Flow visualization in environment
and in technical devices as a way of recording and transmitting information
about various dynamic processes has existed for several centuries.
Understanding of complex non-stationary processes occurring in the flow of gases,
liquids, plasmas is possible by observing the complete flow configuration. In
this respect, panoramic visualization of the flow field is the most important
way to obtain information about such flows in experimental studies in thermal
physics and mechanics of gas, liquid and plasma.
It is generally accepted
that scientific visualization of flows appeared in the Renaissance - dynamic
processes and phenomena of fluid mechanics and physics were recorded in graphic
images. The study of gas and liquid flows helped the genius of the Italian
Renaissance Leonardo da Vinci to think over projects for creating vehicles by
air, water, and land. Since the Renaissance, visualization has developed mainly
in two forms - as a fine art and as a science. The newly discovered physical
principles of image registration were used simultaneously in a new level of
scientific visualization and in art. For example, the invention of photography
was originally used primarily in everyday life; it was only at the end of the
19th century that scientific photography appeared. In 1839 in Paris, at a
meeting with the participation of the Academy of Sciences members, it was
announced that Daguerre had discovered a way to develop and fix photographic
images. After this event, the development of scientific visualization began.
Panoramic visualization
of thermodynamic fields includes two-dimensional, three-dimensional,
four-dimensional presentation of the results of measurements and digital
analysis of the tested fields of gas-dynamic, thermodynamic parameters, as well
as the results of numerical calculations of dynamic processes. Panoramic
visualization of flows is in demand in many natural sciences - thermal physics,
mechanics, geophysics, medicine, plasma physics, biology, psychology, etc.
A. Tepler was the first
to propose the so-called sweep method (a kind of shadow method) for studying
optical inhomogeneities in a transparent medium and shock wave from an electric
spark was observed (1867). E. Mach proposed several flow visualization
techniques. In the 80s of the 19th century, he received the first shadow image
of a bow shock wave in front of a bullet flying at supersonic speed, and later he
received flow interferograms [1]. French naturalist J.E. Marey was the first to
use multi-frame photography to record flow movements and, having built a wind
tunnel, visualized a smoke system around obstacles and the transition to
turbulence. [2].
Over the past two
centuries, the development of panoramic visualization of thermodynamic, hydrodynamic
fields was mainly determined by the following five factors:
1. Progress in the
creation of registration materials;
2. Progress in the
creation of equipment for recording images;
3. Creation of new
sources of generation (and transmission) of probing electromagnetic radiation;
4. Development of visual
information storage methods.
5. Development of digital
methods of visualization data analysis and processing (beginning from the 80th
of the 20th century).
Registration materials
have progressed from solid carriers (paper, photographic plates, photographic
films) to digital matrices. Recording equipment, respectively - from direct
observation with the eyes, then observation using pinhole cameras, optics. From
the middle of the 19th century, photographic equipment began to be used for
scientific visualization, then film cameras, video cameras, and high-speed drum
cameras. In the middle of the 20th century, electro-optical devices appeared.
Light sources are also constantly developing: from - solar radiation, candles,
electric discharges, lasers, cumulative sources, LED sources, etc. Modern
sources emit in a wide range of wavelengths and pulse durations.
In the new digital period,
there is a constant renewal of technologies for the manufacture of specialized cameras
for registering processes in flows; there is a competition between companies
for increasing the space-time, spectral characteristics of cameras.
Methods for visualizing
the structure and flows’ parameters are based on the physical properties of electromagnetic
radiation and its interactions with media, including various physical
processes:
• Scattering;
• Refraction;
• Absorption
• Reflection
• Interference
• Dispersion
• Luminescence
• Effects of nonlinear
optics
The listed processes
include the light properties, which can be interpreted within the framework of
geometric, wave, quantum models. Nowadays, for the competent use of
experimental visualization methods in thermal physics, mechanics and analysis
of the results of panoramic experiments, it is necessary to be a expert in
physical optics; thermal physics and mechanics, as well as digital methods of
processing and analysis of scientific images. In recent years, due to the
continuously increasing volume of panoramic experiment visual data, the process
of involving machine learning and artificial intelligence to analyze big data
has begun.
Images of flow fields in
thermal physics are the main source of medium parameters information,
configurations and dynamics of structures in a flow: vortexes, weak
disturbances, strong discontinuities - shock waves, contact discontinuities,
streamlines, turbulent structures, etc. The purpose of thermophysical flows visualization
is qualitative and quantitative research, description of new phenomena and
patterns in flows in various environments, as well as in their demonstration.
The second important goal of accumulating visualization data of dynamic
thermophysical flowfields is - to provide benchmarks for testing programs and
algorithms for numerical modeling in thermal physics and mechanics.
Over the past 10-20
years, there has been a rapid transition to digital technologies for
registration, processing, and analysis of dynamic processes in liquids, gases,
plasma, multiphase media that arise in nature and technology. Relevant editions
(reviews, papers, monographs) are published (and in the last 5 years they have
been mainly posted on the Internet). Classic monographs and albums of flow
visualization of the 80s - 2000s [3-8] are focused on the description of
optical schemes, recording equipment, and analysis of test flow images. The visual
information obtained in the 20th century in the pre-digital period was mainly
of a qualitative origin. In recent decades, the number of foreign review
publications on the topic has increased many times (for example, [9-11] and
others). Among the monographs and reviews of the last 20 years on panoramic
methods in Russian, it should be noted [12-18].
In the works of the last
decade, the focus is on methods of computer processing, tools, technologies for
analyzing and recognizing of flow fields images, which make it possible to
obtain quantitative information about flows. One of the goals of obtaining
visual information about dynamic processes in flows is the creation of
experimental data bases for verification of three-dimensional calculation
programs [19-20]. Actual problems of the development of an experimental base
for verification of CFD codes when used in nuclear power [19].
With the advent of
powerful computers, it became possible to carry out computational experiments
based on the numerical solution of equations used in a mathematical model of
the physical phenomenon or process under study. Scientific visualization
systems allow presenting the calculations results and comparing them with
experimental data [20-21].
The actual problem of
analyzing the panoramic thermophysical experiment data is the problem of big
data. A huge amount of digital information is accumulated in the experiments,
obtained during video filming with digital cameras, thermal imagers, etc. When
working with big data, the problem of machine learning is coupled when
analyzing large data arrays (in our case, flow images). So far, very few papers
have been devoted to this problem, but their number is growing rapidly.
Panoramic study of
thermophysical fields includes a number of stages: 1. Visualization of the
flow. 2. Registration of the image of the changing area (field) of the flow. 3.
Digital image (film) processing. 4. Earning quantitative information. 5. Receiving
the patterns of the physical parameter. 6. Analysis and interpretation,
physical model.
At present, the
development of methods for panoramic visualization and digital analysis of
thermophysical fields is mostly determined by the introduction into experimental
practice of modern software and electronic means for entering images obtained
during visualization into a computer and their digital processing. With the
help of specialized software, images are processed both in thermal physics and
in fluid mechanics, medicine, geophysics, and biology. Similar basic tasks are
posed: the noise level reducing on the original image, highlighting the
structural elements of the objects under study, saving the results obtained in
a form convenient for further work and presentation.
Especially a lot of
quantitative information about the parameters of thermophysical fields has been
obtained in recent years due to the use of cross-correlation image processing
algorithms. These algorithms are the main in modifications of the Background
Oriented Schlieren
(BOS), Particle Image Velocimetry (PIV), including micro, stereo,
tomographic modifications, seedless anemometry, thermographic PIV, when
measuring velocity in viscous coatings, etc.
To visualize many types of optically transparent
flows in thermal physics and dynamics of continuous media, visualization
methods are used based on the phenomenon of light deflection when it passes
through the density inhomogeneities of a transparent medium: the shadow method,
schlieren method, interferometry, and their modifications. The optical
refractive index of the medium n is equal to the ratio of the speed of light in
the medium to the speed of light in vacuum and is related to the local medium density
by the Lorenz - Lorentz formula, which for gases is:
,
where k is a constant value, (for air equal to
0.22635 cm3 / g).
The shadow method for detecting density inhomogeneities in a gas
was proposed in 1867 by the German scientist Toepler. Schlieren visualization
method is sometimes called the Toepler method [22].
If the gas flow is inhomogeneous, then the
optical refractive index of the medium in the tested flow region depends on the
coordinates (x, y, z). When a flow region with a variable density is transparent,
a ray propagating parallel to the z axis and passing through an inhomogeneity
deviates from the initial propagation direction by an angle:
The main disadvantage of shadow methods is that
all density changes are summed along the propagation of the probe radiation
beam direction, and thus the integral value of the density change is recorded.
Therefore, shadow and interference methods are successfully used to visualize
two-dimensional, as well as some axisymmetric flows. When visualizing the gas
flow field by the shadow method, the change in illumination is proportional to
the degree of change in the gas density gradient. In the presence of strong
density gradients in the flow (in particular, discontinuity surfaces),
additional beam deflections occur on the discontinuity surface. The shadow
image of the shock wave is a dark stripe on the side of the incident flow,
which is followed by a bright light stripe, which intensity gradually decreases
(Fig. 1). During laser probing, the appearance of diffraction fringes in the
area of discontinuities in shadow images is possible.
Fig. 1. Shadow image of a
quasi-two-dimensional process of a shock wave interaction with a region of a
pulsed surface discharge (laser illumination).
In [23], using the shadow method, four scenarios of circular
hydrogen microjet diffusion combustion were investigated depending on the
velocity of its outflow. In [24], a gas jet outflowing from a nozzle was
investigated by shadow and schlieren methods.
The schlieren visualization method (or Toepler's
method) is a sophisticated shadow method. The basic principle of the schlieren
system is that part of the light deflected when passing through the gas density
inhomogeneity is retained by the edge of the knife installed in the focal plane
of the beam that has passed through the region under study. On the screen, as a
result, the illumination of the corresponding parts of the image will decrease
or increase depending on where the deviation is directed. The change in
illumination at a point associated with an inhomogeneity is determined by the
value of the beam deflection angle, the focal length of the second lens, and
the size of the light source. When visualizing the gas flow field by the
schlieren method, the change in illumination is proportional to the gas density
gradient in the studied area in the direction perpendicular to the edge of the
knife, and not to the degree of density gradient change, as in the shadow
method. Schlieren method visualizes better vortices, rarefaction waves; the
position of the discontinuities is recorded more accurately by the shadow
method.
Fig. 2. Schlieren registration of a flow in a
supersonic jet; optical discharge in a supersonic flow.
The visualization of a flow close to
axisymmetric by the schlieren method can be considered using the example of an
image of a gas jet (Fig. 2) outflowing from a nozzle [24].
Complicated modification of the schlieren method
- the color schlieren method [25, 26]. There are several ways to get a colored
shadow picture. Most often, a multicolor diaphragm is installed in the
receiving part of the optical system or in the focal plane of the collimator.
Colored schlieren images of the flow give more information about the flow field
than black-and-white ones, since instead of one blackening density, three
components change at once, namely, color, color saturation, and image
brightness. The human eye is able to distinguish more color shades than gray gradations;
therefore schlieren systems with a color image usually provide more quality
information. The advantage of the color shadow method is that the details of
the flow spectrum in the color picture can be easily distinguished from the
boundaries of models and other objects that obscure the flow picture.
