基于物理信息神经网络和发射光谱测量的火焰多参数反演:模型构建与实验验证  

Retrieval of Multiple Flame Parameters Based on Physics-Based Neural Network and Emission Spectrum Measurement:Model Development and Experimental Validation

作　　者：李洪绪 陈玮 张辰飚 任涛 Li Hongxu;Chen Wei;Zhang Chenyang;Ren Tao(China-UK Low Carbon College,Shanghai Jiao Tong University,Shanghai 201306,China)

机构地区：[1]上海交通大学中英国际低碳学院,上海201306

出　　处：《光学学报》2025年第1期291-302,共12页Acta Optica Sinica

基　　金：国家自然科学基金(52276077)。

摘　　要：提出一种基于物理信息驱动的神经网络逆辐射模型,用于从红外发射光谱测量中准确重建火焰内部的温度场和多组分摩尔分数场。该模型将测量系统的物理信息耦合到神经网络的训练过程中,使其优化目标既能与测量数据匹配,又符合物理方程,从而无需训练数据集或复杂的反演算法。利用模拟的发射光谱重建乙烯层流扩散火焰的温度,二氧化碳、一氧化碳、水的摩尔分数,以及碳烟体积分数分布。结果表明该模型具有较高的反演精度和较强的抗噪声鲁棒性。通过傅里叶变换红外光谱仪测量乙烯火焰的中红外辐射强度,进一步验证了该模型在实验火焰标量场反演重建中的有效性。基于物理信息驱动的神经网络逆辐射模型能够有效地从红外光谱数据中重建火焰内部的多个标量场,显示出高精度和强鲁棒性。Objective Mid-infrared hyperspectral emission measurements provide wide-band,highly detailed spectral information,enabling spatial distribution retrieval of multiple scalar values in combustion flames.However,inferring temperature and species concentrations from these spectra poses significant challenges due to the nonlinear,ill-posed,and potentially highdimensional nature of the related inverse problems.The ill-posedness can result in slow convergence and high sensitivity to initial parameter guesses and experimental noise.It is often necessary to introduce appropriate prior information and apply regularization constraints to yield physically reasonable and stable retrieval results.However,in the reconstruction of multiple fields,it is challenging to accurately define prior conditions and effectively incorporate them into the model.Additionally,the choice of regularization methods and the tuning of parameters significantly influence the retrieval results.As a result,traditional methods struggle to achieve accurate and simultaneous reconstruction of temperature and multispecies concentrations in combustion fields.Artificial neural networks provide a promising solution by learning complex,implicit relationships between input and output data without explicit modeling of the underlying physical and chemical laws required.This capability makes them particularly well-suited for nonlinear inverse problems.However,while data-driven approaches have shown potential in solving various inverse problems,they often rely on extensive datasets from experiments or simulations to build a supervised training database.Consequently,neural networks are frequently treated as“black boxes”,with their predictive capability heavily dependent on the training data.While these models may generalize well within data-rich regions,they often struggle with accurate predictions for data outside the training distribution.Meanwhile,transferring a neural network trained on simulation datasets to predict experimental data faces challenges such as

关 键 词：中红外发射光谱 物理信息神经网络 温度 气体摩尔分数 碳烟体积分数 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]