155 research outputs found
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
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