ALBERT

Description: The article is devoted to the practical problem of computer simulation of the dynamic behaviour of horizontal axis wind turbine composite rotor blades. This type of wind turbine is the dominant design in modern wind farms, and as such its dynamics and strength characteristics should be carefully studied. For this purpose, in this paper the mechanical model of a rotor blade with a composite skin possessing a stiffener was developed and implemented as a finite element model in ABAQUS. On the basis of this computer model, modal analysis of turbine blade vibrations was performed and benchmark cases for the dynamic response were investigated. The response of the system subjected to a uniform underneath pressure was studied, and the root reaction force and blade tip displacement time histories were obtained from the numerical calculations conducted.

Description: This paper demonstrates the differences between popular transformation-based input representations for vibration-based machine fault diagnosis. This paper highlights the dependency of different input representations on hyperparameter selection with the results of training different configurations of classical convolutional neural networks (CNNs) with three common benchmarking datasets. Raw temporal measurement, Fourier spectrum, envelope spectrum, and spectrogram input types are individually used to train CNNs. Many configurations of CNNs are trained, with variable input sizes, convolutional kernel sizes and stride. The results show that each input type favors different combinations of hyperparameters, and that each of the datasets studied yield different performance characteristics. The input sizes are found to be the most significant determiner of whether overfitting will occur. It is demonstrated that CNNs trained with spectrograms are less dependent on hyperparameter optimization over all three datasets. This paper demonstrates the wide range of performance achieved by CNNs when preprocessing method and hyperparameters are varied as well as their complex interaction, providing researchers with useful background information and a starting place for further optimization.

Description: Mechanical seals play an important role in the reliability of a process. Currently, the condition monitoring of mechanical seals is restricted due to the limitations of the traditional monitoring methods, including classical vibration analysis. For this reason, the objective of the present work is the detection and analysis of friction mechanisms inside a mechanical seal that are unfavorable and induce fault conditions using the acoustic emission technique, which allows the measurement of high-frequency vibrations that arise due to material fatigue processes on a microscopic scale. For this purpose, several fault condition modes were induced on a test rig of an agitator vessel system with a double-acting mechanical seal and its buffer fluid system. It was possible to detect the presence of inadequate friction mechanisms due to the absence and limited use of lubrication, as well as the presence of abrasive wear, by measuring a change in the properties of the acoustic emissions. Operation under fault condition modes was analyzed using the acoustic emission technique before an increase in the leakage rate was evaluated using traditional monitoring methods. The high friction due to the deficient lubrication was characterized by a pattern in the high-frequency range that consisted of the harmonics of a fundamental frequency of about 33 kHz. These results demonstrate the feasibility of a condition monitoring system for mechanical seals using the acoustic emission technique.

Description: The blades of the horizontal axis wind turbine (HAWT) are generally subjected to significant forces resulting from the flow field around the blade. These forces are the main contributor of the flow-induced vibrations that pose structural integrity challenges to the blade. The study focuses on the application of the gradient boosting regressor (GBR) for predicting the wind turbine response to a combination of wind speed, angle of attack, and turbulence intensity when the air flows over the rotor blade. In the first step, computational fluid dynamics (CFD) simulations were carried out on a horizontal axis wind turbine to estimate the force distribution on the blade at various wind speeds and the blade’s attack angle. After that, data obtained for two different angles of attack (4° and 8°) from CFD acts as an input dataset for the GBR algorithm, which is trained and tested to obtain the force distribution. An estimated variance score of 0.933 and 0.917 is achieved for 4° and 8°, respectively, thus showing a good agreement with the force distribution obtained from CFD. High prediction accuracy and less time consumption make GBR a suitable alternative for CFD to predict force at various wind velocities for which CFD analysis has not been performed.

Description: High fidelity finite element (FE) models are widely used to simulate the dynamic responses of geometrically nonlinear structures. The high computational cost of running long time duration analyses, however, has made nonlinear reduced order models (ROMs) attractive alternatives. While there are a variety of reduced order modeling techniques, in general, their shared goal is to project the nonlinear response of the system onto a smaller number of degrees of freedom. Implicit Condensation (IC), a popular and non-intrusive technique, identifies the ROM parameters by fitting a polynomial model to static force-displacement data from FE model simulations. A notable drawback of these models, however, is that the number of polynomial coefficients increases cubically with the number of modes included within the basis set of the ROM. As a result, model correlation, updating and validation become increasingly more expensive as the size of the ROM increases. This work presents simultaneous regression and selection as a method for filtering the polynomial coefficients of a ROM based on their contributions to the nonlinear response. In particular, this work utilizes the method of least absolute shrinkage and selection (LASSO) to identify a sparse set of ROM coefficients during the IC regression step. Cross-validation is used to demonstrate accuracy of the sparse models over a range of loading conditions.

