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Optimization of blade geometry by neural network in natural convection and entropy generation of nanofluids within an oblique square cavity with the effects of volumetric radiation with LB method

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Abstract

In the current paper, the impacts of the blade on the heat transfer rate (HTR) and entropy production (EP) of nanofluids (NFs) inside an oblique square cavity exposed to a magnetic field (MF) and the impacts of radiation are investigated numerically. The blade fixed on a left horizontal wall with thermal conductivity (TC). The D2Q9 lattice Boltzmann (LB) technique is used to solve the governing equations. The impact of cavity angle, length, width blade, radiation parameter on EP and HTR has been investigated. According to the accurate results obtained from artificial neural networks (ANNs) in predicting values, the use of this method in predicting values has been considered. In this method, by having a limited number of input data and their desired output, the network can be trained in such a way that for a wide range of input data, the desired output can be predicted with great accuracy. The results show that increasing the cavity angle at low Rayleigh numbers (Ra) does not have a significant impact on HTR, EP and Bejan number (Be). As the blade width increases, the average Nusselt number (Nu) and EP reduce. While increasing the blade length, this process first increases and then decreases. As the blade length or width increases, the Be increases. The increase in HTR is more dramatic with the intensification of the radiation parameter in higher Rayleigh numbers. Finally, the ANN has been used to optimize the HTR. The optimization method is based on the lowest entropy value and the highest average Nu. The highest value of Nu occurs in blade length of 0.6 and blade width of 0.1 at TC of 200, which is equal to 9.45. The lowest amount of entropy production also occurs in the same condition, which is equal to 3.57.

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Acknowledgements

This work was sponsored in part by Welfare Research Program of Zhejiang Province (LGG20E050001);Zhejiang Province Science Foundation(Q20E060028); Ningbo "Belt And Road" Key Project of Vocational Education.

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Authors and Affiliations

  1. School of Mechanical and Electrical Engineering, Zhejiang Business Technology Institute, NingBo, 315000, China

    Zhongliang Shen, Zijun Zheng, Lijun Zhu & Yunxiao Gao

  2. Zhejiang Molded Products Surface Decoration and Intelligent Forming Technology Collaborative Innovation Center, NingBo, 315012, China

    Zhongliang Shen

  3. School of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou, 325006, China

    Mingge Wu

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  1. Zhongliang Shen
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Shen, Z., Zheng, Z., Zhu, L. et al. Optimization of blade geometry by neural network in natural convection and entropy generation of nanofluids within an oblique square cavity with the effects of volumetric radiation with LB method. J Therm Anal Calorim 147, 10827–10843 (2022). https://doi.org/10.1007/s10973-022-11252-4

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