19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

Dynamic Masking Application Examples in Two-Phase Flow PIV Measurements
F. Gökhan Ergin1 *, Jimmy Olofsson2, Bo B. Watz3, Nicolai Fog Gade-Nielsen3
1: Microfluidics Product Manager, Dantec Dynamics, DENMARK
2: LIF Systems Product Manager, Dantec Dynamics, DENMARK
3: Software Development Engineer, Dantec Dynamics, DENMARK*Corresponding author: gokhan.ergin@dantecdynamics.com
Keywords: Phase-separated PIV processing, dynamic masking, histogram thresholding, phase boundary detection, flame front detection,
rigid object stabilization

ABSTRACT
Dynamic masking and phase-separated PIV processing are powerful analysis tools during two-phase flow field
diagnostics. This paper aims to summarize an overview of several dynamic masking techniques used in ten
different application examples, and to make recommendations on masking strategy in different applications. The
examples cover a wide range of applications: all possible two-phase flow combinations, micrometer to decimeterscales, flows with and without temperature gradients can be found among the examples. The application examples
include: droplet formation in micro-channels, mixing of two liquids in microscale using magnetic forces, bubbly
flow in water, combustion in a low-swirl lifted flame, a vibrating airplane model in a wind tunnel, two puller-type
micro swimmers with one pulling breaststrokes and the other using its flagellum, dolphin kick of a humanswimmer, spinning micro-rafts on the air-water interface, and a breaking wave experiment in a wave tank.

1. Introduction
Dynamic masking and phase-separated PIV processing have become standard routines during
two-phase flow field diagnostics. Different dynamic masking strategies have been reported in
the literature, which can be grouped under three categories (Brücker 2000): (i) during image
recording using additional optical systems and components to record one of the phases (opticalphase separation, OPS) (ii) during image processing before PIV processing (digital phaseseparation, DPS) and (iii) post-PIV analysis using differentiators in the cross-correlation function(post-phase separation, PPS). Furthermore, the digital phase separation techniques can begrouped under four categories: (ii-a) size based discrimination (SBD), (ii-b) histogram
thresholding (HT), (ii-c) phase boundary detection (PBD), and (ii-d) rigid object stabilization(ROS) techniques. In challenging cases, dynamic masks are often more successful if both opticaland digital phase separation are used. This paper aims to summarize an overview of dynamic
masking techniques used in ten different application examples and to make recommendations

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

on masking strategy in different situations. The examples cover a wide range of applications: allpossible two-phase flow combinations, micrometer to decimeter scales, and flows with andwithout temperature gradients can be found among the examples. A summary of the application
examples are listed in Table 1, where HT, PBD and ROS techniques are used.
Experiment

PhasesScale

Equipment

Masking

Seeding

Illumination

Frequency

1.1µm

Continuous

20 000 Hz

Strategy
Droplet

Liq-Liqµm

TR µPIVDPS (HT)

formation
Flagellum

broadband
Sol-Liqµm

TR µPIVDPS (HT)1.0 µmswimmer
DolphinGreen

12.5 Hzpulsed LED
Sol-Liqdm

TR PIV

Sol-Liqµm

TR

DPS (HT)

100 µmTR PIV laser

250 Hz

5 µm

TR PIV laser

300 Hzkick
Spinning
micro-rafts
Breaking

Stereo DPS (HT)µPIV
Gas-Liqdm

TR PIVDPS (HT)

10µm

TR PIV laser

96 Hz

Liq-LiqµmµPIV

DPS(PBD)

1.0 µm

Green

4 Hz

wave
Magnetic
micromixer
Bubbly flow
Lifted flame

pulsed LED
Gas-Liq
Gas-Gascm
cm

TR PIV

OPS+PBD

TR PIV + OPS+PBD

20µm-

PIV Laser + 400 Hz

50µm

red LED

1µm

TR PIV and TR

TR OH LIF
Airplane

Sol-Gasmm

model
Breaststroke Sol-Liq
swimmermm

4 000 Hztunable dye laser

Long-range

DPS

µPIV

(ROS)

Long-rangeDPS

TR µPIV(ROS)

1.0 µm

PIV laser

10 Hz

2.0 µmInfrared CW 2 000 Hz
laser

Tab. 1 An overview of the application examples with different dynamic masking strategies.

