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Numerical-Experimental geometric optimization of the Ahmed body and analyzing boundary layer profiles

Abdolmaleki, M ; Sharif University of Technology | 2022

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  1. Type of Document: Article
  2. DOI: 10.1007/s10957-021-01932-w
  3. Publisher: Springer , 2022
  4. Abstract:
  5. The trade-off between the fuel consumption and drag coefficient makes the investigations of drag reduction of utmost importance. In this paper, the rear-end shape optimization of Ahmed body is performed. Before changing the geometry, to identify the suitable simulation method and validate it, the standard Ahmed body is simulated using k − ω shear stress transport (SST) and k-epsilon turbulence models. The slant angle, rear box angle, and rear box length as variables were optimized simultaneously. Optimizations conducted by genetic algorithm (GA) and particle swarm optimization (PSO) methods indicate a 26.3% decrease in the drag coefficient. To ensure the validity of the results, a numerical-experimental study is conducted on the optimized model. Thereafter, the velocity profiles and flow structure in the boundary layers of the original geometry were compared to those of the optimized geometry at different sections. The results indicate that there are points where the velocity profile in the boundary layer can exceed the free stream velocity and return to it again, an overlooked observation in the previous studies. In addition to the streamlines, to better understand the formation of three-dimensional vortexes, the Q-criterion factor is computed and illustrated. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. GA ; Optimization ; PSO ; Velocity profile ; Boundary layers ; Drag coefficient ; Economic and social effects ; Geometry ; Particle swarm optimization (PSO) ; Shape optimization ; Shear stress ; Turbulence models ; Velocity ; Ahmed body ; Geometric optimization ; Numerical experimental ; Optimisations ; Particle swarm ; Particle swarm optimization ; Shape-optimization ; Swarm optimization ; Trade off ; Velocity profiles ; Genetic algorithms
  8. Source: Journal of Optimization Theory and Applications ; Volume 192, Issue 1 , 2022 ; 00223239 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s10957-021-01932-w