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IChemE SYMPOSIUM SERIES NO. 153 # 2007 IChemE

EXPERIMENTAL INVESTIGATION OF A CATASTROPHIC TANK FAILURE WITH A HIGHSPEED VIDEO RECORDER. IMAGE PROCESSING AND HYDRODYNAMICCHARACTERIZATION OF THE LIQUID JET

N. Lecysyn1, F. Heymes1, A. Dandrieux1, P. Slangen1, G. Dusserre1, L. Munier2, E. Lapebie2 and C. Le Gallic2

1Ecole des Mines d’Ales, 6 Avenue de Clavieres, 30319 Ales Cedex, France; e-mail: Nicolas Lecysyn, [email protected] d’Etudes de Gramat, Delegation Generale pour l’Armement, 46500 Gramat, France

Liquid jets are an important concern in risk assessment. Full scale tests were performed at the CEG

(Centre d’etudes de Gramat, DGA), in order to study liquid vessel destruction after high velocity

projectile impact. An important rupture of the vessel resulted on both faces and liquid jets were

created on each side of the vessel. The phenomenon was recorded with high speed cameras at a

sampling rate of 6000 Hz during one second. This paper deals with the image processing method-

ology employed to study some features of the jet. This methodology leads to compute binary

images composed of extracted edges of the hydrodynamic phenomenon. Liquid expansion and

break-up data are presented.

KEYWORDS: liquid jet, hydrodynamics, tank failure, video, image processing

INTRODUCTIONIn this paper an image processing comparative methodology,leading to jet break-up characterization is proposed.

Studying the consequences of a liquid-filled vesseldestruction after projectile impact is an important concernin risk assessment. Previous authors worked on this domain,either to study the consequences on the tank (fracture,shock wave propagation, and kinetic energy decay)[STEPKA, 1965], [TOWNSEND, 2003], [BORG, 2001(a)]or on the consequent liquid instability [BORG, 2000, 2001(b)].

Full scale tests were recently carried out at the CEG(Centre d’etudes de Gramat), in order to assess the conse-quences a high velocity impact (up to 1000 m.s21) on atank filled with ammonium hydroxide solutions. Theimpact of the bullet creates a strong and brutal pressureincrease in the liquid (up to 250 bars). A strong rupture ofthe vessel resulted on both faces and liquid jets werecreated on each side of the vessel. The two jets werefound not to be symmetric.

The study of these very high speed phenomenarequires specific equipment as high speed video acquisitionwhich is particularly appropriate to this study. Thephenomenon was therefore recorded with a sampling rateof 6000 Hz during one second. Three video cameras wereused simultaneously to get a 3D record of the liquid jet.A focus is made on three trials filmed by three camerason three different points of view ((O, x, y), (O, x, z), (O,y, z)).

A comparative image processing methodology wasapplied to video sequences, in order to improve the visualappearance of images to human viewer, and to prepareimages for measurement of the objects present in the image[RUSS, 1999]. Fundamental processes are detailed, likethreshold, spatial filtering and morphology [National Instru-ments, 2004], [SOILLE, 1998]. It is shown that the mostefficient step is background subtraction. Experimental resultsare presented, such jet break-up length and expansion, decay

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of projectile kinetic energy, and vessel’s fracture dynamic.Those experimental values are of a great interest, in orderto understand the vessel’s envelope break-up and the sub-sequent liquid dispersion in the environment.

EXPERIMENTAL DEVICE

OBJECTIVESThe vessel destruction phenomenon is expected to producean expanding liquid jet. Characterizing fluid movement isthe aim of this experimental set up. Optical technique waschosen in order to use an non-intrusive instrumentation.Three fast cameras enable the visualization and the record-ing sequences with a frame rate of 6000 Hz in full resolution(1024 � 1024 pixesl). Cameras were placed at different pos-itions in order to achieve a 3D visualization of the main partof the jet.

EXPERIMENTAL SET UPThe experimental set up is presented in Figure 1 andFigure 2. A cylindrical steel vessel (diameter ¼ 360 mm,height ¼ 622 mm, volume ¼ 60 L, thickness ¼ 0,6 mm)was slightly attached on two poles, spaced as contactpoints between the vessel and two pseudo neighbouringvessels. In order to be representative of industrial storagesthe liquid vessel was not completely filled (height of gasphase ¼ 40 mm) with different kinds of solutions consti-tuted of water, ammonium hydroxide, polyethylene glycol(PEG 400) and rhodamine (dye) in various proportions.Ammonium hydroxide allowed studying the evaporationkinetics and rhodamine was added to give a red colourto the initial colourless solutions. All solutions were pre-pared at a 0% or 10% mass fraction composition of hydrox-ide ammonium. Vapour pressure of ammonia in thewater solution is equal to 10 400 Pa (T ¼ 294 K,[NH4OH] ¼ 10%).

