An Improved Full-Spectrum Correlated-k-Distribution Model for Non-Gray Radiative Heat Transfer in Combustion Gas Mixtures

Abstract

To improve the efficiency of full-spectrum correlated-k-distribution (FSCK), a new method FSCK-RSM has been proposed based on response surface methodology (RSM) in this paper. The k-distributions of FSCK was used to fit the response surface model based on radial basis function and the radiative calculation efficiency was improved by avoiding multiple computations and interpolation in the FSCK-RSM. The thermal radiation heat transfer of five combustion gases (H2O, CO2, CO, C2H2 and C2H4) in a one-dimensional layer was investigated and the radiative sources calculated by the LBL, SNB, FSCK and FSCK-RSM methods were given at different distributions of temperature and gas concentration. The results showed that the needed amount of input data was reduced by 677 times using FSCK-RSM comparing to the FSCK and the maximum of the average normalized deviation for FSCK-RSM was 2.46% in the non-isothermal homogeneous medium. The model was finally used for radiation reabsorption calculations in planar C2H4/O2/N2/CO2 flames with full coupling to heat transfer and multi-species chemistry. The computational time using the FSCK-RSM was found to be at most half of that using the FSCK method. This FSCK-RSM model was an effective method for addressing the radiation problems that occur in combustion systems.

Department(s)

Mechanical and Aerospace Engineering

Comments

This research was supported by the National Key Research Development Program of China (No. 2017YFB0601900), the National Natural Science Foundation of China (No. 51976057, 51922040 and 51827808), the China Scholarship Council and the Fundamental Research Funds for the Central Universities (No. 2017ZZD005). RS acknowledges the Swiss National Science Foundation postdoctoral fellowship (Award Number 178619).

Keywords and Phrases

Efficiency improvement; Full-spectrum correlated-k-distribution (FSCK); Planar flames; Radiative heat transfer

International Standard Serial Number (ISSN)

0735-1933

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2020 Elsevier Ltd, All rights reserved.

Publication Date

01 May 2020

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