The flow field plays a significant role in the performance of proton exchange membrane (PEM) fuel cells. However, its complex structure leads to unacceptable development costs and time commonly using the trial-and-error method based on many experiments. Herein, we propose a digitally-assisted method to accelerate the development process and reduce costs. Comprehensive experiments and tests are conducted using the commercial-size PEM fuel cell with an active area of 332 cm², including the investigation of polarization curves, five sensitivity parameters under seven different current densities, and spatial distributions. A high-resolution printed circuit board with 408 segments of 0.8 cm² is employed to explore the current density distribution. The commercial-size PEM fuel cell is further digitalized with a self-developed fuel cell numerical model, which is strictly verified in terms of all experimental data. The digital multi-physics information inside PEM fuel cells is obtained and evaluated via this efficient numerical model in order to search for the structure defects quickly and accurately. Afterwards, targeted structure optimization is effectively carried out to achieve a better performance, with the maximum deviation of oxygen concentration in the channels decreasing from 26.33% to 3.78%. This digital method is very valuable for the forward design of flow field structures to considerably reduce the development cost and time.

中文翻译：

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大型质子交换膜燃料电池的数字辅助结构设计


流场在质子交换膜 （PEM） 燃料电池的性能中起着重要作用。然而，其复杂的结构导致了不可接受的开发成本和时间，通常使用基于许多实验的试错法。在此，我们提出了一种数字辅助方法来加速开发过程并降低成本。使用有效面积为 332 cm² 的商用尺寸 PEM 燃料电池进行了全面的实验和测试，包括极化曲线、七种不同电流密度下的五个灵敏度参数和空间分布的研究。采用具有 408 个 0.8 cm² 段的高分辨率印刷电路板来探索电流密度分布。商用尺寸的 PEM 燃料电池通过自主开发的燃料电池数值模型进一步数字化，该模型在所有实验数据方面都经过严格验证。通过这种高效的数值模型获得和评估 PEM 燃料电池内部的数字多物理场信息，以便快速准确地搜索结构缺陷。随后，有效地进行靶向结构优化以获得更好的性能，通道中氧浓度的最大偏差从 26.33% 降低到 3.78%。这种数字方法对于流场结构的正向设计非常有价值，可以大大降低开发成本和时间。