Pablo Escot Bocanegra

Vincent Sarou-Kanian

Iskender Christian Chauveau

P Escot Bocanegra

I Gökalp

Studies on the burning of micro-and nano-aluminum particle clouds

published or not. The documents may come

Introduction

The available literature shows that Al nanopowder can significantly improve the combustion properties of some energetic materials, especially for propulsion applications. Solid propellants containing Alex nanopowder exhibit burning rates much higher (in some cases as much as 5 to 20 times higher) than the propellant formulations containing regular Al powder [START_REF] Ivanov | Challenges in Propellants and Combustion 100 Years after Nobel[END_REF]. Further, HTPB-based (HTPB: hydroxyl-terminated polybutadiene) solid-fuel formulations containing Alex or other nanopowders demonstrated mass burning rates in a hybrid rocket up to 50% higher than the baseline formulations with regular Al powder [START_REF] Risha | AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit[END_REF]. These superior performances of the energetic compositions containing Al nanoparticles compared to the conventional microsized particles are related to higher specific area and reactivity of nanopowders.

In the present work, the investigation is related to combustion studies of aluminum particles injected into a tube and ignited by an electric spark after the formation of a cloud. The experiments are conducted for two purposes: first to measure the flame front propagation velocities for different sizes of the Al particles, second to measure the temperature of the gas and the condensed phases during cloud combustion. particles in the tube. The injection pressure and duration are adjusted to achieve an optimum distribution of the powder inside the tube. After suspension, the cloud is stationary due to low sedimentation velocities of Al nanoparticles within the range of 10 -5 -10 -3 cm/s. The particle cloud is ignited by a spark placed on the axis at 30 mm from the top section of the tube. In these experiments, this ignition section was selected as providing the optimum ignition probability for all the combinations between the injected powder quantity, the injection time and pressure. A high-speed video camera (Phantom V5.0 with 1000 fps at a maximum resolution of 1024 x 1024 pixels, or higher frequency by reducing the resolution) is used for the flame front observations and propagation velocity measurements. The propagation velocity of the burning cloud is determined for different Al nanopowder samples by image analysis.

Experimental setup and methods

For the temperature measurements, particle cloud flame emission spectra are recorded using a high resolution spectrometer in the visible range. The fitting of bands ∆ν = -1, 0, +1 of the AlO emission is used for the determination of the gas phase temperature during cloud combustion. Equations used for the simulation of the AlO emission spectra are given below.

The specific intensity assuming that medium is optically thick, ( ) ( )

' 1 l I T B T e λ α λ λ - ⎡ ⎤ = - ⎣ ⎦ (1)
The absorption coefficient corrected for stimulated emission, ( ) ( )

' E T B T λ λ λ α = (2)
The spontaneous emission with medium optically thin, ( ) ( ) ( )

3 4 ' '' ' ' 11 ' '' '' '' 16 10 3 2 ' 1 u vv J v v J cN T E T S S J λ π ν Λ - Λ ⎡ ⎤ = × ⎢ ⎥ + ⎣ ⎦ (3)
The number of AlO molecules in the upper state is given by The method to determinate the condensed phase temperature is based on the continuous part of the flame spectra. The temperature is derived of the condensed emitters in the flame. This was achieved using the polychromatic fitting of the low resolution continuum spectra in the 400-900 nm spectral range to Planck's low.

( ) ( ) 2 ' 1 u E kT u N J N T e Q - + = (
( ) ( ) Nevertheless, if the condensed phase emitters are not considered like « grey », so for fine aluminium oxide particles (d<1 µm) witch are the main source of continuum spectra, the emissivity is not equal to 1. It can be considered that for such small particles the light emission cross-section decreases with wavelength as

Flame propagation velocities: results and discussion

Figure 2 shows a sequence of Al particle cloud combustion. In that sequence the time between frames is 10 ms. The first picture of the sequence shows the ignition point of the cloud due to the spark. Then the combustion front propagates both downwards and upwards along the tube axis. The upwards propagation ends quickly because of the expansion of the hot products. After a stabilization time, only the downwards propagation remains and can be analysed.

Figure 3a: Example of numerical analysis of one flame extract from the movie of the Al particle cloud combustion

Numerical analysis of each frame gives two parameters: time t and flame front position x. It's done for each frame extracted from the movie. All these data are reported in Figure 3b, which illustrates how the mean velocity is taken from the gradient of x(t).

