Enhanced outer peaks in turbulent boundary layer using uniform blowing at moderate Reynolds number

Hasanuzzaman, Gazi; Merbold, Sebastian; Motuz, Vasyl; Egbers, Christoph

doi:10.1080/14685248.2021.2014058

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…The exhalation volume flow rate was 8 l/min, corresponding to the respiratory minute volume of quietly seated occupants. For further details on the DEHS particle, interested readers are referred to [7], [8] and [9].…”

Section: Functional Testmentioning

confidence: 99%

DATIV – Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi Based Distributed Sensors

Hasanuzzaman,

Buchwald,

Schunk

et al. 2024

Preprint

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Enclosed public spaces are the hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of Particulate Matter Sensors has been developed and named as Dynamic Aerosol Transport for Indoor Ventilation or DATIV system. This system can use multiple Particulate Matter Sensors (PMS) simultaneously and can be remotely controlled using a Raspberry Pie based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or Smartphone with corresponding IP address. The software architecture and validation measurements are presented together with possible future developments.

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Section: Functional Testmentioning

confidence: 99%

DATIV – Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi Based Distributed Sensors

Hasanuzzaman,

Buchwald,

Schunk

et al. 2024

Preprint

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“…The 2D3C stereo PIV measurements used for the present PINNs predictions are from [10,12]. The measurements were taken at the boundary-layer wind-tunnel facility at Laboratoire de Mécanique des Fluides de Lille-Kampé de Fériet [7] in France, within the European High Performance Infrastructures in Turbulence (EuHIT) framework.…”

Section: Pinns Predictions Of the Piv Flow Fieldmentioning

confidence: 99%

“…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]. Application of another measurement technique only to infer wall-shear data in conjunction with PIV is often not possible depending on the experimental setup.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to spurious data from the near-wall region due to unavoidable laser reflection from the wall. A reduction of the boundary-layer thickness (δ) makes these near-wall measurements even more challenging a consequence of increased flow velocity [13]. Consequently, all these issues limit the capacity of classical PIV approaches such as 2D2C and 2D3C (to some extent all other PIV approaches) to accurately measure near-wall data.…”

Section: Introductionmentioning

confidence: 99%

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Enhancement of PIV measurements via physics-informed neural networks

Hasanuzzaman

Eivazi

Merbold

et al. 2023

Meas. Sci. Technol.

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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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“…The flow visualization was done with a continuous-wave laser (model Ray power 450 from manufacturer Dantec Dynamics with a wavelength of 532 nm and power approx. 0.5 W) and Di-Ethyl-Hexyl-Sebacat (DEHS) particles with ∼0.3 μm size [9]. Figure 2 shows the flow visualization images where heated dummies create significant changes in the room flow, up-and down-washing due to thermal convection is indicated by the yellow and blue arrows respectively.…”

Section: Cottbus Aerosol Particlementioning

confidence: 99%

Reference experiment on aerosol particle transport for dynamic situations

Merbold

Hasanuzzaman

Buchwald

et al. 2023

Tm - Technisches Messen

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To study airborne transport of aerosol particles by mixed convection and dynamic situations within a closed room, the Cottbus Aerosol Particle Reference Experiment (CARE) was built and equipped, which includes thermal manikins and a spreader dummy. For various flow configurations (location of spreader, heating bodies, windows opened, air ventilation with and without air purification systems) flow visualisation was performed, particulate matter sensors (PMS) measured local particle concentrations, head-mounted camera systems counted particle concentrations of individuals and finally, large field of view Shake-The-Box Particle Tracking delivered velocity fields. The comprehensive experimental configuration of different measurement systems are discussed in terms of their aerosol transport properties and quantitative results, effective application and comparative efficiency explaining the flow dynamics. The findings from these experiments also provide information under which circumstances particularly high concentrations of aerosol particles can be found on which locations.

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Enhanced outer peaks in turbulent boundary layer using uniform blowing at moderate Reynolds number

Cited by 10 publications

References 31 publications

DATIV – Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi Based Distributed Sensors

DATIV – Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi Based Distributed Sensors

Enhancement of PIV measurements via physics-informed neural networks

Reference experiment on aerosol particle transport for dynamic situations

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