14th International Symposium on Particle Image Velocimetry – ISPIV 2021
August 1–5, 2021

Volumetric velocimetry for small seeding tracers in
large volumes

Y. Zhang1∗, H. Abitan1, S. L. Ribergård1, C. M. Velte1

1 Technical University of Denmark, Department of Mechanical Engineering, Kgs. Lyngby, Denmark
∗ yiszh@mek.dtu.dk

Abstract
This paper presents the volumetric velocity measurement method of small seeding tracer with diameter
5µm ∼ 100µm for volumes of ≥ 500cm3. The size of seeding tracer is between helium-filled soap bubbles
(HFSB) and di-ethyl-hexyl-sebacic acid ester(DEHS) droplets. The targeted measurement volume dimen-
sion is equivalent to the volume of HFSB, which will give a higher resolution of turbulence study. The
relations between particle size, imaging and light intensity are formulated. The estimation of the imaging
results is computed for the setup design. Finally, the methodology is demonstrated for turbulence velocity
measurements in the jet flow, in which the velocities of averaged diameter 15µm air filled soap bubbles are
measured in a volume of 7200cm3.

1 Introduction
There is a strong push for large volumetric velocity measurement using small tracers in Barros et al. (2021).
Normally, due to light scattering and characterics length scale considerations, big tracers are used for tracing
large eddies in big volumes, and smaller tracers are applied for microscale eddies in smaller volumes. As
can be expected from Figure 1, there is a noticeable gap between attainable volumetric domains for large
(≥ 300cm3) and small (1µm∼ 20µm) tracers in the range between 102 cm3 ∼ 104 cm3.
As part of a long-term combined theoretical and experimental initiative, we are setting out to experimentally

test the degree of locality of the interactions between wavenumber components. This requires measurements
that cover the ‘global’ dynamics of the flow, while also capturing as wide a bandwidth of the turbulent (tem-
poral and spatial) spectrum as possible. Soap bubbles of diameter ∼ 15µm can follow the flow accurately to
the degree of spatio-temporal resolution in the experimental design while at the same time they are highly
resilient and long-lived and therefore the most viable option from a practical point of view. Therefore, these
smaller seeding particles are the key factor to these experiments.
Discetti and Coletti (2018) summarized methods that have been developed to measure the full volumet-
ric velocity (3D–3C, i.e. three-dimensional and three-component). In the past two decades, volumetric
velocimetry has evolved from experiments at moderate scale of 10 cm3 up to large scales of 104 cm3 by
various experiments. The initial tomo-PIV experiment done by Elsinga et al. (2006) was conducted with
1µm droplets in a volume of 13 cm3. Later the larger measurement volumes were obtained by volumetric
velocimetry in air conducted by Schröder et al. (2009), Humble et al. (2009), Violato et al. (2011), Ghaemi
and Scarano (2011), Schröder et al. (2011) and Atkinson et al. (2011), which did not exceed 102 cm3 mea-
surement volume with micron-scale seeding tracers.
The main factors limiting the scale up of PIV to macroscopic dimensions summarized by Scarano et al.
(2015) are the limited illumination energy from the illumination source, the scattering efficiency of the trac-
ers, the small optical aperture (due to large depth of field), and the sensitivity and spatial resolution of the
camera sensors. With low repetition rate (prolonging exposure time) and millimeter scale HFSB (increasing
scattering efficiency), larger measurement volumes can be observed to characterize large scale flow structure
in the experiments of Lobutova et al. (2009) and Kühn et al. (2011).
For turbulent flows, the inherent instability and three dimensions requires high resolution time-resolved
volumetric velocimetry. It is impossible to utilize millimeter scale tracers to measure the same scale turbu-
lent structure, because the length scale of the structure is smaller than the tracer diameter, and the cut-off



