The effect of internal pipe wall roughness on the accuracy of clamp-on ultrasonic flow meters

Clamp-on transit-time ultrasonic flowmeters (UFMs) suffer from poor accuracy compared with spool-piece UFMs due to uncertainties that result from the in-field installation process. One of the important sources of uncertainties is internal pipe wall roughness which affects the flow profile and also causes significant scattering of ultrasound. This paper purely focuses on the parametric study to quantify the uncertainties (related to internal pipe wall roughness) induced by scattering of ultrasound and it shows that these effects are large even without taking into account the associated flow disturbances. The flowmeter signals for a reference clamp-on flowmeter setup were simulated using 2-D finite element analysis including simplifying assumptions (to simulate the effect of flow) that were deemed appropriate. The validity of the simulations was indirectly verified by carrying out experiments with different separation distances between ultrasonic probes. The error predicted by the simulations and the experimentally observed errors were in good agreement. Then, this simulation method was applied on pipe walls with rough internal surfaces. For ultrasonic waves at 1 MHz, it was found that compared with smooth pipes, pipes with only a moderately rough internal surface (with 0.2-mm rms and 5-mm correlation length) can exhibit systematic errors of 2 in the flow velocity measurement. This demonstrates that pipe internal surface roughness is a very important factor that limits the accuracy of clamp on UFMs

Stochastic stability of viscoelastic systems under Gaussian and Poisson white noise excitations

As the use of viscoelastic materials becomes increasingly popular, stability of viscoelastic structures under random loads becomes increasingly important. This paper aims at studying the asymptotic stability of viscoelastic systems under Gaussian and Poisson white noise excitations with Lyapunov functions. The viscoelastic force is approximated as equivalent stiffness and damping terms. A stochastic differential equation is set up to represent randomly excited viscoelastic systems, from which a Lyapunov function is determined by intuition. The time derivative of this Lyapunov function is then obtained by stochastic averaging. Approximate conditions are derived for asymptotic Lyapunov stability with probability one of the viscoelastic system. Validity and utility of this approach are illustrated by a Duffing-type oscillator possessing viscoelastic forces, and the influence of different parameters on the stability region is delineated

Fluctuations and scaling of inverse participation ratios in random binary resonant composites

We study the statistics of local field distribution solved by the Green's-function formalism (GFF) [Y. Gu et al., Phys. Rev. B {\bf 59} 12847 (1999)] in the disordered binary resonant composites. For a percolating network, the inverse participation ratios (IPR) with $q=2$ are illustrated, as well as the typical local field distributions of localized and extended states. Numerical calculations indicate that for a definite fraction $p$ the distribution function of IPR $P_q$ has a scale invariant form. It is also shown the scaling behavior of the ensemble averaged $$ described by the fractal dimension $D_q$. To relate the eigenvectors correlations to resonance level statistics, the axial symmetry between $D_2$ and the spectral compressibility $\chi$ is obtained.Comment: 7 pages, 6 figures, accepted by Physical Review

A systematic study of Rayleigh-Brillouin scattering in air, N2 and O2 gases

Spontaneous Rayleigh-Brillouin scattering experiments in air, N2 and O2 have been performed for a wide range of temperatures and pressures at a wavelength of 403 nm and at a 90 degrees scattering angle. Measurements of the Rayleigh-Brillouin spectral scattering profile were conducted at high signal-to-noise ratio for all three species, yielding high-quality spectra unambiguously showing the small differences between scattering in air, and its constituents N2 and O2. Comparison of the experimental spectra with calculations using the Tenti S6 model, developed in 1970s based on linearized kinetic equations for molecular gases, demonstrates that this model is valid to high accuracy. After previous measurements performed at 366 nm, the Tenti S6 model is here verified for a second wavelength of 403 nm. Values for the bulk viscosity for the gases are derived by optimizing the model to the measurements. It is verified that the bulk viscosity parameters obtained from previous experiments at 366 nm, are valid for wavelengths of 403 nm. Also for air, which is treated as a single-component gas with effective gas transport coefficients, the Tenti S6 treatment is validated for 403 nm as for the previously used wavelength of 366 nm, yielding an accurate model description of the scattering profiles for a range of temperatures and pressures, including those of relevance for atmospheric studies. It is concluded that the Tenti S6 model, further verified in the present study, is applicable to LIDAR applications for exploring the wind velocity and the temperature profile distributions of the Earth's atmosphere. Based on the present findings, predictions can be made on the spectral profiles for a typical LIDAR backscatter geometry, which deviate by some 7 percent from purely Gaussian profiles at realistic sub-atmospheric pressures occurring at 3-5 km altitude in the Earth's atmosphere

Acoustic based safety emergency vehicle detection for intelligent transport systems

A system has been investigated for the detection of incoming direction of an emergency vehicle. Acoustic detection methods based on a cross microphone array have been implemented. It is shown that source detection based on time delay estimation outperforms sound intensity techniques, although both techniques perform well for the application. The relaying of information to the driver as a warning signal has been investigated through the use of ambisonic technology and a 4 speaker array which is ubiquitous in most modern vehicles. Simulations show that accurate warning information may be relayed to the driver and afford correct action

Motor current signal analysis using a modified bispectrum for machine fault diagnosis

This paper presents the use of the induction motor current to identify and quantify common faults within a two-stage reciprocating compressor. The theoretical basis is studied to understand current signal characteristics when the motor undertakes a varying load under faulty conditions. Although conventional bispectrum representation of current signal allows the inclusion of phase information and the elimination of Gaussian noise, it produces unstable results due to random phase variation of the sideband components in the current signal. A modified bispectrum based on the amplitude modulation feature of the current signal is thus proposed to combine both lower sidebands and higher sidebands simultaneously and hence describe the current signal more accurately. Based on this new bispectrum a more effective diagnostic feature namely normalised bispectral peak is developed for fault classification. In association with the kurtosis of the raw current signal, the bispectrum feature gives rise to reliable fault classification results. In particular, the low feature values can differentiate the belt looseness from other fault cases and discharge valve leakage and intercooler leakage can be separated easily using two linear classifiers. This work provides a novel approach to the analysis of stator current for the diagnosis of motor drive faults from downstream driving equipment

Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition

In this paper, a new method is proposed by combining ensemble empirical mode decomposition (EEMD) with order tracking techniques to analyse the vibration signals from a two stage helical gearbox. The method improves EEMD results in that it overcomes the potential deficiencies and achieves better order spectrum representation for fault diagnosis. Based on the analysis, a diagnostic feature is designed based on the order spectra of extracted IFMs for detection and separation of gearbox faults. Experimental results show this feature is sensitive to different fault severities and robust to the influences from operating conditions and remote sensor locations

Optimal Controller and Filter Realisations using Finite-precision, Floating- point Arithmetic.

The problem of reducing the fragility of digital controllers and filters implemented using finite-precision, floating-point arithmetic is considered. Floating-point arithmetic parameter uncertainty is multiplicative, unlike parameter uncertainty resulting from fixed-point arithmetic. Based on first- order eigenvalue sensitivity analysis, an upper bound on the eigenvalue perturbations is derived. Consequently, open-loop and closed-loop eigenvalue sensitivity measures are proposed. These measures are dependent upon the filter/ controller realization. Problems of obtaining the optimal realization with respect to both the open-loop and the closed-loop eigenvalue sensitivity measures are posed. The problem for the open-loop case is completely solved. Solutions for the closed-loop case are obtained using non-linear programming. The problems are illustrated with a numerical example