Optical
interferometry was used mainly for quantitative studies of the density of
two-dimensional transparent fluxes. With interferometric registration of the
flow field, the distribution pattern of the light intensity bands reflects the
distribution of the refractive index of the medium. The interference pattern is
a system of bands, the distance between the maxima of which at a given
wavelength is determined by the convergence angle of the interfering waves. The
introduction of an optical inhomogeneity into one of the interferometer arms
changes the optical path length of the corresponding beam in comparison with
the unperturbed one and causes an interference fringes shift. Michelson,
Zhamen, Mach-Zehnder, Fabry-Perot interferometers are used to measure density
in liquid and gas flows. When studying two-dimensional flow regions (in which
the gas parameters in the direction of rectilinear beam propagation do not
change), the Fabry-Perot interferometer has increased sensitivity. When the
interference fringes are shifted by δ
(in fractions of the distance between the fringes), the density value changes
by Δρ/ρ=δλ/kL,
where L is the geometric length of the ray path in the inhomogeneity, the
Gladstone-Dale k constant. The Mach - Zehnder interferometer has some
advantages in gas dynamics studies. In this interferometer, using a system of
semitransparent plates and mirrors, an amplitude division of a light wave into
two is carried out and the subsequent superposition of waves that have passed
through different optical paths. For axisymmetric inhomogeneities, the
displacement of the interference fringes in the cross section perpendicular to
the flow symmetry axis is associated with a change in density by solving the
Abel integral equation.
where
R is the boundary of the heterogeneity in the section; r, x are cylindrical
coordinates, ρ0
is the density of the
unperturbed medium. A great difficulty in analyzing interferograms is to
determine the change in density when crossing the discontinuity surface,
especially for plane flows. It is difficult or impossible to establish the
correspondence of the fringes in the interferograms when passing through the
shock wave when shooting with a monochromatic light source. To solve this problem,
there are special methods of interferometric measurements. Shadow
visualization of the shock waves interactions with obstacles made it possible
to establish the regularities of the restructuring of discontinuous flows. In
particular, upon reflection of a shock wave from a solid surface, the existence
of several types of configurations was found, which are realized under
different conditions of reflection. For the first time this phenomenon was
studied by E. Mach, research is being carried out now, since the question of
the critical angles of transition from one type of reflection to another has
not been solved unambiguously. It is important to analyze the density field in
the vicinity of the point of intersection of the shock wave with the surface
from the point of view of establishing the value of the critical angle.
Therefore, investigations of the flow field of a flow upon reflection of a
shock wave are carried out using both shadow and interference methods.
Fig. 3.
Reflection of a shock wave from a concave and convex cylindrical surface.
Figure 3
shows two interferograms (Gvozdeva L.G., and Sysoev N.N., 1970s) of a
two-dimensional unsteady flow - reflection of a shock wave from a concave
convex cylindrical surface.
In [27],
the results of interferometry of the inhomogeneity of the gas-dynamic windows
of a laser with a supersonic flow of the active medium with the simultaneous
registration of two interferograms with a mutually orthogonal shear direction
are described. Data processing software has been tested.
Experimental
time dependences of the radial distribution of electron density for a
femtosecond laser microplasma of optical breakdown of gases (air, nitrogen,
argon, and helium) at various pressures (from 1 to 10 atm) were obtained using
the probe microinterferometry technique [28].
Figure 4
shows an interferogram visualizing convective structures and a phase transition
in a vertical layer of fresh water [29].
Fig. 4. Shear interferogram.
In [30], using a Mach-Zehnder interferometer, phase shifts were
obtained from interferograms for a time of 1000 ns by the fast Fourier
transform method, and then the refractive index values were
derived using the Abel inversion. However, in general, the number of works on
interferometry of thermophysical fields has noticeably decreased over the past
decades. To some extent, the BOS method has come to replace interferometry.
Shadow and schlieren methods have been used since about the 19th century,
but several important improvements have occurred in recent decades [31]. Shadow
imaging is still the main panoramic imaging technique for ruptured and
turbulent flows, including fluid flows. Figure 5 shows shadow photographs of
typical events on the water surface, leading to the generation of drops [32].
Fig. 5.
Formation and rupture of structures during the droplets formation.
The use of high-speed digital cameras has
made it possible to study long-term high-speed non-stationary processes with a
shooting frequency of more than a million frames per second.
Figure 6 shows
4 sequential shadow frames of the opposite motion of shock waves initiated by a
surface discharge of nanosecond duration when shooting with a high-speed
digital camera (124 thousand frames / s).
Fig.
6. Shadow recording of high-speed shooting of shock waves from a surface
discharge.
The visualization of gas-dynamic flows by an
electric discharge is based on the fact that the gaseous medium, being ionized,
is a source of radiation (the phenomenon of electroluminescence), which intensity
is related with local gas-dynamic parameters. When a stationary volumetric
discharge burns in a gas flow, spatial density inhomogeneities cause a redistribution
of the discharge plasma radiation intensity. This effect is often used to
visualize supersonic flows in wind tunnels at low pressures [33]. The advantage
of this method is the simplicity of the instrumentation and the possibility of
observing dynamic changes in the flow pattern during the experiment. The flow density
inhomogeneity leads to a redistribution of the electric current due to the
strong dependence of the electron concentration and conductivity on the
ionization coefficient value. The ionization coefficient is a non-linear
function of the ratio of the electric field strength to the concentration of
neutral particles E / N. Depending on the ratio of the linear scale value
characterizing the particle concentration gradient to different characteristic
physical scales (Debye radius, electron free path, etc.), various physical
effects associated with gas density gradients are possible. Local radiation
intensity of discharge plasma I(x,y,z) at a certain time instant is:
I(x,y,z,)=
f(W, U, i, N, P, T, d, Se)
Where W is the electrical energy supplied to the
discharge area, U is the voltage between the electrodes, i is the discharge
current, N is the particle density, P is the pressure, T is the temperature, d
is the distance between the electrodes in mm, Se is the parameter
characterizing the geometry of the electrodes. Since the 1950th, flow studies
have been carried out around various models in TsAGI wind tunnels using a
stationary high-voltage discharges [34–37]. In particular, stationary shock waves
were visualized in the flow around the cone model on a cylindrical holder at
different angles of attack. The visualized flow elements are vortexes in flow
along the cone surface. Investigations of vortex flows were carried out around
other isolated forms, form with a wing; a bow shock wave near a cylinder with a
hemispherical head was visualized.
Since the 90s, in the works of Japanese researchers,
the visualization of supersonic flows has also been carried out by the method
of a stationary electric discharge. Shock-wave configurations were visualized
around different models with a Mach number up to 10 [38–39]. A stationary
discharge was realized between the linear and point electrodes. When the
discharge passes through the section where the shock wave is visualized as a
dark area. Cross-sectional images of the shock wave configuration relative to
the model were obtained. To increase the area of the flow that
can be visualized, additional point electrodes were introduced.
In
India, work was carried out to visualize supersonic flows around models: (blunt
cone, blunt cone with a spike and a disc [40-41]). The method of visualization
by a stationary electric discharge was used, as in the previous case. The
elements of the flow near the model were visualized: a bow shock wave, a point
of attachment of a flow, a Mach stem. Numerical calculations were carried out
for comparison with the obtained experimental data.
Nowadays,
not many studies are carried out using the gas-dynamic flows visualization with
the electric discharge method. Basically, visualization is carried out with a continuous
electric discharge [42, 43]. Figure 7 shows an integral image of a supersonic
rarefied nitrogen flow under illumination by a transverse glow discharge. At
the shock front, during discharge burning in a supersonic stationary flow,
layers with a space charge are formed, in which the electric field strength,
the concentration of charged particles, and the gas conductivity change, and
the nature of the change in the quantities depends significantly on the
polarity of the field.
It
is known that discharge burning in a gas flow can lead to gas heating,
significant changes in the structure and shape of discontinuities, an increase
in the shock layer, an increase in weak disturbances, and a number of other
effects, including those leading to qualitative changes in the flow structure.
Thus, the visualization of the flow by a stationary gas discharge cannot be
regarded as a contactless method.
Fig. 7. Glow discharge visualization
of a supersonic jet from a nozzle.
Some disadvantages of visualization by a gas
discharge are eliminated by using a pulsed, initially spatially uniform
discharge with a glow time much shorter than the characteristic times of
gas-dynamic interactions. In studies on wind tunnels, these times are tens and
hundreds of microseconds, in studies of fast gas-dynamic processes, including
unsteady interactions of shock waves, in units of microseconds. When creating a
nonequilibrium spatially uniform flow region, a pulsed volume discharge with
preionization by ultraviolet radiation from plasma sheets is effective [44-47].
The use of such a discharge ensures the minimum time for the development of
breakdown, the diffuse nature of the glow at the initial stages of the discharge
development, and excludes the possibility of the inhomogeneities appearance in
the discharge volume that can uncontrollably affect the gas-dynamic flow during
image recording. The correct rectangular shape of the discharge region makes it
possible to instantly ionize the section of the gas-dynamic flow, in
particular, the flow in shock, wind tunnels. Figures (7-9) show the results of non-stationary
and stationary gas-dynamic flows visualization using such a discharge (MAI, Lomonosov
Moscow State University, 2000-2020). Gas flows were visualized in a 48x24 mm
shock tube channel with a specially designed test chamber of the same section.
The two walls of the chamber are quartz windows; upper and lower - flat plasma
electrodes 100 mm long, ignited at a given moment of the process; a volume
discharge is simultaneously ignited. The glow time of the ionized air flow in
the operating pressure range is 150 - 200 ns. With integral registration of the
flow field glow, the "exposure" is instantaneous from the point of
view of the gas-dynamic time scale. During the exposure time - highlighting the
flow elements - at maximum shock wave velocities of 2000 m / s, the wave moves
by 0.4 mm, at average velocities of a plane shock wave of about 1000 m / s, the
displacement is 0.2 mm. Disturbances moving at a subsonic speed are
"smeared" during the exposure by hundredths of a millimeter. Figure
8a shows an image of the flow field near a blunt cylinder 9 mm in diameter at
the Mach number of supersonic flow M = 1.5 (artificial colors). Bow shock wave,
oblique shock, reflected shock are visualized. In the photo 8b. the flow behind
the passing shock wave (left) forms a separation zone at the sphere rear side.
|
|
à
|
b
|
Fig. 8. A pulsed volumetric discharge visualization
of a stationary flow near a blunt cylinder and an unsteady flow around a sphere.
Unlike
optical methods, which require an object probing, this method allows
visualizing the flow through one window of the test chamber. On the other hand,
several images can be simultaneously obtained through both windows of the test
chamber, while the total viewing angle can reach 200
°
- 250
°
[44].
The
visualization of irregularities using electroluminescence allows one to study
three-dimensional flow structures. Shadow methods can give an image of the flow
lateral projection with the registration of integral characteristics in the
transmission direction; the overlapping of the optical path by the model
excludes probing of the central (axial) region. Fig. 9 shows two images of the
flow around an axisymmetric model that combines the main geometric axisymmetric
forms, which often become an object of research, being an element of many elements
in aerodynamics. The model is a cone with a half angle of 10 °, which is
attached to the cylinder, then the diameter of the cylinder increases with a
step. The photo in Fig. 9 shows an image of a supersonic flow near a model
entirely placed in the discharge gap. The moment of the flow formation around
the model after the passage of the shock wave with the Mach number
Ì
= 2.8 was recorded. The flow structure is completely visualized in two images
recorded through opposite camera windows.
Fig. 9 Two-perspective visualization
of the flow around the axisymmetric model.
The method of visualization by a pulsed volume
discharge made it possible to study the dynamics of a three-dimensional vortex
ring in the bottom region during diffraction of a shock wave on a cone with a
step [45]. We also visualized unsteady quasi-two-dimensional flows in a channel
with steps, which arises in the channel behind the incident shock wave. On the
upper and lower walls of the channel from glass to glass, there were
rectangular obstacles 2x6x48 mm [46, 47]. In Figure 10, a pulsed volumetric
discharge visualizes a plane wave bending around an obstacle on the bottom wall
and a cylindrical vortex in the separation zone. The volume discharge glow is instantly
redistributed to the low-pressure zone in front of the shock wave and to the
separation zone behind the step.