Figure 1: Compilation of volumetric velocimetry investigations in air flows: the particle diameter and the
imaged volume. The labeled points refers to the researches in the references: 1, Atkinson et al. (2011); 2,
Kühn et al. (2011); 3,Cafiero et al. (2015); 4, Caridi et al. (2016); 5, Boushaki et al. (2017); 6, Pröbsting et al.
(2013); 7, Schneiders et al. (2016); 8,Schneiders and Scarano (2016); 9, Schneiders and Scarano (2016);
10, Elsinga et al. (2006); 11, Schröder et al. (2007); 12, Humble et al. (2009); 13, Terra et al. (2017); 14,
Scarano et al. (2015); 15, Michaelis et al. (2012); 16, Hou et al. (2021); 17, Barros et al. (2021); 18, Bosbach
et al. (2009). The red square is the current study and the red area is the interested study area.

diameter of the particles deduced from cut-off frequency could be micron-scale (see Mei (1996)). The cur-
rent investigation aims at characterizing both the ’global’ flow structure and millimeter scale structure with
micron-scale tracers.
With the capabilities of low resolution cameras,, the only option to capture the turbulence structure is to
observe small tracers in a small measurement volume. Now with the development of camera sensor tech-
niques, the high resolution cameras in recent years provide the possibility of capturing the intermediate-size
turbulence structure in a larger volume, in which the whole evolution process of the turbulent spectrum can
be observed. However, there is still a problem between the visibility of seeding tracers and the following
effect of intermediate-size turbulence structure. In order to visualize small structures in the turbulence flow,
micron-scale tracers are needed to follow the unstable and three dimensional millimetre scale structures.
And as the result of reducing the tracer dimension, the signal from seeding tracers to camera would drop in
quadratic proportion.
For the purpose of solving this problem, this paper thus details the computation of particle image size with
tracers, lens and camera parameters. Then with the desired turbulent scales, the relation among camera, light
source and measurement volume is formulated to make sure that the particle images cannot travel across pix-
els (to avoid streaky particle images). Two examples of approximate 15µm particles in a 7200 cm3 volume,
that are 10 times larger than Barros et al. (2021), are demonstrated at an acquisition frequency of ∼ 2KHz.
The current work thus documents how to design a time-resolved volumetric velocimetry experiment in a
manner that the gap of smaller seeding particles for the target turbulent micro-scale structure in big volume
for ’global’ flow is filled.
The paper is organized as follows. In the Section 2, the methodology that related from flow characteristics,
seeding, illumination and imaging was formulated. To demonstrate the computation, a jet flow example
was computed in Section 3, where we use the all the formulas in Section 2. The setup and experiment re-
sult corresponding to the computation in Section 3 was provided in Section 4. Finally, our conclusion and
perspectives are presented.



2 Methodology

2.1 Flow characteristics
The target smallest structure in the flow is characterized with length-scale ` and characteristic velocity u(`).
The timescale of these structure (see Pope (2000)) is:

τ(`)≡ `

u(`)
. (1)

Based on the timescale, the interested frequency is

fi =
1

τ(`)
. (2)

According to the Shannon’s sampling theorem, the camera minimum repetition rate fc should be more than
twice of the interested frequency fc ≥ 2 fi. Both the spatial resolution and the camera repetition are based
on the flow length-scale and timescale.

2.2 Seeding
There are six factors that affects the particles to follow the flow from the study of Mei (1996), Adrian and
Westerweel (2011) and Raffel et al. (2018). Response time and Stokes number are related to the fidelity
of tracer particles accurately following the flow. Response time (τt) represents the characteristic time re-
quired for a tracer particle to reach an equilibrium condition after a flow disturbance. The response time is
commonly calculated using the following form

τt = d2
p

ρp

18ν
(3)

where dp is the particle diameter, ρp is the particle density and ν is the kinematic viscosity of the ambient
fluid. The Stokes number (Sk) is defined as the ratio of the characteristic time of a particle to a characteristic
time of the flow (τ f ),

Sk =
τt

τ f
(4)

