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Neural Computing and Applications

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12.06.2020 | Original Article

Neural network and multi-objective optimization of confined flow characteristics on circular cylinder in standing double vortex region

verfasst von: Rajendran Senthilkumar, Premalatha Vasudevan, Sethuramalingam Prabhu

Erschienen in: Neural Computing and Applications | Ausgabe 4/2021

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Abstract

The unsteady state and isothermal two dimensional numerical computations were carried out using Ansys Fluent-18 between the Reynolds number ranges 10 to 50. The blockage ratios (Domain height to the circular cylinder diameter) range 1.54–112. The flow characteristics such as drag coefficients and length of recirculation are optimized and correlated as a function of various Reynolds numbers at different blockage ratios. Gradual decrease in blockage ratio which means the increase in blockage effect postponed the flow separation, transition and reduces the length of recirculation and also makes the flow steady. In this study optimum flow characteristics exist at maximum blockage ratio, i.e. with minimum blockage effect and maximum Reynolds number. The artificial neural networks model proved to predict values of the total drag coefficient (R² = 0.979) and length of recirculation (R² = 0.992) closer to simulated data at 95% (α = 0.05) confident interval.

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Titel: Neural network and multi-objective optimization of confined flow characteristics on circular cylinder in standing double vortex region
verfasst von: Rajendran Senthilkumar
Premalatha Vasudevan
Sethuramalingam Prabhu
Publikationsdatum: 12.06.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 4/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05079-z

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