Towards large-scale commercialization of fuel cells: robust design optimization considering dimensional uncertainties in the flow distributors

The present and future challenges that humanity is facing regarding consumption and supply of energy constitute the context of this research. The technology in which we are interested is the fuel cell, mainly because of its high efficiency for the conversion of fuels into electricity and heat. More specifically, we considered solid-oxide and polymer electrolyte fuel cells. To take part in the reduction of the consumption of fossil fuels and of the emissions of greenhouse gases and of pollutants, fuel cells should first become a more attractive alternative technology. The finality of this study is hence to tackle remaining obstacles hindering their large-scale commercialization; namely, to reach a balanced and competitive combination of production cost, lifetime, and density of performance. The originality of this research lies in the simultaneous tackling of these challenges via the management of uncertainties during the design of fuel cell stacks. The approach is hence to take actions "upstream" rather than "downstream". Particularly, a novelty is to account for the effect of the manufacturing variability on the homogeneity of the performance and for the related risk of degradation, or even failure. We focus on dimensional tolerances of the parts whose function is to distribute the flows as homogeneously as possible into the fuel cell. The technical objective is to find a robust optimal solution, i.e., a solution which is optimal also in terms of a lowered sensitivity to imperfections, such as geometrical distortions. Besides, this research also deals with the challenges associated with the management of uncertainties in the context of combining optimization of geometries (design) and modeling based on computational fluid dynamics. Taken alone, these techniques were proven to be powerful tools of analysis and of synthesis. They are however computer intensive. When used together, the insight they can offer is even greater, but we face, even with today's high-performance computing infrastructures, the dilemma of accuracy versus tractability, which is even more problematic in the context of uncertainty management as will be shown. Therefore, efforts were dedicated to find ways to unravel this dilemma, in the prospect of achieving the optimization, under uncertainty, of the design of fuel cells. In particular, approximate models were investigated, notably reduced-order modeling and meta-modeling techniques. The results of this research relate to both methodology and technology. Among the methodological results, surrogate models are evaluated (tractability vs. accuracy). Guidelines are given for the management of uncertainties in this context, and for future researches. From a technological point of view, it was shown, first, that accounting for dimensional tolerances in the design of fuel cells is crucial. Then, the effect of these uncertainties were quantified, giving clearer insight on the best ways to deal with them. Last but not least, optimization of the design was carried out accounting for the uncertainties. Deterministic optima were compared with stochastic optima, revealing weaknesses of the former and potential for improvements of the designs when considering, quantitatively, the uncertainties. Last, and maybe more important, while conducting these investigations, we were able to raise numerous original (or re-formulated) questions, giving birth to novel tracks for improvements and to a fertile ground for further researches.


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