Because the smallest eddies are characterized the timescale τ(`), the flow characteristic time equals to the
timescale τ f = τ(`). In general, a Stokes number of 0.1 or less given by Tropea et al. (2007) could return an
acceptable flow tracing accuracy with errors below 1%.
In dynamic conditions like turbulent flows studied by Mei (1996), the particle frequency response function
Hp that is defined as the sinusoidal response of particle to a sinusoidal oscillation of the flow relates to the
bias between the particle velocity and flow velocity,

v̂(ω) = Hp(ω)û(ω) (5)

where v̂(ω) is the particle velocity amplitude at frequency ω, and û(ω) is the flow velocity amplitude,
both of which are deduced from particle oscillation function v(t) = v̂(ω)e−iωt and flow oscillation function
u(t) = û(ω)e−iωt .
Defining Stokes number in frequency domain ε with the frequency variable ω (see Mei (1996)),

ε =

√
ωd2

p

2ν
, (6)

the energy transfer function deduced from the particle frequency response function is

|Hp(ω)|= |Hp(ε)|=
(1+ ε)2 +(ε+ 2

3 ε2)4

(1+ ε)2 +[ε+ 2
3 ε2 + 4

9(ρ−1)ε2]2
. (7)



where, ρ is the density ratio of particles and ambient flow ρ = ρp/ρ f , and ρ f is the density of the ambient
flow.
In the study of Mei (1996), the cut-off frequencies of the particles are based the 50% and 200% energy
response, which means that 0.5 < |Hp(ω)|2 < 2 implies very good response of the seeding particles. So the
cut-off frequencies fcuto f f , the cut-off Stokes number εcuto f f and the cut-off particle diameter dcuto f f can be
computed from the follow equations:

εcuto f f = {ε : |Hp(ω)|2 = 0.5 or 2}

εcuto f f ≈ [2.3800.93 +
( 0.659

0.561−ρ
−1.175

)0.93
]

1
0.93 , for ρ < 0.561

εcuto f f ≈ [
( 3

2ρ1/2

)1.05
+
( 0.932

ρ−1.621

)1.05
]

1
1.05 , for ρ > 1.621

fcuto f f ≈ ν

π

(
εcuto f f

dp

)2

dcuto f f ≈ εcuto f f

√
π fcuto f f

ν

(8)

However, in practice the computation process is the reversed version: a. find the particle parameter like
diameter dp and density ρp; b. compute the density ratio ρ and Stokes frequency domain number ε; c. get
the energy transfer function result |Hp(ω)|2 and decide if the seeding type is suitable for the flow; d. compute
the cut-off frequency to see if it can cover the interested frequency range.
To emphasize the unit of the parameters in the equations, kinematic viscosity in the equation 8 and 6 is
expressed in terms of cm2/s, and the diameter of particles is in the term of µm.

2.3 Imaging
The observed volume V = L×H ×W is imaged by cameras with the fixed optical magnification based
on the sensor size. Defining the pixel resolution ls, pixel pitch ∆pix, and aperture number fs, the optical
magnification of the imaging system can be deduced from the ratio of the focal lens to the working distance.

M =
ls∆pix

L
=

di

do
. (9)

Where do is the distance from the object geometrical center to the lens center and di is the distance from the
image geometrical center to the lens center. The particle image τp on camera sensor can be computed from:

τp =

√
d2

op +d2
s +d2

f

∆pix
(10)

where dop = Mdp is the particle diameter; ds is diffraction limit spot diameter, which can be computed with
aperture stop number fs and light wave length λ by ds = 2.44(1+M) fsλ; and d f is the blur circle diameter.
The depth of focus (DOF) δz is

δz = 2ds fs
1+M

M2 = 4.88 f 2
s λ

(1+M)2

M2 (11)