The motivation behind dynamic masking is to perform phase-separated PIV measurements andthereby reduce the measurement uncertainty significantly along the phase boundaries. This is
crucial, since often important fluid dynamics phenomena occur close to a phase boundary, i.e.
surface of a swimmer, flying object, combustion zone or a mixing layer. Therefore, detection andseparation of phases using dynamic masking provides more accurate PIV measurements andthereby a better understanding of fluid phenomena near phase boundaries.

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

The application examples include: droplet formation in micro-channels (Fig. 1), a puller-type
micro swimmer using a flagellum (Fig. 2), dolphin kick of a human swimmer (Fig. 3), spinningmicro-rafts on the air-water interface (Fig. 4), a breaking wave experiment in a wave tank (Fig.
5), mixing of two liquids in microscale using magnetic forces (Fig. 6), combustion in a low-swirl
lifted flame (Fig. 7), bubbly flow in water (Fig. 8), a vibrating airplane model in a wind tunnel(Fig. 9), and a micro swimmer pulling double-breaststrokes (Fig. 10).
2. Histogram Thresholding (HT) Examples
HT-based dynamic masking relies on the pixel intensity differences (bimodal histograms)
between the phases for phase discrimination. The phase to be masked (can be solid, liquid orgas) should have a certain pixel value distribution that is lower or higher than a certain
threshold. Once such a threshold is selected, the pixel values lower than the threshold can be set
to zero, and the rest to 1. The new image ensemble is then called the mask ensemble, andmasking is performed by a simple image multiplication at each time step of the ensemble.
Selecting a threshold level in combination with some other image pre-processing functions (lowpass filters, morphology filters etc.) allows one to generate a dynamic mask that follows thephase or the object in the image ensemble. In the following five examples, we demonstrate thesuccessful application of HT-based dynamic masking, at the same time highlighting someunwanted local artifacts observed in the field of view (FoV).

Fig. 1 Snapshot of the flow field at the rupture instance during the droplet break-up experiment performed in a
microchannel. (from Carrier et al. 2015). Colors indicate velocity magnitude, max velocity 270 mm/s. Histogram
thresholding based dynamic mask is able to follow morphological changes during droplet formation.
(Collaboration: Université de Lorraine, CNRS)

HT-based dynamic masks are quite popular because they are relatively simple to apply and arequite powerful in applications where the phase to be masked has a complex shape andundergoing morphological changes (merging of bubbles, rupturing of droplets etc). Fig. 1 shows19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

an example of an HT-based dynamic mask applied in a micro-droplet formation experiment at
the rupture instance. In order to capture the rupture event, the MicroPIV experiment was
performed with single-frame image acquisition at 20 000 fps. A movie of this experiment isavailable in Ref. M1. As can be seen in the movie, the droplet goes through growth, necking,rupture and recoil phases where its shape, size, and morphology changes and this can be tracked
successfully by the dynamic mask. Further details can be found in Carrier et al. (2015).
The second example on HT-based dynamic masking is the masking of a puller-type microscopic
swimmer, where E. Gracilis (Fig. 2a) is meandering through water by pulling strokes using its
anterior flagellum, and changing direction by rotating, bending and stretching its body (Fig. 2ef). HT-based mask is generated (Fig. 2c) after background subtraction (Fig. 2b) and the mask isapplied using a number of image processing functions (Fig. 2d). Although HT masking is very
successful in following flexible objects that are changing shape (bending, stretching etc.), someunwanted artifacts can be observed. For example in this case, excessive use of the morphology
filters (erosions and dilations) makes the mask size larger than the actual object size close to theedges of the FoV (compare top of Fig. 2e-f). Further details can be found in Ergin (2015, 2017).