Fig. 10. Flow of a pulsed volumetric
discharge with preionization during diffraction of a shock wave by an obstacle
on the lower wall.
The actual problem of a panoramic experiment data
analysis is the problem of big data. In the experiments today a huge amount of
digital information is accumulated, obtained during video filming with digital
cameras, thermal imagers, etc. The development of digital technologies leads to
a multiple increase in the data array on the parameters of thermophysical flow fields;
large arrays of digital data appear which make it impossible to process it
manually. Thus, modern video films recording the evolution of turbulent fluid
flows based on shadow methods, tracing, thermography require processing and
qualified analysis. The transition to another level of data analysis is
inevitable.
There is a need to automate the process and data
analysis - experimental images of thermophysical fields, using various
approaches, including machine vision and learning methods using deep learning,
convolutional neural networks ( CNN). So far, there are few works devoted to
this problem, but their number is growing rapidly. Work in this direction
begins in a number of scientific laboratories. Software is being developed to
recognize the structural elements of flows in gases, liquids and plasma. For
software testing and training of neural networks, arrays of images of various
currents are used, registered by shadow, schlieren, and PIV methods.
Today, the most promising approaches
to solving these problems are based on digital image processing with various
algorithms for edge detection and object recognition [48] to identify complex
stream structures. Various image edge detection algorithms are suitable for
shock wave detection. Various researchers have shown that the Canny algorithm
[51] is best suited for processing shadow and schlieren images. The authors of
[48] developed software for detecting and tracking shock waves. The mixing
process in the nozzle improved by means of boundary detection in schlieren
images was analyzed in [52]. Image processing was performed using the Canny
algorithm. Work is underway to improve the quality of images by removing noise,
subtracting background images by various methods. It was shown in [53] that
subtraction of the background image in frequency representation (performed
using the fast Fourier transform) yields the best quality. Also promising for
digital processing of experimental images by computer vision methods are
methods of image segmentation using algorithms such as k-means, energy
minimization, etc .; feature detection methods (SURF, LESS, HOG, etc.).
Currently, the topic of machine
learning for fluid dynamics is actively developing. A fairly detailed review of
research on this topic is given in [54]. An approach based on machine learning
for identifying flow structures in schlieren images is also developing. In
[55], a system for classification and processing of schlieren images of objects
in a wind tunnel is proposed. The system was able to extract three parameters
from the images: the angle of refraction of the bow shock wave, the difference
in line intensity and the average line width. Based on these data, the system
calculates the flow velocity near the model. Neural networks can extract not
only shock waves, but also any other elements. For example, the authors of [56]
have successfully applied a neural network to classify wake vortexes behind a
wing profile. Neural networks are beginning to be used to predict [57] or
reconstruct [58] flow dynamics. New physics-based methods for calculating the
loss function [58]. Deep machine learning allows simulation of turbulence and
other large-dimensional gas-dynamic systems [59].
To study the evolution of
gas-dynamic flow over 6-10 milliseconds, in [60-61] results of quantitative
analysis of high-speed shadow shooting shock tube flow were tested. Using
machine vision and learning [61] [62], three programs for processing shadow and
schlieren images were developed. The first one works on machine vision
algorithms, the second uses the convolutional neural network; we trained to
recognize and automatically track various flow structures, the third program
uses the cross-correlation method to estimate the flow velocity from the
displacement of turbulent structures and particles suspended in the flow - by
analogy with the PIV method. For edge detection, the Canny edge detection
algorithm was used, which turned out to be the most effective for analyzed
shadow and schlieren images. The algorithm for finding the angle of an oblique
shock wave includes the selection of boundaries, the search for equations of
straight lines (shock waves) using the Hough transform, and filtration of the
found straight lines by length, angle, and position. The moment of transition
to the subsonic regime has been determined. The dynamics of plane shock waves
was automatically measured using a convolutional neural network. The speed of objects
recognition in images (shock waves, tracer particles in a flow) by the neural
network was 15 fps. The time dependences of the incident speed, reflected shock
wave were automatically measured and plotted. The dynamics of a pseudoshock
train in a channel is investigated. The use of machine vision and learning
algorithms made it possible to speed up the processing and analysis of large
arrays of experimental digital images (Fig. 11) and to fully automate this
process. The manual processing of one shot of the flow in the shock tube (about
1000 frames at a shooting speed of 150,000 frames / s) could take a whole
working day. The developed software solves this problem in one or two minutes.
Thus, the acquisition of new physical information was significantly
accelerated.
Fig. 11. Recognition of a plume
from a pulsed surface discharge using CNN and an automatically plotted graph of
its growth.
The method of infrared (IR) thermography of
thermal fields is based on measuring the distribution of thermal radiation and
converting it into a temperature map. Thermal radiation arises in solids,
liquids and gases at temperatures above absolute zero due to vibrations of
atoms or rotational-vibrational motion of molecules [63]. As it is known,
objects can absorb, reflect or transmit radiation energy. Corresponding
coefficients are introduced to describe these physical processes. IR radiation is
in the range of the electromagnetic waves spectrum from 0.75
μm
to 1000
μm
between visible light and radio waves. In turn, the IR spectrum is usually
subdivided into short-wavelength (0.75–1.5 µm), medium-wavelength (1.5–20 µm),
and long-wavelength regions (20–1000 µm), although in the literature there are
different divisions of the IR range depending on the further application.
Infrared thermography is a non-contact method
for measuring and analyzing the thermal radiation of objects or flows.
Registration of thermal radiation provides extensive information about the
energy state of the object of study, which is used in thermal physics,
medicine, geology, biology, energy saving, etc. This method is used in such
engineering applications as regulation of thermal insulation, non-destructive
testing, etc. Fig. 12 shows examples of thermograms: heat outflow through the
slots of the window frame and the image of an “energy-saving” lamp; thermal
imager was with a registration range of 3.7 - 4.8 µm.
Fig. 12. Thermograms of the thermal
fields: window frame and an energy-saving lamp.
Thermography
is used to diagnose the human psychoemotional state in the infrared (and
terahertz) range [64 - 66].
The
increased interest in thermography is due to both the emergence of new
generation of thermal imagers and the possibilities of digital processing,
analysis, and storage of thermographic images and films. The total flux of
thermal radiation W recorded by the thermal imager is equal to:
This is a common measurement formula used in most
commercial thermal imaging systems. The thermal imager gives an uncorrected
value of the object temperature, obtained taking into account all the thermal
radiation received by the detector. Thermography can be used to heat transfer experimentally
study on flat and relief surfaces with different geometries [67, 68].
Despite the fact that gaseous media are transparent
in the IR region of the spectrum, thermography is widely used to study air
flows and their effects on surfaces. The earliest known attempts to measure
heat transfer coefficients in a high-speed air flow using thermography were
carried out in a hypersonic mode in a wind tunnel [69]. In [70] implemented
shadow visualization of rarefied flows in infrared light (gas density in the shock
region ≈ 10–3
kg / m3), which is not available for
visualization in visible light. Since molecular nitrogen cannot emit in the
infrared range, the visualization of shock waves (M = 21) is associated with
the refraction of infrared radiation reflected from the rear wall of the test
chamber at the shock waves. There is a "direct shadow" visualization
of the flow in the infrared range, where the refractive index of gases
increases significantly.
One of the important applications of thermography is
the determination of the area of a laminar-turbulent transition
on a surface in a gas or liquid flow. The problem of controlling the
laminar-turbulent transition in gaseous media is of great interest for optimizing
the geometry of aircraft, both from the point of view of gas dynamics and from
the point of view of heat transfer on a surface in a streamlined flow.
Thermography made it possible to determine the zones of laminar-turbulent
transition in flows around aircraft by measuring the temperature maps on the
wings and blades [71-73].
The work of a group from Dryden Flight Research
Center described a flight experiment to study the pressure distribution when
flowing around a flat plate in flight at supersonic speeds up to Mach 2.0 [74].
The boundary layer was investigated in flight. The aim was to determine the
characteristics of the boundary layer transition and the efficiency of the
surface coating for future flight tests using IR thermography. Infrared imaging
was used to record the shock wave incident on the surface in addition to
determining the transition line of the laminar boundary layer to the turbulent
one.
An important area of thermography is the analysis of
near-surface fluid flows. In the literature, there are mainly papers devoted to
the study of the gas-liquid interface configurations and slow flows with low
Reynolds numbers. IR radiation is absorbed directly on the surface of the
liquid. Thermography can be used to measure the temperature fields of a liquid
in laminar and turbulent modes of convective flows from the free surface of a
liquid [75]. The space-time characteristics of multiscale convective structures
were obtained [76].
The modes of air flow and liquid flows in
inclined pipes were investigated using infrared thermography on the basis of
thermal images and fields of local heat transfer coefficients on a heated
surface [77]. In [78], IR thermography, together with the PIV method, is used
to analyze the structure of a free liquid jet falling on a metal plate in air.
A large number of works are devoted to the use
of infrared thermography for studying the heat transfer of jet flows by
registering the heat flux from the outer surface of the wall [79]. Of practical
interest were the problems of monitoring and scanning the temperature field of
the outer mixers walls during the flow of water and liquid metal coolants
[77-81]. For impact jets, measurements of the spatial and temporal
characteristics of a turbulent water flow were usually carried out through a
thin metal wall (plate, foil) [82–85]. The averaged thermal fields obtained due
to heat transfer of the investigated flow to a solid wall were recorded. The
problem with measurements through metal substrates is the attenuation of
temperature fluctuations by the test surface. In [86], it was proposed to
compensate for the attenuation of temperature pulsations by restoring the
initial heat signal from the inner side of the surface by solving the inverse
heat conduction equation.
The development of high-speed thermographic
technology has led to the possibility of registering sufficiently fast
processes, in particular, the characteristics of heat transfer in a turbulent
flow. In [87], IR imaging is used to visualize the turbulent flow of water in
acrylic round pipes with a high temporal resolution. In [88], the starting
processes and dynamics of a non-submerged high-speed liquid jet on a waterjet
cutting machine were recorded. The research was aimed at obtaining new data on
two-phase flows in extreme conditions, and can be applied to improve
engineering hydraulic jet structures. Figure 13 shows two thermographic images
of the development of a supersonic hydro jet when shooting with a frame rate of
up to 415 Hz; observation is carried out from the peripheral jet region and its
air-water covering. The outflow velocity of the jet on the axis reaches 270 m /
s, (Re ≈ 107).
Fig. 13.
Thermograms of high-speed hydraulic jet starting processes in the period up to
0.005 s after starting.
The property of water to absorb infrared (IR)
radiation on a submillimeter scale has made it possible to propose a method for
studying non-isothermal unsteady turbulent fluid flows in a boundary layer [89]
based on IR thermography. When registering through an IR-transparent window,
the method allows visualizing thermal radiation from a thin near-surface layer
of a liquid. It is shown that through a wall transparent to infrared radiation
during a thermal imaging study of a moving liquid, the pulsating, energy
characteristics of a non-isothermal turbulent boundary liquid layer can be
measured with a frequency resolution of 100 Hz. For the model of a flat T-junction,
the presence of inertial intervals of energy spectra in the range from 4 to 40
Hz was revealed. The application of the method for studying impact jets is
described in [90-91]
Figure 14 shows an example of the transition
region visualization of an impact jet flow. Time sweeps of temperature
pulsations at four different distances from the stagnation point are shown.
Fig. 14. An example of an instantaneous
thermogram and temperature evolution at four different distances from the
stagnation point.
