Estimation Of Source Characteristics Research Articles

Direct estimation and experimental validation of the acoustic source characteristics of two-cylinder naturally aspirated diesel engine exhaust system

In this paper, acoustic source characteristics of the exhaust system of two-cylinder naturally aspirated engines are estimated using a novel direct method. Each cylinder (the cylinder cavity and exhaust valve) is a non-linear time-variant acoustic source for the exhaust system. The source impedance of each cylinder is estimated assuming that the other cylinder is not exhausting at the same time. In order to work with linear time-invariant model of the acoustic source, the impedance of each cylinder is assumed as the reciprocal of source admittance averaged over different crank-angle steps. The source pressure is product of acoustic mass velocity at the source-load junction and the sum of the source impedance and the load impedance. Computation of source impedance and acoustic mass velocity calls for the aero-thermodynamic computation in the time-domain making use of the values of pressure and temperature of the in-cylinder gas at the exhaust valve opening (EVO) along with the basic geometrical details of the cylinder, intake and exhaust valves and ports. The estimated source characteristics for each cylinder at the downstream of exhaust valve are then transferred downstream to the end of the runner. The source characteristics so obtained are then combined for the two cylinders. These values are then transferred to the beginning of the acoustic test load (exhaust pipe or exhaust muffler). The estimation of source characteristics is dependent on the exhaust mass flow rate which depends on the exhaust system to which cylinders are discharging. Thus, the IC engine acoustic source is load-dependent, not unique. Yet, the resultant source characteristics help to predict unmuffled sound pressure level (SPL) spectra that agree reasonably with the experimentally measured spectra.

Single-Channel Blind Separation Using Pseudo-Stereo Mixture and Complex 2-D Histogram

A novel single-channel blind source separation (SCBSS) algorithm is presented. The proposed algorithm yields at least three benefits of the SCBSS solution: 1) resemblance of a stereo signal concept given by one microphone; 2) independent of initialization and a priori knowledge of the sources; and 3) it does not require iterative optimization. The separation process consists of two steps: 1) estimation of source characteristics, where the source signals are modeled by the autoregressive process and 2) construction of masks using only the single-channel mixture. A new pseudo-stereo mixture is formulated by weighting and time-shifting the original single-channel mixture. This creates an artificial mixing system whose parameters will be estimated through our proposed weighted complex 2-D histogram. In this paper, we derive the separability of the proposed mixture model. Conditions required for unique mask construction based on maximum likelihood are also identified. Finally, experimental testing on both synthetic and real-audio sources is conducted to verify that the proposed algorithm yields superior performance and is computationally very fast compared with existing methods.

Hybrid stochastic finite fault modeling of 2003, M6.5, Bam earthquake (Iran)

Accelerographic time series of the M ∗ 6.5 Bam (Iran) earthquake of December 26, 2003, are used to calibrate the source and propagation path characteristics based on a hybrid stochastic approach, which includes stochastic finite fault and analytical modeling. Estimation of source characteristics is based on the calibration of finite-fault modeling to near-source observed time series, while propagation characteristics are estimated using far-field recorded ones. The distance-dependent Kappa factor is obtained from the slope of smoothed amplitude of acceleration Fourier spectrum at higher frequencies. The estimated zero-distance Kappa value is 0.06. Calibration based on near-source time series indicates a stress drop of 130 bars for the Bam earthquake. The strong impulsive long-period motion, recorded at the only near-source station, is modeled using analytical modeling of Mavroeidis and Papageorgiou (2003).