2.4 Scattered Light
In the PIV experiment, part of the incident light is scattered by the tracers onto the camera sensors. The light
scattering properties of a homogeneous and isotropic sphere in a plane wave, which leads to the Lorenz-Mie
Theory, are well documented in van de Hulst (1981), thus only related final results are presented to obtain
the light source budget.
The first step is to project the incident wave vector onto the scattering plane. From camera lens, the received
electric field vector can be expressed as

Er =
e−ikωrp

kωrp
MβMSMϕE0 (12)



where E0 is incident plane wave vector, kω is the incident wave vector, and rp is the scattering vector
between the particle center and lens center, the angle between kω and rp is ϑ, the scattering plane lies at
an angle of ϕ, MS is the scattering matrix with sum of scattering functions, Mβ, and Mϕ is plan transform
matrix (details to see Albrecht et al. (2003) pages:79-126).
One dimensionless parameter defined as Mie parameters is important to the scattering intensity,

xM =
πdp

λ
. (13)

For particles with Mie parameter xM > 10, the scattered intensity increases with the second power of the
particle diameter, which means geometrical optics can be applied for approximate computation. For the
Lorenz-Mie region with 1≤ xM ≤ 10, the scattered intensity exhibits strong oscillations. The received light
intensity is

Ir(λ,r,ϑ) =
cε

2
|E0|2 (14)

where ε is the permittivity of the medium, c is the speed of light.
The photons signal Ic received by the cameras is the inner product of the light intensity distribution function
and the camera sensor quantum efficiency function Φqe(λ),

Ic =
∫ ∫

Φqe(λ)Ir(λ,r,ϑ)dλdA. (15)

The integral of the area depends on the aperture diameter. If the distance between particle and lens is much
larger than the efficient aperture r� D and the scattering angle is in the range of geometrical optics, the
scattering angle can be consider as constant. Thus the integral is only the constant intensity multiply the
efficient aperture area.
The signal-to-noise ratio SNR in this paper follows the definition of Scharnowski and Kahler (2016), which
used standard deviations of the image intensity and the noise:

SNR =
σA

σn
, (16)

where σn is the standard deviation of the noise level, and σA is the standard deviation of the signal level,
which can be approximated with number particle images per pixel Nppp,

σA =
Ic

2

√
Nppp · (

π

4
τ2

p−1) (17)

The loss-of-correlation due to image noise Fσ is defined as,

Fσ =
σ2

A

σ2
A +σ2

n
=
(
1+

1
SNR2

)−1
. (18)

Before experiments start, the noise level σn can be estimated from the camera manuals. From the theory
analysis and experimental evaluation in Scharnowski and Kahler (2016), the loss-of-correlation Fσ increase
strongly from 0.4 to 0.8 with linearly increment of light power. So the main focus of the light design is to
pursuit the higher Fσ below 0.8 under the limited light source budget.

3 Measurement computation in a jet flow example
In this section, we demonstrate the computation process of the visibility problem in a jet flow with approxi-
mate 15µm diameter air-filled bubbles in a volume of 30cm×30cm×8cm. The whole estimation is carried
out under the condition of International Standard Atmosphere (ISA).



3.1 Axisymmetric jet flow
The axisymmetric jet flow is one of the best canonical flow for understanding the turbulence. It has been
interrogated using both experimental and theoretical methods. We model the jet velocity field based on the
experimental investigation results by Hussein et al. (1994).
According to measurement result in Buchhave and Velte (2017), for 1 cm jet at Re= 19868, the length Taylor
microscales are 2.2mm and the temporal Taylor microscales are 1ms at the center line at 30D downstream
location and , which requires particle cut-off frequency higher than 1KHz and camera repetition rate higher
than 2KHz. From 13mm radial distance off the jet centerline, the temporal Taylor microscales are 2ms,
which requires particle cut-off frequency 500Hz.