The Background
Oriented Schlieren method which is
based on the physical phenomenon of refraction and
cross-correlation analysis of images deserves a separate consideration. When
visualizing the fields of the refractive index of transparent inhomogeneous
media, the displacements of the background points separated in the image space,
placed behind the object under study, are determined. Quantitative measurements
of density fields are possible in the case of a two-dimensional and
axisymmetric flow of a transparent medium using the photometric shadow method.
The angles of light deflection are related to the distribution of the
refractive index within the studied inhomogeneity by means of the Abel
relations. For example, in Ref. [92], the density fields of helium were
measured for a hypersonic flow around a cone.
In English-language
literature, method is known as BOS (Background Oriented Schlieren). The method
was proposed almost simultaneously by Mayer [93] and Daltsil [94], at the same
time the first experimental surveys were made. The predecessor of this method
can be considered the technique of speckle photography, which was developed at
the Institute of Heat and Mass Transfer (ITMO) named after V.I. A. V. Lykov of
the Academy of Sciences of the BSSR since the mid-1980s. Quantitative
visualization of flows based on speckle technologies is described in [95].
Techniques for digital speckle photography have also been developed in the PIV
technique, Talbot interferometry, [96].
The essence
of the BOS consists in comparing two images of the same background, taken in
the absence and in the presence the investigated transparent object with
inhomogeneities between the camera and the background of [97; 98]. The
background screen must meet certain requirements in order to obtain
high-quality data with the maximum amount of useful information and a low noise
level during further digital processing of experimental images.
A change in
the refractive index along the observation line in the case of shooting the
background through an object leads to a discrepancy between the reference and
"working" images (Fig. 15). By analyzing the displacement of the
background elements in the images, one can obtain quantitative information
about the integral change in the refractive index of the medium under study
along the observation direction. Cross-correlation methods for digital
comparison of experimental images have previously been significantly developed
within the framework of the digital tracer anemometry (PIV) method.
Fig. 15. Optical scheme of BOS:
1 - background, 2 - object under
study, 3 - lens / objective, 4 - digital recorder
The Y-component of the beam deflection
coming from the background is expressed from the law of refraction as follows:
Then the corresponding background
element will be displaced in the working image relative to the reference one by
the amount:
The relationship between the density
of a homogeneous gas and its refractive index can be expressed by the
Gladstone-Dale relation:
where G is the Gladstone-Dale
constant. The recorded BOS displacement of the background element in the image
is directly proportional to the density gradient in the plane perpendicular to
the optical axis.
Here
is the distance from the object
to the background,
is the thickness of the flow
under study along the scheme optical axis. The displacement of the image is
influenced by all flow elements along the optical beam - in fact, information
about the studied flow is averaged along it. Reconstruction of the density
field directly from the results of one-angle BOS is possible only for a flow
close to two-dimensional (which is rare in the experiment), or for an
axisymmetric flow (based on the use of the inverse Radon transform.).
To obtain quantitative values of the
density field in complex three-dimensional flows, a multi-angle BOS survey is
required with subsequent reconstruction of the three-dimensional field from
two-dimensional images using tomographic reconstruction approaches. In this
case, the shooting is carried out simultaneously from several (usually at least
5-6) angles. For stationary flows, the survey can be performed sequentially
from different angles with the same camera [99]. However, even the displacement
fields obtained on the basis of BOS (associated with density fields) can serve
as a useful source of information about thermophysical objects, for example,
data on the position and dynamics of characteristic gas-dynamic structures
(discontinuities, vortices, etc.).
In Russia, the first work with the
use of BOS was carried out in the early 2000s at the Institute
"MPEI". Unlike shadow methods, BOS visualization does not require the
use of optical elements comparable in size to the object under study. This
makes it convenient for field research and experiments, and other cases when
visualization of large-scale currents is required. The authors of [100] used BOS
together with PIV for combined visualization of a flow with a Mach number M = 8
in a wind tunnel. BOS studies of unsteady transonic flows containing shock
waves in the channel and at the exit from the shock tube channel were carried
out at Lomonosov Moscow State University ([101, 102]. Figure 16 shows 3 BOS
images of the shock wave exit and the flow from the shock tube end. – distance up
to 40 cm.
Fig. 16. BOS images of a supersonic
flow at the exit of a shock wave into the atmosphere.
In the study of combustion
processes, BOS can also be widely used, since the method is able to visualize
not only heat fluxes and flames, but also to determine the concentrations of
components in various fuel mixtures [103]. BOS was also used for full-scale
field survey of explosive tests, [104 - 106]. The main result was the
determination of the shock front generated by the explosion dynamics. The
results of the work unambiguously indicate that the BOS has exceptional
capabilities for practical field applications, although it is not always
possible to make quantitative measurements.
Almost from the very beginning of
the use of the Background Oriented Schlieren, significant errors were found
that arise when visualizing strong density gradients (in particular, shock
waves) by this method. The results of numerous studies show that the
quantitative determination of the density jump at the shock front using the
classical BOS scheme is a difficult problem. The effect recorded at the shock
fronts often does not correlate with the physical parameters of the flow. In
papers [107], [101] it was shown that this problem is actually caused by the
fact that the detected value of refraction goes beyond the sensitivity of the
method. Due to the strong light refraction at the shock front, the deflected
beam can go beyond the optical scheme and not be detected.
To date, most of the work related to
BOS considers flows and processes occurring in gas. However, by its principles,
BOS is also applicable to the study of processes in transparent liquid and
amorphous transparent media. Thus, the method was used to register internal
waves in water [108] and thermal processes in water and plexiglass (MPEI 2008).
Fig. 17 shows the images of the displacement fields during the movement of
convective thermals in a flat vessel.
Fig. 17. BOS images of convective
flows in water.
BOS occupies the position of classical qualitative shadow
methods, outperforming them due to the greater ease of use [109] where high
spatial resolution is not required. The greater simplicity of the BOS hardware
in comparison with other methods makes multi-angle BOS visualization more
accessible.
With all the advantages of BOS, a number of
drawbacks are obvious [110, 111]. Algorithms for cross-correlation of images
are averaged over the size of the polling window; TMF always provides a lower
image resolution than comparable traditional shadow schemes. Increasing the
sensitivity requires a larger interrogation window and thus resolution decreases.
Resolution loss is most noticeable for objects such as shock waves and
interfaces; while traditional shadow methods produce a finished image in real
time (no processing), BOS requires processing. Problems with focusing
simultaneously on the object and background arise. Method is also sensitive to
vibrations. One of the drawbacks complicating quantitative measurements using
the BOS is the non-parallelism of the light beams probing the volume under
investigation, which leads to the non-uniformity of the spatial scale of the BOS
fields depending on the position of the fixed irregularities along the optical
beam. The disadvantage of introducing a large converging lens into the optical
system, matched with the lens of the recording camera, cancels one of the
essential advantages of the method, since it limits the dimensions of the
investigated inhomogeneity to the dimensions of the main optical element.
The movement of gas and liquid
elements can be visualized by introducing marked, colored particles, plumes of
smoke, threads, silk threads, etc. into the flow. etc. This visualization
method - the tracing method - is one of the oldest methods of direct flow
visualization. With the integral registration of a particle trace in a gas, the
tracing method allows one to observe the trajectories of particles,
streamlines. By registering with the exposure
δt the
movement of tracer particles in the flow, it is possible to obtain images of
the segments of the path
δs traversed by the particles in
δt.
The quantity v =
δs /
δt is the
average velocity of the particle in this segment. Digital processing of tracing
results has developed into a special direction providing the reconstruction of
the dynamics of three-dimensional velocity fields - the PIV method (particle
image velocimetry).
The method of digital tracing
(anemometry from particle images, Particle Image Velocimetry method) is based
on statistical analysis of particles moving with the flow displacements of
images under study, visualizing the flow over a short time interval when
registering these particles in a selected plane using an optical knife. The
optical knife (laser radiation) is formed in the form of two short pulses with
a certain time interval between radiation pulses. The result of PIV method measuring
is the instantaneous flow velocity fields. Many reviews and monographs are
devoted to the description of modifications and method applications [112, 114].
In Russia, the method has been actively developed since the 90s at the
Institute of Thermophysics named after V.I. S.S. Kutateladze RAS. The review [15]
provides an analysis of the history and current trends in the development of the
PIV method in applications to aerodynamic installations. The authors consider
the basics of the anemometry method, options for its implementation, history
and current state of hardware.
The appearance of the term PIV dates
back to the 80s, when it was identified as a special case of the LSV (Laser
Speckle Velocimetry) method [115 - 117] based on the optical Fourier transform
of brightness pictures.
Planar modifications of the method
include Particle Image Velocimetry (PIV), Micro Particle Image Velocimetry
(Micro PIV), Particle Tracking Velocimetry (PTV), etc.
Volumetric methods for studying the
kinematic structure of a flow include Stereo Particle Image Velocimetry (Stereo
PIV), Tomographic Particle Image Velocimetry (Tomographic PIV), etc.
The PTV method is almost identical
to the PIV method. The PTV method also results in instantaneous two-component
velocity fields. But unlike the PIV method, the velocity vector is measured by
the movements of individual tracers in the flow, not a group. The PTV method is
used when the density of particle images is very low. Image processing is also
performed using correlation algorithms.
One of the most important advantages
of the method is the absence of a significant disturbing effect on the flow.
The limitations of PIV include the finite size of the tracer particles, as a
result of which the tracers do not always accurately follow the flow. This is
especially true for areas of strong gradients and discontinuities. The size of
the particles used limits the size of the elementary region, while the use of
smaller particles leads to the effect of Brownian motion on their position.
Tracers for gaseous media can be liquid droplets with a size of 1-100 microns;
as a rule, various natural and synthetic oils are used. For high-speed streams,
solid particles are used, most often from titanium oxide and aluminum. They can
be smaller (on the order of hundreds of nanometers), and due to this, it is
better to follow the flow.
The area of PIV method application,
in particular, includes fundamental scientific research aimed at studying the
dynamics and scales of vortex structures in liquid and gas flows [118-120]. In
works [121-122] visualization by the PIV method of velocity profiles behind the
blast (shock) waves formed during the explosion of the wire is demonstrated.
With the help of an 8-pulse laser system, successive images of the velocity
fields behind the front of a spherical blast wave were obtained. It is noted
that a significant broadening of the velocity profile was recorded at the wave
front, apparently due to the predominantly inertial impedance of liquid tracer
particles. This effect, noted in other works, limits the applicability of the
method to investigations of flow fields during explosions.
PIV imaging of supersonic flows remains a
challenge due to the uneven particle density in the images. In this regard, the
visualization of supersonic flows requires a particularly careful selection of
tracer particles, flow seeding mechanisms, algorithms and parameters for PIV
image processing.
Another issue, closely related to the previous
one, is the adjustment of PIV data in flows with large velocity gradients. Due
to the delay of particles, the data in such flows can differ significantly from
the true ones. However, in some cases it is possible to take these errors into
account and restore the true gas velocity field [123]. In [124], the PIV method
was used to measure nonstationary velocity fields arising during the development
of flows behind shock (blast) waves initiated by a pulsed surface sliding
discharge in air. Plasma sheets (nanosecond discharges sliding across the
dielectric surface) were initiated on the walls of a rectangular chamber. The
distribution of flow velocities behind these waves showed that the impulse
energy deposition is uniform along the discharge channels of the plasma sheet,
while the integral apparent intensity of the plasma glow decreases in the
direction of the channel propagation. Figure 18 shows the velocity field behind
a plane shock wave (left side) and behind a cylindrical shock wave initiated by
a nanosecond plasma channel.
Fig. 18. Velocity field behind a
shock wave initiated by a surface sliding discharge.
