10TH INTERNATIONAL SYMPOSIUM ON PARTICLE IMAGE VELOCIMETRY - PIV13 Delft, The Netherlands, July 1-3, 2013
Measurements with a redesigned 3D-PTV system in natural and mixed
convection
Patrik Steinhoff1, Martin Schmidt1, Christian Resagk2and Dirk M ¨uller1
1E.ON Energy Research Center, Institute for Energy Efficient Buildings and Indoor Climate (EBC), RWTH Aachen University, Aachen, Germany, psteinhoff@eonerc.rwth-aachen.de
2Institut f ¨ur Thermo- und Fluiddynamik, Ilmenau University of Technology, Ilmenau, Germany
ABSTRACT
The paper reports on the application of scientific CMOS cameras and high power LED light sources for three-dimensional particle tracking in large enclosures. An open source software is adapted to work with 16 bit grey value images. As fixed thresholds pointed out to be inappropiate for particle detection in 16 bit images due to temporal and spatial brightness variations in large fileds of view a method has been implemented to select a threshold based on the histogram of an image or image area. The redesigned system has so far been used for investigations in two experimental facilities. The settings and methods as well as an extract of the results are presented in the following.
1. INTRODUCTION
The effects of thermal convection are present everywhere around us in buildings, vehicles, outdoor and in many other situations. With focus on the indoor airflow the thermal convection very often superposes with forced convection from mechanical ventilation systems to a mixed convection flow. The evolving flow structures have a significant impact on the ventilation effectiveness, a factor which is important for the evaluation of the pollutant transport, heat transfer and the thermal comfort of occupants. Today the knowledge about formation processes of these flow structures and their transient behaviour is not sufficient to predict the behaviour of airflows at low turbulent Reynolds numbers without large computational afford like for example a direct numerical simulation (DNS). The investigation of real-scale experiments is also a complex topic, as the pool of measuring methods, that enable volumetric and time resolved measurements is small. In most real scale experiments thermal anemometers (punctual) or planar systems like particle image velocimetry (PIV) are used to measure flow velocities. Working with these techniques the required time for an averaged volumetric flow measurement is large as a lot of different points or slice-postions are needed to generate a volumetric measurement field. A temporal resolution that is sufficient for a structure detection is still hard to achieve, because the information from the flow field is limited to one period of time. As the small scales of the investigated settings are of minor interest with focus on the detection and observation of large scale flow structures, the three-dimensional particle tracking velocimetry (3D-PTV) is a promising method for application in large scale experiments like the “Barrel of Ilmenau” (BOI) as presented by Lobutova[1].
The experimental study focusses on the application of 3D-PTV and detection of flow structures in enclosures with a volume about or larger than 3 m3. Therefore 3D-PTV was applied in two different experimental facilities, the “Barrel of Ilmenau” (BOI), a setup for investigation of turbulent natural convection, and in a facility for the investigation of turbulent mixed
convection flows, called “Aachen Modelroom” (AMoR). In the BOI a lot of experimental works were performed with PIV [2] and 3D-PTV [3]. For AMoR, so far, experimental works were performed with omnidirectional thermal anemometers as presented by Kandzia [4].
In the following (sec. 2.1 & 2.2) the employed hard- and software and their characteristics are described. A short description of the presented experimental facilities and the applied setups are given in the sections 2.3 and 2.4. In section 3 selected results from two different experimental facilities (sec. 3.1 BOI, sec. 3.2 AMoR) are presented. Finally in section 4 the progress for 3D-PTV achieved by the presented work is summarized.
2. EXPERIMENT
For the experimental investigation of large scale flow structures in natural and mixed convective air flows the threedimensional particle tracking velocimetry (3D-PTV) is chosen. The fluid motion is visualized using Helium filled soap-bubbles (HFSB) with an average diameter of 2 mm as described in [5] and [6] as neutrally buoyant tracer particles. By reconstruction of the three-dimensional particle trajectories the observed flow structure is determined. The transient behaviour of flow structures is resolved by a variation of the acquistion settings and dividing the sequence into stacks, that are acquired with different time-shifts.
2.1 3D-PTV - Hardware
With respect to the area of application various hardware settings for 3D-PTV systems are conceivable. From one fast (≥ 500 frames per second (fps)) camera with a multi-view image splitter system and an observation volume in the order of cm3 [7],[8] up to an arrangement with four consumer cameras, a framerate about 3 fps and a volume about 90 m3[3].
