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The purpose of this paper is to develop a new formulation using spectral approach, which can predict the wave behavior to uncertain parameters in mid and high frequencies.

The work presented is based on a hybridization of a spectral method called the “wave finite element (WFE)” method and a non-intrusive probabilistic approach called the “polynomial chaos expansion (PCE).” The WFE formulation for coupled structures is detailed in this paper. The direct connection with the conventional finite element method allows to identify the diffusion relation for a straight waveguide containing a mechanical or geometric discontinuity. Knowing that the uncertainties play a fundamental role in mid and high frequencies, the PCE is applied to identify uncertainty propagation in periodic structures with periodic uncertain parameters. The approach proposed allows the evaluation of the dispersion of kinematic and energetic parameters.

The authors have found that even though this approach was originally designed to deal with uncertainty propagation in structures it can be competitive with its low time consumption. The Latin Hypercube Sampling (LHS) is also employed to minimize CPU time.

The approach proposed is quite new and very simple to apply to any periodic structures containing variabilities in its mechanical parameters. The Stochastic Wave Finite Element can predict the dynamic behavior from wave sensitivity of any uncertain media. The approach presented is validated for two different cases: coupled waveguides with and without section modes. The presented results are verified vs Monte Carlo simulations.

This paper aims to propose numerical-based and experiment-based identification processes, accounting for uncertainties to identify structural parameters, in a wave propagation framework.

A variant of the inhomogeneous wave correlation (IWC) method is proposed. It consists on identifying the propagation parameters, such as the wavenumber and the wave attenuation, from the frequency response functions. The latters can be computed numerically or experimentally. The identification process is thus called numerical-based or experiment-based, respectively. The proposed variant of the IWC method is then combined with the Latin hypercube sampling method for uncertainty propagation. Stochastic processes are consequently proposed allowing more realistic identification.

The proposed variant of the IWC method permits to identify accurately the propagation parameters of isotropic and composite beams, whatever the type of the identification process in which it is included: numerical-based or experiment-based. Its efficiency is proved with respect to an analytical model and the Mc Daniel method, considered as reference. The application of the stochastic identification processes shows good agreement between simulation and experiment-based results and that all identified parameters are affected by uncertainties, except damping.

The proposed variant of the IWC method is an accurate alternative for structural identification on wide frequency ranges. Numerical-based identification process can reduce experiments’ cost without significant loss of accuracy. Statistical investigations of the randomness of identified parameters illustrate the robustness of identification against uncertainties.

The purpose of this paper is to extend statistical energy analysis (SEA)‐like modeling to fluid‐structure coupled systems.

An equivalent approach of aerodynamic loads is applied to a SEA‐like modeling of a panel‐cavity coupled system with rain‐on‐the‐roof excitation. Two aerodynamic excitations are presented: turbulent boundary layer (TBL) and diffuse field excitation. The energetic description of the coupled system is studied with both aerodynamic excitations, taking in account the coincidence effects. In order to extent the approach to more general systems, some parameters of the coupled system are also modified and the accuracy of the coupled system modeling is investigated.

The boundary conditions of the panel and the coupling strength between the panel and the cavity have been modified. As it was expected, the accuracy of equivalent approach is shown to be independent of such modifications. The interest of such calculation is thus highlighted: modelings of systems and aerodynamic excitations are independent, and can be treated separately.

This result is interesting in the space industry, for launch vehicles are excited by different types of random excitations. Those excitations can be modeled by SEA‐like with low calculation time and memory and applied to a unique system modeling.

This study aims to carry out numerical modeling to predict aerodynamic noise radiation from four different Savonius rotor blade profile.

Incompressible unsteady reynolds-averaged navier-stokes (URANS) approach using gamma–theta turbulence model is conducted to obtain the time accurate turbulent flow field. The Ffowcs Williams and Hawkings (FW-H) acoustic analogy formulation is used for noise predictions at optimal tip speed ratio (TSR).

The mean torque and power coefficients are compared with the experimental data and acceptable agreement is observed. The total and Mono+Dipole noise graphs are presented. A discrete tonal component at low frequencies in all graphs is attributed to the blade passing frequency at the given TSR. According to the noise prediction results, Bach type rotor has the lowest level of noise emission. The effect of TSR on the noise level from the Bach rotor is investigated. A direct relation between angular velocity and the noise emission is found.

The savonius rotor is a type of vertical axis wind turbines suited for mounting in the vicinity of residential areas. Also, wind turbines wherein operation are efficient sources of tonal and broadband noises and affect the inhabitable environment adversely. Therefore, the acoustic pollution assessment is essential for the installation of wind turbines in residential areas.