In recent years, the number of PIV applications
for microflow researches has been increasing [125-126]. The last paper presents
the results of using the micro-PIV method to visualize the flow structure in a
water droplet located on a glass substrate (Fig. 19). At different moments of
droplet evaporation, two-component velocity fields were obtained in different
droplet height sections.
Fig. 19. Instantaneous velocity
field in an evaporating droplet. Section - 45 microns from the substrate.
In [127], the PIV visualization and study of the
flow developing when a shock wave exits the shock tube channel into the
atmosphere was carried out (Fig. 20).
Fig. 20. Streamlines of flow behind
the shock wave leaving the channel
(color codes flow velocity).
In recent years, due to the spreading of
cross-correlation image processing algorithms, a significant amount of papers
has appeared using the seedless tracing - tracking structural elements, markers
present in the flow itself. This “schlieren PIV” technique uses natural
inhomogeneities and turbulent vortices in the flow as virtual “seeding
particles” on which the velocity is measured [128-131].
An example of visualization of boundary layer
inhomogeneities in high-speed shadow image is shown in Fig. 21. The result of
cross-correlation processing of such images can be used for a lower estimate of
the gas velocity in the boundary layer.
Fig. 21. A frame of high-speed
shadow shooting of the flow in the test section of the shock tube.
The smoke visualization method proposed at the
Kazan Scientific Center of the Russian Academy of Sciences [132] is based on
digital processing of smoke visualization video recorded on a light sheet. The
gas flows are seeded by aerosol generators that follows the gas flow in the
same way as in PIV, but due to their higher concentration, they appear in the
image as not illuminated individual particles, but smoke with a continuous
intensity. The method makes it possible to use relatively primitive equipment
for measuring the dynamics of two-component vector velocity fields with a
frequency of about 10 kHz.
One of the seedless tracer visualization methods
is a new thermal point tracing (TPT) method based on thermographic
visualization of the fluid boundary layer [133]. The cross-correlation program
measures the displacement of points of equal temperature (in grayscale) on two
adjacent thermograms. Figure 22 shows the field of flow velocities averaged
over 100 frames in the boundary layer of water based on the TPT method. We used
high-speed imaging of an impact submerged non-isothermal jet with a thermal
imager through a window transparent to infrared radiation. In the center is the
area of laminar flow.
Fig. 22. Method of seedless tracing
by thermal points. Velocity field in the boundary layer of an impact submerged
jet.
Hydrodynamic flow on a streamlined solid surface
can be visualized using specially developed methods. They make it possible to
detect areas of transition of a laminar boundary layer to a turbulent one,
visualize zones of flow separation, areas of intersection of shock waves with
the surface, distribution of thermophysical parameters over the surface, etc.
In the transitional region of the flow (dynamic boundary layer), the velocity
changes from zero on the wall to some final value in the external flow. It is
also possible to distinguish a thermal (temperature) boundary layer formed in
the case of a mismatch between the surface temperature and the gas temperature.
When a chemical reaction proceeds on the wall or during injection, a
concentration (diffusion) boundary layer is formed. The heat flow from the gas
to the model can be calculated by measuring the heating rate of the model surface
with known thermophysical properties.
Optical panoramic methods differ in the way they
convert the surface temperature into an optical signal: fluorescent temperature
converters, liquid crystals, thermal imaging. The use of special methods for
visualizing the surface flow makes it possible to obtain pictures created by
the distribution of velocity, temperature, pressure, and wall shear stresses in
the boundary layer. The visualization of the surface flow is carried out, as a
rule, as follows: a special paint, liquid film or other coating is applied to
the streamlined surface of the model, which responds to the local flow
parameters. Then the pattern created by the distribution of pressure,
temperature, velocity, shear stresses on this surface is recorded. Coatings
used to visualize surface flow, are the following: liquid crystals, coatings
for infrared thermography, coatings that are sensitive to pressure (barotropic
coatings, PSP) and to temperature (thermal indicator paints TSP), oil coatings,
viscous oil, some special coatings, used in special cases. The methods provide
both quantitative and qualitative images of the flowfields parameter on the
model surface without introducing disturbances into the flow (non-contact
diagnostic methods). The resulting image (film) of the flow on the surface is
recorded digitally during the experiment (or after it). The problem of
restoring the visualizing properties of the coating during the experiment is
essential; special methods are used to solve it.
The ability to measure the light intensity and
frequency has opened the way for the development of methods in which the light intensity
is related to the parameter being measured (pressure, temperature, frictional
stress, etc.). First of all, we focus on the methods of luminescent pressure
and temperature converters.
In the late 70s, the attention of TsAGI
specialists (Central Aerohydrodynamic Institute, Zhukovsky) was attracted by
the work of I. Zakharov, associate professor of the Leningrad Technological
Institute, who investigated the quenching of the luminescence of organic dyes
(luminophores) by oxygen molecules. They used this phenomenon to measure the
distribution of air pressure (which contains oxygen) on the model surface in
wind tunnels. Scientists patented the idea itself in 1980, and the first
pressure-sensitive coating that implements it (a layer of silica gel with
organic phosphor molecules adsorbed in it) - in 1981. This coating - the
authors called it a baro-indicators - turned out to be imperfect, but with its
help it was already possible to see the pressure field. By the end of 1989,
several different types of pressure-sensitive coatings had been developed
(called luminescent pressure transducers).
The use of the phenomenon of luminescence in
optical methods has the advantage over the use of scattered and reflected light
that, due to the spectral shift and time delay, it makes it possible to detune
from the incident (exciting) light and, thereby, increase the measuring accuracy
of the intensity of light carrying useful information.
Today, the determination of the pressure
distribution on a streamlined surface using coatings that are sensitive to
local pressure (PSP - Pressure Sensitive Paint) is one of the main modern
panoramic non-contact methods for diagnosing surface flows in thermal physics.
To visualize the pressure distribution, the surface
of the model is covered with several special layers of paint; one of the layers
contains a fluorescent substance with optical activity depending on the partial
pressure of oxygen. The physical phenomenon underlying the visualization of
pressure fields is the quenching of the luminescence of organic dyes by
atmospheric oxygen. Excited by light of the appropriate wavelength, the
phosphor can emit light (luminescence) or lose energy by transferring it to
oxygen molecules (luminescence quenching). The share of lost energy is
proportional to the oxygen concentration in the polymer and its mobility.
According to Henry's law, the oxygen concentration in the polymer layer is
directly proportional to its partial pressure above the polymer surface. Thus,
the quantum yield of luminescence is inversely proportional to the partial
pressure of oxygen. If the oxygen concentration in the air is constant, then
the luminescence quenching effect can be used to measure the air pressure. A
luminescent temperature converter (TSP) is a coating that, under the action of
exciting radiation, luminesces with an intensity that depends on its
temperature.
The first publications on the use of the method
were published by the employees of TsAGI [134]. Similar foreign works appeared
only 4-5 years later. In [135] a polymer was investigated in a laboratory shock
tube, and in [136], the results of measurements of pressure and temperature in
a hypersonic shock tube at the University of Kolspan are presented. The most
complete reviews on the topic are presented in the monograph [137] and in the
book [138]. This new edition describes pressure and temperature sensitive
paints (PSP and TSP) for panoramic surface pressure and temperature
measurements in aerodynamics and fluid mechanics. The book includes the latest
achievements in the field of paint formulations, the results of stationary and
non-stationary measurements in various aerodynamic installations, including
supersonic and hypersonic wind tunnels, are presented. Technical aspects
including calibration, lighting, image processing, - are described. The
measurement uncertainty of PSP and TSP (Temperature Sensitive Paints) are
discussed.
The papers [139-141] describe methods and
results of measuring pressure and temperature fields and visualizing surface
flow lines and shear stresses. Figure 23 shows the pressure field on the
surface of the model, obtained using luminescent pressure transducers.
Fig. 23.
Pressure distribution on the plate.
(http://www.tsagi.ru/research/measurements/lpd2.jpg)
Surface streamlines or the direction
of flow at the surface of the model can be visualized by applying an oil film
to the surface. Optically contrasting (luminescent) solid particles are added
to the viscous oil film, and at least two distributions of these particles are
recorded on the surface of the model in the flow at a given time interval using
a digital camera. Processing these images using cross-correlation analysis
(similar to how it is done in the PIV method) gives the particle displacement
vectors. To calculate the shear stress field, it is also necessary to know the
thickness of the oil film and the oil dynamic viscosity at each point of the
model surface [143, 142]. The method has become widespread in the study of
separated flows. Figure 24 shows a picture of the streamlines visualization and
friction stress obtained by the method of oil coatings.
Fig. 24. Thick oil method. Wing flow
of a passenger aircraft.
http://www.tsagi.ru/research/measurements/ofi/measurements_maslo.jpg
Surface interferometry in oil films is a method
of measuring the resistance of the aircraft skin in flow. A layer of
transparent oil applied to an air-flowed surface becomes thinner in the region
where shear stress occurs. Local change in layer thickness can be measured using
the interference phenomenon in thin films. Interference occurs when a surface
covered with an oil film of varying thickness is illuminated by laser
radiation. Interference in thin films occurs due to the interaction of light
reflected from the air-oil interface and the oil-solid interface. To obtain
quantitative information about the resistance of the skin, the value of the
displacement of the strips is measured.
A simple but very effective method for
visualizing the structure of the detonation wave front is used in combustion
gas dynamics to analyze the structure of the detonation front [143]. The
existence of overcompressed and undercompressed sections on the plane shock
front of the detonation wave leads to the appearance of inhomogeneities.
Periodic inhomogeneities in the detonation front in the form of lines of double
and triple intersection of wave sections leave traces on a smoked glass plate
placed at the end of the tube. This phenomenon is still used today to study the
structure of the detonation front and the scale of the front inhomogeneity.
Liquid crystal coatings are used in both air and
water currents. High spatial resolution, short response times, multicolor,
reversibility, and relatively low cost allow using liquid crystals to visualize
the temperature and shear stress distribution on the model surface, streamlines
in spatial flows in a wide range of flow regimes [144]. Thermosensitive liquid
crystals change color with temperature. Outside the temperature range, they
become transparent on a black surface. As the temperature rises, the color
changes and the border of the color area indicates the position of the isotherm
on the surface. The range of temperatures measured using liquid crystal
coatings is approximately from -40
°
to 280
°
C. However, in one experiment, a range of several degrees is visualized, which
means, in fact, the visualization of one isotherm with a wide range of
temperatures on the surface.
A
characteristic feature of the modern stage of visualization of thermophysical
flows is the rapid convergence of numerical and experimental panoramic images
of flowfields. This convergence became possible due to the introduction of digital
technologies in the methods of flows registration and analysis on the one hand,
and the growth of the possibilities of flows numerical modeling on the other
hand. On the basis of comparison numerical calculations with the data of
experimental flows visualization, the verification of models and algorithms of CFD
is carried out. On the other hand, the results of the experiments are interpreted
and refined based on the data of the numerical experiment data. Figure 25 shows
an instantaneous image of the unsteady process of a plane shock wave diffraction
in a 48x24 mm rectangular channel over a rectangular obstacle on the channel
wall. Image was obtained by the method of flow visualization by a pulsed volume
discharge. The experimental image is combined with a gas-dynamic calculation of
a two-dimensional flow (density isolines, Navier-Stokes equations). The
calculation helps to decode the details of the experimental image.
Numerical simulation data are visualized, if
possible, in the form of experimental results simulation (numerical
interferograms, pseudo-shadow images, luminescent coatings, PIV, BOS, etc.).
