Non-linear identification of a squeeze-film damper

Described is an experimental study to identify the damping laws associated with a squeeze-film vibration damper. This is achieved by using a non-linear filtering algorithm to process displacement responses of the damper ring to synchronous excitation and thus to estimate the parameters in an nth-power velocity model. The experimental facility is described in detail and a representative selection of results is included. The identified models are validated through the prediction of damper-ring orbits and comparison with observed responses

Active Control of a Nonlinear Flexible Aircraft Wing

The avoidance of flutter still remains a key constraint in the design of all aircraft. In this endeavour, the need to develop models that accurately reproduce physical phenomena is of growing importance; one such phenomenon is nonlinearity. Whereas in many cases it may be justifiable to neglect nonlinearity and treat the system as linear, substantial nonlinear behaviour (such as limit cycle oscillations) have been observed in several aircraft, making evident the need to account for nonlinearity. The present work gains motivation from this need. In the first section, the Feedback Linearisation method is applied to a structurally nonlinear flexible aircraft wing, and Active Control is performed to extend the flutter boundary of the system. The flexible wing is modelled as an aeroservoelastic system containing ailerons that will provide the required control forces. The ailerons are treated purely as an excitation source, and do not participate in the dynamics of the system. In the second section, some uncertainty is incorporated into the parameters describing the nonlinearity, and Adaptive Feedback Linearisation is performed to account for this uncertainty. The advantage of this method is the guaranteed closed-loop stability of the system, despite a lack of knowledge of the exact description of the nonlinearity. Results from numerical simulations demonstrate the effectiveness of the method in suppressing flutter

Attitudes towards neurology among medical undergraduates

Diseases of the nervous system are an important contributor to clinical and social problems. Therefore there is a need to provide undergraduates and postgraduates of medical faculties with adequate training in neurology. However, many shortcomings have been observed in this field, often associated with students’ negative perception of neurology. The aim of this study was to explore attitudes towards neurology amongst undergraduates of the medical faculty at Wroclaw Medical University, and the reasons for these attitudes. As a qualitative component of the study, a focus group discussion was conducted with six fifth year undergraduates. The findings of the focus group and a literature search informed the content of a questionnaire distributed among fifth year students of the medical faculty, including non-Poles attending English Division. The responses to the closed questions were analysed quantitatively and subjected to statistical analysis while the free text comments were analysed qualitatively. Triangulation of the findings from the focus group and the survey was performed. 134 Polish students and 75 English-speaking ones responded to the survey. The majority of participants perceived neurology to be interesting and important for medical education, and it was highly ranked as a potential future speciality. The majority of the survey respondents regarded neurology as difficult and mentioned specific drawbacks. In spite of similar general perceptions of neurology, Polish and English- -speaking students differed in their perceptions of particular aspects, conditioned by diversity in cultural backgrounds and earlier experiences associated with neurology. The course in neurology affected attitudes towards the subject more than preceding experiences, mostly in a positive manner. The fifth year medical undergraduates expressed mostly positive attitudes towards neurology. Cultural background and the course in neurology were the main factors contributing to attitudes in these students

Stochastic Model Updating with Uncertainty Quantification: An Overview and Tutorial

This paper presents an overview of the theoretic framework of stochastic model updating, including critical aspects of model parameterisation, sensitivity analysis, surrogate modelling, test-analysis correlation, parameter calibration, etc. Special attention is paid to uncertainty analysis, which extends model updating from the deterministic domain to the stochastic domain. This extension is significantly promoted by uncertainty quantification metrics, no longer describing the model parameters as unknown-but-fixed constants but random variables with uncertain distributions, i.e. imprecise probabilities. As a result, the stochastic model updating no longer aims at a single model prediction with maximum fidelity to a single experiment, but rather a reduced uncertainty space of the simulation enveloping the complete scatter of multiple experiment data. Quantification of such an imprecise probability requires a dedicated uncertainty propagation process to investigate how the uncertainty space of the input is propagated via the model to the uncertainty space of the output. The two key aspects, forward uncertainty propagation and inverse parameter calibration, along with key techniques such as P-box propagation, statistical distance-based metrics, Markov chain Monte Carlo sampling, and Bayesian updating, are elaborated in this tutorial. The overall technical framework is demonstrated by solving the NASA Multidisciplinary UQ Challenge 2014, with the purpose of encouraging the readers to reproduce the result following this tutorial. The second practical demonstration is performed on a newly designed benchmark testbed, where a series of lab-scale aeroplane models are manufactured with varying geometry sizes, following pre-defined probabilistic distributions, and tested in terms of their natural frequencies and model shapes. Such a measurement database contains naturally not only measurement errors but also, more importantly, controllable uncertainties from the pre-defined distributions of the structure geometry. Finally, open questions are discussed to fulfil the motivation of this tutorial in providing researchers, especially beginners, with further directions on stochastic model updating with uncertainty treatment perspectives

