Active boundary layer control in linear cascades using CFD and artificial neural networks

Svorcan, Jelena; Stupar, Slobodan; Trivković, Srđan; Petrašinović, Nikola; Ivanov, Toni

doi:10.1016/j.ast.2014.09.010

Cited by 20 publications

(8 citation statements)

References 12 publications

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“…Although performed analyses are two-dimensional, they provide insight into possibilities to relatively easily simulate active flow control which has become an interesting and widely considered topic in recent years [16][17][18][19][20][21]. The increase of aerodynamic performances, achieved without the change of undisturbed velocity or angle-of-attack, is proven and quantified.…”

Section: S650mentioning

confidence: 99%

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Two-dimensional numerical analysis of active flow control by steady blowing along foil suction side by different urans turbulence models

Svorcan¹,

Fotev²,

Petrović³

et al. 2017

THERM SCI

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The effects of active separation control by steady blowing jets were investigated numerically on three different examples: subsonic flow past Aerospatiale A airfoil at 13º angle-of-attack, transonic flow past NACA 0012 airfoil at 4º angle-ofattack, and transonic flow in linear compressor/turbine cascade. Performed analyses are two-dimensional, flow is turbulent (or transitional) while fluid is viscous and compressible. Jets are positioned along the suction sides of the foils, the first one being located just upstream of the separation point, and modeled by source terms added to flow equations. Several different jet diameters and intensities are investigated. As the choice of turbulence model affects the final solution of Reynolds equations, turbulence is modeled by four different models: Spalart-Allmaras, realizable k-ε, k-ω SST, and γ-Re θ , and a comparison of obtained results is performed. Goals of the study include definition of an adequate numerical setting that enables sufficiently correct simulation of the problems in question as well as evaluation of the possible increase in aerodynamic performances. Lift coefficients, lift-to-drag ratios or relative pressure differences are improved for all controlled cases.

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Section: S650mentioning

confidence: 99%

“…Various jet configurations are tested and their efficiencies are compared. Finally, although such studies on subsonic flows around airfoils [16][17][18][19] and cascades [20] can be found, they have rarely been performed for transonic regimes [21].…”

Section: S650mentioning

confidence: 99%

Two-dimensional numerical analysis of active flow control by steady blowing along foil suction side by different urans turbulence models

Svorcan¹,

Fotev²,

Petrović³

et al. 2017

THERM SCI

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“…Finally, the authors also previously used ANNs to control boundary layers around foils in linear cascades. 18 The paper is organized as follows: since this is primarily a computational study, adopted numerical approaches are firstly validated in the next section. Chapter 3 provides a description of the parameterized concentrator model that is used in flow computations covered in section 4.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, the authors also previously used ANNs to control boundary layers around foils in linear cascades. 18…”

Section: Introductionmentioning

confidence: 99%

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

Svorcan

Peković

Simonović

et al. 2021

Advances in Mechanical Engineering

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Wind energy extraction is one of the fastest developing engineering branches today. Number of installed wind turbines is constantly increasing. Appropriate solutions for urban environments are quiet, structurally simple and affordable small-scale vertical-axis wind turbines (VAWTs). Due to small efficiency, particularly in low and variable winds, main topic here is development of optimal flow concentrator that locally augments wind velocity, facilitates turbine start and increases generated power. Conceptual design was performed by combining finite volume method and artificial intelligence (AI). Smaller set of computational results (velocity profiles induced by existence of different concentrators in flow field) was used for creation, training and validation of several artificial neural networks. Multi-objective optimization of concentrator geometric parameters was realized through coupling of generated neural networks with genetic algorithm. Final solution from the acquired Pareto set is studied in more detail. Resulting computed velocity field is illustrated. Aerodynamic performances of small-scale VAWT with and without optimal flow concentrator are estimated and compared. The performed research demonstrates that, with use of flow concentrator, average increase in wind speed of 20%–25% can be expected. It also proves that contemporary AI techniques can significantly facilitate and accelerate design processes in the field of wind engineering.

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“…Also, an evaluation on natural turbulence transition sensitivity of RANS turbulence models, implemented on the PAB3D code, was performed by Pao and Abdol-Hamid, [8]. Although some of these models have an apparable to actively fine tune the transition onset of propulsive system as shown in the work of Svorcan et al [17]. Possession of a turbulence model that can accurately predict transition onset, as well as correctly compute the transition length, has become a must amongst Computational Fluid Dynamics, or CFD, engineers who deal with transitional flows on a daily basis.…”

Section: Introductionmentioning

confidence: 99%

Analysis of transitional flow in 3D geometries using a novel phenomenological model

Vizinho

Morgado

Páscoa

et al. 2015

Aerospace Science and Technology

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Active boundary layer control in linear cascades using CFD and artificial neural networks

Cited by 20 publications

References 12 publications

Two-dimensional numerical analysis of active flow control by steady blowing along foil suction side by different urans turbulence models

Two-dimensional numerical analysis of active flow control by steady blowing along foil suction side by different urans turbulence models

Design of optimal flow concentrator for vertical-axis wind turbines using computational fluid dynamics, artificial neural networks and genetic algorithm

Analysis of transitional flow in 3D geometries using a novel phenomenological model

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