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Aerodynamic shape optimization of porous fences with curved deflectors using surrogate modelling

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Abstract

Wind fences are commonly used to mitigate dust emission from coal stockpiles in an open storage yard. There has substantial progress in the trial-and-error study of traditional straight fences, but only limited research focused specifically on the shelter effect of curved fence and its shape optimization. This paper introduces an innovative wind fence with curved deflector, and applies the aerodynamic optimization to enhance its shelter efficiency. Computational fluid dynamics (CFD) techniques are employed to simulate the flow field around a triangular coal stockpile behind the fence using RNG k-ε turbulence model. The simulation results have confirmed the role of curved fences in reducing wind erosion through comparative analysis of different fence geometries. In order to efficiently and automatically explore the optimal design parameters including fence porosity and external shape, the aerodynamic optimization via surrogate modelling is implemented. Two conflicting objectives namely minimization of total absolute surface pressure coefficients and reduction of peak velocity ratios around the stockpile are considered. The weighted sum method is utilized to solve the proposed multi-objective model. The optimization results suggest that the optimal curved fence is associated with a significant improvement of shelter performance in comparison with the traditional straight fence.

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Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (Grant No. 51808194), and the Fundamental Research Funds for the Central Universities (Grant No. B220202012).

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  1. College of Civil and Transportation Engineering, Hohai University, Nanjing, 210098, China

    Ye Qiu, Youming Yuan, Rui Yu & Jie Liu

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  1. Ye Qiu
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  2. Youming Yuan
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  3. Rui Yu
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  4. Jie Liu
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Correspondence to Ye Qiu.

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Qiu, Y., Yuan, Y., Yu, R. et al. Aerodynamic shape optimization of porous fences with curved deflectors using surrogate modelling. Optim Eng 24, 2387–2408 (2023). https://doi.org/10.1007/s11081-022-09777-6

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  • DOI: https://doi.org/10.1007/s11081-022-09777-6

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