Figure 3b: Example of determination of the mean velocity for one sequence of an Al particle cloud combustion and an illustration of the path of the flame front leading point.

The experimental velocities thus obtained are collected in Figure 4. versus dust (particle) concentration for different particle sizes, [START_REF] Goroshin | Proc. Combust. Inst[END_REF], [START_REF] Boichuk | [END_REF], [START_REF] Risha | AIAA Aerospace Sciences Meeting and Exhibit[END_REF]. The condensed phase temperature is also exanimated in this study. Two curves are shown in Figure 7, one of micro-sized powder and the other of nano-sized powder. Each point represents the temperature of condensed emitters present in the flame for different time. The measurement zone is fixed and the flame propagates so the measurement position is along the axis of the flame. The two curves have the same profile, the temperature increases in the beginning and after a stabilisation decreases at the end. The first phase is the increasing which is fast in both cases corresponds of the ignition zone. It is clear that the distance between the unburned and the burned mixing is small. The second phase is the constant temperatures which correspond of the burning zone. Then the last phase is the decreasing of the temperature. This phase is linked with the tail of the flame. The tail of the flame is longer in the case of the nano-sized powder than in the case of micro-sized powder. The resulting temperature decreases slower with nano-sized powder than with micro-sized powder. The temperature rise faster to the maximum in the case of nanoparticle cloud combustion but that maximum is lower than in the case of microparticle cloud combustion. But the decreasing is faster for the nanoparticle cloud combustion.

The maximum temperature of the condensed phase and the temperature of the gas phase are the same for microparticle cloud combustion. Whereas for the nanoparticle cloud combustion, the temperature of the gas phase is 280 K lower than the condensed phase temperature.

Conclusions

The present results show that nano-sized Al particle clouds burn faster than micro-sized particle clouds for the same global particle mass concentration in air. The cloud flame propagation velocity depends also on the particle concentration. The global evolution the velocity versus concentration of nano-sized and microsized Al particle clouds show different trends; the nanosized Al particle clouds show two maxima whereas the micro-sized particle clouds only one. The simulated AlO emission spectra give a constant value of 3300 K and of 2900 K for the nanoparticle and for the microparticle cloud combustion, respectively. The condensed phase temperature during the microparticle cloud combustion is higher of 180 K than during the nanoparticle cloud combustion.

Figure 1

 1 Figure 1 shows the experimental setup used for studying the combustion of nanoparticle clouds in air

Figure 1 :

 1 Figure 1: Combustion apparatus.The powder sample is placed in the bottom of the tube. Injection of particles and formation of the cloud inside the tube are achieved by a pulse air injection, using an electrical valve, to carry and disperse the

  body function which is calibrated with a black body furnace at 1600K, l the geometric path length, c the speed of light, ' '' v v ν the frequency of emitted radiation, ( ) factor, N the number density of molecules, Q the partition function, u E the spontaneous emission in the upper state, k the Boltzmann constant and T the temperature.

  emissivity of the condensed emitter, if the emitters are considered like « grey », the factor ( ) ,T ε λ is a constant inferior or equal to 1, so independent of wavelength. The resulting curve is

1 λ, [ 3 ] 1 λ

 131 . At the same time the authors claim that the absorption index of the molten bulk aluminium oxide also decreases as in the 400-900 spectral range. The resulting factor ( ) powders are used in the experiments: a nanopowder (250 nm) and a micropowder (6 µm) provided by SNPE Matériaux Énergétiques.

Figure 2 :

 2 Figure 2: Sequence of Al particle cloud combustion (10 ms between frames). Particle cloud flame propagation velocities are obtained by numerical image analysis of the combustion light emission, Figure 3a. It's presented the different phases of the analysis.

Figure 4 :

 4 Figure 4: Flame front velocity of Al particle cloudsversus dust (particle) concentration for different particle sizes,[START_REF] Goroshin | Proc. Combust. Inst[END_REF],[START_REF] Boichuk | [END_REF],[START_REF] Risha | AIAA Aerospace Sciences Meeting and Exhibit[END_REF].

Figure 4

 4 Figure 4 compares cloud burning velocities determined for various micro-aluminum and nanoaluminum particle concentrations in air with different data from the literature. Microparticle clouds experiments are in a good accordance with the data from the literature. The results for nanoparticle clouds give two maxima: one next to the stoichiometric concentration and the second one at a higher concentration. The second maximum can be due to the increasing total area of particle surfaces when the particle concentration rises. The experiments indicate that for the same global concentration the nanoparticle clouds burn faster than microparticle clouds.

