Model updating using uncertain experimental modal data

The propagation of parameter uncertainty in structural dynamics has become a feasible method to determine the probabilistic description of the vibration response of industrial scale �nite element models. Though methods for uncertainty propagation have been developed extensively, the quanti�cation of parameter uncertainty has been neglected in the past. But a correct assumption for the parameter variability is essential for the estimation of the uncertain vibration response. This paper shows how to identify model parameter means and covariance matrix from uncertain experimental modal test data. The common gradient based approach from deterministic computational model updating was extended by an equation that accounts for the stochastic part. In detail an inverse approach for the identi�cation of statistical parametric properties will be presented which will be applied on a numerical model of a replica of the GARTEUR SM-AG19 benchmark structure. The uncertain eigenfrequencies and mode shapes have been determined in an extensive experimental modal test campaign where the aircraft structure was tested repeatedly while it was 130 times dis- and reassembled in between each experimental modal analysis

Model-based displacement estimation of wind turbine blades using strainmodal data

For wind turbine rotor blades, the use of strain sensors is preferred over acceleration sensors for the purpose of permanent monitoring. Experimental modal analysis during operation is thus constrained to strain information, yielding strain modal data including strain mode shapes. For follow-up investigations such as aerodynamic load assessment or flutter monitoring it is however advantageous to have this information as displacement mode shapes or as displacements of the blade contour over time. This research applies a generic approach that converts strain mode shapes to displacement mode shapes utilizing an FE shell model as a basis for approximation. The accuracy of the approach is assessed by comparison with experimentally identified high-resolution displacement mode shapes which are acquired with accelerometers and serve as a reference. In the process the conversion procedure is illustrated with the help of strain data that has been obtained using a sensor instrumentation installed for certification testing of the blade. The requirements for successful usage of the employed conversion scheme and its suitability for rotor blade data are discussed

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

Techniques pour réaliser l'essais de vibrations au sol du Beluga XL d'Airbus

International audienceAfter the completion of the Airbus A350 XWB 900 Ground Vibration Test (GVT) in 2013 and the A320 NEO GVT in July 2014, the ONERA-DLR joined team performed the GVT of the new BelugaXL aircraft in May-June 2018. The test was completed in 8 measurement days for 2 mass configurations, thanks to efficient methods and tools. The originality of this structure comes from its very large fuselage and its rear section with two large additional vertical planes, which made the modal identification particularly challenging. This paper describes the process followed, the methods used, and the interactions with engineers in charge of aeroelastic computations during the test, and how they allowed the success of this campaign.Après les essais de vibrations au sol de l'Airbus A350 XWB 900 en 2013 et de l'Airbus A320 NEO en juillet 2014, l'équipe commune ONERA-DLR a réalisé l'essai du nouvel avion d'Airbus, le Beluga XL, en mai-juin 2018. Le test fut effectué en 8 journées de mesure pour 2 configurations massiques, grâce à des outils et des méthodes efficaces. L'originalité de cette structure réside en la présence d'un fuselage de très grande dimension et d'une partie arrière comportant deux grands plans verticaux additionnels, ce qui rend l'identification modale particulièrement difficile. Cette communication décrit le procédé suivi, les méthodes utilisées et les interactions avec les ingénieurs en charge des calculs aéroélastiques durant le test, et comment ils ont permis le succès de cette campagne d'essais

Phase resonance method for linearized identification of nonlinear mechanical structures

Ground Vibration Tests provide data to validate the prediction of structural dynamics of the aircraft before first flight. For this purpose, a modal substitute model is identified from experimental data. However, nonlinear dynamic behavior is often observed during those test campaigns, resulting in frequency and damping variations for specific modes. %uncertain results. This work deals with the modal identification of aircraft with structural nonlinearities. Phase resonance method is embedded into the theory of describing functions for an improved linearized identification. Also, modal phase control is employed for automatic tuning of the excitation frequency in phase resonance testing in order to speed up the identification process. This methodology is then applied to aircraft structures and the results are discussed in the context of the assumptions made within this work. Improved results are achieved with a laboratory structure, while limits of this method are found with a real airplane structure

