PINN-Based Modeling Approach for Combustion of Low-concentration Chemical Species Forward and Inverse Problems

ZHANG Weina; LI Gangyang; CUI Hongyang; BI Zhongqin; DAI Dan

doi:10.19805/j.cnki.jcspe.2026.250709

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PINN-Based Modeling Approach for Combustion of Low-concentration Chemical Species Forward and Inverse Problems

更新时间：2026-10-06

- PINN-Based Modeling Approach for Combustion of Low-concentration Chemical Species Forward and Inverse Problems
- Chinese Journal of Power Engineering Vol. 46, Issue 3, Pages: 95-105(2026)
- 作者机构：
  
  1. 上海电力大学计算机科学与技术学院,上海,201306
  2. 中国联合重型燃气轮机技术有限公司,北京,100016
  3. 阿斯顿大学计算机科学与数字技术学院,伯明翰,英国,B4 7ET
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2026.250709
  CLC：
- Online First：10 March 2026，
  
  Published：10 March 2026
- 稿件说明：
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张伟娜,李港扬,崔弘杨,毕忠勤,戴丹. 基于PINN的含低浓度化学物种燃烧正逆问题建模方法动力工程学报, 2026, 46(3): 95-105 https://doi.

org/10.19805/j.cnki.jcspe.2026.250709
张伟娜,李港扬,崔弘杨,毕忠勤,戴丹. 基于PINN的含低浓度化学物种燃烧正逆问题建模方法动力工程学报, 2026, 46(3): 95-105 https://doi. DOI： 10.19805/j.cnki.jcspe.2026.250709.

org/10.19805/j.cnki.jcspe.2026.250709 DOI：

摘要

随着燃气轮机向更高效、更清洁方向发展

基于高保真模型的燃烧室设计需求日益迫切。提出基于变分自编码器火焰(variational auto-encoder flame

VAEF)的物理信息神经网络(physics-informed neural networks

PINN)对含低浓度化学物种的层流预混火焰正逆问题进行建模:通过基于自适应上下界或观测数据的约束映射;通过平滑窗划分计算域与局部加权;在稀疏观测条件下的逆问题中将变分自编码器重构误差与相对熵项加入观测条件损失计算中。结果表明:以上措施显著提升了训练稳定性与低浓度物种预测精度

PINN模型的决定系数均大于0.95

相对误差绝对值均小于15%

展现良好的全局拟合精度和参数反演能力

为复杂化学机理下的燃烧模拟与排放监测提供了鲁棒性更好的PINN方案。

Abstract

As gas turbines progress towards higher efficiency and cleaner performance

the demand for combustion chamber designs grounded in high-fidelity models is becoming increasingly pressing. To meet this demand

a physics-informed neural network (PINN) based on variational auto-encoder flame (VAEF) was proposed for modeling both the forward and inverse problems of laminar premixed flames containing low-concentration chemical species. Following strategies were employed in this method: constrained mapping was implemented based on adaptive upper and lower boundaries or observational data; the computational domain was partitioned using smoothing windows with regional weighting; for inverse problems under sparse observation conditions

the variational auto-encoder reconstruction error and the Kullback-Leibler (KL) divergence term were incorporated into the calculation of the observation condition loss. Results indicate that these measures substantially enhance training stability and prediction accuracy for low-concentration species. A coefficient of determination greater than 0.95 and an relative error below 15% are achieved by PINN model

demonstrating excellent global fitting precision and parameter inversion capability. A PINN solution with better robust performance is thus provided for combustion simulation and emission monitoring under complex chemical mechanisms.

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