Today, most of the results of computational studies
of supersonic gas-dynamic processes are presented in the form of numerical
shadow pictures. This occurs most effectively in structured flows - with
discontinuities, distinct inhomogeneities, and vortex structures.
Fig. 25. Discharge glow and
calculated density field in a channel with a step during the shock wave
diffraction.
Figure 26 presents the calculated and experimental
images of the density fields of a complex gas-dynamic quasi-two-dimensional
flow in a shock tube after the shock wave interaction with a pulsed volume
discharge with preionization from plasma electrodes. Comparison of experimental
shadow images with the results of CDF images based on the Euler and Navier –
Stokes equations made it possible to calculate the value of the energy input to
the flow by solving the inverse problem [145-147].
Fig. 26. Comparison of calculated
and experimental shadow images.
In the 90th the first computerized
interferograms of two-dimensional flows were published [148, 149].
In the book [20], approaches are considered and
numerous examples are given in the visual representation of a number of
problems’ solutions in gas dynamics, associated with calculations of inviscid
and viscous compressible gas flows containing weak and strong gas-dynamic
discontinuities. The data of supersonic jet visualization and separated flows
obtained in experimental and numerical studies are presented.
|
|
à
|
b
|
Fig. 27. Supersonic two-dimensional
flow with a shock wave and co-flow. BOS method (displacement field) (a) and
numerical simulation (b).
Figure 27 shows images of a supersonic flow after
the shock wave exit from a rectangular channel, obtained by the BOS method (on
the left) and numerical simulation of the corresponding flow (MSU). Figure 28
shows the result of visualization of the numerical simulation of the same flow
- image of the velocity field - simulation of the digital tracing method (PIV).
Fig. 28. Numerical simulation - the
velocity field of a supersonic flow with a shock wave.
The reported study was funded
by RFBR, project number 20-17-50081.
1.
Mach, E.,
Salcher, P.
"Photographische
Fixirung der durch Projectile in der Luft eingeleiteten Vorgänge".
Sitzungsber. Kaiserl. Akad. Wiss., Wien, Math.-Naturwiss.
Cl. (in German). 95 (Abt. II): 764–780. 1887. Doi
10.1002/andp.18872681008.
2.
Etienne-Jules Marey, ‘‘Des mouvements de l’air
lorsqu’il rencontre des surfaces de differentes formes,’’ Comptes Rendus des
Seances de l’Acad ´emie des Sciences 131 (July 16, 1900): 160–63.
3.
Van Dyke,
M. An album of fluid motion. Stanford, CA: Parabolic Press. 1982.
4.
Yang W.J.
(ed.) Handbook of Flow Visualization // NY.: Hemisphere Publishing Corporation.
1989.
5.
Merzkich W. Flow Visualization // 2nd edition. NY.:
Academic
Press.
1987.
6.
Mishin G.I. Optical research methods in a ballistic
experiment. // L.: Nauka.
1979.
7. 7. Klimkin V.F., Papyrin A.N., Soloukhin R.N. Optical methods of registration of fast processes // N .: Nauka. 1980.
8.Glotov G.F., Maikapar. Aerothermodynamics of aircraft in photographs. 2003, TsAGI, Zhukovsky.
9.
Panigrahi P.
K., Muralidhar K., Schlieren and Shadowgraph Methods in Heat and Mass Transfer
Springer, 2012.
10.
Settles G.S.
Schlieren and Shadowgraph Techniques. Visualizing Phenomena in Transparent
Media Springer. 2001.
11.
Ronald J.
Adrian,
Jerry
Westerweel,
Particle Image Velocimetry
(Cambridge
Aerospace),
2010.
12. Dubnischev Yu. N., Arbuzov V. A., Belousov P. P., Belousov P. Ya. Optical methods of investigation of flows. Novosibirsk: Siberian University Publishing House, 2003, 418 pp.
13. Belozerov A.F. Optical methods for visualization of streams. Kazan: publishing house KSTU, 2007, 747 pp.
14. Bazylev N.B., Fomin N.A.Quantitative visualization of currents based on speckle technologies Minsk: Belaruskaya Navuka, 2016.
15.V.P. Vavilov Infrared thermography and thermal control. 2nd edition, add. M. Publishing House Spectrum. 2009.S. 544.
16. Modern optical methods for the study of flows / ed. B.S.Rikevichyus. - M .: Overlay, 2011.
17.
Bilsky, A. V. Evolution and recent trends
of particle image velocimetry for an aerodynamic experiment (review) / A. V.
Bilsky, O. A. Gobyzov, D. M. Markovich // Thermophysics and Aeromechanics. –
2020. – Vol. 27. – No 1.
18.
Pimshtein, V.G. Aeroacoustic interactions in turbulent jets
/ Moscow, 2010. – 88ñ. – ISBN 9785922112321.
19. Bolshukhin MA et al. Actual problems of development of an experimental base for verification of CFD codes when used in nuclear power. Proceedings of NSTU im. R.E. Alekseeva, 2013, T 2 (99) pp 117-125.
20. Volkov K.N., “Visualization of data of physical and mathematical modeling in gas dynamics” Moscow: Fizmatlit, 2018. - 356 p.
21.
E.
Bondarev, VA Galaktionov, VM Chechetkin, “Analysis of the development of
concepts and methods of visual data representation in problems of computational
physics”, Zh. Vychisl. mat. and mat. Fiz., 51: 4 (2011), 669–683.
22.
Krehl P., Engemann S. August
Toepler // Shock Waves. 1995. V. 5.
Is. 1–2. P. 1–18.
23.
Litvinenko, Y.A., Grek, G.R., Kozlov, V.V., Litvinenko, M.V.,
Shmakov, A.G. Diffusion Combustion of a Round Hydrogen Microjet at Sub- and
Supersonic Jet Velocity. Doklady Physics, V. 65, I9, 2020, P.312-316
24.
Zudov V.N., Tretyakov P.K., Tupikin A.V.
Ignition and stabilization by the
optical discharge of homogeneous burning in high-speed jet,
2016, Scientific Visualization, V. 8, no. 2,
24 – 36 pp.
25. Vasiliev L.A. Shadow methods // Nauka. 1968.
26. Kleine H., Hiraki K., Maruyama H. et al. High-speed time-resolved color schlieren visualization of shock wave phenomena // Shock Waves. 2005. V. 14. Is. 5-6. P. 333–341.
27. Inshakov S.I., Rodionov A.Yu., Shirin A.S., Shekhtman V.N. Eleventh International Scientific and Technical Conference "Optical Methods for Studying Flows", Moscow, June 27 - 30, 2011 interferometer for simultaneous registration of two interferograms with an orthogonal shear direction.
28. Bukin V.V., Garnov S.V., Malyutin A.A., Strelkov V.V. Interferometric diagnostics of femtosecond laser microplasma in gases // Trudy instituta obshchey fiziki im. A.M. Prokhorova, 2011, Volume 67, p. 3.
29. Dubnishchev Yu.N., Arbuzov V.A., Arbuzov E.V., Berdnikov V.S., Kislytsin S.A., Melekhina O.S. Optical diagnostics of convective structures induced by non-stationary boundary conditions in a vertical water layer // Scientific Visualization, 2018, V. 10, no. 4, 134 – 144 pp.
30. Wei W., Lia X., Wu J., Yang Z., Jia S., Qiu A. Interferometric and schlieren characterization of the plasmas and shock wave dynamics during laser-triggered discharge in atmospheric air // Physics of Plasmas, Vol. 21, No. 8, 2014.
31. Hargather J., Settles S. A review of recent developments in schlieren and shadowgraph techniques // Meas. Sci. Technol 2017. V. 28. N. 4.
32. A.A. Kandaurov, D.A. Sergeev, O.S. Ermakova, Yu.I. Troitskaya. Investigation of the mechanisms of spray generation induced by wind-wave interactionusing shadow technique. Scientific Visualization V.9 no. 3, 103 – 107 pp.
33. Alferov V.I., Bushmin A.S. Electric discharge in a supersonic air flow // ZhETF, 1963, V. 44, V. 6, pp. 1775-1779.
34. Alferov, V.I. Kalachev, B.V., Visualization of supersonic flows by means of a prebreakdown discharge, Journal of Applied Mechanics and Technical Physics, 9(4), pp. 468-470.
35. Alferov V.I. Investigation of the structure of a high-power electric discharge in a high-speed air flow // Izvestiya. AN SSSR, MZhG, 2004, No. 6, pp. 163-175, 1968.
36. Alferov V.I. On the question of determining the flux density in the visualization of vortex bundles by the method of high-voltage discharge // Tr. TsAGI. 1972. Issue. 1421.S. 13-2.
37. Alferov V.I., Dmitriev L.M. Electric discharge in a gas flow in the presence of density gradients // TVT, 1985, T. 23, No. 4, pp. 677-682.
38. Nishio M., Sezaki S., Nakamura H. Visualization of Flow Structure Around a Hypersonic Re-entry Capsule Using the Electrical Discharge Method // J. of Visualization, Vol.7, No.2, 2004.
39. Nishio M., Nakamura H., Sezaki Sh., Manabe K. Flowfield Around Space Plane Traveling at Mach 10 (Comparison of Visualization and Calculation) // The 10th International Symposium on Flow Visualization, Aug.26-29, 2002, Kyoto, Japan.
40. Jagadeesh G., Srinivasa Rao B.R., Nagashetty K., Reddy N.M., Reddy K.P.J. Electrical discharge technique for three-dimensional flow field visualisation in hypersonic shock tunnel // J. Flow Visualisation and Image Processing, Vol. 4, N0. 1, pp. 51-57, 1997.
41. Jagadeesh G., Srinivasa Rao B.R., Nagashetty K., Reddy K.P.J., Viren M. Visualization studies around spiked blunt cones using electrical discharge technique at Mach 5.75 // The 10th International Symposium on Flow Visualization, Aug. 26-29, 2002, Kyoto, Japan.
42. Chen X., Sha X., Wen S., Lu H.B., Ji F. Visualization of three dimension shock wave in hypersonic gun tunnel using electric discharge. Proceedings 18th International Symposium on Flow Visualization. Zurich, Switzerland, 2018.
43. Ieshkin A.E, Danilov A.V, Chernysh V.S, Ivanov I.E, Znamenskaya I.A. Visualization of supersonic flows with bow shock using transversal discharges. Journal of Visualization, Vol. 22, pp. 741–750, 2019.
44. Znamenskaya I. A., Kuli-zade T. A. , Kulikov V. N., Perminov S. P. Transonic 3d non-stationary flow visualization using pulse transversal discharge // Journal of Flow Visualization and Image Processing. — 2011. — Vol. 18, no. 3. — P. 214-224.
45. I. A. Znamenskaya, T. A. Kuli-zade, “Visualization of toroidal vortex instability by the impulse volume discharge”, Dokl. Akad. Nauk, 348:5 1996, 617–619.
46. Znamenskaya I. A., Ivanov I. E., Kryukov I. A., Kuli-Zade T. A. Pulsed volume discharge with preionization in a two-dimensional gas-dynamic flow// JETP, Vol. 95, No. 6, p. 1033, December 2002
47. Znamenskaya I. A., Tatarenkova D. I., Kulizade T. A. Nanosecond ionization of an area of flowing around a rectangular ledge by a high-speed flow // Technical Physics Letters. — 2020. — Vol. 46. — P. 5-7.
48. Li G. et al. Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems. // Journal of Physics: Conference Series, vol. 1215, 2019.
49. Edla D. et al. Advances in Machine Learning and Data Science: Recent Achievements and Research Directives. Springer, 2018.
50. Ye S. et al. A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network. Scientific Reports, vol. 10, 2020.
51. Canny J. A. Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8(6), p. 679-698, 1986.
52. Srisha Rao M., Jagadeesh G. Visualization and Image Processing of Compressible Flow in a Supersonic Gaseous Ejector. // Journal of the Indian Institute of Science, vol. 93, no. 1, 2013.
