amplitudes caused by high pressure loads during operational flight. Activation of these nonlinearities often affect the dynamic response of the missile and in some cases lead to structural failures in the air vehicle. In this context, identifying and predicting the vibration response of aerodynamic structures with nonlinearities will be of great advantage to the present aerospace industries. In this paper the nonlinear dynamic behaviour of a prototype missile is examined using established nonlinear identification methods applied to measured data obtained from experimental test. The nonlinear identification is achieved using the acceleration surface method and the Hilbert transform FORCEVIB method, these methods are applied to sine-sweep excitation and stepped sine excitation measurements to obtain nonlinear parametric coefficients. The nonlinear experimental model was developed using the white box identification process (Detection, Characterisation and Parameter Estimation). In addition, Force controlled stepped sine experiments at several excitation levels were conducted to gain useful insight into the amplitude dependant behaviour of the missile in the existence of structural nonlinearities.

Model Upgrading T0 Augment Linear Model Capabilities into Nonlinear Regions

Identification of nonlinear dynamical systems have enjoyed significant progression over the past few years with the outcome of various developed identification methods, however there is still no generalised method applicable to structures with arbitrary nonlinearity. In the analysis of nonlinear dynamical system, it is essential to establish accurate and reliable tools that are capable of estimating the parameters from measured data for both the linear and nonlinear system. This paper presents a modular framework approach for upgrading a valid linear finite element structural model to accommodate any nonlinearities present in a system. To validate the efficiency of the proposed method, numerical and experimental studies are conducted on a “Multiple Beam Test Structure”, the method uses an iterative process to upgrade the nonlinear terms in the system. The results are verified by comparing predicted new response with measured data.

Application of Artificial Neural Network to Predict Static Loads on an Aircraft Rib

Aircraft wing structures are subjected to different types of loads such as static and dynamic loads throughout their life span. A methodology was developed to predict the static load applied on a wing rib without load cells using Artificial Neural Network (ANN). In conjunction with the finite element modelling of the rib, a classic two layer feed-forward networks were created and trained on MATLAB using the back-propagation algorithm. The strain values obtained from the static loading experiment was used as the input data for the network training and the applied load was set as the output. The results obtained from the ANN showed that this method can be used to predict the static load applied on the wing rib to an accuracy of 92%.