This study aims to investigate the radiated noise level of four common Savonius rotor blade profiles, namely, Bach type, Benesh type, semi-elliptic and conventional. As stated above, numbers of studies exploit the URANS method coupled with the FW-H analogy to predict the aeroacoustics behavior of wind turbines. Therefore, this approach is chosen in this research to deal with the aeroacoustics and aerodynamic calculation of the flow field around the aforementioned Savonius blade profiles. The effect of optimal TSR on the emitted noise and the contribution of thickness, loading and quadrupole sources are of interest in this study.

The purpose of this paper is to present the extension of plane wave based method.

The mixed functional are discretized using enriched finite elements. The fluid is discretized by enriched acoustic element, the structure by enriched structural finite element and the interface fluid‐structure by fluid‐structure interaction element.

Results obtained show the potentialities of the proposed method to solve a much larger class of wave problems in mid‐ and high‐frequency ranges.

The plane wave based method has previously been applied successfully to finite element and boundary element models for the Helmholtz equation and elastodynamic problems. This paper describes the extension of this method to the vibro‐acoustic problem.

The vibration of the rails is a significant source of railway rolling noise, often forming the dominant component of noise in the important frequency region between 400 and 2000 Hz. The purpose of the paper is to investigate the influence of the ground profile and the presence of the train body on the sound radiation from the rail.

Two-dimensional boundary element calculations are used, in which the rail vibration is the source. The ground profile and various different shapes of train body are introduced in the model, and results are observed in terms of sound power and sound pressure. Comparisons are also made with vibro-acoustic measurements performed with and without a train present.

The sound radiated by the rail in the absence of the train body is strongly attenuated by shielding due to the ballast shoulder. When the train body is present, the sound from the vertical rail motion is reflected back down toward the track where it is partly absorbed by the ballast. Nevertheless, the sound pressure at the trackside is increased by typically 0–5 dB. For the lateral vibration of the rail, the effects are much smaller. Once the sound power is known, the sound pressure with the train present can be approximated reasonably well with simple line source directivities.

Numerical models used to predict the sound radiation from railway rails have generally neglected the influence of the ground profile and reflections from the underside of the train body on the sound power and directivity of the rail. These effects are studied in a systematic way including comparisons with measurements.

Describes some of the new techniques and materials developed over the last ten years in sound insulation. Discusses common problems in controlling external noise, intrusion, sound insulation between dwellings and in the office environment, and through particular construction weakness areas. Briefly outlines the problems and solutions discovered in insulating a multiplex cinema.

The purpose of this paper is to propose a novel method using support vector machine (SVM) classifiers for objective seam pucker evaluation. Features are extracted using wavelet analysis and gray-level co-occurrence matrix (GLCM), and the samples are evaluated using SVM classifiers. The study aims to solve the problem of inappropriate parameters and large required samples in objective seam pucker evaluation.

Initially, seam pucker image was captured, and Edge detection and Hough transform were utilized to normalize the seam position and orientation. After cropping the image, the intensity was adjusted to the same identical level through histogram specification. Then, the standard deviations of the horizontal image and diagonal image, reconstructed using wavelet decomposition and reconstruction, were calculated based on parameter optimization. Meanwhile, GLCM was extracted from the restructured horizontal detail image, then the contrast and correlation of GLCM were calculated. Finally, these four features were imported to SVM classifiers based on genetic algorithm for evaluation.

The four extracted features reflected linear relationships among five grades. The experimental results showed that the classification accuracy was 96 percent, which catches up to the performance of human vision, and resolves ambiguity and subjective of the manual evaluation.

There are large required samples in current research. This paper provides a novel method using finite samples, and the parameters of the methods were discussed for parameter optimization. The evaluation results can provide references for analyzing the reason of wrinkles during garment manufacturing.

A ship unloading system based on a new concept in the mechanical bulk handling of materials has been introduced by Simon‐Carves Ltd of Stockport.

This paper aims to investigate the performance of estimators of the bid-ask spread in a wide range of circumstances and sampling frequencies. The bid-ask spread is important for many reasons. Because spread data are not always available, many methods have been suggested for estimating the spread. Existing papers focus on the performance of the estimators either under ideal conditions or in real data. The gap between ideal conditions and the properties of real data are usually ignored. The consistency of the estimates across various sampling frequencies is also ignored.

The estimators and the possible errors are analysed theoretically. Then we perform simulation experiments, reporting the bias, standard deviation and root mean square estimation error of each estimator. More specifically, we assess the effects of the following factors on the performance of the estimators: the magnitude of the spread relative to returns volatility, randomly varying of spreads, the autocorrelation of mid-price returns and mid-price changes caused by trade directions and feedback trading.

The best estimates come from using the highest frequency of data available. The relative performance of estimators can vary quite markedly with the sampling frequency. In small samples, the standard deviation can be more important to the estimation error than bias; in large samples, the opposite tends to be true.

There is a conspicuous lack of simulation evidence on the comparative performance of different estimators of the spread under the less than ideal conditions that are typical of real-world data. This paper aims to fill this gap.