3.2 Seeding computation
The seeding tracers that we use to verify the method are the air-filled micron-scale bubble tracers, which
have mean diameters of approximately 15µm. Barros et al. (2021) investigated the characteristics of the
seeding in a wind tunnel experiment. The theoretical response time computed from equation 3 is 20µs, and
the measured mean response time was 40µs. More details of the microbubble tracer and the bubble generator
can be found in Barros et al. (2021).
The reason we choose this type of seeding is that the tracers size is big enough to reflect light signal, and
is small enough to show trajectory of Taylor micro-scale, and the response time is small enough to follow
Taylor micro-scales. As the temporal Taylor micro-scales measured by Buchhave and Velte (2017) is 1ms
at the center line at 30D downstream location for 1 cm jet at Re = 19868, the Stokes number of the micro-
bubbles was found to be 0.04, which is in the acceptable upper limit of 0.1 given by Tropea et al. (2007).
According to Kerho and Bragg (1994) and Afanasyev et al. (2011), the wall thickness of soap bubbles lies
in the range 0.1µm∼ 0.3µm, the density of the bubbles is 20∼ 70kg ·m−3 in Barros et al. (2021). Taking the
visible bubble diameter from 10µm ∼ 30µm, which have the frequency domain Stoke number at 0.26 and
0.47, the cut-off frequencies varies from 752.2Hz for large high density bubbles to 6.77KHz for small low
density bubbles. And the mean cut-off frequency is 2.21KHz for 15µm particles with 0.2µm wall thickness.
So in the radial range of 13mm at 30D downstream, only minor parts of the tracers (2.5% computed from
statistics in Barros et al. (2021)), of which the diameter is larger than 30µm and the wall thickness is thicker
than 0.3µm, cannot represent the Taylor microscale in the 10mm jet. Out the radial range of 13mm at 30D
downstream, all the tracers follows the Taylor microscale eddies well. For the average diameter particles,
the energy transfer function in equation 7 equals to 0.7088, which is in the efficient response range.

3.3 Imaging computation
Considering the sensor size 27.6mm×26.3mm with resolution 2048 × 1944px and pixel pitch 13.5µm, the
magnification is 0.0875. Only computing the aperture number with λ = 625 that is the highest quantum
efficient point of the camera used in the Section 4.2, the aperture number 13 is as DOF=8cm. So the F-stop
is set at 16 due to the discrete number. The particle image τp is 1.97. To avoid to interfere the flow, the
lens and the jet nozzle are in the same plane parallel to the front plane of the investigated volume, thus the
distance from the object geometrical center to the lens center do is 35cm with the camera angle at 30°. So
the focal length f is 28.16mm. The 35mm lens is adapted due to discrete product models.

3.4 Light budget computation
The emission spectra of a white light LED consists of a wide wavelength band from 440-760 nm. In order
to simplify the estimation of the image signal, we consider the optical power as if it is emitted at the single
wavelength λ = 625nm. i.e. the following computation is based on λ = 625 nm and corresponding photon
energy of 3.2×10−19 Joules. At this wavelength the Mie parameters xM equals 75.4, which is in the range
of geometrical optics approximate computation. At the scattering angle of 90°, the light brightness at the
target is 106 lux per LED. The averaged Mie scattering coefficient is about 8.6× 10−12m−2. The received
light energy of the CMOS detector is 2.95×10−18 Joules. The quantum efficiency of the CMOS detector of
the camera at this photon energy is 0.95.
Plugging these parameters into the aforementioned model for estimating, the signal from one particle gives
8.8 counts for one LED set. If a LED can be pivoted to the cameras for acquiring a better scattering angle
like , for example, 75°, the received light energy would be 1.39× 10−17 Joules, and the particle signal on



(a) Drawing of the current FF setup (preliminary). (b) Drawing of the current FF setup (preliminary).

Figure 2: The current setup in the Full Field section in Turbulence Research Laboratory at Technical Uni-
versity of Denmark.

the sensor is about 41.6 counts for each LED set. The noise level of the CMOS detector is σn = 7.2, the
signal level is 10.8 and the required light density Ic is 148 counts with assuming Nppp = 0.01. So the number
of required LED sets is 3.5. Because all the computation are conserved, we choose to use 3 LEDs in the
experiment.

