Simple Full-Spectrum Correlated k-Distribution Model based on Multilayer Perceptron

• URL: http://arxiv.org/abs/2403.12993v1
• Date: Tue, 5 Mar 2024 08:04:01 GMT
• Title: Simple Full-Spectrum Correlated k-Distribution Model based on Multilayer Perceptron
• Authors: Xin Wang, Yucheng Kuang, Chaojun Wang, Hongyuan Di, Boshu He,
• Abstract summary: The simple FSCK (SFM) model is developed to compensate among accuracy, efficiency and storage. Several test cases have been carried out to compare the developed SFM model and other FSCK tools including look-up tables and traditional FSCK (TFM) model. Results show that the SFM model can achieve excellent accuracy that is even better than look-up tables at a tiny computational cost.
• Score: 6.354085763851961
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While neural networks have been successfully applied to the full-spectrum k-distribution (FSCK) method at a large range of thermodynamics with k-values predicted by a trained multilayer perceptron (MLP) model, the required a-values still need to be calculated on-the-fly, which theoretically degrades the FSCK method and may lead to errors. On the other hand, too complicated structure of the current MLP model inevitably slows down the calculation efficiency. Therefore, to compensate among accuracy, efficiency and storage, the simple MLP designed based on the nature of FSCK method are developed, i.e., the simple FSCK MLP (SFM) model, from which those correlated k-values and corresponding ka-values can be efficiently obtained. Several test cases have been carried out to compare the developed SFM model and other FSCK tools including look-up tables and traditional FSCK MLP (TFM) model. Results show that the SFM model can achieve excellent accuracy that is even better than look-up tables at a tiny computational cost that is far less than that of TFM model. Considering accuracy, efficiency and portability, the SFM model is not only an excellent tool for the prediction of spectral properties, but also provides a method to reduce the errors due to nonlinear effects.

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