Acoustic performance of hoses - A parametric study

Modeling of wave propagation in hoses, unlike in rigid pipes or waveguides, introduces a coupling between the inside medium, the hose wall, and the outside medium, This alters the axial wave number and thence the corresponding effective speed of sound inside the hose resulting in sound radiation into the outside medium, also called the breakout or shell noise, The existing literature on the subject is such that a hose cannot be integrated into the,whole piping system made up of sections of hoses, pipes, and mufflers to predict the acoustical performance in terms of transmission loss (TL), The present paper seeks to fill this gap, Three one-dimensional coupled wave equations are written to account for the presence of a yielding wall with a finite lumped transverse impedance of the hose material, The resulting wave equation can readily be reduced to a transfer matrix form using an effective wave number for a moving medium in a hose section, Incorporating the effect of fluid loading due to the outside medium also allows prediction of the transverse TL and the breakout noise, Axial TL and transverse TL have been combined into net TL needed by designers, Predictions of the axial as well as transverse TL are shown to compare well with those of a rigorous 3-D analysis using only one-hundredth of the computation time, Finally, results of some parametric studies are reported for engineers involved in the acoustical design of hoses. (C) 1996 Institute of Noise Control Engineering.

A two timescale stochastic approximation scheme for simulation-based parametric optimization

A two timescale stochastic approximation scheme which uses coupled iterations is used for simulation-based parametric optimization as an alternative to traditional "infinitesimal perturbation analysis" schemes, It avoids the aggregation of data present in many other schemes. Its convergence is analyzed, and a queueing example is presented.

Parametric study of MSW landfill settlement model

A newly developed and validated constitutive model that accounts for primary compression and time-dependent mechanical creep and biodegradation is used for parametric study to investigate the effects of model parameters on the predicted settlement of municipal solid waste (MSW) with time. The model enables the prediction of stress strain response and yield surfaces for three components of settlement: primary compression, mechanical creep, and biodegradation. The MSW parameters investigated include compression index, coefficient of earth pressure at-rest, overconsolidation ratio, and biodegradation parameters of MSW. A comparison of the predicted settlements for typical MSW landfill conditions showed significant differences in time-settlement response depending on the selected model input parameters. The effect of lift thickness of MSW on predicted settlement is also investigated. Overall, the study shows that the variation in the model parameters can lead to significantly different results; therefore, the model parameter values should be carefully selected to predict landfill settlements accurately. It is shown that the proposed model captures the time settlement response which is in general agreement with the results obtained from the other two reported models having similar features. (C) 2011 Elsevier Ltd. All rights reserved.

Multiscale stochastic approximation for parametric optimization of hidden Markov models

A two-time scale stochastic approximation algorithm is proposed for simulation-based parametric optimization of hidden Markov models, as an alternative to the traditional approaches to ''infinitesimal perturbation analysis.'' Its convergence is analyzed, and a queueing example is presented.

Design enhancement of the underwater noise control linings through a parsimonious parametric study

Control of sound transmission through the structure and reflection from the structure immersed in fluid media impose highly conflicting requirements on the design of the carpeted noise control linings. These requirements become even more stringent if the structure is expected to be moving with considerable speed particularly under intense hydrostatic pressure. Numerous configurations are possible for designing these linings. Therefore, in this paper, a few lining configurations are identified from the literature for parametric study so that the designer is provided with an environment to analyze and design the lining. A scheme of finite element analysis is used to analyze these linings for their acoustic performance. Commercial finite element software, NISA®, is used as a platform to develop a customized environment wherein design parameters of different configurations can be varied with consistency checks and generate the finite element meshes using the 8-noded hexahedral element. Four types of designs proposed and analysed here address the parameters of interest such as the echo reduction and the transmission loss. Study of the effect of different surface distributions of the cavities is carried out. Effect of static pressure on different designs is reported.

Analysis and Synthesis of Reverberation for Parametric Stereo Audio

We propose a parametric stereo coding analysis and synthesis directly in the MDCT domain using an analysis by synthesis parameter estimation. The stereo signal is represented by an equalized sum signal and spatialization parameters. Equalized sum signal and the spatialization parameters are obtained by sub-band analysis in the MDCT domain. The de-correlated signal required for the stereo synthesis is also generated in the MDCT domain. Subjective evaluation test using MUSHRA shows that the synthesized stereo signal is perceptually satisfactory and comparable to the state of the art parametric coders.