4 Experiment and results
Experiments are conducted in a facility of the Turbulence Research Laboratory (TRL) of the Department of
Mechanical Engineering at Technical University of Denmark. The TRL consists of two separate test cells –
the High-Resolution experiment test cell and the Full-Field (FF) experiment test cell (see Ribergaard et al.
(2021)). The experiment in this paper are implemented in the Full-Field testcell.

4.1 Jet facility
In the Full-Field test cell, a jet with 10mm diameter nozzle was placed in the center of injection wall. At
the end of jet downstream direction, a sucking plate was mounted on the wall. The whole Full-Field test
cell has a volume of 4600mm (streamwise length) ×4010mm (width) ×4930mm (height). Outside of the test
section, there is an air recirculation controlling the air flow and the seeding density without influencing flow
in the test cell.

4.2 Camera
The visualization system that we used is a commercial LaVision PIV/PTV system, which consists of four
2048 × 1944px CMOS high-speed video cameras (v2640), a Programmable Timing Unit(PTU) and system
software Davis10. The sensor size is 27.6mm× 26.3mm, and the pixel pitch is 13.5µm with 12-bit depth.
The cameras were arranged symmetrically facing the volume of interest(VOI), as shown in Figure 2(b).
The cameras were angled at α = 30◦ to the jet center line. The cameras were mounted on a 4 degree of
freedom frame, which help to adjust the view angle at the minimum point independently. According to
computation from Section 3, the lens focal length is 35mm, and the aperture is set at f# 8.



(a) The sub-image of the measurement. (b) The signal intensity is 80-160 counts.

Figure 3: A sample image acquired from one camera.

4.3 Lights
The light sources we use in the experiment are an array of Flashlight 300 LEDs, which is produced by
LaVision GmbH. These high power LEDs consist of 72 diodes each. The opening angle is 10°. In the
pulsed-overdrive mode, the LEDs are operated above the nominal LED current to generate short pulses at
about 5 times high light intensities than the free trigger mode. but to protect the LEDs at such high current,
the duty cycle is limited to a maximum of 10%. The brightness can be measured above 106 lux at 1m
distance.
In order to acquire better light conditions, forward scattering is applied by rotating the LEDs to the camera.

4.4 Bubble generator
The air-filled soap bubble generator used in this paper was developed by TSI and had been applied for several
wind tunnel experiements. It consists of a 57 liter reservoir filled with a 5% surfactant-water solution. The
surfactant was common non-color and non-perfume pure dish-washing detergent. At bottom of the reservoir,
a piston pump is used to draw and pressurize the solution through a high pressure tube with a set of 10
nozzles. The outlet diameter of the nozzles is 0.2mm, and could be blocked with special blank nozzle heads
to reduce the seeding generating rate.
The air-filled-soap-bubble tracers have mean diameter of 14.7µm, response time 40µs with a dispersion of
30µm. The bubbles can be generated at high rates of 50 tracers per cm3. The Stokes number of the micro-
bubbles was 0.04, which could show the flow trajectory faithfully.

4.5 Result
For each snapshot, the number of the traced particles was in the range 7000–9000. A sample image acquired
by one camera was shown in Fig. 3(a), in which the particles can be clearly identified by the LaVison Davis
system. The particle size is 2-4 pixels, in agreement with computation. As shown in Fig. 3(b), the signal
intensity from the camera sensor is about 80-160 counts, which is in the same level with the estimated 147
counts in section 3. The main difference may be due to the different angle of LED to different cameras.

