Abstract:Uniform blowing in wall bounded shear flows is well known for its drag reducing effects and has long been investigated ever since. However, many contemporary and former research on the present topic has confirmed the drag reducing effect but very less is known regarding how blowing mechanism is effecting the coherent structures, more importantly, their influence on the Reynolds stresses at high Reynolds number. Therefore, effect of uniform blowing has been experimentally investigated in a zero pressure gradien… Show more
“…Wind tunnel measurements of airfoil drag are thus often based on wake measurements. This is also the case for experiments with micro blowing which showed partially good agreement with numerical results but also revealed some considerable deviations [26][27][28][29][30]. Eto et al [26] report drag reduction based on numerical results which was difficult to reproduce experimentally.…”
The present study considers uniform blowing in turbulent boundary layers as active flow control scheme for drag reduction on airfoils. The focus lies on the important question of how to quantify the drag reduction potential of this control scheme correctly. It is demonstrated that mass injection causes the body drag (the drag resulting from the stresses on the body) to differ from the wake survey drag (the momentum deficit in the wake of an airfoil), which is classically used in experiments as a surrogate for the former. This difference is related to the boundary layer control (BLC) penalty, an unavoidable drag portion which reflects the effort of a mass-injecting boundary layer control scheme. This is independent of how the control is implemented. With an integral momentum budget, we show that for the present control scheme, the wake survey drag contains the BLC penalty and is thus a measure for the inclusive drag of the airfoil, i.e. the one required to determine net drag reduction. The concept of the inclusive drag is extended also to boundary layers using the von Kàrmàn equation. This means that with mass injection the friction drag only is not sufficient to assess drag reduction also in canonical flows. Large Eddy Simulations and Reynolds-averaged Navier-Stokes simulations of the flow around airfoils are utilized to demonstrate the significance of this distinction for the scheme of uniform blowing. When the inclusive drag is properly accounted for, control scenarios previously considered to yield drag reduction actually show drag increase.
“…Wind tunnel measurements of airfoil drag are thus often based on wake measurements. This is also the case for experiments with micro blowing which showed partially good agreement with numerical results but also revealed some considerable deviations [26][27][28][29][30]. Eto et al [26] report drag reduction based on numerical results which was difficult to reproduce experimentally.…”
The present study considers uniform blowing in turbulent boundary layers as active flow control scheme for drag reduction on airfoils. The focus lies on the important question of how to quantify the drag reduction potential of this control scheme correctly. It is demonstrated that mass injection causes the body drag (the drag resulting from the stresses on the body) to differ from the wake survey drag (the momentum deficit in the wake of an airfoil), which is classically used in experiments as a surrogate for the former. This difference is related to the boundary layer control (BLC) penalty, an unavoidable drag portion which reflects the effort of a mass-injecting boundary layer control scheme. This is independent of how the control is implemented. With an integral momentum budget, we show that for the present control scheme, the wake survey drag contains the BLC penalty and is thus a measure for the inclusive drag of the airfoil, i.e. the one required to determine net drag reduction. The concept of the inclusive drag is extended also to boundary layers using the von Kàrmàn equation. This means that with mass injection the friction drag only is not sufficient to assess drag reduction also in canonical flows. Large Eddy Simulations and Reynolds-averaged Navier-Stokes simulations of the flow around airfoils are utilized to demonstrate the significance of this distinction for the scheme of uniform blowing. When the inclusive drag is properly accounted for, control scenarios previously considered to yield drag reduction actually show drag increase.
“…For a better understanding of the patterns observed in the space-time plots, the quadrant map of the fluctuating components of u r and ω is shown in figure 16. The quadrant analysis is a turbulence data-processing technique, and it has been applied over time in various experimental analyses, mainly in the investigation of turbulent shear flows (Wallace 2016;Hasanuzzaman et al 2020).…”
Section: Spatial and Temporal Behaviour Of The Shear Stressmentioning
confidence: 99%
“…The quadrant analysis is a turbulence data-processing technique, and it has been applied over time in various experimental analyses, mainly in the investigation of turbulent shear flows (Wallace 2016; Hasanuzzaman et al. 2020). Figure 16.Joint PDFs of radial velocity fluctuations and angular velocity fluctuations for radial positions (top row) close to the inner cylinder (figures 10–12, first row), (second row) centre of the gap (figures 10–12, third row) and (bottom row) close to the outer cylinder (figures 10–12, fifth row).…”
Turbulent Taylor–Couette flow between two concentric independently rotating cylinders with a radius ratio of
$\eta = 0.1$
is studied experimentally. While the scope is to study the counter-rotating cases between both cylinders, the radial and azimuthal velocity components are recorded at different horizontal planes with high-speed particle image velocimetry. The parametric study considered a set of different shear Reynolds numbers in the range of
$20\,000 \leq Re_s \leq 1.31 \times 10^5$
, with different rotation ratios of
$-0.06 \leq \mu \leq +0.008$
. The observed flow fields had a clear dependence on the rotation ratio, where flow patterns evolved with a more pronounced axial dependence. The angular momentum transport is computed as a result of the recorded flow fields and given by a quasi-Nusselt number. The dependence of the Nusselt number on the different rotation ratios shows a maximum for the low counter-rotating case and
$\mu _{max}$
is found between
$-0.011 < \mu _{max} < -0.0077$
. The Nusselt number decreases for stronger counter-rotation until a minimum is reached, where it tends to increase again for higher counter-rotation rates. The space–time behaviour of the turbulent flow showed the existence of patterns propagating from the inner region towards the outer region for all studied counter-rotating cases. In addition, patterns have been found that tend to propagate from the outer region towards the inner region with a novel character at high counter-rotation cases. These patterns enhance the angular momentum transport where a second maximum in the transport mechanism has to be expected.
“…Wall-shear stress plays an important role in the analysis of TBL data, not only when it comes to scaling the mean profile but also for quantify the role of skin friction in TBL-control experiments [9]. Traditionally, the determination of wall-shear stress through direct measurement was mostly based on direct oil-film interferometry (OFI) [21], surface hot-film interferometry (SHFA) [31] or non-intrusive laser-doppler anemometry (LDA) [13].…”
Section: Introductionmentioning
confidence: 99%
“…Hasanuzzaman et al [10] investigated uniform-blowing effects in ZPGTBL at moderately-high Reynolds numbers based on momentum thickness: Re θ = U ∞ θ/ν = 7500-19 763, where U ∞ and θ represent free stream velocity and momentum thickness, respectively. Uniform blowing was applied at an upstream location and the measurement was conducted immediately after.…”
Physics-informed neural networks (PINN) are machine-learning methods that have been proved to be very successful and effective for solving governing equations of fluid flow. In this work we develop a robust and efficient model within this framework and apply it to a series of two-dimensional three-component (2D3C) stereo particle-image velocimetry (SPIV) datasets, to reconstruct the mean velocity field and correct measurements errors in the data. Within this framework, the PINNs-based model solves the Reynolds-averaged-Navier-Stokes (RANS) equations for zero-pressure-gradient turbulent boundary layer (ZPGTBL) without a prior assumption and only taking the data at the PIV domain boundaries. The TBL data has different flow conditions upstream of the measurement location due the effect an applied flow control via uniform blowing. The developed PINN model is very robust, adaptable and independent of the upstream flow conditions due to different rates of wall-normal blowing while predicting the mean velocity quantities simultaneously. Hence, this approach enables improving the mean flow quantities by reducing errors in the PIV data. The PINNs-predicted data have less than 1\% error and is in excellent agreement with reference data. This shows that PINNs has potential applicability to shear-driven turbulent flows with different flow histories for predicting high-fidelity data.
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