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Physics and Modeling of Phonon Wave Behaviors in Nanoscale Heat Conduction
Bin LIU, Yunfan HUANG, Moran WANG
Chinese Journal of Computational Physics    2024, 41 (6): 746-771.   DOI: 10.19596/j.cnki.1001-246x.9000
Abstract193)   HTML9)    PDF (28602KB)(302)      

Nanoscale heat conduction has attracted considerable attention due to the unique transport behaviors exhibited by its intrinsic heat-carrying phonons, as well as its significant potential in addressing thermal management issues in electronic devices and enhancing thermoelectric conversion efficiency. This paper reviews the research progress in nanoscale heat conduction theory and simulations over the past three decades. It particularly introduces and discusses methods based on both wave and particle perspectives, focusing on nanoscale coherent thermal conduction mechanisms. Finally, the paper analyzes the challenges facing the field of nanoscale heat conduction research and outlines potential future directions, aiming to deepen the understanding of the wave-particle duality of heat-carrying phonons and promote their application in critical areas.

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A Novel Random Walk Algorithm for Optimal Configuration of Micro-grid
Lei PAN, Guomin CUI, Ruifang ZHANG, Hongbin LIU, Yuan XIAO, Zhikang YI
Chinese Journal of Computational Physics    2024, 41 (3): 392-402.   DOI: 10.19596/j.cnki.1001-246x.8703
Abstract131)   HTML4)    PDF (3818KB)(530)      

In order to solve the configuration optimization problem of isolated micro-grid, wind driven generator, photovoltaic, diesel generator and energy storage battery optimization model described in the form of energy flow matching is established, which can flexibly form the node connection relation representing the output of equipment at each time. At the same time, in view of the precocious convergence of swarm intelligence algorithm applied to optimal configuration of micro-grid, a random walk optimization algorithm suitable for optimal configuration of micro-grid is proposed. Guided by reducing the annual comprehensive cost of the system, the algorithm realizes synchronous optimization of continuous variable (equipment output) and integer variable (equipment quantity) by randomly increasing or decreasing the hourly output of equipment. By accepting the differential solution mechanism, the algorithm has the ability to jump out of the local optimal solution and better take into account the global search and local search in the capacity optimization process of micro-grid. Applying the random walk algorithm to the simulation example, the annual comprehensive cost is 552 826.39 yuan. Compared with particle swarm optimization algorithm, a better result is obtained. The superiority of the algorithm in optimization accuracy is verified.

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Individual Reconstruction Optimization Method Applied to Mass Exchanger Networks
Guanglin JIN, Guomin CUI, Yuan XIAO, Hongbin LIU, Yinrui FU, Zhikun ZHANG
Chinese Journal of Computational Physics    2024, 41 (2): 245-257.   DOI: 10.19596/j.cnki.1001-246x.8682
Abstract125)   HTML7)    PDF (1719KB)(611)      

A random walk algorithm compulsive evolution with individual reconstruction strategy is proposed to solve the problem that the optimization of mass exchanger network is easily trapped in local extremum due to the weakening of the ability of structural variation and the loss of population diversity. In the process of receiving differential solutions, real-time monitoring is carried out on individuals, and different reconstruction methods are adopted to stimulate the network structure updating and variation of backward individuals, so as to improve the structural variation ability and population diversity of the algorithm. At the same time, according to the characteristics of the unstructured model with shunt nodes, a new individual network structure after cross reconstruction is repaired. Finally, the R2S2 and R4S2 examples are used to verify the effectiveness of the proposed strategy, and the optimization results are all lower than the results in the current literature, which proves that the proposed strategy can effectively enhance the structural variation ability and global optimization ability of the algorithm.

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