(a)

(b)

(c)

(d)
(e)

(f)

Fig. 2 (a, b, c, d) Snapshot of a microorganism (E. Gracilis) swimming horizontally in water at t=1.92 s.
(a) Raw
image (b) after pixel inversion and background subtraction (c) HT-based dynamic masking (d) particle image afterimage masking. (e, f) Quality of dynamic masking: (e) Recorded position (f) dynamic mask of E. Gracilis every 0.96s.
From Ergin (2015).

HT-based dynamic masking can be used successfully for macroscopic swimmers as well as for
microscopic swimmers. Fig. 3 shows the flow field around a human swimmer's feet while
performing a dolphin kick. A movie of this experiment is available in Ref. M2. The experiment isperformed in a swimming pool, using a time-resolved PIV system with a powerful laser where19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018the human subject was wearing proper laser protection equipment. The masking strategy reveals
the large trailing vortex caused by the kick, and the downwash of the water above the foot sole.
However, due to the focused laser light sheet, high-intensity reflections were observed aroundthe toes, where the fluid phase was registered as a mask, and therefore in the immediate vicinityof the toes flow field information could not be obtained. On the contrary, some regions on the leg
and heels were not masked due to a low pixel value found in these regions. Further details on
undulatory swimming can be found in Hochstein et al. (2012).

Fig. 3 Flow field around the feet of a human swimmer performing a “Dolphin kick” in a swimming pool. One side
effect of the HT- based dynamic masking is that vectors appear on the swimmers left foot in the dark regions, andno vectors are found around the toes of the right foot due to strong reflections in the fluid phase. (Collaboration:
Friedrich-Schiller-Universität, Jena)

HT-based dynamic masking can be applied to multiple-camera systems (f. ex. Stereoscopic PIV)
as well as single camera systems. Fig. 4 shows the application of an HT-based masking strategy
in a stereoscopic MicroPIV experiment using two cameras. The experiment intends to capture
the three-dimensional motion of the free surface while a pair of micro-rafts are spinning in theclockwise direction. Although the generated dynamic masks are circular like the rafts, themasked areas in the Stereoscopic velocity fields are not circular in certain time instances. Thiscan be explained by the lack of seeding near the rafts (red circles in Fig. 4a): Due to the highrotation speed (2500 rpm) used in this experiment, the seeding particles are pushed away fromthe rafts due to centrifugal forces. The lack of seeding in a high velocity region introduces ahigher uncertainty in the cross-correlation based displacement estimation in the immediate
vicinity of the rafts in individual camera views. As a result of this, the Stereoscopic construction
accuracy is lower, and these vectors are discarded by the algorithm. In short, although the raw
images of the rafts and the dynamic masks generated from these are quite round and symmetric(Fig. 4a), the resulting flow field information may not be due to low seeding density (Fig. 4b).
Further details can be found in Ergin et al. (2017a).

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

(a)

(b)

Fig. 4 (a) Raw image of the spinning micro-rafts. Red arrows indicate rotation direction; red dashed circles show theboundary for low seeding concentration (b) Stereoscopic PIV results at the same instant where colors indicate out ofplane velocity component and white color indicates where vectors fall in the circular masked areas. From Ergin et al.
(2017a) (Collaboration: Max Planck Institute).

Another successful example for HT-based dynamic masking in variable seeding density is froma wave tank experiment. In this case the measurement area is illuminated from below and due toexpanding light sheet propagating upwards, the lower left and lower right corners of the FoV is
not sufficiently illuminated. Additionally, the bubbles and droplets over the free surface and theimage of the free surface is not desirable (Fig. 5a). Following a background subtraction,difference of Gaussian filters, opening and closing filters a dynamic mask ensemble was
generated to mask everything except the seeded area (Fig. 5b). The flow field is obtained using a
PIV/PTV algorithm where the low seeding areas can be seen (Fig. 5c). Further details can befound in Vested et al. (2018).

