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contributor authorDehghanian, A.
contributor authorHosseini Sarvari, S. M.
date accessioned2019-02-28T11:00:38Z
date available2019-02-28T11:00:38Z
date copyright8/29/2017 12:00:00 AM
date issued2018
identifier issn0022-1481
identifier otherht_140_02_022701.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4251689
description abstractThe aim of this study is to present a reduced spectral line-based weighted sum of gray gases (SLW) model to simulate the radiation heat transfer in nongray media at high temperatures. Inverse approach is used to divide the absorption cross section band into a clear gas with one gray gas and two gray gases, which are called the S-1 and S-2 approaches, respectively. The unknown absorption cross sections are determined from the knowledge of measured total incident intensities received by wall surfaces. In order to simulate the exact solution of radiation heat transfer in nongray gaseous media, the discrete transfer method (DTM) in combination with S-20 model is used, where the nongray medium is replaced with a set of a clear gas and 20 gray gases. The inverse problem is formulated as an optimization problem to minimize a least square objective function, which is solved by the conjugate gradient method (CGM). The accuracy of the present method is verified by comparing with previous researches and the S-20 approach with a large number of gray gases. The effects of noisy data on the inverse solution are investigated by considering an extreme case with large measurement error. The results show that the unknown absorption cross sections are retrieved well, even for noisy data.
publisherThe American Society of Mechanical Engineers (ASME)
titleInverse Estimation of Main Parameters of Spectral Line-Based Weighted Sum of Gray Gases Model With Few Gray Gases to Simulate the Radiation in Nongray Media
typeJournal Paper
journal volume140
journal issue2
journal titleJournal of Heat Transfer
identifier doi10.1115/1.4037496
journal fristpage22701
journal lastpage022701-10
treeJournal of Heat Transfer:;2018:;volume( 140 ):;issue: 002
contenttypeFulltext


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