Titre :

On the use of recurrent neural networks for fast and accurate non-uniform gas radiation modeling

Auteur(s) :

Andrea, Frédéric [Auteur]
Centre d'Energétique et de Thermique de Lyon [CETHIL]
Cornet, Celine [Auteur]
Laboratoire d'Optique Atmosphérique (LOA) - UMR 8518
Delage, Cindy [Auteur]
Centre d'Energétique et de Thermique de Lyon [CETHIL]
Dubuisson, Philippe [Auteur]
Laboratoire d'Optique Atmosphérique (LOA) - UMR 8518
Galtiera, Mathieu [Auteur]
Centre d'Energétique et de Thermique de Lyon [CETHIL]

Titre de la revue :

Journal of Quantitative Spectroscopy and Radiative Transfer

Nom court de la revue :

J. Quant. Spectrosc. Radiat. Transf.

Numéro :

293

Pagination :

-

Date de publication :

2022-11-13

ISSN :

0022-4073

Mot(s)-clé(s) en anglais :

Gas radiation
Godson
L-distribution
Propagative form
Recurrent neural network

Discipline(s) HAL :

Planète et Univers [physics]/Océan, Atmosphère

Résumé en anglais : [en]

This paper focuses on the construction of a fast though accurate gas radiation model based on a Recurrent neural network (RNN) formulation. The model is founded on recent works in which a solution to a non-uniform technique ...
Lire la suite >This paper focuses on the construction of a fast though accurate gas radiation model based on a Recurrent neural network (RNN) formulation. The model is founded on recent works in which a solution to a non-uniform technique proposed by Godson in the 50s was derived explicitly. The method uses a non-linear transformation of a set of physical / geometrical paths, directly related to a non-uniform path which is first discretized into uniform sub-layers, into a sequence of equivalent absorption lengths. This process, studied thoroughly within the frame of the development of the -distribution approach, can be naturally handled using an algorithm that takes the form of an RNN model. The method is assessed against LBL calculations in non-uniform high temperature gaseous media and found to provide more accurate results than a CKD (Correlated K-Distribution) model with 16 gray gases. This paper is the first to suggest an RNN to treat radiative transfer in non-uniform gaseous media. Moreover, all the weights involved in the RNN have a clear physical meaning so that the structure of the RNN can be readily interpreted, avoiding the black box disadvantage of most brute force machine learning strategies.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS

Collections :

• Laboratoire d'Optique Atmosphérique (LOA) - UMR 8518

Date de dépôt :

2024-01-16T22:52:26Z
2024-02-14T09:11:03Z