Description: Current models describing the effect of crowd-induced loading require a full-scale validation. To measure the lower-back accelerations during such validation, low-cost accelerometers are used to ensure a sufficient scalability. The goal is to verify to what extent the low-cost sensors can be used for the contact force reconstruction in case the pedestrian walks on a vibrating surface. First, a data set is collected comprising the simultaneous registration of the lower-back accelerations and the contact forces. Three contact force reconstruction methods are presented to accurately reconstruct the contact force in case of walking on a rigid surface. Second, the focus is on the contact force reconstruction in case of walking on a vibrating surface. A numerical study is performed adopting quantities of the Eeklo Benchmark Dataset providing a realistic framework. The additional lower-back accelerations as a result of the vibrating surface are estimated numerically. It is found that directly reconstructing the total contact force leads to inaccurate results. Instead, it is more suited to reconstruct the contact force one would induce on a rigid surface and combine this with an independent model to account for human–structure interaction. The conclusions of this numerical example are case-specific while the presented methodology is generic and can be readily extended to virtually any other structure.

Description: The aim of this contribution is to present numerical comparisons of model-order reduction methods for geometrically nonlinear structures in the general framework of finite element (FE) procedures. Three different methods are compared: the implicit condensation and expansion (ICE), the quadratic manifold computed from modal derivatives (MD), and the direct normal form (DNF) procedure, the latter expressing the reduced dynamics in an invariant-based span of the phase space. The methods are first presented in order to underline their common points and differences, highlighting in particular that ICE and MD use reduction subspaces that are not invariant. A simple analytical example is then used in order to analyze how the different treatments of quadratic nonlinearities by the three methods can affect the predictions. Finally, three beam examples are used to emphasize the ability of the methods to handle curvature (on a curved beam), 1:1 internal resonance (on a clamped-clamped beam with two polarizations), and inertia nonlinearity (on a cantilever beam).

Description: In this paper, the three-layer model of ballasted railway track with discrete supports is analyzed to access its applicability. The model is referred as the discrete support model and abbreviated by DSM. For calibration, a 3D finite element (FE) model is created and validated by experiments. Formulas available in the literature are analyzed and new formulas for identifying parameters of the DSM are derived and validated over the range of typical track properties. These formulas are determined by fitting the results of the DSM to the 3D FE model using metaheuristic optimization. In addition, the range of applicability of the DSM is established. The new formulas are presented as a simple computational engineering tool, allowing one to calculate all the data needed for the DSM by adopting the geometrical and basic mechanical properties of the track. It is demonstrated that the currently available formulas have to be adapted to include inertial effects of the dynamically activated part of the foundation and that the contribution of the shear stiffness, being determined by ballast and foundation properties, is essential. Based on this conclusion, all similar models that neglect the shear resistance of the model and inertial properties of the foundation are unable to reproduce the deflection shape of the rail in a general way.

Description: Helicopters are known to exhibit higher vibratory levels compared to fixed-wing aircraft. The consequences of vibrations depend on the affected helicopter component or subject. Specifically, pilots are in contact with several parts of the helicopter; vibrations can spoil the pilot-vehicle interaction. To evaluate the effects of vibration exposure on pilots, comfort levels resulting from whole-body vibration are computed. However, specific body parts and organs, e.g., hands, feet, and eyes are also adversely affected, with undesirable effects on piloting quality. Therefore, a detailed assessment is necessary for a more accurate estimation of pilot vibration exposure when comparing different configurations, tracking changes during design, and determining the safety of the flight envelope. A generalized assessment is presented by considering vibrations at the seat surface, hand-grip of controls, eyes, and feet. The suggested vibration measure includes comfort, handling, feet-contact, and vision in a single formulation. It is illustrated by coupling a high-fidelity biodynamic model of the pilot to a helicopter aeroservoelastic model in a comprehensive simulation environment. Using appropriate modeling techniques, vibration exposure of helicopter pilots could be evaluated during all stages of design, to achieve a more comfortable and safer flying environment.

Description: Inverse dynamics problems and associated aspects are all around us in everyday life[...]