Unsteady Coanda effect and drag reduction for a turbulent wake

Haffner, Yann; Borée, Jacques; Spohn, Andreas; Castelain, Thomas

doi:10.1017/jfm.2020.494

Cited by 25 publications

(12 citation statements)

References 56 publications

(111 reference statements)

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“…2016; Haffner et al. 2020), which also induce a sign change of streamline curvature leading to a local pressure increase near the wall. The pressure increase associated with a more symmetric and slender bubble has also been reported in the vertical rear surface of a Ahmed body using rounded corners by Rossitto et al.…”

Section: Drag Optimization Of An Ahmed Body With 10 Actuation Parametersmentioning

confidence: 99%

Explorative gradient method for active drag reduction of the fluidic pinball and slanted Ahmed body

Cui

Jia

et al. 2021

J. Fluid Mech.

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We address a challenge of active flow control: the optimization of many actuation parameters guaranteeing fast convergence and avoiding suboptimal local minima. This challenge is addressed by a new optimizer, called the explorative gradient method (EGM). EGM alternatively performs one exploitive downhill simplex step and an explorative Latin hypercube sampling iteration. Thus, the convergence rate of a gradient based method is guaranteed while, at the same time, better minima are explored. For an analytical multi-modal test function, EGM is shown to significantly outperform the downhill simplex method, the random restart simplex, Latin hypercube sampling, Monte Carlo sampling and the genetic algorithm. EGM is applied to minimize the net drag power of the two-dimensional fluidic pinball benchmark with three cylinder rotations as actuation parameters. The net drag power is reduced by 29 % employing direct numerical simulations at a Reynolds number of $100$ based on the cylinder diameter. This optimal actuation leads to 52 % drag reduction employing Coanda forcing for boat tailing and partial stabilization of vortex shedding. The price is an actuation energy corresponding to 23 % of the unforced parasitic drag power. EGM is also used to minimize drag of the $35^\circ$ slanted Ahmed body employing distributed steady blowing with 10 inputs. 17 % drag reduction are achieved using Reynolds-averaged Navier–Stokes simulations at the Reynolds number $Re_H=1.9 \times 10^5$ based on the height of the Ahmed body. The wake is controlled with seven local jet-slot actuators at all trailing edges. Symmetric operation corresponds to five independent actuator groups at top, middle, bottom, top sides and bottom sides. Each slot actuator produces a uniform jet with the velocity and angle as free parameters, yielding 10 actuation parameters as free inputs. The optimal actuation emulates boat tailing by inward-directed blowing with velocities which are comparable to the oncoming velocity. We expect that EGM will be employed as efficient optimizer in many future active flow control plants as alternative or augmentation to pure gradient search or explorative methods.

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Section: Drag Optimization Of An Ahmed Body With 10 Actuation Parametersmentioning

confidence: 99%

Explorative gradient method for active drag reduction of the fluidic pinball and slanted Ahmed body

Cui

Jia

et al. 2021

J. Fluid Mech.

View full text Add to dashboard Cite

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“…The linear GP control identified the openloop multi-frequency forcing as the best control. Haffner et al [48] also combined pulsed jets and curved surfaces. They analysed the influences of various parameters (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…[4]pulsed blowing013 ( f e , α , C m )1.71126.5Haffner et al. [48]pulsed blowing coupled with Coanda surfaces014 ( C μ , f e , r , U 0 )4.4, 5.5, 6.6, 7.7, 8.812no valueLi et al. [28]steady blowing35510 ( U i , θ i , i = 1, 2, 3, 4, 5)2.217no value…”

Section: Introductionmentioning

confidence: 99%

“…Haffner et al. [48] also combined pulsed jets and curved surfaces. They analysed the influences of various parameters (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…The response model is an important means for sensitivity analysis of control parameters and the examination of control efficiency. Previous parameter analysis was conducted on one variable after another with others fixed [44,48]. The approach is difficult to find the optimum with both large DR and control efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Sensitivity analysis of large body of control parameters in machine learning control of a square-back Ahmed body

et al. 2023

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Active drag reduction (DR) of a square-back Ahmed body is experimentally studied based on machine learning or artificial intelligence (AI) control. The control system consists of four independently operated arrays of pulsed microjets, 25 pressure taps and an explorative downhill simplex method controller. Two strategies, i.e. asymmetric and symmetric actuations, are investigated, with 12 and 9 control parameters, respectively. Both achieve a DR by 13%, though with distinct flow physics and control mechanisms behind. A model linking the control parameters with the cost is developed based on Taylor expansion around the K-nearest neighbours of the smallest cost obtained from the AI control, resulting in a substantially reduced deviation between measured and predicted costs, especially when involving a large number of control parameters, compared with that based on Taylor expansion around the optimum cost. Sensitivity analysis, conducted based on the model, indicates that the control efficiency, i.e. the ratio of the power saving from DR to the total power consumption, may reach 55 and 78 for the symmetric and asymmetric strategies, respectively, given a 1–2% sacrifice on DR. This efficiency greatly exceeds that (26.5) obtained by Fan et al. (Fan et al. 2020 Phys. Fluids 32 , 125117. ( doi:10.1063/5.0033156 )), whose independent control parameters are only three.

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