Calcium carbonate is promising thermochemical heat storage material for next-generation solar power systems due to its high energy storage density,low cost,and high operation temperature.Researchers have tried to impr...Calcium carbonate is promising thermochemical heat storage material for next-generation solar power systems due to its high energy storage density,low cost,and high operation temperature.Researchers have tried to improve energy storage performances of calcium carbonate recently,but most researches focus on powders,which are not suitable for scalable applications.Here,novel granular porous calcium carbonate particles with very high solar absorptance,energy storage density,abrasive resistances,and energy storage rate are proposed for direct solar thermochemical heat storage.The average solar absorptance is improved by 234%compared with ordinary particles.Both cycle stability and abrasive resistances are excellent with almost no decay of energy storage density over 25 cycles nor apparent particle weight loss over 24 h of continuous operation insides a planetary ball mill.In addition,the decomposition temperature is reduced by 2.8%–5.6%while the reaction rate of heat storage is enhanced by 80%–205%depending on the CO_(2) partial pressure.The decomposition process of doped granular porous CaCO_(3) particles is found to involve three overlapping processes.This work provides new routes to achieve scalable direct solar thermochemical heat storage for next-generation high-temperature solar power systems.展开更多
The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status rec...The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status recognition. In this paper, a novel knowledge-and-data-hybrid driven recognition model is proposed for solving the problem of difficult learning of discriminative visual features of molten pool/keyhole images. In addition, a label semantic attention mechanism(LSA) is designed with three modules: representation of image visual feature, representation of labels semantic feature, and generation of label semantic attention. For learning discriminative features in visual space, LSA uses discriminative information in label semantics to guide the convolutional neural network. The experimental results show that the proposed LSA method has faster convergence and higher accuracy than the traditional attention mechanism. Further comparative experiments reveal that LSA is less dependent on the amount of training data and model complexity. The results of visualization experiments show that the visual features learned by the proposed method are more discriminative.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.51820105010 and 52076106)the support from Natural Science Foundation of Jiangsu Province(Grant No.BK20202008).
文摘Calcium carbonate is promising thermochemical heat storage material for next-generation solar power systems due to its high energy storage density,low cost,and high operation temperature.Researchers have tried to improve energy storage performances of calcium carbonate recently,but most researches focus on powders,which are not suitable for scalable applications.Here,novel granular porous calcium carbonate particles with very high solar absorptance,energy storage density,abrasive resistances,and energy storage rate are proposed for direct solar thermochemical heat storage.The average solar absorptance is improved by 234%compared with ordinary particles.Both cycle stability and abrasive resistances are excellent with almost no decay of energy storage density over 25 cycles nor apparent particle weight loss over 24 h of continuous operation insides a planetary ball mill.In addition,the decomposition temperature is reduced by 2.8%–5.6%while the reaction rate of heat storage is enhanced by 80%–205%depending on the CO_(2) partial pressure.The decomposition process of doped granular porous CaCO_(3) particles is found to involve three overlapping processes.This work provides new routes to achieve scalable direct solar thermochemical heat storage for next-generation high-temperature solar power systems.
基金supported by the Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University(Grant No.CUSF-DH-D-2020053)。
文摘The degree of penetration can directly reflect the forming quality of laser welding. The fine-grained feature of the molten pool/keyhole image brings challenges to the vision-based laser welding penetration status recognition. In this paper, a novel knowledge-and-data-hybrid driven recognition model is proposed for solving the problem of difficult learning of discriminative visual features of molten pool/keyhole images. In addition, a label semantic attention mechanism(LSA) is designed with three modules: representation of image visual feature, representation of labels semantic feature, and generation of label semantic attention. For learning discriminative features in visual space, LSA uses discriminative information in label semantics to guide the convolutional neural network. The experimental results show that the proposed LSA method has faster convergence and higher accuracy than the traditional attention mechanism. Further comparative experiments reveal that LSA is less dependent on the amount of training data and model complexity. The results of visualization experiments show that the visual features learned by the proposed method are more discriminative.
