Correlation between drag variation and rear surface pressure of an Ahmed body

Liu, Kai; Bingfu, Zhang; Zhou, Yu

doi:10.1007/s00348-021-03214-7

Cited by 5 publications

(20 citation statements)

References 56 publications

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“…(2016) and Liu et al. (2021), who observed a decline in C D 0 from 0.32 to 0.26 from Re = 1.7 × 10 5 to 1.4 × 10 6 .…”

Section: Resultsmentioning

confidence: 99%

“…Lienhart & Becker 2003; Liu et al. 2021). The upper bubble is much bigger than the lower one, covering both the rear window and most of the upper part of the base, which is highlighted by the thick solid contour in figure 5( a ).…”

Section: Resultsmentioning

confidence: 99%

“…Liu et al. (2021) carried out a relatively thorough investigation on the flow structures around this body and proposed a conceptual model of the flow structures. Unlike the well-known classical flow structure model developed by Ahmed et al.…”

Section: Methodsmentioning

confidence: 99%

“…(1984), which is constructed based on time-averaged data, Liu et al. 's (2021) model embraces both steady and unsteady coherent structures around the body and even the predominant frequencies of the unsteady structures. Based on this model, five different actuations based on constant blowing, referred to as C 1 , C 2 , C 3 , C 4 and C 5 (figure 2 a ), were deployed.…”

Section: Methodsmentioning

confidence: 99%

“…This instability has never been observed in the low-drag regime (Liu et al. 2021). Therefore, the square-back Ahmed body is often considered to be a special case, not included in the low-drag regime.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence control of a low-drag Ahmed body using distributed jet arrays

2023

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This work proposes a machine-learning or artificial intelligence (AI) control of a low-drag Ahmed body with a rear slant angle φ = 35° with a view to finding strategies for efficient drag reduction (DR). The Reynolds number Re investigated is 1.7 × 105 based on the square root of the body cross-sectional area. The control system comprises of five independently operated arrays of steady microjets blowing along the edges of the rear window and vertical base, twenty-six pressure taps on the rear end of the body and a controller based on an ant colony algorithm for unsupervised learning of a near-optimal control law. The cost function is designed such that both DR and control power input are considered. The learning process of the AI control discovers forcing that produces a DR up to 18 %, corresponding to a drag coefficient reduction of 0.06, greatly exceeding any previously reported DR for this body. Furthermore, the discovered forcings may provide alternative solutions, i.e. a tremendously increased control efficiency given a small sacrifice in DR. Extensive flow measurements performed with and without control indicate significant alterations in the flow structure around the body, such as flow separation over the rear window, recirculation bubbles and C-pillar vortices, which are linked to the pressure rise on the window and base. The physical mechanism for DR is unveiled, along with a conceptual model for the altered flow structure under the optimum control or biggest DR. This mechanism is further compared with that under the highest control efficiency.

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“…(2016) and Liu et al. (2021), who observed a decline in C D 0 from 0.32 to 0.26 from Re = 1.7 × 10 5 to 1.4 × 10 6 .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%