Application of Nanofluid Electrochemistry in Heat Dissipation of Permanent Magnet Synchronous Motors

Lin, Xiongping; Xie, GuoDong

doi:10.1155/2022/3681749

Cited by 6 publications

(2 citation statements)

References 24 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple researchers have extensively investigated the heat transfer characteristics of N.F. They have great potential, receiving a great deal of research attention, such as electrochemistry [ 7 , 8 ], optics [ 9 , 10 ], pharmacology [ 11 ], solar energy [ 12 , 13 ], tribological lubrication [ 14 , 15 , 16 ]. One of the most significant indicators of thermophysical properties is thermal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Heat Transfer Behaviour of Ethylene Glycol by the Introduction of Silicon Carbide Nanoparticles: An Experimental and Molecular Dynamics Simulation Study

et al. 2023

View full text Add to dashboard Cite

As the critical component of automotive engine coolant, ethylene glycol (E.G.) significantly matters in heat dissipation. In this study, the key aim is to investigate the heat transfer behaviour of E.G. as nano-additives base fluid. The heat transfer capability of E.G./SiC nanofluid (N.F.) was experimentally and theoretically evaluated via transient hot wire methods and equilibrium molecular dynamics (EMD) simulation, respectively. M.D. simulation exhibited a great ability to accurately forecast the thermal conductivity of N.F. compared with the experiment results. The results confirmed that the thermal stability of N.F. is relatively greater than that of E.G. base fluids. An improvement mechanism of thermal conductivity and thermal stability under an atomic scale via the analysis of mean square displacement (MSD) and radial distribution function (RDF) calculation was elaborately presented. Ultimately, the results indicated that the diffusion effect and the increasing transition rate of liquid atoms are responsible for thermal conductivity enhancement.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing Heat Transfer Behaviour of Ethylene Glycol by the Introduction of Silicon Carbide Nanoparticles: An Experimental and Molecular Dynamics Simulation Study

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

mentioning

confidence: 99%

Retracted: Application of Nanofluid Electrochemistry in Heat Dissipation of Permanent Magnet Synchronous Motors

2023

Journal of Chemistry

View full text Add to dashboard Cite

Recent advances in deep learning for retrosynthesis

Zhong,

Song,

Feng

et al. 2023

WIREs Comput Mol Sci

View full text Add to dashboard Cite

Retrosynthesis is the cornerstone of organic chemistry, providing chemists in material and drug manufacturing access to poorly available and brand‐new molecules. Conventional rule‐based or expert‐based computer‐aided synthesis has obvious limitations, such as high labor costs and limited search space. In recent years, dramatic breakthroughs driven by deep learning have revolutionized retrosynthesis. Here we aim to present a comprehensive review of recent advances in AI‐based retrosynthesis. For single‐step and multi‐step retrosynthesis both, we first introduce their goal and provide a thorough taxonomy of existing methods. Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets. In the next part, we introduce popular databases and established platforms for retrosynthesis. Finally, this review concludes with a discussion about promising research directions in this field.This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Computer Algorithms and Programming Data Science > Chemoinformatics

show abstract

Application of Nanofluid Electrochemistry in Heat Dissipation of Permanent Magnet Synchronous Motors

Cited by 6 publications

References 24 publications

Enhancing Heat Transfer Behaviour of Ethylene Glycol by the Introduction of Silicon Carbide Nanoparticles: An Experimental and Molecular Dynamics Simulation Study

Enhancing Heat Transfer Behaviour of Ethylene Glycol by the Introduction of Silicon Carbide Nanoparticles: An Experimental and Molecular Dynamics Simulation Study

Retracted: Application of Nanofluid Electrochemistry in Heat Dissipation of Permanent Magnet Synchronous Motors

Recent advances in deep learning for retrosynthesis

Contact Info

Product

Resources

About