A Calculation Method of Leading-Edge Separation Bubbles

Gleyzes, C.; Cousteix, J.; Bonnet, J. L.

doi:10.1007/978-3-662-09014-5_10

Cited by 23 publications

(20 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The laminar bubble bursting could be reproduced when using transition prediction that was also found to initiate the dynamic stall on test case DS5 in the experiment (Refs. 9,12,25). Test case DS2 is a case with higher Mach number where the influence of a laminar separation bubble is likely to be less important and the impact of compressibility effects more prominent (Ref.…”

Section: Influence Of Transition Predictionmentioning

confidence: 95%

Improved Two-Dimensional Dynamic Stall Prediction with Structured and Hybrid Numerical Methods

Richter¹,

Pape²,

Knopp³

et al. 2011

j am helicopter soc

View full text Add to dashboard Cite

A joint comprehensive validation activity on the structured numerical method elsA and the hybrid numerical method TAU was conducted with respect to dynamic stall applications. To improve two-dimensional prediction, the influence of several factors on the dynamic stall prediction was investigated. The validation was performed for three deep dynamic stall test cases of the rotor blade airfoil OA209 against experimental data from two-dimensional pitching airfoil experiments, covering low-speed and high-speed conditions. The requirements for spatial discretization and for temporal resolution in elsA and TAU are shown. The impact of turbulence modeling is discussed for a variety of turbulence models ranging from one-equation Spalart-Allmaras-type models to state-of-the-art, seven-equation Reynolds stress models. The influence of the prediction of laminar/turbulent boundary layer transition on the numerical dynamic stall simulation is described. Results of both numerical methods are compared to allow conclusions to be drawn with respect to an improved prediction of dynamic stall. Nomenclatureb span, m c chord, m c d , c d drag coefficient, difference in drag coefficient c l , c l lift coefficient, difference in lift coefficient c m , c m pitching moment coefficient, difference in pitching moment coefficient f frequency, Hz M Mach number r radius, m Re Reynolds number s, s LE , s max cell size, cell size at airfoil leading-edge, maximum cell size, m T period, s v ∞ freestream velocity, m/s x, y coordinates, m y + normalized wall distance α, α angle of attack, difference in angle of attack, deg t timestep, s ω * reduced frequency, ω * = 2πfc/v ∞

show abstract

Section: Influence Of Transition Predictionmentioning

confidence: 95%

Improved Two-Dimensional Dynamic Stall Prediction with Structured and Hybrid Numerical Methods

Richter¹,

Pape²,

Knopp³

et al. 2011

j am helicopter soc

View full text Add to dashboard Cite

show abstract

“…In this paper we present and evaluate a recently developed [41] transition modeling formulation that intends to account for the turbulence growth within a LSB and in the downstream relaxation flow region. To this purpose, laminar separation transition triggering (hereafter LSTT) in the RANS model is progressive in the streamwise direction (s), from the criteria-determined [42][43][44] transition onset to full turbulence. The assumption is made that the streamwise length between the laminar separation point, which is exactly determined by the solution of the laminar Navier-Stokes equations, and the criteria-determined transition onset is the characteristic lengthscale of the LSTT-process.…”

Section: Introductionmentioning

confidence: 99%

RANS modeling of Laminar Separation Bubbles around Airfoils at Low Reynolds conditions

Bernardos¹,

Richez²,

Gleize³

2019

AIAA Aviation 2019 Forum

View full text Add to dashboard Cite

remain separated for longer distances (long bubble globally modifying wall-pressure distribution [1]). In both cases, airfoil lift, drag and moment are influenced by the presence of the bubble. RANS (Reynolds-Averaged Navier-Stokes) turbulence models require specific transition information and/or modification to handle the separation-induced transition which dominates the flow [7, 10]. Numerous early [1, 6, 9, 11, 12] and more recent [8, 13-15] studies, both experimental and computational, highlight the complex physics of the transitional flow inside LSBs, such as pronounced three-dimensionality, high unsteadiness, turbulent breakdown driven by multiple coherent structures and complex oscillator and amplifier instability mechanisms that may lead to notable changes in the flow topology. Several approaches of varying degree of empiricism have been used to predict this difficult flow. Ultimately, improved transport-equation based transition models [16] are expected to accurately predict such flows. Correlation-based models [17, 18] reformulate widely used transition correlations [19] into coefficients of transport equations for the intermittency [20, 21] and other transition parameters, to obtain local, and recently [22] Galilean invariant, transition models. Physics-based models [23-25] generally rely on the concept [26] of laminar (pre-transitional) kinetic energy k L ≈ 1 2 u 2 (essentially streamwise, in agreement with transition physics [27] and with the observed rapid increase of the Reynolds-stress tensor anisotropy in very low Reynolds number channel flows [28]), which is computed by a specific transport equation, to trigger and control transition in the turbulence model. Transport-equation based transition models are quite successful in mimicking transitional flow effects and in detecting transition [16]. Given the extreme difficulty of accurately predicting transition, algebraic nonlocal transition models are also developed [29], including specific adaptations of additional transport equations [30-32]. Numerous authors have contributed to RANS modeling of LSBs on airfoils, especially at low chord-based Reynolds number Re c. Yuan et al. [33] and Windte et al. [34] performed extensive RANS modeling of LSBs for the flow around a SD7003 airfoil at Re c =60,000, with various models, and concluded that Menter's BSL [35] k-ω performed quite well. Source terms were disabled in the laminar regions upstream of transition onset which was determined using an approximate envelope method [34]. Lian and Shyy [36] proposed a nonlocal intermittency model for the activation of Wilcox's 1994 k-ω [37], through direct weighting of the eddy viscosity ν effective