5 Conclusions
To cover the whole range from the global eddies to the smallest interested eddies, the relations of particle
size, imaging, light intensity are formulated. From the characteristic timescale and length scale of the
smallest interested eddies, the tracer type can be chosen by the diameter and density. Then the light intensity
can be estimated from the particle and the necessary minimum distance of the setup. With all the computed
parameters, one setup for jet flow measurement was prepared for the volumetric velocimetry in the volume



of ≥ 5000 cm3. The selected seeding tracer is air-filled soap bubbles with diameter of approximately 15µm,
of which the size of seeding tracer is between millimeter scale HFSB and 1µm scale DEHS. The targeted
measurement volume dimension is equivalent to the volume of HFSB, which will give a higher resolution
of turbulence study. The evaluation method in Section 2 can successfully predict the visibility of the small
tracers.
Our furthur work is to observe Taylor micro-scale in the same time to catch the global eddies at Re= 15×103

in the 1 cm jet flow. A higher Reynolds number is possible to be achieved with well designed illumination
source.

Acknowledgements
This project has received funding from the European Research Council (ERC) under the European Unions
Horizon 2020 research and innovation program (grant agreement No 803419). The Department of Mechan-
ical Engineering at the Technical University of Denmark is also acknowledged for their generous additional
support in establishing the laboratory.

References
Adrian RJ and Westerweel J (2011) Particle image velocimetry. Cambridge University Press

Afanasyev Y, Andrews G, and Deacon C (2011) Measuring soap bubble thickness with color matching.
American Journal of Physics 79:1079–1082

Albrecht H, Borys M, Damaschke N, and Tropea c (2003) Laser Doppler and Phase Doppler Measurement
Techniques. Springer-Verlag Berlin Heidelberg

Atkinson C, Coudert S, Foucaut J, Stanislas M, and Soria J (2011) The accuracy of tomographic particle
image velocimetry for measurements of a turbulent boundary layer. Experiments in Fluids 50:1031–1056

Barros DC, Duan Y, Troolin DR, and Longmire EK (2021) Air-filled soap bubbles for volumetric velocity
measurements. Experiments in Fluids 62

Bosbach J, Kühn M, and Wagner C (2009) Volumetric velocity measurements (v3v) on turbulent swirling
flows. Experiments in Fluids 46:539–547

Boushaki T, Koched A, Mansouri Z, and Lespinasse F (2017) Volumetric velocity measurements (v3v) on
turbulent swirling flows. Flow Meas Instrum 54:46–55 54:46–55

Buchhave P and Velte CM (2017) Measurement of turbulent spatial structure and kinetic energy spectrum
by exact temporal-to-spatial mapping. PhysFluids 29:085109

Cafiero G, Discetti S, and Astarita T (2015) Flow field topology of submerged jets with fractal generated
turbulence. Phys Fluids 27:115

Caridi G, Ragni D, Sciacchitano A, and Scarano F (2016) Hfsb-seeding for large-scale tomographic piv in
wind tunnels. Experiments in Fluids 52:190

Discetti S and Coletti F (2018) Volumetric velocimetry for fluid flows. Meas Sci Technol 29

Elsinga G, Scarano F, Wieneke B, and van Oudheusden B (2006) Tomographic particle image velocimetry.
Experiments in Fluids 41:933–947

Ghaemi S and Scarano F (2011) Counter-hairpin vortices in the turbulent wake of a sharp trailing edge.
Journal of Fluid Mechanics 689:317–356

Hou J, Kaiser F, Sciacchitano A, and Rival DE (2021) A novel single-camera approach to large-scale,
three-dimensional particle tracking based on glare-point spacing. Experiments in Fluids 63

Humble R, Elsinga G, Scarano F, and Van Oudheusden B (2009) Three dimensional instantaneous structure
of a shock wave/turbulent boundary layer interaction. Journal of Fluid Mechanics 622:33–62



Hussein H, Capp S, and George W (1994) Velocity measurements in a high-reynolds-number, momentum-
conserving, axisymmetric, turbulent jet. Journal of Fluid Mechanics 285:31–75

Kerho M and Bragg M (1994) Neutrally buoyant bubbles used as flow tracers in air. Experiments in Fluids
16:393–400