(a)

(b)

(c)

Fig. 5 Snapshot from the breaking wave experiment in a wave tank. (a) Inverted raw particle image of the breaking
wave traveling to the left. (b) Result of image pre-processing just before histogram thresholding is applied (c)PIV/PTV results displayed on a regular grid where colors indicate velocity magnitude (Collaboration: Technical
University of Denmark).

3. Phase Boundary Detection (PBD) Examples
As discussed in the previous section, HT-based masking strategies are quite powerful ingenerating dynamic masks, but some unwanted artifacts can be observed on the boundaries due
to reflections, excessive use of certain image processing functions and/or due to lack of seeding19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

in stereoscopic reconstruction. One solution is to use phase boundary detection (PBD), a novel,
hybrid dynamic masking technique reported recently (Ergin et al. 2017b), where both pixel
intensities and/or intensity gradients are used for phase discrimination. In the following three
examples, the successful application of PBD-based dynamic masking is presented.

(a)

(b)

Fig. 6 (a) Snapshot from the magnetic micromixer experiment reported in Ergin et al. (2014) with magnetic fluid(dark phase) and water (bright phase). (b) The detected phase boundary (red line). Phase boundary detection
technique is powerful in detecting phases with a large spectrum of local pixel value gradients. (Collaboration:
University of Latvia)

Pixel intensity differences exist frequently in the liquid-liquid mixing applications. One example
is taken from a magnetic micromixer (Fig. 6), where there is a clear difference between the
optical absorption rates of liquid water (brighter phase) and magnetic fluid (darker phase), in
this case, water doped with magnetic nanoparticles. In this experiment (reported in Ergin et al.
2014 among others) the two liquids are forced into each other using magnetic forces. A movie of
this experiment is available in Ref. M3. A shadow imaging technique was used and thedifference in the optical absorption rates was sufficient to create an overall distinction between
the phases (OPS was not necessary). However, since this is a mixing experiment, the intensity
gradients were found to lie in a wide range (very shallow to very steep gradients), and often it
was difficult to decide where to place the phase boundary (red line in Fig. 6b) on a shallow
intensity gradient. Therefore, for the magnetic micromixer experiment shown in Fig. 6a, only the
pixel intensities in a local and finite bounding box were used for phase discrimination by
applying Otsu thresholding. This strategy is not only a systematic way to place the phaseboundary, but also a more powerful one compared to the simpler HT-based dynamic masking.
Further details can be found in Ergin et al. 2017b.
In other cases, pixel intensity gradients should be considered in addition to the pixel intensities
during phase discrimination. Fig. 7 shows the phase separation of raw images (Fig. 7a-b) and theresulting flow field (Fig. 7c) across a turbulent, premixed lifted flame. The experiments were19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

performed using a time-resolved PIV and OH-LIF system triggered simultaneously. Since the
OH distribution was used for the generation of the dynamic mask, this application example iscombining OPS and DPS. In the combustion process, the OH radical is produced very close to
the reaction zone and diminishes gradually with increasing distance from the flame front. In
other words, very steep gradients of the OH radical marks the flame front and the phaseboundary and shallower gradients indicate the regions where OH reacts with other molecules toform the combustion products. For this reason, the pixel intensity gradients in the OH image(not shown) are used for phase discrimination in conjunction with the pixel intensity differences.
Immediately apparent from the raw images is the substantial difference in the seeding densitiesof the fuel phase (Fig. 7a) and the combustion products (Fig. 7b) across the flame front. This isdue to the sudden heat release in the reaction zone which results in an expansion in the
combustion products phase. The PBD based dynamic masking together with OPS lends itself tobetter phase discrimination and to phase-separated PIV measurements (Fig. 7c) where themeasurement accuracy is improved significantly compared to mixed-phase PIV analysis. Further
details can be found in Ergin et al. (2017b).