show abstract

“…(13) and (14) are obtained from Eppler 18 for laminar flow, and Drela and Giles 15 for turbulent flow. Transition in attached flow is detected using the e" method with correlation developed by Gleyzes et al, 9 while laminar separation is detected using the criteria from Liu and Sandborn, 19 and Curie and Skan. 20 Airfoil drag C D is then calculated by the widely used Squire and Young formula 21 which relates C D to the trailing-edge, boundary-layer condition.…”

Section: Incorporation Bubble Model Into Airfoil Analysismentioning

confidence: 99%

Separation bubble model for low Reynolds number airfoil applications

Shum

Marsden

1994

Journal of Aircraft

View full text Add to dashboard Cite

Midchord laminar separation bubbles, which act as a transition mechanism on low Reynolds number airfoils, make a contribution to wing section profile drag that becomes increasingly important at low Reynolds number. A model for the analysis of the boundary layer through the bubble is needed. The model developed here, which is based on Horton's method, provides a simple computationally efficient analysis that matches the integral boundary-layer analysis methods used on most existing boundary-layer codes. The bubble calculation is initiated by the detection of laminar separation. Transition location and boundary-layer growth in the laminar region are determined using Van Ingen's shortcut e" method and Schmidt's correlations, respectively. Following Horton, the turbulent region is calculated using an iterative scheme that also functions as a bursting criterion, but the original linear velocity distribution has been replaced by Wortmann's concave velocity distribution. Both computation efficiency and prediction accuracy were improved after this change. Testing against experimental data showed that the bubble model greatly improved the drag prediction accuracy of the analysis in the Reynolds number range from 0.2 to 1.5 x 10 6 , especially in cases when the midchord bubble was dominant. The validity of the bubble model was further confirmed by accurate prediction of bubble size and reattachment velocity gradient. B-Nomenclature laminar separation angle parameter drag coefficient dissipation integral, 2 /« r(du/drj) skin friction coefficient, [2r/(pw^)] TJ=0 lift coefficient chord shape factor, 81/82 shape factor, 8 3 /8 2 maximum amplification integral total bubble length, l v + / 2 length of bubble laminar region, S T -s s length of bubble turbulent region, S R --S T velocity distribution exponent defined in Eq. (9) exponential growth of Tollmien-Schlichting waves in the e" method chord Reynolds number, u^clv distance Reynolds number, u^slv momentum thickness Reynolds number, u e 8 2 /v distance along surface from stagnation point freestream turbulence level velocity component, tangential to surface external velocity position along the chord line angle of attack velocity distribution parameter used in Eq. (8) displacement thickness, /o (1 -u/u e ) drj momentum thickness, Jo (u/u e )[l -(u/u e )] dr; energy thickness, /" (ulu e ][l -(ulu e ) 2 ] dr/ distance normal to airfoil surface velocity gradient parameter, (8 2 /u e )(du e /ds) Pohlhausen velocity gradient parameter, P T = density = shear stress v = kinematic viscosity Subscripts m = mean value R = reattachment S = separation T = transition Superscripts = lengths and velocities normalized by 8 2S and u es , respectively A = lengths and velocities normalized by 8 2T and u eT , respectively

show abstract

A Calculation Method of Leading-Edge Separation Bubbles

Cited by 23 publications

References 0 publications

Improved Two-Dimensional Dynamic Stall Prediction with Structured and Hybrid Numerical Methods

Improved Two-Dimensional Dynamic Stall Prediction with Structured and Hybrid Numerical Methods

RANS modeling of Laminar Separation Bubbles around Airfoils at Low Reynolds conditions

Separation bubble model for low Reynolds number airfoil applications

Contact Info

Product

Resources

About