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首页> 外文期刊>Journal of power sources >Advanced computational tools for PEM fuel cell design Part 1. Development and base case simulations
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Advanced computational tools for PEM fuel cell design Part 1. Development and base case simulations

机译：用于PEM燃料电池设计的高级计算工具，第1部分。开发和基础案例模拟

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摘要

This paper reports on the development and numerical implementation of a comprehensive 3D computational fuel cell dynamics code for PEMFCs. The code solves a set of coupled non-linear conservation equations (mass, momentum, species, energy, electrical potential and liquid water saturation) for an entire unit cell. A phenomenological model for water transport in the membrane is solved separately for the membrane domain, in conjunction with calculation of the water content on the boundary such that that water balance is satisfied on both sides of the membrane interface, and the numerical implementation of the model is validated against an analytical solution. The global polarization curve predicted with the CFD code is found to compare favorably with reported data. A detailed validation of the CFD code against spatially resolved experimental data is presented in a companion Part 2 paper, and in this paper base case simulations for a unit cell with straight channels are presented to illustrate and analyze basic physical features, transport of species along the channel and coupling between heat and mass transfer processes. Analysis of the results shows that many of the variables of interest, including mass fractions and current densities, exhibit similar profiles along the channel, which suggests that reduced dimensional model based on appropriate similarity variables might be suitable for rapid calculations.
机译：本文报道了用于PEMFC的全面3D计算燃料电池动力学代码的开发和数值实现。该代码解决了整个单元电池的一组耦合的非线性守恒方程（质量，动量，种类，能量，电势和液态水饱和度）。结合膜边界处的水含量计算，使膜界面两侧的水平衡得到满足，并结合该模型的数值实现，分别针对膜域求解膜中水传输的现象学模型。已针对分析解决方案进行了验证。发现使用CFD代码预测的全局极化曲线可以与报告的数据进行比较。在第二部分的配套文件中，针对CFD代码针对空间解析的实验数据进行了详细的验证，并在本文中针对具有直通道的基本单元进行了基本案例模拟，以说明和分析基本物理特征，物种沿物种的运输。传热过程之间的通道和耦合。结果分析表明，许多感兴趣的变量（包括质量分数和电流密度）沿通道显示相似的轮廓，这表明基于适当相似性变量的降维模型可能适合快速计算。

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