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In this work, an inverse design method that couples the multi-physics model for a solar trough thermochemical reactor (SPTR) and shape optimization model is proposed to find out optimal solar flux distribution for maximizing overall reactor performance. The gradient-based segmentation method is applied to convert the continuous solar flux into step-like flux to guide the concentrator system design. Performance comparisons among uniform flux, linear decreasing flux, and the optimized non-linear flux are also conducted to discuss the reliability of SPTR performance improvement. The results show that the optimized non-linear solar flux can improve the methanol conversion, solar thermochemical conversion, and hydrogen yield of SPTR by 2.5, 3.3, and 2.4%, respectively, compared with the uniform flux. This is attributed to the fact that the optimized non-uniform flux distribution enhances the synergy between temperature and reaction fields, and achieves a better match between spatial solar flux supply and local energy demand by reactions. Also, it is shown that the optimized step-like flux, achieved by regressing the optimized non-linear flux, can perfectly maintain SPTR performance and is effective in boosting SPTR performance under different operating conditions.

In this work, the solar-thermal-chemical integrated design for a methane dry reforming reactor with cavity-type solar absorption was numerically performed. Combined with a multiphysical reactor model, the gradient optimization algorithm was used to find optimal radiation flux distribution with fixed total incident solar energy for maximizing overall hydrogen yield, defined as the ratio of molar flow of exported hydrogen to imported methane, which can be applied for guiding the optical property design of solar adsorption surface. The comprehensive performances of the reactor under the conditions of original solar flux and optimal solar flux were analyzed and compared. The results show that for the inlet volume flow rate of 8–14 L·min−1, the hydrogen production rate was increased by up to 5.10%, the energy storage efficiency was increased by up to 5.55%, and the methane conversion rate was increased by up to 6.01%. Finally, the local absorptivities of the solar-absorptive coating on the cavity walls were optimized and determined using a genetic algorithm, which could realize the predicted optimal radiation flux distribution.