According to the needs of our planned PTV-experiments in AMoR four cameras1 with a novell scientific CMOS2-sensor (sCMOS) are chosen. These sCMOS-sensors enable the acquisition of images with 5.5 MPix resolution and a depth of 16 bit per pixel at up to 100 fps. A programmable timing device synchronises the four cameras, that are connected to two workstations with 20 GB virtual RAM disk and a RAID 0 hard disk array each for continuous acquisition and data spooling. For long time acquisition frame rates up to 25 Hz, mainly limited by the timing device and data tansfer rates, are used. The acquisition time at higher frame rates up to 100 fps is primarily limited to the cameras internal memory of 4 GB. Lenses with a fixed focal distance of 25 mm are a compromise between field/angle and depth of view. The observation volume is illuminated by up to twelve compact pulsed light sources each
1Type: Neo sCMOS, Andor technology
assembled with six Cree XP-G R5 LED. Each light source has a diameter of 90 mm and a height of 15 mm. Their flat spherical design (fig. 1) reduces the disturbance of the surroundig and allows a mobile positioning in the interior of large observation volumes.
Figure 1: Snapshort of a LED light source with a diameter of 90 mm, height of 15 mm and an adjustable illumination angle. Assembled LED type: Cree XP-G R5
2.2 3D-PTV- Software
The deployed software for calibration, image processing, determination of 3D positions and tracking is the open source code from ETH Z¨urich, which is available via the open source community OpenPTV3. Examples for the usage and accuracy of this source code are given in different publications of Maas [10], Wilneff [11] and many others ([3],[7],[8],[9]). Processing 16 bit image data required some modifications in the source code for image processing operations. To make use of the
Figure 2: Example for the threshold selection based on a histogram.
benefit from the 16 bit data range thresholds for the particle detection are calculated based on the histogram of the image respective of an image region. This is especially helpful, if the illumination of the observation volume is not homogeneous. It also compensates brightness fluctuations over measuring-time as the threshold is reselected for each acquired image. Based on the histogramm of a pixel area two criterias are defined for the estimation of a threshold: limiter for pixel count and limiter for peak size ratio. The limiter for peak size ratio returns, counting backwards from the maximum grey value of the histogram, the selected threshold at a given ratio of the
3www.openptv.net
maximum to the actual grey value of the histogram (fig. 2). The principle of the limiter for pixelcount is shown in figure 2. Counting donwards from the highest 16 bit value (65535) the histogram counts are summed up until the given percentage (shaded area in figure 2) of the pixel array size is reached. The last added grey value is set as threshold for the pixel array. The criteria which is fulfiled first is taken to select the threshold for the image area. In order to handle images with non-uniform illumination, where a global threshold for some areas might be improper for a reliable particle detection, the full image is divided into an array with n x m grid elements. For each of this equal spaced elements the threshold is computed as described above. The threshold values are stored in an threshold image. This additional used memory is acceptable as processing time stays low by using pointers arithmetics in the C code for image processing. A further advantage of handling the threshold values in an image: possible discontinuities of two adjacted elements can be reduced by a simple low pass filtering of the threshold image.
However sometimes the number of detected particles does not match with the expected number of detections. Therefore an additional iterative routine has been implemented, which manipulates the global detection criteria for each camera image, the so-called peak fit factor, until the number of detections matches the expected range or the maximum number of iterations is exceeded. The additional computational effort is distributed via parallelization with OpenMP4to one thread per image on the processing computer, which retains the ability to integrate additional cameras in the 3D-PTV system.
2.3 Setup at Barrel of Ilmenau, BOI
The “Barrel of Ilmenau” (BOI) is a facility for Rayleigh-B´enard experiments. BOI has an insulated cylindrical casting with an inner diameter of di= 7.15 m. The experiments aspect ratio Γ is adjustable as the height of the cell can be modified in a range from 0.05 m up to 6.30 m. Setting up a temperature difference between the bottom and the top of the BOI, caused by the heat transfer, temperature differences of fluids and the corresponding differences in density fluid motion is generated. According to
Figure 3: Sketch of the setup at the “Barrel of Ilmenau” for experiments at a height of h=3.58 of the cooling plate (2) leading to an aspect ratio of Γ = 2. The twelve LED light sources (1) are placed at the edge of the heated plate on the bottom (5). Four Neo sCMOS cameras from Andor (4) are mounted next to the wall with about 1 m vertical and 1.4 m horizontal distance. The inner surface of the wall (3) is coated with a low reflecting black layer.
the measurements presented by Lobutova [1],[3] a setting with a height of h = 3,58 m and an aspect ratio of Γ =di
h = 2 is chosen as reference. The temperature difference between the cooled upper and heated lower plate is set to ∆T = 20 K, that leads to a Rayleigh-number of 8.45 · 1010, where the continuous transition of flow structures is expected to occur. As shown in figure 3 the four Neo sCMOS cameras look at an extract of the whole cylindrical volume. Restricted by the length of the connector cables to the cameras the distances between the cameras are limited to 1 m vertical and 1.4 m horizontal distance. To estimate the orientation of the cameras a calibration with a three-dimensional calibration target is performed. A layer of black paint on the curved surface area reduces light reflections and enhances the contrast between the backround and the light reflection of the tracer particles. The helium filled soap-bubbles (HFSB) with a diameter of about 2 mm are continuously seeded to the flow, because the expected lifetime is, due to thermal stresses, reduced to less than 120 seconds.