Kühn M, Ehrenfried K, Bosbach J, and Wagner C (2011) Large-scale tomographic particle image velocime-
try using helium-filled soap bubbles. Experiments in Fluids 50:929–948

Lobutova E, Resagk C, Rank R, and Müller D (2009) Extended three dimensional particle tracking ve-
locimetry for large enclosures. In Imaging measurement methods for flow analysis. Springer

Mei R (1996) Velocity fidelity of flow tracer particles. Experiments in Fluids 22:1–13

Michaelis D, Bomphrey R, Henningsson P, and Hollis D (2012) Reconstructing the vortex skeleton of the
desert locust using phase averaged pod approximations from time resolved thin volume tomographic piv.
in 16th International symposium on applications of laser techniques to fluid mechanics, Lisbon, Portugal

Pope SB (2000) Turbulent Flows. Cambridge University Press

Pröbsting S, Scarano F, Bernardini M, and Pirozzoli S (2013) On the estimation of wall pressure coherence
using time-resolved tomographic piv. Experiments in Fluids 54:1567

Raffel M, Willert C, Scarano F, Kähler C, Wereley S, and Kompenhans J (2018) Particle Image Velocimetry:
A Practical Guide Third Edition. Springer International Publishing

Ribergaard S, Zhang Y, Abitan JS Hand Nielsen, Jensen N, and Velte C (2021) A novel laboratory pushing
the limits for optics-based basic turbulence investigations. in 14th International Symposium on Particle
Image Velocimetry- ISPIV 2021, Chicago, Illinois, USA, August 1-5

Scarano F, Ghaemi S, Caridi G, Bosbach J, Dierksheide U, and Sciacchitano A (2015) On the use of
helium-filled soap bubbles for large-scale tomographic piv in wind tunnel experiments. Experiments in
Fluids 56

Scharnowski S and Kahler C (2016) On the loss-of-correlation due to piv image noise. Experiments in Fluids
57

Schneiders J, Caridi G, Sciacchitano A, and Scarano F (2016) Largescale volumetric pressure from tomo-
graphic ptv with hfsb tracers. Experiments in Fluids 57

Schneiders JF and Scarano F (2016) Dense velocity reconstruction from tomographic ptv with material
derivatives. Experiments in Fluids 57

Schröder A, Geisler R, Elsinga G, Scarano F, and Dierksheide U (2007) Investigation of a turbulent spot
and a tripped turbulent boundary layer flow using time-resolved tomographic piv. Experiments in Fluids
44:305–316

Schröder A, Geisler R, Sieverling A, Wieneke B, Henning A, Scarano F, Elsinga G, and Poelma C (2009)
Lagrangian aspects of coherent structures in a turbulent boundary layer flow using tr-tomo piv and ftv. in
8th international symposium on particle image velocimetry

Schröder A, Geisler R, Staack K, Elsinga G, Scarano F, Wieneke B, and Westerweel J (2011) Eulerian and
lagrangian views of a turbulent boundary layer flow using time-resolved tomographic piv. Experiments in
Fluids 50:1071–1091

Terra W, Sciacchitano A, and Scarano F (2017) Aerodynamic drag of a transiting sphere by large-scale
tomographic-piv. Experiments in Fluids 58

Tropea C, Foss J, and Yarin A (2007) Springer Handbook of Experimental Fluid Mechanics. Springer

van de Hulst H (1981) Light scattering by small particles. Dover Publications

Violato D, Moore P, and Scarano F (2011) Lagrangian and eulerian pressure field evaluation of rod-airfoil
flow from time-resolved tomographic piv. Experiments in Fluids 50:1057–1070


	Introduction
	Methodology
	Flow characteristics
	Seeding
	Imaging
	Scattered Light

	Measurement computation in a jet flow example
	Axisymmetric jet flow
	Seeding computation
	Imaging computation
	Light budget computation

	Experiment and results
	Jet facility
	Camera
	Lights
	Bubble generator
	Result

	Conclusions