Comparison of Bayesian and interval uncertainty quantification : application to the AIRMOD test structure

This paper concerns the comparison of two inverse methods for the quantification of uncertain model parameters, based on experimentally obtained measurement data of the model's responses. Specifically, Bayesian inference is compared to a novel method for the quantification of multivariate interval uncertainty. The comparison is made by applying both methods to the AIRMOD measurement data set, and comparing their results critically in terms of obtained information and computational expense. Since computational cost of the application of both methods to high-dimensional problems and realistic numerical models can become intractable, an Artificial Neural Network surrogate is used for both methods. The application of this ANN proves to limit the computational cost to a large extent, even taking the generation of the training dataset into account. Concerning the comparison of both methods, it is found that the results of the Bayesian identification provide less over-conservative bounds on the uncertainty in the responses of the AIRMOD model

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

Inverse quantification of epistemic uncertainty under scarce data: Bayesian or Interval approach?

This paper introduces a practical comparison of a newly introduced inverse method for the quantification of epistemically uncertain model parameters with the well-established probabilistic framework of Bayesian model updating via Transitional Markov Chain Monte Carlo. The paper gives a concise overview of both techniques, and both methods are applied to the quantification of a set of parameters in the well-known DLR Airmod test structure. Specifically, the case where only a very scarce set of experimentally obtained eigenfrequencies and eigenmodes are available is considered. It is shown that for such scarce data, the interval method provides more objective and robust bounds on the uncertain parameters than the Bayesian method, since no prior definition of the uncertainty is required, albeit at the cost that less information on parameter dependency or relative plausibility of different parameter values is obtained.Matthias Faes is a post-doctoral researcher of the Flemish Research Foundation under grant number 12P3519N ("Generalized Inverse Uncertainty Quantification")

Artilysation' of endolysin λSa2lys strongly improves its enzymatic and antibacterial activity against streptococci

Endolysins constitute a promising class of antibacterials against Gram-positive bacteria. Recently, endolysins have been engineered with selected peptides to obtain a new generation of lytic proteins, Artilysins, with specific activity against Gram-negative bacteria. Here, we demonstrate that artilysation can also be used to enhance the antibacterial activity of endolysins against Gram-positive bacteria and to reduce the dependence on external conditions. Art-240, a chimeric protein of the anti-streptococcal endolysin λSa2lys and the polycationic peptide PCNP, shows a similar species specificity as the parental endolysin, but the bactericidal activity against streptococci increases and is less affected by elevated NaCl concentrations and pH variations. Time-kill experiments and time-lapse microscopy demonstrate that the killing rate of Art-240 is approximately two-fold higher compared to wildtype endolysin λSa2lys, with a reduction in viable bacteria of 3 log units after 10min. In addition, lower doses of Art240 are required to achieve the same bactericidal effect

Wind tunnel flutter testing on a highly flexible wing for aeroelastic validation in the transonic regime within the HMAE1 project

The aircraft manufacturer Embraer, the German Aerospace Center (DLR), the Netherlands Aerospace Centre (NLR) and German-Dutch Wind Tunnels (DNW) have tested an innovative highly flexible wing within an aeroelastic wind tunnel experiment in the transonic regime. The HMAE1 project was initiated by Embraer to test its numerical predictions for wing flutter under excessive wing deformations in the transonic regime. A highly elastic fiberglass wing-body pylon nacelle wind tunnel model (see Figure 1), which is able to deform extensively, was constructed for the experiment. The model was instrumented with a large number of pressure orifices, strain gauges, stereo pattern recognition (SPR) markers and accelerometers. The wing was tested from Ma = 0.4 to Ma = 0.9 for different angles of attack and stagnation pressures. The static and dynamic behavior of the wing model was monitored and a new method to analyze its eigenfrequencies and damping ratios was used. In the past, the large amounts of data acquired during such experiments could only be evaluated with a time lag. An efficient method developed by DLR now allows performing the data analysis in real time [1, 2]. As a result, it was possible during the test to identify exactly which safety margins remained before the onset of flutter and the resulting possible destruction of the model