2.4 Setup at Aachen Modelroom, AMoR
The experimental facility Aachen Modellrom “AMoR” is a generic mock-up for investigations on indoor airflow and passenger cabins at RWTH Aachen University. The cabin has a depth of L = 5 m, a height of H = 3 m and a width of B = 4 m (fig 4). For optical access of the inner volume equal spaced acryl glass windows with an refraction index of 1.49 and a width of 0.1 m are embedded in the wall mounting. These can either be used for illumination purposes or camera mounting. Four inner cuboids with a width of 0.4 m and a height of 0.6 m provide a thermal load up to 6000 watt. Starting from the outer
Figure 4: Drawing of the geometry of “Aachen Modelroom”. A thermal load is provided using the four inner cuboids with an over all power of up to 6000 watt.The air supply, a plane wall jet with an height of 20 mm, is placed at the left and right side next to the top. The exhaust air is extracted at both sides next to the bottom with a height of 150 mm.
walls the spacing of the elements is 0.4 m, only the distance between the second an third cuboid is 0.8 m. A distance of 0.15 m between the bottom of the heat sources and the ground of AMoR is representative for the geometric parameters of cabin ventilation systems. The ventilation system is a mixing ventilation setup with slot inlets of 20 mm height on the top of both sides and the outlets placed next to the ground with
an height of 150 mm. In consequence of special interests on three-dimensional structures a case with 0.8ms inlet velocity and a temperature difference of ∆T = 7.5 K between heat sources and air inlet temperature is selected, where the effects of natural and forced convection are in the same order of magnitude. In the base configuration AMoR’s inner surface is completely covered with a thin aluminium sheet to eliminate the radiative heat transfer. As the high coefficient of reflection of aluminium would lead to heavy light reflections in the acquired images, the surface in front of the cameras is covered by a matt black plastic layer of 3 ˙mm thickness. Embedded LED with a horizontal and vertical distance of 200 mm are used for calibration of the camera system. The camera system is mounted at one front end of the facility as schown in figure 5. A detection volume of 9.5 m3 is realized with 0.5 m vertical and 0.8 m hoirzontal distance between the cameras. For this setup HFSB with a diameter of 2 mm are continuously seeded to both plane wall jets from the top. The illumination of the measuring volume is
Figure 5: Sketch of the measuring volume (9.5 m3) and camera postions at one front end of the “Aachen Modelroom” done by ten of the described LED light sources. Two are placed on each sidewall at z =L2, y = H2 and y =34H, one on top of each heat source at z = L and two on the ground at z =34L. 3. RESULTS
The parameters of the experimental setups at BOI and AMoR and a selection of the postprocessed results are described in the following. A transfer of the experimental data into the vkt-data format enabled the post-processing of the reconstructed trajectories and particle information with the open source software ParaView5.
3.1 Results BOI
In order to investigate transient structures an aspect ratio of Γ = 2 and a Rayleigh number of 8.45 · 1010are selected. With these boundary conditions unstable and changing structures are expected to occur [2]. According to limited experimental time only one camera setup was calibrated and used for image recording. In refence to the geometry of the barrel and the camera settings only less than one fifth of the volume was observed by the cameras. The sequences were acquired using framerates 17 fps and 25 fps. Acquisition sequences are splitted into stacks with a short period without image recording after each stack to extend the overall measuring time. Stack sizes from 50 up to 1000 images have been used to investigate flow
(a) Pattern at t = t0− 10 s (b) Pattern at t = t0 (c) Pattern at t = t0+ 10 s
Figure 6: Flow patterns before, at and after moment of transition. Stack size of 100 images at 25 fps. The trajectories are colour coded by the velocity in x-direction Umean x.