53. Fujimoto T.R., Kawasaki T., Kitamura K. Canny-Edge-Detection/Rankine–Hugoniot-Conditions Unified Shock Sensor for Inviscid and Viscous Flows // J. Comput. Phys., vol. 396, pp. 264–279, 2019.
54. Brunton S. L. et al. Machine Learning for Fluid Mechanics. Annual Review of Fluid Mechanics, vol. 52, pp. 477-508, 2020.
55. Dehghan Manshadi M. et al. Speed Detection in Wind-tunnels by Processing Schlieren Images. IJE TRANSACTIONS A: Basics, vol. 29, no. 7, pp. 962-967, 2016.
56. Colvert B. et al. Classifying vortex wakes using neural networks. Bioinspiration & Biomimetics, vol 13, no. 2, 2018.
57. Harel R., Rusanovsky M., Fridman Y., Shimony A., Oren G. Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities. High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science, Vol 12321, p. 61-80, 2020.
58. Ott C., Pivot C., Dubois P., Gallas Q., Delva J., Lippert M., Keirsbulck L. Pulsed jet phase-averaged flow field estimation based on neural network approach. Experiments in Fluids, Vol. 62, No. 79, 2021.
59. Kutz J. Deep learning in fluid dynamics. // Journal of Fluid Mechanics, Vol. 814, pp 1-4, 2017.
60. Znamenskaya I. A., Doroshchenko I.A. Edge detection and machine learning for automatic flow structures detection and tracking on schlieren and shadowgraph images // Journal of Flow Visualization and Image Processing. — 2021. — Vol. 28, no. 4. — P. 1–26.
61. I. A. Znamenskaya, I. A. Doroshchenko, N. N. Sysoev, and D. I. Tatarenkova. Results of Quantitative Analysis of High-Speed Shadowgraphy of Shock Tube Flows Using Machine Vision and Machine Learning. Doklady Rossiiskoi Akademii Nauk. Fizika, Tekhnicheskie Nauki, Vol. 497, pp. 16–20, 2021.
62. Znamenskaya, I.A. et al. Edge detection and machine learning approach to identify flow structures on schlieren and shadowgraph images. Proceedings of the 30th International Conference on Computer Graphics and Machine Vision. CEUR Workshop Proceedings, vol. 2744, pp. 1–14, 2020.
63. V.P. Vavilov Infrared thermography and thermal control. 2nd edition, add. M. Publishing House Spectrum. 2013. - P. 544.
64.
Chernorizov A., Isaychev S., Znamenskaya I.,
Koroteyeva E., Khakhalin A., Shishakov V. Remote Detection of Human Emotional
States by Facial Areas // International Journal of Psychophysiology. – 2018. –
131. – S. 85.
65.
Berlovskaya E.E., Isaychev S.A., Chernorizov A.
M., Ozheredov I.A., Adamovich T.V., Isaychev E.S., Cherkasova O.P., Makurenkov
A.M., Shkurinov A.P., Varaksin A.N., Gatilov S.B., Kurenkov N.I., Manaenkov
A.E. Diagnosing Human Psychoemotional States by Combining Psychological and
Psychophysiological Methods with Measurements of Infrared and THz Radiation
from Face Areas // Psychology in Russia: State of the Art. – 2020. – 13. – ¹.
2. – P. 64-83.
66.
Carlomagno G.M., Cardone G. Infrared
thermography for convective heat transfer measurements // Experiments in
fluids. – 2010. – 49. – ¹. 6. – P. 1187-1218.
67.
Leontiev A.I., Kiselev N.A., Burtsev S.A.,
Strongin M M., Vinogradov Y.A. Experimental investigation of heat transfer and
drag on surfaces with spherical dimples // Experimental Thermal and Fluid
Science. – 2016. – 79. – P. 74-84.
68.
Kiselev N.A., Leontiev A.I., Vinogradov Yu.A. et
al. Effect of large-scale vortex induced by a cylinder on the drag and heat
transfer coefficients of smooth and dimpled surfaces // International Journal
of Thermal Sciences. – 2019. – 136. – P. 396-409.
69.
Thomann H., Frisk B. Measurement of heat
transfer with an infrared camera //International Journal of Heat and Mass
Transfer. – 1968. – 11. – ¹.
5. –
P. 819-826.
70.Bazovkin V.M., Kovchavtsev A.P., Kuryshev G.L., Maslov A.A., Mironov S.G., Khotyanovskiy D.V., Tsarenko A.V., Tsyryulnikov I.S. Numerical and experimental investigation of hypersonic flow around a two-dimensional compression angle. // Journal Vestnik NSU. Series Physics. 2007. Volume 2, No. 1. P. 3–9.
71.
Simon B., Filius A., Tropea C., Grundmann S.
// Experiments in Fluids. – 2016. – 57. – ¹. 5. – P. 93.
72.
Raffel M., Merz C.B. Differential Infrared
Thermography for Unsteady Boundary-Layer Transition Measurements // AIAA
journal. – 2014. – 52. – ¹. 9. – P. 2090-2093.
73.
Richter K., Schulein E. Boundary-layer
transition measurements on hovering helicopter rotors by infrared thermography
// Experiments in fluids. – 2014. – 55. – ¹. 7. – P. 1755.
74.
Banks D.W., Frederick M.A., Tracy R.R.,
Matisheck J.R., Vanecek N.D. In-flight boundary-layer transition on a large
flat plate at supersonic speeds. 15th International Symposium on Flow
Visualization ISFV15 – Minsk / Belarus – 2012 0-62.
75.
V. S. Berdnikov, V. A. Grishkov, K. Y. Kovalevskii, V. A. Markov.
Thermal Imaging Studies of the Laminar-Turbulent Transition in the
Rayleigh-Benard Convection. Avtometriya, 2012, n.3, P. 111-120.
76.
Ivanitskii G R, Deev A A,
Khizhnyak E P "Water surface structures observed using infrared
imaging" Phys. Usp. 2005, V 48, P. 1151–1159.
77.
Hetsroni G., Mewes D., Enke C., Gurevich M. et al. Heat transfer to
two-phase flow in inclined tubes // International Journal of Multiphase Flow. –
2003. – 29. – ¹. 2. – P. 173-194.
78.
Violato D., Ianiro A., Cardone G., Scarano F. Three-dimensional
vortex dynamics and convective heat transfer in circular and chevron impinging
jets // International Journal of Heat and Fluid Flow. – 2012. – 37. – P.
22-36.
79.
Carlomagno G.M., Ianiro A. Thermo-fluid-dynamics of submerged jets
impinging at short nozzle-to-plate distance: A review // Experimental thermal
and fluid science. – 2014. – 58. – P. 15-35.
80.
Judd K.P.,
Smith G.B., Handler R.A., Sisodia A. The thermal signature of a low Reynolds
number submerged turbulent jet impacting a free surface //Physics of Fluids. –
2008. – 20. – ¹.
11. – 115102.
81.
Kashinskii, O.N., Lobanov, P.D., Kurdyumov, A.S. et
al. Experimental simulation of a liquid-metal heat-transfer fluid flow in
a T-shaped mixer. Tech. Phys. 61, 783–785 (2016).
82.
Zaitsev D.K., Smirnov E.M., Kolesnik E.V.,
Bolshukhin M.A., Budnikov A.V., Sveshnikov D.N. Computational and experimental
study of temperature pulsations in a tee joint with oblique injection //
Collection of reports "Problemy primeneniya i verifikatsii CFD kodov v
atomnoy energetike". - 2018. - C. 104-105.
83.
Bol’shov L., Pribaturin N., Kashinsky O.,
Lobanov P., Kurdyumov A. Experimental Study of Mixing Fluid Flows with
Different Temperatures in a T-Junction // Journal of Applied Mechanics and
Technical Physics. – 2020. – 61. – ¹. 3. – P. 368-376.
84.
Nakamura H., Shiibara N., Yamada S. Quantitative measurement of
spatio-temporal heat transfer to a turbulent water pipe flow // International
Journal of Heat and Fluid Flow. – 2017. – 63. – P. 46-55.
85.
Roux S., Fenot M., Lalizel G., Brizzi L.-E., Dorignac E. Evidence of
flow vortex signatures on wall fluctuating temperature using unsteady infrared
thermography for an acoustically forced impinging jet // International journal
of heat and fluid flow. – 2014. – 50. – P. 38-50.
86.
Nakamura H. Measurements of time-space distribution of convective
heat transfer to air using a thin conductive film // Fifth International
Symposium on Turbulence and Shear Flow Phenomena. – Begel House Inc., 2007. –
P. 1906–1914.
87.
Shiibara N., Nakamura H., Yamada S. Visualization of turbulent heat
transfer to a water flow in a circular pipe using high-speed infrared
thermography //Journal of Flow Visualization and Image Processing. – 2013. –
20. – ¹.
1-2.
88.
Znamenskaya I.A., Koroteeva E.Yu., Shirshov Ya.N., Novinskaya A.M.,
Sysoev N.N. High speed imaging of a supersonic waterjet flow //Quantitative
InfraRed Thermography Journal. – 2017. – V. 14. – ¹. 2. – P. 185-192.
89.
Bolshukhin, M.A., Znamenskaya, I.A. & Fomichev, V.I. A method of
quantitative analysis of rapid thermal processes through vessel walls under
nonisothermal liquid flow. Dokl. Phys. 2015, V. 60, P. 524–527.
90.
Koroteeva E., Shagiyanova A.,
Znamenskaya I., Sysoev N. Time-resolved thermographic analysis of the near-wall
flow of a submerged impinging water jet //
Experimental Thermal
and Fluid Science.
— 2021. — P. 110 – 264.
91.
Znamenskaya I.,
Koroteeva E., Shagiyanova A. Thermographic analysis of turbulent
non-isothermal water boundary layer // Journal of Flow Visualization
and Image Processing. — 2019. — Vol. 26, no. 1. — P. 49–56.
92.
P.P.Khramtsov et al. Diagnostics of
Density Fields in Hypersonic Flows around a Cone in a Light-Gas Gun by the
Shadow Photometric Method. Tech. Phys. 64, 1424–1429 (2019).
93.
Meier G.,
(2002). Computerized background-oriented schlieren. Exp. Fluids 33 (1),
pp.181–187.
94.
Dalziel
S.B., Hughes G.O., Sutherland B.R., (2000). Whole-field density measurements by
“synthetic schlieren.” Exp. Fluids 28 (4), pp.322–335.
95.Bazylev N.B., Fomin N.A.Quantitative visualization of currents based on speckle technologies - Minsk: Belaruskaya Navuka, 2016 .-- 392 p.
96.
Khramtsov P.P., Penyazkov O.G., Shatan I.N. Temperature measurements
in an axisymmetric methane–air flame using Talbot images. Exp Fluids 56, 31
(2015).
97.
Raffel M. Background-Oriented Schlieren (BOS) techniques. Exp
Fluids 56, 60 (2015).
98. Skornyakova N.M. Background Oriented Schlieren and its applications. In the book Modern optical methods for the study of flows / ed. B.S.Rikevichyus. - M .: Overlay, 2011.S. 93-107.
99.
Hazewinkel J., Maas L. R. M., Dalziel S. B. Tomographic
reconstruction of internal wave patterns in a paraboloid. Experiments in
Fluids, vol. 50, no. 2, pp. 247–258, Jul. 2010.
100.
Kirmse T., Agocs J., Schröder A., Martinez
Schramm J., Karl S., Hannemann, K., (2011). Application of particle image
velocimetry and the Background-Oriented Schlieren technique in the
high-enthalpy shock tunnel Göttingen. Shock Waves 21 (3), pp.233–241.
