Applications of Artificial Intelligence for Analysis of Two-Phase Flow in PEM Fuel Cell Flow Fields
Authors: Santamaria Anthony D. , Mortazavi Mehdi , Chauhan Vedang , Benner Jingru , Philbrick Oliver , Clemente Riccardo , Jia Hongfei , Ling Chen 
Published: December 2020

Many electrochemical devices rely upon flow-field channels to transport reactants and byproducts to and from critical electrode sites. Often, this results in multiphase flow, which, if not managed properly, can reduce system performance and lifespan. In the case of PEM fuel cells, two-phase flow occurs when liquid water forms as a byproduct of the cathode oxygen reduction reaction. Over the past two-decades much effort has been invested to understand the nature of this stochastic transport phenomena and how to optimize flow-field design to mitigate its effects. This work probes the use of artificial intelligence to guide our understanding of complex relationships between liquid phase behavior and pressure drop in these reactant channels [1]. Using a supervised decision tree (DT) machine learning algorithm, the liquid distributions in ex-situ experimental reactant channels are correlated to the induced pressure drop for a range of air flow rates; this process is diagrammed in Fig. 1. Liquid phase data collection is accomplished by capturing 2D images via a CCD camera of liquid water distributions in a transparent flow channel simulating PEM fuel cell conditions. In this setup, liquid water is injected through the bottom of the GDL in the same manner that water must travel from the catalyst layer through the GDL to reach the channel an operating cell. Pressure transducers measure pressure drop along the length of the channel and are synchronized with the image collection. The images are processed for noise reduction and feature extraction and then used with pressure data to train a regression DT. The models produced using the DT show the potential to improve predictions over earlier methods that relied solely upon liquid area [2]. Overall, the DT models predicted pressure drop using liquid distributions with 88.9% to 98.54% accuracy and were able to capture complex behavior such as pressure changes due to the breakup of liquid slugs. In general, the methods may be valuable for attaining deeper understanding of the impact variables such as geometry, hydrophobicity, and system operating conditions have on two-phase flow pressure drop trends. Additionally, these models may serve as a validation tool for numerical efforts aimed at flow-field design. A reliable model that can accurately predict the liquid-gas two-phase flow pressure drop may be used to estimate the amount and potentially the distribution of the water content within the flow channel by inputting the pressure drop measured during the operation of the cell.

Figure 1: Overview of the regression DT training and testing processes.

[1] Santamaria AD, Mortazavi M, Chauhan V, Benner J. Two-Phase Flow Characterization in PEM Fuel Cells Using Machine Learning. In Meeting Abstracts 2019 May 1 (No. 30, pp. 1538-1538). The Electrochemical Society.

[2] Hussaini IS, Wang CY. Visualization and quantification of cathode channel flooding in PEM fuel cells. Journal of Power Sources. 2009 Feb 15;187(2):444-51.

Figure 1