Tomographic Reconstruction of the Local PDFS of Soot Volume Fraction and Temperature
Y. R. Sivathanu, Anthony Hamins, Robert Hagwood, Takashi Kashiwagi
Deconvolution of local properties from line-of-sight measurements is important in a wide variety of applications such as x-ray tomography, nuclear magnetic resonance imaging, atmospheric sciences, optical inteferometry and flow field diagnostics. The Radon Transforms form the theoretical basis for retrieving local properties from path integrated measurements under steady state conditions. These methods have found wide-spread application in tomographic spectroscopy of laminar flames. For turbulent flow fields, conventional deconvolution algorithms cause greater difficulty due to the transient nature of the phenomena being studied. Progress has been made in obtaining ultra-fast multiple angle and multiple ray measurements in a turbulent flow field over a small time interval. This technique has limited temporal resolution and suffers from a high degree of deconvolution noise due to the asymmetric nature of the instantaneous flow field. Recently, a discrete probability function (DPF) method was developed to deconvolute path integrated measurements in order to obtain the local PDFs of soot volume fractions in turbulent flames. The objective of this work is to extend the DPF method to obtain local PDFs of soot volume fraction and temperature from path integrated measurements of emission intensities. The deconvolution method is evaluated by synthetic noise-free data as well as experimental data obtained using an intrusive optical pyrometer.
Combustion Institute/Central and Western States (USA) and Combustion Institute/Mexican National Section and American Flame Research Committee
, Hamins, A.
, Hagwood, R.
and Kashiwagi, T.
Tomographic Reconstruction of the Local PDFS of Soot Volume Fraction and Temperature, Combustion Institute/Central and Western States (USA) and Combustion Institute/Mexican National Section and American Flame Research Committee, San Antonio, TX, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=909968
(Accessed February 25, 2024)