Flame temperature: results and discussionFigure 5

 5 Figure5presents different stages of the analysis of emission spectra from microparticle combustion. First is the initial spectrum obtained after the combustion, then the spectrum is divided by the apparatus function and finally the continuous spectrum is subtracted to use the AlO spectrum for simulation.

Figure 5 :

 5 Figure 5: Different stages of the analysis of emission spectra from microparticle combustion.The simulated AlO emission spectra are in a good accordance with the experimental spectra and give a constant value of 3300 K for the microparticle cloud combustion and 2900 K for the nanoparticle cloud combustion.

Figure 6a :

 6a Figure 6a: Experimental and simulated AlO emission spectra during aluminium microparticle cloud combustion in air.

Figure 6b :

 6b Figure 6b: Experimental and simulated AlO emission spectra during aluminium nanoparticle cloud combustion in air.

Figure 7 :

 7 Figure 7: Condensed phase temperature of microand nano-particle cloud combustion in air.

mm

Acknowledgments

This study is financially supported by SME groupe SNPE and Région Centre.


Nicholas Journet
email: nicholas.journet@labri.fr

Boris Mansencal
email: boris.mansencal@labri.fr

Muriel Visani
email: muriel.visani@univ-lr.fr

Massive, free and reproducible grountruthed document image databases generation with DocCreator

Whether your research is focused on image restoration, layout analysis, text-graphic separation, binarization, OCR, etc. you need a groundtruthed database to train your method or to evaluate it. This article presents DocCreator, a multi-platform and open-source software able to create many synthetic image documents with controlled groundtruth. With DocCreator, you can create complete synthetic images choosing the text, font, background and layout to use, add various realistic degradations (bleedthrough, light defect, paper deformation, ink degradation, etc.) on original images, or combine both to increase the size of your database. DocCreator comes as an online (easy to test version) and a desktop solution (fast calculation process, and no need to upload copyrighted data). DocCreator is useful for retraining tasks and to know precisely whether your algorithm is robust. It has already been used favorably and could help other DIAR researchers to produce and share groundtruthed databases.

I. Introduction

One important challenge for the Document Image Analysis and Recognition (DIAR) community is to increase the proportion of open source software and public datasets. Currently, most DIAR experiments are difficult to reproduce, results are impossible to verify. It should become a habit to share our softwares and also our databases to improve the robustness and reproducibility of DIAR reasearch.

Of course some public DIAR datasets are available. Among the most popular ones, one can cite for printed documents: Washington UW31 , LRDE2 , RETAS-OCR 3 , PaRADIIT 4 , etc; for handwritten documents IAM database 5 , RIMES 6 , etc; for graphical documents: archi-tectural symbol database 7 or musical symbol database CVC-MUSICMA 8 for instance. The International Association for Pattern Recognition, for instance, gathered some interesting datasets 9 mostly used for different conferences competitions over the last two decades. However, very few of them are reliably annotated, copyrightfree, up-to-date and easily available for downloading.

In this article we present DocCreator 10 , a multiplatform and open-source (LGPL v3) software able to create myriad of synthetic image documents with controlled groundtruth. DocCreator can be especially useful for DIAR researchers or digital curators willing to train or evaluate DIAR tools. DocCreator aims at facilitating datasets exchange and help make DIAR research more reproducible. The first section of this article details a state of the art about groundtruth creation software programs created over the last 20 years. In the second section, we present the architecture of DocCreator and in the last section the features available in DocCreator for data augmentation or synthetic image creation.

