Elastic heat transfer tube bundles are widely used in the field of flow-induced vibration heat transfer enhancement. Two types of mainly used tube bundles, the planar elastic tube bundle and the conical spiral tube bu...Elastic heat transfer tube bundles are widely used in the field of flow-induced vibration heat transfer enhancement. Two types of mainly used tube bundles, the planar elastic tube bundle and the conical spiral tube bundle were comprehensively compared in the condition of the same shell side diameter. The natural mode characteristics, the effect of fluid-structure interaction, the stress distribution, the comprehensive heat transfer performance and the secondary fluid flow of the two elastic tube bundles were all concluded and compared. The results show that the natural frequency and the critical velocity of vibration buckling of the planar elastic tube bundle are larger than those of the conical spiral tube bundle, while the stress distribution and the comprehensive heat transfer performance of the conical spiral tube bundle are relatively better.展开更多
As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of u...As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.展开更多
基金Projects(xjj2013104,08143063)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2011CB706606)supported by the National Basic Research Program of China
文摘Elastic heat transfer tube bundles are widely used in the field of flow-induced vibration heat transfer enhancement. Two types of mainly used tube bundles, the planar elastic tube bundle and the conical spiral tube bundle were comprehensively compared in the condition of the same shell side diameter. The natural mode characteristics, the effect of fluid-structure interaction, the stress distribution, the comprehensive heat transfer performance and the secondary fluid flow of the two elastic tube bundles were all concluded and compared. The results show that the natural frequency and the critical velocity of vibration buckling of the planar elastic tube bundle are larger than those of the conical spiral tube bundle, while the stress distribution and the comprehensive heat transfer performance of the conical spiral tube bundle are relatively better.
基金the National Natural Science Foundation of China(Grant Nos.61905115,62105151,62175109,U21B2033)Leading Technology of Jiangsu Basic Research Plan(Grant No.BK20192003)+2 种基金Youth Foundation of Jiangsu Province(Grant Nos.BK20190445,BK20210338)Fundamental Research Funds for the Central Universities(Grant No.30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(Grant No.JSGP202105)to provide fund for conducting experiments。
文摘As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.