(a)

(b)

(c)

Fig. 7 Application of phase boundary detection (PBD) based dynamic masking used in combination with opticalphase separation (OPS) using an OH-LIF system in a swirling lifted flame experiment. (a) 1st frame of the fuel phaseof the raw particle image pair, (b) same for the combustion products phase (c) Close-up of the phase-separated PIV
measurement results at the same instant. Red vectors are in the products phase, blue vectors are in the fuel phase,and the continuous black line depicts the flame front. From Ergin et al. (2017b) (Collaboration: Lund University)

Like the HT-based techniques, the PBD-based dynamic masking techniques can handle
morphological changes (merging and separating) quite well. In the next example, the PBD-basedmasking technique is applied to a flow field with rising bubbles (Fig. 8a) where two bubbles are
merging later during the process (Fig. 8b). This is the second example where PBD is used in
combination with OPS, because a separate shadow illumination/recording system is used.
However, because of the choice of the wavelengths, some cross-talk was observed in the images.
This can be observed in the particle images within the large bubble in the middle in Fig. 8a.
These are either particles located behind the bubble refracting through the bubble, or in front of
the bubble, reflecting from the bubble surface. Additionally, the bubbles are transparent and19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

appear as hollow rings in the raw images and a simple HT-based masking cannot be used here.
In fact, this example is the most challenging example in terms of the number of challenges
involved: (i) arbitrary shape and direction, (ii) phase to be masked is hollow, (iii) morphologicalchanges (merging and separation), (iv) cross-talk between the two optical systems, and (v)intensity differences in the particle images due to shadows cast by the bubbles. Despite thechallenges, image pre-processing and PBD-based dynamic masking provides very good results(Fig. 8)(a)(b)

Fig. 8 (a) Close up of flow field around 3 bubbles rising in water at t=8ms in a bubbly flow setup. (b) Same 3 bubbles
at t=73ms. Phase boundary detection based dynamic masking is able to detect morphological changes such as
bubbles merging or separating. Colors indicate velocity magnitude.

4. Rigid Object Stabilization (ROS) Examples
So far we have provided examples where the phase or object to be masked is changing shape orundergoing morphological changes, which is the case in many experiments. In other cases theobject to be masked could be rigid, and simply translating and rotating within the FoV. In thenext two examples, we discuss how dynamic masks can be produced in these situations. The
basic idea is changing from a global coordinate system to an object-fixed coordinate system.
The first example is concerning boundary layer measurements of an airplane model in a windtunnel (Fig. 9a) where a conventional 15Hz two-frame PIV acquisition mode is used. Due toaerodynamic forces, the model was observed to be moving a few pixels to a different position(Fig. 9b) from one acquisition to the next (i.e. from one pulse pair to the next), but seemed
stationary within each pulse pair, thanks to the short pulse separation. If the aim is to performunmasked PIV analysis, the movement of the model does not create any complications forindividual flow fields. However, the accuracy gets worse when one wants to perform f. ex.
statistical or modal analysis simply because computed velocity vectors lie in a different position
with respect to the object. If the objective is to perform masked PIV analysis, or subsequentstatistical or modal information is required, then the solid object must be first stabilized in the19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

FoV, before applying a static masking strategy. In this particular case, the solid object wasstabilized by first extracting a rough shape of the object from, say, the first image, andcorrelating this sub-image to the full images of the ensemble. This results in the displacement of
the object from one acquisition to the next (Fig. 9b) which can be corrected for in the next step byshifting the original images by the negative amount of each displacement. Once the object isstabilized in the FoV, and static masking is performed; the vector statistics results are much moreaccurate in the boundary layer compared to a similar analysis without ROS (Fig. 9c). Please notethat the static masking performed after object stabilization is actually dynamic masking
performed on the original images.

