A Dual-Path neural network model to construct the flame nonlinear thermoacoustic response in the time domain
<|reference_start|>A Dual-Path neural network model to construct the flame nonlinear thermoacoustic response in the time domain: Traditional numerical simulation methods require substantial computational resources to accurately determine the complete nonlinear thermoacoustic response of flames to various perturbation frequencies and amplitudes. In this paper, we have developed deep learning algorithms that can construct a comprehensive flame nonlinear response from limited numerical simulation data. To achieve this, we propose using a frequency-sweeping data type as the training dataset, which incorporates a rich array of learnable information within a constrained dataset. To enhance the precision in learning flame nonlinear response patterns from the training data, we introduce a Dual-Path neural network. This network consists of a Chronological Feature Path and a Temporal Detail Feature Path. The Dual-Path network is specifically designed to focus intensively on the temporal characteristics of velocity perturbation sequences, yielding more accurate flame response patterns and enhanced generalization capabilities. Validations confirm that our approach can accurately model flame nonlinear responses, even under conditions of significant nonlinearity, and exhibits robust generalization capabilities across various test scenarios.<|reference_end|>
@article{wu2024a,
    title={A Dual-Path neural network model to construct the flame nonlinear
  thermoacoustic response in the time domain},
    author={Jiawei Wu, Teng Wang, Jiaqi Nan, Lijun Yang, Jingxuan Li},
    journal={arXiv preprint arXiv:2409.05885},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.05885},
    primaryClass={cs.LG cs.CE}
}