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


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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ζ :

Distinguishing coefficient

F T :

Tangential force

ρ :

Fluid density

C DF :

Frictional drag coefficient

U :

Free stream velocities

F N :

Normal pressure force

C Dp :

Pressure drag coefficient


Reynolds number

d :

Diameter of the cylinder

η ij :

Signal noise ratio

Γ :

Grey relational grade

Χ :

Grey relational coefficient

R j :

Actual output of flow simulation

tR j :

Predicted neural network total drag coefficient output

N :

Total number of measurement values

L w :

Length of recirculation

L :

Length of computational domain

r :

Radius of circular cylinder


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We sincerely acknowledge the computing facilities in product development and computational fluid dynamics laboratories at Department of Mechanical engineering, SRMIST.

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Correspondence to Sethuramalingam Prabhu.

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Senthilkumar, R., Vasudevan, P. & Prabhu, S. Neural network and multi-objective optimization of confined flow characteristics on circular cylinder in standing double vortex region. Neural Comput & Applic 33, 1379–1398 (2021).

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  • Reynolds number
  • Neural network
  • Flow characteristics