Low-dissipation model of three-terminal refrigerator: performance bounds and comparative analyses

[EN]In the present paper, a general non-combined model of three-terminal refrigerator beyond specific heat transfer mechanisms is established based on the low-dissipation assumption. The relation between the optimized cooling power and the corresponding coefficient of performance (COP) is analytically derived, according to which the COP at maximum cooling power (CMP) can be further determined. At two dissipation asymmetry limits, upper and lower bounds of CMP are obtained and found to be in good agreement with experimental and simulated results. Additionally, comparison of the obtained bounds with previous combined model is presented. In particular it is found that the upper bounds are the same, whereas the lower bounds are quite different. This feature indicates that the claimed universal equivalence for the combined and non-combined models under endoreversible assumption is invalid within the frame of low-dissipation assumption. Then, the equivalence between various finite-time thermodynamic models needs to be reevaluated regarding multi-terminal systems. Moreover, the correlation between the combined and non-combined models is further revealed by the derivation of the equivalentJGA thanks financial support for a postdoctoral contract from University of Salamanca under Program I

An alkali metal thermoelectric converter hybridized with a Brayton heat engine: Parametric design strategies and energetic optimization

[EN]A model for a novel integrating system consisting of an alkali metal thermoelectric converter and a non-recuperative irreversible Brayton heat engine is presented. The efficiency and power output density of the overall system is analyzed at light of the main characteristic losses in each subsystem: the thickness of the electrolyte, the current density of the converter, and the internal losses of the Brayton cycle coming from the compressor and turbine. A detailed study on the behavior of the overall maximum power and maximum efficiency regimes is also presented. An analysis on compromise performance regimes from multi-objective and multi-parametric optimization techniques based on the Pareto front, for both the subsystems and the overall system, enhance the obtained results. The numerical results of the present model are compared with those of alkali metal thermoelectric converter working alone and with other different existing hybrid models. It is found that the exhaust heat discharged by the converter can be efficiently utilized by an irreversible Brayton heat engine. So, the maximum efficiency and maximum power output density of the present model attain 41.7% and W/m2 which increase about 44.8% and 158% compared to the values of the alkali metal thermoelectric converter working alone and 20.5% and 80.4% when compared with a hybridized configuration including a thermoelectric energy converter.National Natural Science Foundation of China (No. 11675132) People’s Republic of China and China Scholarship Council (CSC) under the State Scholarship Fund (No. 201806310020) Junta de Castilla y Leon under project SA017P17. J.G.A. acknowledges Universidad de Salamanca contract 2017/X005/

CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution

Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Local Feature Refinement Module (LFRM) to enhance the local facial structure information. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Comprehensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly.Comment: 12 pages, 10 figures, 8 table