Figure 1.


A bullet was shot on the vessel from a distance equalto 15 meters. Experimental device of gunshot allowedspeed projectiles in the range 850–1250 meters persecond when impinging on the vessel. Projectile was shotin the centre of the vessel. Three fast cameras as shownin Figure 1 and Figure 2 enabled the visualization andthe recording sequences with a frame rate of 6000 Hz.The use of CCD (Charge Coupled Device) was chosenwith respect to its ability to catch fast events [SCHMITT,2004]. Cameras were placed at different positions, regard-ing the objectives of the experiment (visualization of theentire jet in one direction, visualization of a part of thejet in the other direction).

Figure

2

OPTICAL CONDITIONSHigh speed framing technique applied to hydrodynamicshas been already used to study diesel jet break-up [YON,2003], where the scene was shined by a backgroundlight, scattered by the jet. In this experiment, because ofspecific conditions due to shot gun, it was chosen toshine the scene directly, with three powerful spotlights of3500 W (Figure 1). However, natural sunshine was themajor source of light, that is why, from one test toanother, optical conditions were not the same. Therefore,resulting video sequences quality is not homogeneous.The field of view of each camera was within 1,5 meters(cameras #2 and #3) to 2,5 meters (camera #1), the focal

2.

Table 1.

Shots Cameras Plan Movie duration

#1 #1 6000 fps Fast Cam MAX 3K (O,x,z) 0,68487sec

#2 6000 fps Fast Cam APX 120 K (O,x,y) 0,68487sec

#3 6000 fps Fast Cam APX 120 K Color (O,y,z) 0,68570sec







Table 2.

Shots Liquid

Projectile velocity

(m/s) before impact

#1 Water 1125

#2 Water 1193

#3 Waterþ 10% ammonium

hydroxide

1148

#4 Waterþ 10%

ammonium hydroxide

1167


distance was chosen with respect to entire coverage ofexperimental field.

SHOTS TEST SERIESFour different shots are described in Table 2. Thousands ofimages are recorded, containing a lot of information. It isthen necessary to process video sequences, in order to sortand extract sorting and extracting essential data.

IMAGE SEGMENTATION METHODOLOGYDifferent steps are described in this part. The aims of thesesteps are to analyze and treat each image in order to prepareit for experimental measurement.

GREY LEVEL PICTUREAfter video recording, the three movies described quantitat-ively in Table 1 were analyzed through image histograms.The most straightforward strategy for image analysis usesthe brightness of regions [RUSS, 1999] in the image as amean of identification: it is assumed that the same type offeature will have the same brightness wherever it appearsin the field of view. The histogram of an image indicatesthe quantitative distribution of pixels per grey-level value.

Two types of histograms can be calculated: the linearand cumulative histograms. The linear histogram is the func-tion H defined on the gray-scale range [0, . . . , k, . . . , 255]such that the number of pixels equal to the gray-level valuek is

H(k) ¼ Nk (1)

Figure

3

The linear histogram provides a general description ofthe appearance of an image and helps identifying various com-ponents such as the background, objects, and noise.

The cumulative histogram is the function HCumul (k)defined on the gray-scale range [0, . . . , k, . . . , 255] suchthat the number of pixels that are less than or equal to k:

Hcumul(k) ¼Xk

0

Nk (2)

This latter will be useful to determine a thresholdvalue, used afterwards, in order to simplify the 256 greylevels image into a two levels binary image.

The gray-level intervals with a concentrated set ofpixels reveal the presence of significant components in theimage and their respective intensity ranges. In Figure 3,the linear histogram reveals that the image is composed ofthree major elements. The cumulative histogram of thesame image in Figure 4 shows that the two leftmost peaks

3.

Figure 4.


compose approximately 80% of the image, while theremaining 20% corresponds to the third peak.

As shown in Figure 5, the gray levels distribution(mean value distribution done on the images sequence) isnot always composed of few distinguishable peaks. Itmeans main elements of images like background, vesseland liquid jet do not have different luminosity levels,because the optical condition were not optimized during

Figur

4

experimental set up. For instance, the concrete wall in thebackground could have been painted in black. It will beshown in the next part, that image processing is necessaryto isolate jet liquid jet from the background.

