ABSTRACT

Seven structural parameters that affected the heat transfer performance were proposed as optimization variables to enhance the heat transfer and thermal reaction capacity of the thin double-layered annular zirconium–cobalt (ZrCo) bed of hydrogen metal reactor and to improve the hydrogen storage performance. We used the Taguchi method to conduct numerical experimental sampling and construct a three-dimensional model of the hydrogen absorption of a hydrogen storage bed. COMSOL software was used to simulate 36 models of the hydrogen absorption process and changes in the temperature and time of the hydrogen storage bed under different conditions were identified. A new hybrid method combining a neural network and the genetic algorithm was proposed by taking the hot-spot temperature of the bed and the cooling time when it was cooled to 300 K as the optimization objectives. The algorithm was implemented, and the relationship between the process parameters and the objective function was established. A model response analysis was conducted to improve the understanding of the behavior of the backpropagation model and to analyze the sensitivity of the parameters. This hybrid method was used to optimize the parameters to obtain an excellent hydrogen storage performance. The results showed that the predicted value of the neural network model was highly consistent with the numerical simulation results. The number of cooling tubes had the greatest impact on the heat transfer performance, and the optimal combination of the input parameters was obtained. When the optimized parameters were used to reach the target temperature, the cooling time was reduced by 78s, which provided guidance for the design and operation of hydrogen storage beds.

摘要

提出了影响传热性能的七个结构参数作为优化变量，以提高氢金属反应器薄双层环形锆钴 (ZrCo) 床的传热和热反应能力，并提高储氢性能。我们采用田口方法进行数值实验采样，构建了储氢床吸氢三维模型。利用COMSOL软件模拟了36个吸氢过程模型，识别了不同条件下储氢床温度和时间的变化。以床的热点温度和冷却到300 K时的冷却时间为优化目标，提出了一种神经网络与遗传算法相结合的新混合方法。实现了算法，建立了工艺参数与目标函数的关系。进行模型响应分析以提高对反向传播模型行为的理解并分析参数的敏感性。该混合方法用于优化参数以获得优异的储氢性能。结果表明，神经网络模型的预测值与数值模拟结果高度一致。冷却管的数量对传热性能的影响最大，并得到输入参数的最优组合。当采用优化参数达到目标温度时，冷却时间减少了78s，为储氢床的设计和运行提供了指导。