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Srinivasan K
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Srinivasan K
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Srinivasan K
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Srinivasan, K.
Srinivasan, Kothandaraman
Kothandaraman, Srinivasan
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2 results
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- PublicationInvestigation of acoustic spectral variations in the pool boiling regimes of water on wire heater(25-05-2023)
;Barathula, Sreeram ;Alapati, Jaswanth K.K.This paper presents the experimental investigation of acoustic spectral variations in saturated pool boiling regimes of water on a heated wire. In the current study, two different wires of standard wire gauge (SWG) 36 and 42 are considered to investigate the acoustic spectral variations through the pool boiling curve. The amplitude and frequency changes are evaluated for each regime of pool boiling. In a single regime, amplitude rise is observed with respect to the heat flux without any significant change in dominant frequencies. On the other hand, frequency shifts are observed in regime transitions. A change in the diameter of the heater wire has no significant effect on the boiling acoustic spectra. However, the number of high-frequency components increased for the SWG – 42 than the SWG – 36 wire. A frequency peak near 2000 Hz is found to be crucial for boiling regime identification. The sound pressure level (SPL) for SWG – 36 is higher than the SWG – 42, and it is further noted that SPL follows an ‘N’ shaped pattern for both wires owing to the frequency shifts and variation of mean bubble departure diameter at that heat flux. - PublicationEvaluation of regularization methods for acoustic pyrometry(01-07-2022)
;Chaitanya, S. K. ;Alapati, Jaswanth K.K.Measurement of temperature distribution is vital in boilers, heat exchangers, and other industrial applications. Acoustic pyrometry offers the advantage of measuring the temperature in the entire domain in a non-intrusive manner. Acoustic pyrometry involves estimating the temperature of the domain using the time of flight information between the transceivers. Since acoustic pyrometry is an inverse problem, it is sensitive to noise and the number of cells in the domain. Regularization methods help in obtaining feasible solutions. Therefore, the performance of four regularization methods, namely, Tikhonov, modified Tikhonov, Total variation (TV), and iterative reweighted least-squares (IRLS) in reconstructing three different temperature profiles, is studied and compared against the pseudo-inverse method. The effect of noise and the cell ratio (CR) on temperature reconstruction is also studied. Overall, the best results are observed for the coarsest cell ratio and modified Tikhonov with sharp filter regularization.