OBJECTIVESSegmentation is one of the critical steps of image analysis,which influences measurement quality done further. Itallows isolating objects on which a focus has to be made;and to separate regions of interest from the background.Among some existing techniques; the simplest is gray levelsthresholding. As seen before, the gray levels distribution ofimages in this study, shown in Figure 5, does not allow to dis-tinguish main elements of movies. Therefore methodologieshave been developed to segment images sequences of thesequences listed in Table 1. A comparative analysis betweenthree methodologies is proposed in the next paragraphs,each of them can be differenced by their first step:

a classical linear gradient is applied to images, in order tohighlight elements;a high contrast value is given to images, in order to separatedark background from others elements;a first sequence image subtraction is done, in order to elim-inate background.

e 5.

Figure 6.


Then, a classical treatment process [SOILLE, 1998] isapplied in order to extract only liquid jet edges, as describedin next paragraphs.

SEGMENTATION METHODOLOGIES

Linear gradientThe transformed image looks like the source image withhighlighted edges. That is why this type of kernel is usedfor grain extraction and perception of texture [nationalInstruments, 2004].

The gradient filter chosen (Figure 6a) is a convolutionbased on this Image Gradient kernel, the larger the kernel,the thicker the edges:

0 �1 0 1 0

�1 �2 0 2 1

�1 �2 1 2 1

�1 �2 0 2 1

0 �1 0 1 0

Image contrast enhancementThis transformation (Figure 6b) can highlight details inareas containing significant information, at the expense ofother areas. It converts input gray-level values (those fromthe source image) into other gray-level values (in the trans-formed image). The function associates an equal amount ofpixels per constant gray-level interval and takes full advan-tage of the available shades of gray.

Image subtractionA subtraction between images (as shown in Figure 7) is apixel-by-pixel transformation. It produces an image in

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which each pixel derives from the values of pixels withthe same coordinates in other images. In this experiment,the image acquisition device (Photron Fast Cam MAX3K) with a gamma of 1.0 that is why subtraction ofthe first sequence image (background) is acceptable[RUSS, 1999]. Results on the image sequences takenduring the three different gun shots are shown inFigure 6d.

IMAGE THRESHOLDINGThresholding consists in segmenting an image into tworegions: a particle region and a background region. Thisprocess works by setting to 1 all pixels that belong to agray-level interval, called the threshold interval, andsetting all other pixels in the image to 0. The aim of thisoperation (Figure 8) is to extract areas that correspond tosignificant structures in an image and to focus the analysison these areas. A critical and frequent problem in segment-ing an image into a particle and a background region occurswhen the boundaries are not sharply demarcated. In such acase, the choice of a correct threshold becomes subjective.Therefore, it is highly recommended that images beenhanced before thresholding to outline where the correctborders lie [National Instruments, 2004]. Observing theintensity profile of a line crossing a boundary area alsocan be helpful in selecting a correct threshold value. Inthis study, manual thresholding is done with differentrange values. Then, morphological transformations aredone on images in order to reshape the binary particlesand therefore correct unsatisfactory selections that occurredduring the thresholding.

Figure 7.

Figure 8.


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Figure 9.


IMAGE MORPHOLOGYMorphological transformations extract and alter the struc-ture of particles in an image. These transformations aimat preparing particles for quantitative analysis, observingthe geometry of regions, and extracting the simplestforms for modeling and identification purposes, and soforth. In this study, the morphologies applied to imagesconsist in:

filling holes (Figure 9);removing small particles (Figure 9);extracting edges (Figure 10).

RESULTSThe best results are obtained applying method #3(Figure 10f). All objects composing background have beeneliminated. The snake tongue boundaries of the jet areclearly marked. Applied to each source image, this processproduces a new video sequence on which jet geometry is

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easily accessible. Therefore, it is possible to make somemeasurements in order to characterize the jet.

RESULTS AND DISCUSSION

VIDEO SEQUENCES ANALYSISThe different phases of the catastrophic tank failure can bedescribed as (Figure 11):

phase 0: impact and penetration of projectile into vessel(less than 0,5 milliseconds).phase 1: small droplets with a high velocity. This masstransfer case corresponds to high mass transfer kinetics,high specific surface area but low ammonia quantity tovaporize (less than 30 milliseconds).phase 2: liquid break-up, ligaments formation anddrops coalescence with low velocity (about 250 milli-seconds).phase 3: gravitory flow of the liquid through the failure hole.This mass transfer case corresponds to low mass transfer

Figure 10.


kinetics but important ammonia quantity. The specific areadepends on the geometry of the area around the vessel(liquid completely spread on the ground or contained by apreventive device).

MEASUREMENTSAs it is explained in paragraph 2.3, video sequences qualityis not homogenous, even after processing, video sequencesfrom shots #1 and #4 are too much noisy to be exploited.Another tests series will be run soon, optimizing opticalconditions. Following experimental results are drawnfrom shots #2 and #3 described in Tables 1 and 2.

