Prediction of Nusselt number of heated cylinder exposed to turbulent flow by deep long short-term memory network optimized by particle swarm algorithm

Document Type : Research Article

Authors

1 mechanical engineering, arak university of technology

2 Department of Mechanical Engineering, Faculty of Engineering, Kharazmi University , Tehran, Iran

Abstract

Leveraging artificial intelligence to forecast heat transfer characteristics across diverse industries holds significant potential for improving thermal equipment design, increasing heat transfer efficiency, optimizing cooling systems, and reducing energy consumption. The main contribution and purpose of the current study is predicting the Nusselt number in the context of turbulent flow-induced vibration around a heated cylinder experiencing unconfined oscillations along both streamwise and transverse axes. The anticipation of the Nusselt number relies on transverse and streamwise displacements of the oscillating cylinder and encompasses three distinct scenarios: displacement input in the x-direction, displacement input in the y-direction, and comprehensive amalgamation of both x and y inputs. This prediction is achieved through a sophisticated deep long short-term memory network, meticulously crafted and fine-tuned using a particle swarm optimization algorithm. The results highlight the effectiveness of the optimized networks across various inputs, with the highest predictive precision observed when employing combined x and y inputs. The correlation coefficients within the test segment are as follows: 0.967 for x input, 0.961 for y input, and 0.975 for combined x and y inputs. By applying the methodology elucidated in this study, the forecasting of heat transfer characteristics for structures subjected to fluid flow emerges as a feasible possibility.

Keywords

Main Subjects

References

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Amirkabir Journal of Mechanical Engineering
Volume 55, Issue 11
February 2024
Pages 1353-1372

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