101.
Glazyrin F.N., Znamenskaya I.A., Mursenkova
I.V., Sysoev N.N., Jin J., (2012). Study of shock-wave flows in the channel by
schlieren and background oriented schlieren methods. Optoelectron. Instrum.
Data Process. 48 (3), pp.303–310.
102.
Glazyrin F., Znamenskaya I., Koroteeva E.,
Mursenkova I., Sysoev N.
Application of Background Oriented
Schlieren Technique for Investigations of a Non-Stationary Flow With Shock Wave
//
Scientific Visualization
– 2013. – V. 5 – ¹ 3 – 65–74 pp.
103.
Tillmann W., Abdulgader M., Rademacher H., Anjami N.,
Hagen L., (2014). Adapting of the Background-Oriented Schlieren (BOS) Technique
in the Characterization of the Flow Regimes in Thermal Spraying Processes. J.
Therm. Spray Technol. 23 (1-2), pp.21–30.
104.
Mizukaki T., Wakabayashi K.,
Matsumura T., and Nakayama K., (2014). Background-Oriented Schlieren with
natural background for quantitative visualization of open-air explosions. Shock
Waves 24 (1), pp.69–78, 2014.
105.
Gerasimov S.I., Trepalov N.
À.
Background-Oriented Schlieren
method for recording of air shock waves.
Scientific Visualization, 2017, 4(9), 1-12 pp.
106.
Sysoev N.N., Znamenskaya
I.A. New possibilities of digital technologies for image analysis during
testing at proving grounds.
Izvestiya
Rossiyskoy akademii raketnykh i artilleriyskikh nauk.
- 2020. - T. 112, No. 2. -
P. 114.
107.
Znamenskaya I.A., Vinnichenko N.A., Glazyrin
F.N., (2012). Quantitative measurements of the density gradients on the flat
shock wave by means of Background Oriented Schlieren. (ISFV, Minsk, Belarus),
p. 060.
108.
Hazewinkel J., Maas L.R.M., Dalziel S.B., (2011).
Tomographic reconstruction of internal wave patterns in a paraboloid. Exp.
Fluids 50 (2), pp.247–258.
109.
Hayasaka K., Tagawa
Y. Mobile visualization of density fields using smartphone Background-Oriented Schlieren. Exp
Fluids 60, 171 (2019).
110.
Hargather M.J., Settles G.S. (2012) A comparison
of three quantitative schlieren techniques. Opt Lasers Eng 50(1):8–17.
111.
Fisher T.B., Quinn M.K., Smith K.L. An
experimental sensitivity comparison of the schlieren and Background-Oriented Schlieren
techniques applied to hypersonic flow //
Measurement Science and Technology, 2019,
V. 30,
N. 6.
112.
Adrian R. J. Particle Image Velocimetry /
Adrian R. J., Westerweel J. Particle Image Velocometry.
Evolution and recent trends of
particle image velocimetry for an aerodynamic experiment (review)
// Cambridge University Press, 2010. – 586 p.
113.
Raffel M.,
Willert C.E.,
Scarano F.,
Kähler C.,
Wereley S.T.,
Kompenhans J.
2018.
Particle image velocimetry, a practical guide 3rd ed. Springer Int.
Publishing.
669 p.
114.
Scarano F.
2012.
Tomographic PIV: principles and practice //
Meas. Sci. Technol. Vol. 24,
No. 1. P. 012001-1- 012001-28.
115.
Grant, I.
Particle image velocimetry: A review. Arch. Proc. Inst. Mech. Eng. C J. Mech.
Eng. Sci. 1997, 211, 55–76.
116.
N. B. Bazylev, N. A. Fomin.
Quantitative visualization of currents based on speckle technologies Minsk:
Belaruskaya Navuka, 2016.392p.(In Russian).
117.
Adrian R. J. Twenty years of
particle image velocimetry // Exp.
in Fluid s. – 2005. – Vol . 39. – P. 159–169.
118.
Mariani R., Kontis K., (2010). Experimental studies on coaxial
vortex loops. pp.126102. Physics of Fluids 22(12).
119.
Skornyakova N.M., Sychev D.G., Varaksin A.Yu.,
Romash M.E.
(2015)
Vizualization of vortex structures by
particle image velocimetry.
Scientific Visualization, 7 (3), pp.15–24.
120.
Koroteeva E.Y., Znamenskaya I.A., Glazyrin F.N.,
Sysoev N.N. Numerical and experimental study of shock waves emanating from an
open-ended rectangular tube // Shock Waves. — 2016. —
Vol. 26, no. 3. — P. 269–277.
121.
Murphy M.J., Adrian
R.J., (2010). PIV space-time resolution of flow behind blast waves. Exp. Fluids
49 (1), pp.193–202.
122.
Murphy M.J., Adrian, R.J., (2011). PIV through
moving shocks with refracting curvature. Exp. Fluids 50 (4), pp.847–862.
123.
Koroteeva E.,
Mursenkova I., Liao Y., Znamenskaya I. Simulating particle inertia for
velocimetry measurements of a flow behind an expanding shock wave //
Physics
of Fluids. — 2018. — Vol. 30, no. 1. — P. 011702.
124.
PIV analysis of the
homogeneity of energy deposition during development of a plasma actuator
channel / F. N. Glazyrin, I. A. Znamenskaya,
I. V. Mursenkova et al. // Technical Physics Letters.
— 2016. — Vol. 42, no. 1. — P. 63–66.
125.
Bin Yang, Yuan Wang,
Wen Bo He “Application of Micro-PIV on the Microscale Flow and a Modified
System Based on Ordinary 2-D PIV.” Advanced Materials Research 346 (2011):
657–63.
126.
Yagodnitsyna A.A., Bilsky A.V., Kabov O.A.
Flow visualization in evaporating
droplet on a substrate by means of micro-PIV technique
//
Scientific Visualization, 2016, V. 8, no. 2, 53-58 pp.
127.
Koroteeva E. Y.,
Znamenskaya I. A., Glazyrin F. N., Sysoev N. N. Numerical
and experimental study of shock waves emanating from an open-ended re
ctangular tube
// Shock Waves.
— 2016. — Vol. 26, no. 3. —
P. 269–277.
128.
Dennis R.
Jonassen, Gary S. Settles, Michael D. Tronosky, Schlieren “PIV” for turbulent
flows, Optics and Lasers in Engineering, Volume 44, Issues 3–4, 2006, Pages
190-207.
129.
Michael
John Hargather
Michael
James Lawson
Gary
S. Settles
Leonard
M. Weinstein.
Seedless Velocimetry Measurements by Schlieren Image Velocimetry 2011 AIAA
Journal 49(3):611-620.
130.
Nematollahi O.,
Samsam-Khayani H., Kim K.C., Nili-Ahmadabadi M., Yoon S.Y A novel self
‑
seeding method for particle image
velocimetry measurements of subsonic and supersonic
flows
Scientific
Reports.
2020. Ò.
10.
¹ 1.
Ñ. 10834.
131.
Bryan E. Schmidt,
Wayne
E. Page,
Jeffrey
A. Sutton
Seedless
Velocimetry in a Turbulent Jet using Schlieren Imaging and a Wavelet-based
Optical Flow Method AIAA 2020-2207 Session: Advancements in Planar, Volumetric,
and High-Speed Imaging Techniques.
132.
Mikheev, N.I.,
Dushin, N.S. A method for measuring the dynamics of velocity vector fields in a
turbulent flow using smoke image-visualization videos. Instrum Exp
Tech 59, 882–889. 2016.
133.
Koroteeva E. Y.,
Znamenskaya I. A., Ryazanov P. A. Velocity-field
measurements in a fluid boundary layer based on high-speed
thermography // Doklady Physics. — 2020. — Vol. 65, no. 3.
— P. 100–102.
134.
Borovoy V., Bykov
A., Mosharov V., Orlov A., Radchenko V., Fonov S. Pressure Sensitive Paint
Application in Shock Wind Tunnel. // 16th ICIASF Congress. - Dayton, Ohio, July
1995, ICIASF 95 record, 1995. P. 34.1–34.4.
135.
Hubner J.P.,
Carroll B.F., Schanze K.S., Ji H.F. Pressure sensitive Paint Measurements in a
Shock Tube. // Experiments in Fluids, 2000. - V. 28. - ¹ 1. – P. 21–28.
136.
Hubner J.P.,
Carroll B.F., Schanze K.S., Ji H.F., Holden M.S. Temperature- and
Pressure-Sensitive Paint Measurements in Short-Duration Hypersonic Flow. //
AIAA Journal, 2001. - V. 39. - ¹ 4. – P. 654-659.
137.
Liu T., Sullivan
J.P. Pressure and Temperature Sensitive Paints //
Springer
2005, 328ñ.
138.
Liu
T., Sullivan J.P., Asai K., Klein C., Egami Y. 2nd ed.
2021.
Springer.
139.
Mosharov V. E.
"Luminescent methods for investigating surface gas flows
(Review)" Instruments and Experimental Techniques January,
Volume 52, Issue 1, pp 1-12. (2009).
140.
Mosharov V., Radchenko V. PSP/TSP activity in
TsAGI // 15th International Symposium on Flow Visualization (ISFV15)
proceedingss, Minsk, Belarus, June 25-28, 2012, CD, ISFV15-009, pp.1-4.
141.
Mosharov V.,
Radchenko V., Tsipilev N. Particle image surface flow visualization and
skin-friction measurement // 31st Congress of the International Council of the
Aeronautical Sciences, ICAS 2018 : 31, Belo Horizonte, 2018.
142.
Mosharov V. Ye., Radchenko V.
N., Tsipilev N. S. Visualization of the flow on the surface from the images of
particles: a step towards the measurement of surface friction //
Modeli i metody aerodinamiki : Shestnadtsataya
Mezhdunarodnaya shkola-seminar,
Evpatoria,
2016– Ñ . 118-119 TsAGI.
143. Shchelkin K.I., Troshin Ya.K. Combustion gas dynamics. M. 1963.
144.
Zharkovà
G.M., Kovrizhina V.N.,
Petrov
À.
Ð.,
Shapoval, E.S.,
Mosharov V.E. and Radchenko V.N. Visualization of boundary layer transition by
shear sensitive liquid crystals.// Proceedings
PSFVIP-8: 2011
Moscow,
Russia.
No. 113. -
P.
1-5.
145.
Znamenskaya I. A.,
Koroteev D. A., Lutsky A. E. Discontinuity breakdown
on shock wave interaction with nanosecond discharge // Physics of
Fluids. — 2008. — Vol. 20. — P. 056101–1–056101–6.
146.
Chetverushkin,
B.N., Znamenskaya, I.A., Lutsky, A.E. et al. Numerical Simulation of the
Interaction and Evolution of Discontinuities in a Channel Based on a
Compact Form of Quasi-Gasdynamic Equations. Math Models Comput Simul 2021, 13, P.
26–36.
147.
Koroteeva E., Znamenskaya I., Orlov D , Sysoev N.Shock
wave interaction with a thermal layer produced by a plasma sheet actuator
// Journal of Physics D - Applied Physics. — 2017. — Vol. 50,
no. 8. — P. 085204.
148.
Tamura Y., Fujii K. Visualization for computational
fluid dynamics and the comparison with experiments, Paper AIAA-90-3031
(1990).
149.
Fursenko A. A., Sharov D. M., Timofeev E. V.,
Voinovich P. A. Numerical Simulation of Shock Wave Interactions with Channel
Bends and Gas Nonunoformities. // Computers Fluids Vol. 21, No. 3, pp. 377-396,
1992.