During the first milliseconds of the phenomenon, ithas been possible to get some experimental values like:

jet break-up length (Figure 12), which trends to growthlinearly;jet width (Figure 13) which is constant;jet expansion (Figure 14);

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adimensionnal vessel’s fracture (Figure 15), which growthreaches rapidly a constant value of 30%;projectile velocity.

JET BREAK-UP LENGTH, WIDTH

AND EXPANSIONThose three characteristics are of a great interest, in order tounderstand the liquid dispersion, consequence of a cata-strophic failure.

Thanks to image processing methodology, describedupward, it has been possible to process jet break-up length(Figure 12), which trends to grow linearly.

Liquid recuperative containers can be seen on camera#2 sight, which induce a noisy image background. Videoanalysis has been made without image processing in thiscase. Phenomenon observation on camera #2 sight allowssaying that jet width is constant (Figure 13).

Jet expansion (Figure 14) trends to increase more inthe (O, x, z) plan than in (O, x, y).

Figure 11.

Figure

Figure 13.


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Those experimental values will be compared toexpanding liquid system models.

VESSEL FRACTURECharacterizing dynamic fracture of the vessel allows under-standing the material response to the projectile impact.

From the camera #3 sight, it has been possible toobserve and measure vessel’s fracture caused by projectileimpact (Figure 15). The phenomenon grows quickly(within 25 milliseconds) to a limit value. A fraction of pro-jectile energy leads to the tearing of the front side of the

12.

Figure 14.

Figure 15.

Figure 16.


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Figure 17.


vessel. The vertical length of tearing always follows thegenerator passing by the centre of the opening and coversthe near total height of the vessel (62 cm). The average vel-ocity of material fracturing is about 25 m/s. These resultswill be used to evaluate the mass flow rate at the breach,as source term for liquid dispersion, and will be comparedto hydraulic models.

PROJECTILE VELOCITY MEASUREMENT

ANDASSESSMENT OF KINETIC ENERGY

LOSS DURING THE IMPACTExperimental set up has been adapted during three newshots in order to see (thanks to the camera #1) what is hap-pening behind the vessel (Figure 15). Acquisition frequencyhas been decreased to 4000 Hz, to improve exposure.

Images sequence has been processed to enhance aninteresting phenomenon.

Optical blur of projectile can be seen on imagessequences (Figure 16). The exposure time (1/6000second) of the camera was long enough (compared to par-ticle velocity .1000 m.s21) to record an “optical smear”.

Then, “optical smear” length is divided by timestep between two frames (4000 frames per second)(Figure 17), in order to get projectile velocities. Measure-ment accuracy depends on optical aberrations which arein the order of 5%.

It has been thus possible to get projectile velocitiesbefore and after impact, in order to plot velocity decay inthe vessel (Table 3).

Table 3.

Shots

Test serie

# 3

Projectile velocity

(m/s) before impact

Projectile velocity

(m/s) after impact

#1 1192 730

#2 1536 881

#3 1003 668

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The ratio of kinetic energy loss in the vessel by theprojectile is about 30%. This dissipated energy due toimpact and projectile penetration is the source term ofliquid jet emission. Those experimental data will be com-pared to a drag model applied to kinetic energy decay intothe vessel, as source term of the strong and brutal pressureincrease in the liquid.

Once source term (mass flow rate, pressure increase,jet expansion and break-up length) characterized, it willbe possible to predict the final break-up of droplets, and tocompare it with granulometry values, which will bemeasured thanks to a specific video analysis process (dropcomputer recognition).

CONCLUSIONSIt is shown that very high speed camera is suitable for dataacquisition of very short duration phenomena. However, thisnon intrusive technique has some inconvenient. Lots ofuseless objects are recorded like background. That is whyit is necessary to process images in order to isolate interest-ing elements. A comparative analysis has been done onbackground elimination and fluid edge extraction. Bestresults are obtained by image subtraction.

This image processing methodology applied to fluidfilled storage impact will allow accessing to hydrodynamicsmeasurements.

Experimental results show that small projectileimpact at high velocity on a liquid filled vessel have import-ant consequences on the environment of the storage, andparticularly just after vessel destruction, which occurswithin ten milliseconds.

The interest of this project is the integrated approachfrom projectile impact to consequences in terms of containerfailure, catastrophic liquid discharge, and final break-upleading to evaporation and atmospheric dispersion (cloudconcentrations).

Video diagnostic of toxic liquid discharge mighthelp to identify safety distances in case of such accidents(in particular domino effects).


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