structures. The presented results show the inversion of a flow pattern next to the rotation axis of the barrel. Figures 6(a) to 6(c) show three different flow patterns, that were acquired in one sequence with 25 fps, a stacksize of 100 images and time shift of 10 seconds between two consecutive stacks. Figure 6(a) shows a nearly laminar flow from the rotation axis of the barrel to the wall. 10 seconds later in figure 6(b) there is no clear pattern left. Taking another sequence, further 10 seconds later, into account it becomes apparent that a process where the structure changes is detected, due to the inversion of flow direction. The flow direction changed and the trajectories are focussing the rotation axis of the barrel, figure 6(c). The lentgh of the displayed trajectories varies between 5 as lower threshold and 25 time steps. As from the chosen form of presentation of the trajectories longer tracks are suggested it has to be mentioned, that most of the trajectories are interrupted for five or less time steps. This connection errors are attributed to temporal and spatial variations of the illumination systems brightness. The reflected light of a transparent tracer particle like a HFSB at least leads to two bright dots in an acquired image. Depending on the positions of the light sources relative to the particles position and the camera, the reflection points in the images move relative to the particle motion. The resulting shift leads to a less accurate position determination of the particle detection, which is used for the estimation of three-dimensional particle positions. In the last processing step, the particle tracking, these jumping positions cause an interruption of the trajectories as the tracking criterias like maximum displacement, acceleration, change of angle are violated.
3.2 Results AMoR
For symmetric boundary conditions of the plane wall jet in mixed convection two kinds of flow structures as shown in figure 7 are defined by Kandzia [13] and Shishkina [12]. If the flow is dominated by the forced convection a two-dimensional main flow structure with two rolls rotating in opposite directions above the heat sources developes 7(a). Increasing the thermal load will not influence the main flow structure up to a certain range, where the structure evolves due to the thermal effects a three-dimensional character. Representative for the three-dimensional flow structure shown in 7(b) the results for a case with two detected vertical plumes is presented in the following section.
For the investigation of mixed convection with the 3D-PTV technique described above a frame rate of 25 fps is selected. Different sequences with a length between 500 and 5000 images were acquired with different schemes like:
• 150 images / ∆t pause / 150 images / ∆t pause / ... • 500 images / ∆t pause / 500 images / ∆t pause / ...
(a) Two-dimensional flow structure in AMoR.
(b) Three-dimensional flow structure in AMoR.
Figure 7: Flow structures in mixed convection in a rectangular cabin with heated obstacles according to Kandzia [13]. 7(a) shows a two-dimensional (stable) flow structure, that is dominated by the forced convection. In 7(b) the flow structure becomes three-dimensional as the forces of natural and forced convection are about the same order of magnitude. This is defined as an unstable flow structure. The arrows mark the in-and outflow of the cabin.
The waiting period ∆t was systematically modified to extend the acquisition time for a identification of transient characteristics of the flow structures. In figure 8 the post processed trajectories from a 5000 image sequence with a stacksize of 500 images and ∆t = 30 s are presented in the front view (fig. 8(a)) and in the side view (fig. 8(b)). The illustrated trajectories, that are colour coded by the velocity in y-direction Uy, show the superposed results from the acquired stacks. Two regions with an upward fluid motion, so called vertical plumes, located on top of the heat sources at the depth of about z/L = 0, 25 are detected. Next to the wall at z/L = 0 a downward fluid motion is shown. Taking a detailed look on the data quality it becomes apparent, that scattered light from the backround causes false detections, which lead to lines of detected particles with a velocity close to zero as displayed in figure 8(b). Regarding to the limited frame rate for long time measurements the seeding density is kept on a medium level to allow accurate tracking. A comparison of the detected structure from one stack with the results of the superposition shows, that no significant change of the flow structure happens. Assuming that the three-dimensinal flow structure changes over time with a low frequency further studies will need to enlarge the measuring time to resolve these low-frequency transition of flow structures.
(a) Front view of the reconstructed trajectories. Vertical plumes occur above the heat sources.
(b) Side view of the reconstructed trajectories. Vertical plumes at z/L = 0, 25.
Figure 8: Flow structure in AMoR for an inlet velocitiy of 0.8ms, a temperature difference of ∆T = 7.5 K between the heat sources averaged surface and air inlet temperature and a thermal load of 900 watt. Acquisition settings: 25 fps, sequence length 5000 images. The trajectories are colour coded by the velocity in y-direction Uy. By a light reflection in the backround noisy lines are generated.
4. CONCLUSION
The presented results approve the usage of cameras with a high dynamic range like cameras with a sCMOS sensors for particle tracking methods. Increasing sensor resolutions and framerates as well as the development of more powerful LED light sources enable a broader field of applications like 3D-PTV in large enclosures with HFSB. The important outcomes of the performed work are modifications in the available open source C code: - Change to 16 bit image processing - Customiz ation of the threshold selection for particle detection - Compensation of additional computational effort by implementation of parallelized particle detection. Surfaces with high reflection rates still lead to low signal to noise ratios and cause the detection of erroneous particles even at processing with 16 bit image information. The development of a generator for neutrally buoyant tracer particles with an adjustable diameter could enable the adjustment of particle characteristics to the experiments needs as for example the reflected light increases with a growing particle diameter. Future work will focus on the migration of the extended processing routines to the OpenPTV library.
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