Single-machine scheduling with past-sequence-dependent setup times and learning effects: a parametric analysis

In this article, we consider the single-machine scheduling problem with past-sequence-dependent (p-s-d) setup times and a learning effect. The setup times are proportional to the length of jobs that are already scheduled; i.e. p-s-d setup times. The learning effect reduces the actual processing time of a job because the workers are involved in doing the same job or activity repeatedly. Hence, the processing time of a job depends on its position in the sequence. In this study, we consider the total absolute difference in completion times (TADC) as the objective function. This problem is denoted as 1/LE, (Spsd)/TADC in Kuo and Yang (2007) ('Single Machine Scheduling with Past-sequence-dependent Setup Times and Learning Effects', Information Processing Letters, 102, 22-26). There are two parameters a and b denoting constant learning index and normalising index, respectively. A parametric analysis of b on the 1/LE, (Spsd)/TADC problem for a given value of a is applied in this study. In addition, a computational algorithm is also developed to obtain the number of optimal sequences and the range of b in which each of the sequences is optimal, for a given value of a. We derive two bounds b* for the normalising constant b and a* for the learning index a. We also show that, when a < a* or b > b*, the optimal sequence is obtained by arranging the longest job in the first position and the rest of the jobs in short processing time order.

Improved parametric models and DPCM techniques for EGG data compiression

This paper presents a novel algorithm for compression of single lead Electrocardiogram (ECG) signals. The method is based on Pole-Zero modelling of the Discrete Cosine Transformed (DCT) signal. An extension is proposed to the well known Steiglitz-Hcbride algorithm, to model the higher frequency components of the input signal more accurately. This is achieved by weighting the error function minimized by the algorithm to estimate the model parameters. The data compression achieved by the parametric model is further enhanced by Differential Pulse Code Modulation (DPCM) of the model parameters. The method accomplishes a compression ratio in the range of 1:20 to 1:40, which far exceeds those achieved by most of the current methods.

A PPCA based non-parametric modeling and retrieval of PD signal buried in excessive noise

The problem of on-line recognition and retrieval of relatively weak industrial signals such as partial discharges (PD), buried in excessive noise, has been addressed in this paper. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) due to the overlapping broad band frequency spectrum of PI and PD pulses. Therefore, on-line, onsite, PD measurement is hardly possible in conventional frequency based DSP techniques. The observed PD signal is modeled as a linear combination of systematic and random components employing probabilistic principal component analysis (PPCA) and the pdf of the underlying stochastic process is obtained. The PD/PI pulses are assumed as the mean of the process and modeled instituting non-parametric methods, based on smooth FIR filters, and a maximum aposteriori probability (MAP) procedure employed therein, to estimate the filter coefficients. The classification of the pulses is undertaken using a simple PCA classifier. The methods proposed by the authors were found to be effective in automatic retrieval of PD pulses completely rejecting PI.

Theoretical gain optimization studies in 10.6 μm CO2–N2 gasdynamic lasers. IV. Further results of parametric study

Based on a method proposed by Reddy and Shanmugasundaram, similar solutions have been obtained for the steady inviscid quasi‐one‐dimensional nonreacting flow in the supersonic nozzle of CO2–N2–H2O and CO2–N2–He gasdynamic laser systems. Instead of using the correlations of a nonsimilar function NS for pure N2 gas, as is done in previous publications, the NS correlations are computed here for the actual gas mixtures used in the gasdynamic lasers. Optimum small‐signal optical gain and the corresponding optimum values of the operating parameters like reservoir pressure and temperature and nozzle area ratio are computed using these correlations. The present results are compared with the previous results and the main differences are discussed.

A Long Memory Process Based Parametric Modeling and Recognition of PD Signal

We address the problem of recognition and retrieval of relatively weak industrial signal such as Partial Discharges (PD) buried in excessive noise. The major bottleneck being the recognition and suppression of stochastic pulsive interference (PI) which has similar time-frequency characteristics as PD pulse. Therefore conventional frequency based DSP techniques are not useful in retrieving PD pulses. We employ statistical signal modeling based on combination of long-memory process and probabilistic principal component analysis (PPCA). An parametric analysis of the signal is exercised for extracting the features of desired pules. We incorporate a wavelet based bootstrap method for obtaining the noise training vectors from observed data. The procedure adopted in this work is completely different from the research work reported in the literature, which is generally based on deserved signal frequency and noise frequency.