Conceitos Básicos
Deep reinforcement learning-based synthetic jet actuation can effectively suppress vortex shedding and reduce drag in elliptical cylinders with aspect ratios ranging from 1 to 0.1, with energy-efficient control strategies.
Resumo
The study investigates the use of deep reinforcement learning (DRL) combined with synthetic jet actuation to control the flow around elliptical cylinders with varying aspect ratios (Ar) and blockage ratios (β).
Key highlights:
DRL training process:
For Ar = 1 and 0.75, the reward function gradually increases with decreasing oscillations before stabilizing.
As Ar decreases, the DRL training becomes less stable, with energy consumption surging for Ar ≤ 0.1.
When β is reduced to 0.12, the DRL training demonstrates robust convergence and consistent full suppression of vortex shedding across all Ar from 1 to 0.1.
Drag reduction and lift suppression:
For Ar = 1 and 0.75, the DRL-based control strategy achieved drag reduction rates of 8% and 15%, while 99% of the lift coefficient is effectively suppressed.
As Ar decreases, the lift and drag coefficients continue oscillating, and vortex shedding remains uncontrolled.
For Ar between 1 and 0.25, the external energy expenditure remains below 1.4% of the inlet flow rate, indicating efficient and energy-conservative control strategies.
For Ar = 0.1, the energy cost escalates to 8.1%, highlighting the higher energy expenditure required for highly elongated geometries.
Vortex shedding suppression:
For Ar = 1 and 0.75, vortex shedding is entirely eliminated with only 0.1% and 1% of the inlet flow rate, respectively.
As Ar decreases, the DRL-based control strategy becomes less effective in fully suppressing vortex shedding.
When β is reduced to 0.12, the DRL-based control strategy achieves complete suppression of vortex shedding across all Ar from 1 to 0.1.
The study demonstrates the effectiveness of DRL-based strategies in controlling the flow around elliptical cylinders with varying geometries, paving the way for future research on more complex flow environments and adaptive control mechanisms.
Estatísticas
The drag reduction rates achieved by the DRL-based control strategy are 8% for Ar = 1 and 15% for Ar = 0.75.
The external energy expenditure remains below 1.4% of the inlet flow rate for Ar between 1 and 0.25.
The energy cost escalates to 8.1% for the extremely slender elliptical cylinder with Ar = 0.1.
Citações
"For Ar = 1 and 0.75, the reward function gradually increases with decreasing oscillations before stabilizing."
"As Ar decreases, the DRL training becomes less stable, with energy consumption surging to 14.5% for Ar ≤ 0.1."
"When β is reduced to 0.12, the DRL training demonstrates robust convergence and consistent full suppression of vortex shedding across all Ar from 1 to 0.1."