(a)

(b)

(c)

Fig. 9 Application of rigid object stabilization (ROS) based dynamic masking in a boundary layer flow around amodel airplane in a wind tunnel. (a) Typical raw particle image where some surface features are visible on the
airplane nose. From Ergin and Alemdaroğlu (2013). (b) Position of the airplane as it is moving around a few pixels
from one acquisition to the next due to aerodynamic forces. (c) Mean of the horizontal velocity component, U(average of 61 vector maps) shown together with the used mask (purple)

The final dynamic masking example is the flow field measurements around a sub-mm sizedmarine organism while performing breast-strokes (Fig. 10a). In a long-distance MicroPIV
experiment performed in the Technical University of Denmark, an individual Acartia Tonsa wascaptured while swimming against the gravitational field in a relatively straight trajectory (Erginet al. 2015). In this acquisition A. Tonsa has performed three cycles of double-power strokes
followed by a recovery stroke. The ROS is performed in a very similar fashion as described in theprevious example: First a sub-image of the object is extracted from the first image of theexample, then this was cross-correlated within the images of the entire ensemble and a region ofinterest (RoI) is extracted around the peak of the cross-correlation function (Fig. 10b). After
performing ROS, applying a static mask and performing phase locked-averaging the flow fieldaround A. Tonsa was obtained more accurately compared to earlier results (Fig. 10c). Further
details can be found in Ergin et al. (2015) and Ergin (2017).

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

(a)

(b)

(c)

Fig. 10 Application of rigid object stabilization (ROS) based dynamic masking in a biological flow around A. Tonsa,
swimming upwards in a water tank (a) First frame of the raw particle image ensemble where some seeding particles
stuck on the swimmer are visible (b) Cross-correlation function of the organism with the 1st frame. (c) Flow field
during the second power stroke shown together with the used mask (dark blue). Colors indicate velocity
magnitude. From Ergin et al. (2015). (Collaboration: Technical University of Denmark)5. Suggestions and Conclusions
Several dynamic masking strategies used in ten different application examples are presented.
Some conclusions / recommendations can be made based on the foregoing discussion and basedon the experience of the authors:
1. Most likely there is no magic masking strategy that works equally well for all the applications,and the masking strategy should be selected case by case.
2. Always prefer OPS in combination with DPS whenever possible.
3. Think about the OPS-based masking strategy already during the planning stage of theexperiment, as f. ex. choice of correct illumination wavelength can make things much easier.
4. Use HT-based or PBD-based masking strategies if the masked phase or object has arbitrary
shape and direction, or going through morphological changes (such as merging and separation)
5. HT-based dynamic masking may be the easiest strategy for certain cases, where a single
thresholding operation can be sufficient for image segmentation. In other cases one may have toperform some image pre-processing to bring the image ensemble to a state ready for pixel-value
thresholding.
6. Prefer the relatively simpler HT-based masking strategy for quick checks and preliminary
results, and choose PBD-based masking strategies for more challenging situations and/or for
more accurate results.
7. Start with an ROS-based masking strategy if the object to be masked is rigid. ROS-basedmasking strategies have the added advantage of measuring the velocity and accelerations of therigid object that is to be masked.

