The vibro-acoustic analysis of built-up systems using a hybrid method with parametric and non-parametric uncertainties

An existing hybrid finite element (FE)/statistical energy analysis (SEA) approach to the analysis of the mid- and high frequency vibrations of a complex built-up system is extended here to a wider class of uncertainty modeling. In the original approach, the constituent parts of the system are considered to be either deterministic, and modeled using FE, or highly random, and modeled using SEA. A non-parametric model of randomness is employed in the SEA components, based on diffuse wave theory and the Gaussian Orthogonal Ensemble (GOE), and this enables the mean and variance of second order quantities such as vibrational energy and response cross-spectra to be predicted. In the present work the assumption that the FE components are deterministic is relaxed by the introduction of a parametric model of uncertainty in these components. The parametric uncertainty may be modeled either probabilistically, or by using a non-probabilistic approach such as interval analysis, and it is shown how these descriptions can be combined with the non-parametric uncertainty in the SEA subsystems to yield an overall assessment of the performance of the system. The method is illustrated by application to an example built-up plate system which has random properties, and benchmark comparisons are made with full Monte Carlo simulations. © 2012 Elsevier Ltd. All rights reserved.

Assessing seasonal precipitation trends in India using parametric and non-parametric statistical techniques

This paper provides an insight into the long-term trends of the four seasonal and annual precipitations in various climatological regions and sub-regions in India. The trends were useful to investigate whether Indian seasonal rainfall is changing in terms of magnitude or location-wise. Trends were assessed over the period of 1954-2003 using parametric ordinary least square fits and non-parametric Mann-Kendall technique. The trend significance was tested at the 95% confidence level. Apart from the trends for individual climatological regions in India and the average for the whole of India, trends were also specifically determined for the possible smaller geographical areas in order to understand how different the trends would be from the bigger spatial scales. The smaller geographical regions consist of the whole southwestern continental state of Kerala. It was shown that there are decreasing trends in the spring and monsoon rainfall and increasing trends in the autumn and winter rainfalls. These changes are not always homogeneous over various regions, even in the very short scales implying a careful regional analysis would be necessary for drawing conclusions regarding agro-ecological or other local projects requiring change in rainfall information. Furthermore, the differences between the trend magnitudes and directions from the two different methods are significantly small and fall well within the significance limit for all the cases investigated in Indian regions (except where noted). © 2010 Springer-Verlag.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Growth rate of YBCO single grains containing Y-2411(M)

Y-Ba-Cu-O (YBCO) single grains have the potential to generate large trapped magnetic fields for a variety of engineering applications, and research on the processing and properties of this material has attracted world-wide interest. In particular, the introduction of flux pinning centres to the large grain microstructure to improve its current density, Jc, and hence trapped field, has been investigated extensively over the past decade. Y 2Ba4CuMOx [Y-2411(M)], where M = Nb, Ta, Mo, W, Ru, Zr, Bi and Ag, has been reported to form particularly effective flux pinning centres in YBCO due primarily to its ability to exist as nano-size inclusions in the superconducting phase matrix. However, the addition of the Y-2411(M) phase to the precursor composition complicates the melt-processing of single grains. We report an investigation of the growth rate of single YBCO grains containing Y-2411(Bi) phase inclusions and Y2O3. The superconducting properties of these large single grains have been measured specifically to investigate the effect of Y2O3 on broadening the growth window of these materials. © 2010 IOP Publishing Ltd.

Wastage of low alloy steels in a fluidized bed environment

The wastage behaviour of four low alloy steels, suitable for use as evaporator tubing in industrial atmospheric fluidized bed combustors (AFBCs), was examined in a laboratory-scale test rig. Specimens exposed in the test apparatus experienced a high flux of impacts at low particle velocities similar to conditions in a FBC boiler. The influence of time, velocity and temperature on the wastage behaviour was examined and incubation times and velocity exponents were determined and their values discussed. Since high-temperature oxidation played an important role in this process, the short-term oxidation rate of each of the steels was measured. The mechanisms of material loss across the temperature range were discussed and the behaviour of the low alloy steels in the current work was compared with that of high alloy and stainless steels in earlier studies. © 1995.

Effects of mechanical and electrical coupling on the parametric sensitivity of mode localized sensors

We compare and contrast the effects of two distinctly different mechanisms of coupling (mechanical and electrical) on the parametric sensitivity of micromechanical sensors utilizing mode localization for sensor applications. For the first time, the strong correlation between mode localization and the phenomenon of 'eigenvalue loci-veering' is exploited for accurate quantification of the strength of internal coupling in mode localized sensors. The effects of capacitive coupling-spring tuning on the parametric sensitivity of electrically coupled resonators utilizing this sensing paradigm is also investigated and a mass sensor with sensitivity tunable by over 400% is realized. ©2009 IEEE.