RESUMO
The parallel measurements of wavelength dependent optical absorption, particle number size distribution have made by a multi wavelength photoacoustic spectrometer (4λ-PAS) and scanning mobility particle sizer (SMPS) respectively at different modes of a diesel engine using two different types of fuel. The thermal evolution of the emission was also investigated using posterior temperature treatment of emission. The bimodal size distribution of emitted particles at a set reference temperature has been observed regardless of the applied fuel at idle. However, the emitted particulate assembly had lognormal size distribution falls into the accumulation mode at all other defined engine modes and both fuel types. The total number- and volume concentration (TNC and TVC) showed retrograde tendency with the increasing torque and rpm independently of the applied fuel types. The TNC values decreased up to 50% for both fuels with engine operation changes from idle engine mode(em#1) to low engine mode(em#2). With further increase in torque and rpm of engine, the change in TNC is negligible. On the other hand, the TVC remains more or less the same for idle to low engine mode transition and increased more than 60% for high mode (em#3) transition. The Optical Absorption Coefficient (OAC) values measured at the operational wavelengths of the 4λ-PAS instrument decreased at all wavelengths with increasing rpm and torque. The wavelength dependency quantified by Aerosol Ängström Exponent (AAE) was applied here for qualitative analysis of the carbonaceous emission and showed decreased values towards the higher engine speed and torque output of the engine. The proposed technique can be used as real-time, precise and accurate measurement of light absorption by DPM aerosols, which opens up novel possibilities for the volatility and thermal evolution investigation of diesel emissions.
RESUMO
We provide a comprehensive multi-aspect study of the performance of a pipeline used by the LIGO-Virgo Collaboration for estimating parameters of gravitational-wave bursts. We add simulated signals with four different morphologies (sine-Gaussians (SGs), Gaussians, white-noise bursts, and binary black hole signals) to simulated noise samples representing noise of the two Advanced LIGO detectors during their first observing run. We recover them with the BayesWave (BW) pipeline to study its accuracy in sky localization, waveform reconstruction, and estimation of model-independent waveform parameters. BW localizes sources with a level of accuracy comparable for all four morphologies, with the median separation of actual and estimated sky locations ranging from 25°.1 to 30°.3. This is a reasonable accuracy in the two-detector case, and is comparable to accuracies of other localization methods studied previously. As BW reconstructs generic transient signals with SG wavelets, it is unsurprising that BW performs best in reconstructing SG and Gaussian waveforms. The BW accuracy in waveform reconstruction increases steeply with the network signal-to-noise ratio (S/Nnet), reaching a 85% and 95% match between the reconstructed and actual waveform below S/Nnet ≈ 20 and S/Nnet ≈ 50, respectively, for all morphologies. The BW accuracy in estimating central moments of waveforms is only limited by statistical errors in the frequency domain, and is also affected by systematic errors in the time domain as BW cannot reconstruct low-amplitude parts of signals that are overwhelmed by noise. The figures of merit we introduce can be used in future characterizations of parameter estimation pipelines.