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

6. Acknowledgements
The authors are indebted to several individuals for providing the raw image ensembles for
dynamic masking and subsequent PIV analysis performed at Dantec Dynamics:
Denis Funfschilling (Fig. 1), Stefan Hochstein (Fig. 3), Ahmet Tabak and Wendong Wang (Fig. 4),
Malene Vested (Fig. 5), Kaspars Erglis and Andrejs Cebers (Fig. 6), Per Petersson (Fig. 7), Zhang
Yao (Fig. 8), and Navish Wadhwa (Fig. 10). Furthermore, the authors wish to thank Giovanni
Noselli for providing the culture for Euglena Gracilis (Fig. 2).
7. Author Contributions
FGE: Definition, development and validation of HT, PBD, ROS techniques, preparation of setup
and image acquisition of airplane model and flagellum swimmer experiment, image processing
and dynamic masking for all application examples, abstract preparation, presentation
preparation, manuscript preparation. JO: PBD definition and development, data acquisition andprocessing for the lifted flame experiment. BW: ROS development in DynamicStudio, image
processing for magnetic micromixer, and airplane model experiments. NGN: PBD development
in DynamicStudio, data processing for lifted flame experiment. All authors reviewed themanuscript.
8. References

Brücker Ch (2000) PIV in two-phase flows. von Karman Institute for Fluid Dynamics,
lecture series 2000–01



Carrier O, Ergin FG, Li HZ, Watz BB & Funfschilling D (2015) Time-resolved mixing &flow-field measurements during droplet formation in a flow-focusing junction. J.
Micromech. Microeng. 15 084014



Ergin FG and Alemdaroğlu N (2013) Long-distance MicroPIV measurements of acommercial aircraft model at low Reynolds number. Proc. 7th Ankara International
Aerospace Conference, Ankara, Turkey, AIAC-2013-013



Ergin FG, Watz BB, Erglis K and Cebers A (2014) Modal analysis of magnetic
microconvection. Magnetohydrodynamics Vol. 50 (2014), No. 4, pp. 339–352



Ergin FG (2015) Flow field measurements during microorganism locomotion using
MicroPIV and dynamic masking. Proc. 11th Int. Symp. on PIV, Santa Barbara, California,
September 14-16, 2015

19th International Symposium on the Application of Laser and Imaging Techniques to Fluid Mechanics・LISBON | PORTUGAL ・JULY 16 – 19, 2018

Ergin FG, Watz BB and Wadhwa N (2015) Pixel-accurate dynamic masking and flow
measurements around small breaststroke-swimmers using long-distance MicroPIV. Proc.
11th Int. Symp. on PIV, Santa Barbara, California, September 14-16, 2015



Ergin FG. (2017) Dynamic masking techniques for particle image velocimetry. J. of
Thermal Science and Technology,Vol 37, 2, pp. 61-74. ISSN 1300-3615.



Ergin FG, Tabak AF, Wang W, Sitti M (2017a) Time-resolved measurements of the freesurface motion due to spinning micro-rafts using Stereo MicroPIV. Proc. 12th
International Symposium on Particle Image Velocimetery June 2017, Pusan, Korea



Ergin FG, Olofsson J and Gade-Nielsen NF (2017b) Phase separated PIV Measurements
Using Phase Boundary Detection. Proc. 12th International Symposium on Particle Image
Velocimetry June 2017, Pusan, Korea



Hochstein S, Pacholak S, Brücker C and Blickhan, R (2012) Experimental and numericalinvestigation of the unsteady flow around a human underwater undulating swimmer. In:
C. Tropea & H. Bleckmann (Eds.) Nature-Inspired Fluid Mechanics, Notes on Numerical
Fluid Mechanics and Multidisciplinary Design, 119, 293-308.



Vested MH, Ergin FG, Carstensen S, and Christensen ED (2018) High-resolution planar
two-component PTV measurements in a breaking wave. Proc. to 19th Int. Symp. on App.
of Laser and Imaging Tech. to Fluid Mechanics, July 2018, Lisbon, Portugal.



M1: https://www.dantecdynamics.com/microfluidics-category/time-resolved-velocitymeasurements-of-droplet-formation-in-a-flow-focusing-junctionM2:

https://www.dantecdynamics.com/hydraulics-hydrodynamics-category/piv-

measurements-during-the-dolphin-kick-of-a-human-swimmer
M3:

https://www.dantecdynamics.com/microfluidics-category/modal-analysis-in-a-

micromixer