Efficient optimization design of flue deflectors through parametric surrogate modeling with physics-informed neural networks

In engineering applications, deflectors play a vital role in regulating the uniformity of flow field distribution in the selective catalytic reduction (SCR) system, and their optimal design is a topic of great concern. However, traditional optimal design methods often suffer from insufficient prediction accuracy or too high computational cost. This paper develops and verifies an efficient and robust parametric surrogate model for SCR systems based on the physics-informed neural networks (PINNs) framework. This study comprises three progressive steps. (1) We predicted the flow field distribution in the original flue based on the PINNs framework and compared the results qualitatively and quantitatively with the traditional computational fluid dynamics (CFD) method. The results show that the maximum relative error of velocity is 12.6%, and the relative error is within 5% in most areas. (2) For the optimal design of the deflector in the SCR system, a parametric surrogate model based on the PINNs framework is developed, and the model inputs include not only the coordinate variables but also the position parameters of the deflector. The accuracy and efficiency of this parametric surrogate model are also compared with the traditional CFD method. (3) Based on the parametric surrogate model developed above, the deflector optimal position for the research object of this study is found through two quantitative indicators (uniformity coefficient and flue gas energy loss). The results demonstrate that the parameterized model based on PINNs can reduce the computational time to about 14% compared to traditional methods. Finally, the sensitivity analysis of the deflector position parameters is carried out. Overall, the results of this study demonstrate that the parametric surrogate model based on the PINNs framework is an efficient and robust tool for system optimization, design, and autonomous control.