用CNN和MLP加速2D炉膛辐射传热模拟，速度提升显著且误差可接受。
Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Participative Gases
- 将气体与壁面属性编码适配CNN结构，用于图像类建模
- 相比传统求解器，速度提升明显，相对误差在工业可接受范围
- CNN精度更高、对超参数更鲁棒，适合工程仿真加速
为降低数值模拟计算成本，本文引入卷积神经网络（CNN）与多层感知机（MLP）构建代理模型，近似求解含光谱参与性气体的二维炉膛辐射传热问题。创新点在于将问题输入（气体与壁面特性）重新编码以适配常用于图像处理的CNN架构。使用经典求解器ICARUS2D（基于离散传递法与统计窄带模型）生成两个高精度数据集。通过Optuna优化超参数，对比了CNN与MLP在速度与精度上的表现。结果表明，两种架构均实现显著加速，相对误差处于工业可接受水平；且CNN在精度上优于MLP，对超参数变化更具鲁棒性。还分析了数据集规模对模型性能的影响，深化了对模型行为的理解。
原文摘要 · Abstract (English)
Aiming to reduce the computational cost of numerical simulations, a convolutional neural network (CNN) and a multi-layer perceptron (MLP) are introduced to build a surrogate model to approximate radiative heat transfer solutions in a 2-D walled domain with participative gases. The originality of this work lays in the adaptation of the inputs of the problem (gas and wall properties) in order to fit with the CNN architecture, more commonly used for image processing. Two precision datasets have been created with the classical solver, ICARUS2D, that uses the discrete transfer radiation method with the statistical narrow bands model. The performance of the CNN architecture is compared to a more classical MLP architecture in terms of speed and accuracy. Thanks to Optuna, all results are obtained using the optimized hyper parameters networks. The results show a significant speedup with industrially acceptable relative errors compared to the classical solver for both architectures. Additionally, the CNN outperforms the MLP in terms of precision and is more robust and stable to changes in hyper-parameters. A performance analysis on the dataset size of the samples have also been carried out to gain a deeper understanding of the model behavior.
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