Model Updating Strategy of the DLR-AIRMOD Test Structure

Considerable progresses have been made in computer-aided engineering for the high fidelity analysis of structures and systems. Traditionally, computer models are calibrated using deterministic procedures. However, different analysts produce different models based on different modelling approximations and assumptions. In addition, identically constructed structures and systems show different characteristic between each other. Hence, model updating needs to take account modelling and test-data variability. Stochastic model updating techniques such as sensitivity approach and Bayesian updating are now recognised as powerful approaches able to deal with unavoidable uncertainty and variability. This paper presents a high fidelity surrogate model that allows to significantly reduce the computational costs associated with the Bayesian model updating technique. A set of Artificial Neural Networks are proposed to replace multi non-linear input-output relationships of finite element (FE) models. An application for updating the model parameters of the FE model of the DRL-AIRMOD structure is presented. © 2017 The Authors. Published by Elsevier Ltd

Parameter selection for model updating with global sensitivity analysis

National Science Foundation of China (NSFC) under Grant No. 11372084 Sem PDF conforme despacho.The problem of selecting parameters for stochastic model updating is one that has been studied for decades, yet no method exists that guarantees the ‘correct’ choice. In this paper, a method is formulated based on global sensitivity analysis using a new evaluation function and a composite sensitivity index that discriminates explicitly between sets of parameters with correctly-modelled and erroneous statistics. The method is applied successfully to simulated data for a pin-jointed truss structure model in two studies, for the cases of independent and correlated parameters respectively. Finally, experimental validation of the method is carried out on a frame structure with uncertainty in the position of two masses. The statistics of mass positions are confirmed by the proposed method to be correctly modelled using a Kriging surrogate.authorsversionpublishe

Experimental Nonlinear Control for Flutter Suppression in a Nonlinear Aeroelastic System

Experimental implementation of input–output feedback linearization in controlling the dynamics of a nonlinear pitch–plunge aeroelastic system is presented. The control objective is to linearize the system dynamics and assign the poles of the pitch mode of the resulting linear system. The implementation 1) addresses experimentally the general case where feedback linearization-based control is applied using as the output a degree of freedom other than that where the physical nonlinearity is located, using a single trailing-edge control surface, to stabilize the entire system; 2) includes the unsteady effects of the airfoil’s aerodynamic behavior; 3) includes the embedding of a tuned numerical model of the aeroelastic system into the control scheme in real time; and 4) uses pole placement as the linear control objective, providing the user with flexibility in determining the nature of the controlled response. When implemented experimentally, the controller is capable of not only delaying the onset of limit-cycle oscillation but also successfully eliminating a previously established limit-cycle oscillation. The assignment of higher levels of damping results in notable reductions in limit-cycle oscillation decay times in the closed-loop response, indicating good controllability of the aeroelastic system and effectiveness of the pole-placement objective. The closed-loop response is further improved by incorporating adaptation so that assumed system parameters are updated with time. The use of an optimum adaptation parameter results in reduced response decay times

Sensitivity or Bayesian model updating: a comparison of techniques using the DLR AIRMOD test data

Deterministic model updating is now a mature technology widely applied to large-scale industrial structures. It is concerned with the calibration of the parameters of a single model based on one set of test data. It is, of course, well known that different analysts produce different finite element models, make different physics-based assumptions, and parameterize their models differently. Also, tests carried out on the same structure, by different operatives, at different times, under different ambient conditions produce different results. There is no unique model and no unique data. Therefore, model updating needs to take account of modeling and test-data variability. Much emphasis is now placed on what has become known as stochastic model updating where data are available from multiple nominally identical test structures. In this paper two currently prominent stochastic model updating techniques (sensitivity-based updating and Bayesian model updating) are described and applied to the DLR AIRMOD structure