II. Data augmentation or groundtruth generation

Many researchers choose to create their own groundtruth. For many basic tasks such as segmentation, it consists in annotating document images by manually cropping different elements in the documents (using rectangles or polygons), and possibly assigning labels to these elements. For text recognition tasks, the groundtruthing work usually consists of two steps: manual transcription of the text and alignment of text lines and image zones. In order to assist them in the tedious task of groundtruth creation, multiple software have been proposed during the last two decades. [START_REF] Baird | Document Image Defect Models[END_REF] no n/a n/a + no 1990 Zhao et al [START_REF] Jiuzhou | Creation of Synthetic Chart Image Database with Ground Truth[END_REF] no n/a n/a + no 2005 Delalandre et al [START_REF] Delalandre | Generation of Synthetic Documents for Performance Evaluation of Symbol Recognition & Spotting Systems[END_REF] no n/a n/a + no 2010 Yin et al [START_REF] Yin | Transcript mapping for handwritten chinese documents by integrating character recognition model and geometric context[END_REF] no n/a n/a ++ no 2013 Mas et al [START_REF] Mas | An interactive transcription system of census records using word-spotting based information transfer[END_REF] no n/a n/a ++ yes 2016 Seuret et al [START_REF] Seuret | Gradient-domain degradations for improving historical documents images layout analysis[END_REF] no TABLE I Technical and functional characteristics of existing annotation or data augmentation software. Softwares are compared according 6 features : export format, distribution licence, desktop/online software, ease-of-use (one ""+"" means that the user needs to set a lot of parameters or to understand the algorithms to use the software , four ""+"" means that its simple to use the software ; most features are working automatically), collaborative/crowdsourcing software , Year of distribution

As detailed in Table I, some are fully manual standalone software, while others provide semi-automatic annotation modules. Some of the most recent solutions are based on an online collaborative platform. These software assist the user in creating the groundtruth associated with real documents, intrinsically limited in number because of acquisition procedures.

Even with a very good groundtruth creation software, this task remains tedious and time-consuming and most of these manually groundtruthed database are not shared by their owners. This inherently limits the amount of groundtruthed data available.

Another solution is available for getting (quickly and with lower human cost) large groundtruthed document image datasets. This solution, investigated since the beginning of the 90's [START_REF] Baird | Document Image Defect Models[END_REF], is to generate, from real images, synthetic images with controlled groundtruth. The authors of [START_REF] Jiuzhou | Creation of Synthetic Chart Image Database with Ground Truth[END_REF], [START_REF] Ishidera | A study on top-down word image generation for handwritten word recognition[END_REF] propose different approaches. Some consist in using a text editor (e.g. Word-office, Latex IDE) to automatically create multiple documents with varied contents (in terms of font, background, layout) where others consist in applying degradation models to original images [START_REF] Baird | Document Image Defect Models[END_REF], [START_REF] Zhai | A Line Drawings Degradation Model for Performance Characterization[END_REF], [START_REF] Liang | Geometric Rectification of Camera-Captured Document Images[END_REF].

With DocCreator, we propose to go further than existing synthetic generation methods. DocCreator is a semisupervised software able, from a real document image, to create several synthetic document images that looks like the original one: same character font, same background and same structure but with any text users wish (see Figure 1). Besides, these synthetic images are created with an XML groundtruth.

DocCreator also proposes many image transformation algorithms that mimic the degradations observed in real images. These degradations might be due to poor printing materials, bad conservation procedures or unforeseeable events. For any document image, DocCreator is able to add bleed-through, ink spots, make some parts of ink characters disappear, distort the paper, add holes, etc. Combining these features allows to generate an infinity of document images, directly with the corresponding groundtruth. It should hopefully help to increase the number of shared databases.

III. DocCreator

A. How synthetic images are created

According to the needs of the DIAR researcher, it is possible to generate synthetic document images (and their groundtruth) by different ways. First possibility: if a researcher has real document images but without any groundtruth, DocCreator can generate synthetic images that look like the real ones, and of course, with the associated groundtruth. To do so, layout structure is extracted form the original image using a hybrid segmentation method [START_REF] Ramel | User-driven page layout analysis of historical printed books[END_REF]. Using an OCR, the characters are extracted and labeled (a GUI allows the user to correct the results). A clean background is obtained using an inpainting algorithm 11 . At last, given an input text, everything is assembled in order to build the final output, that is the created synthetic document image and the associated XML groundtruth. See Figure 1 (left part) for an example of a real image and Figure 1 (right part) for the automatic generation of its synthetic version. Desktop and online version proposes a GUI for making some tests before generating a huge amount of synthetic images (see figure 4) Thus, when the font is used to write a character, DocCreator will randomly choose an image among different versions. This will make the final output look more realistic by reducing the strict uniformity between similar letters. A font extracted by the user can be used later to compose a new document Second possibility: a researcher has a groundtruthed database but it is too small or not heterogeneous enough. DocCreator provides several degradation algorithms to augment the dataset. By degrading text ink, paper shape or background colors it is possible to create a representative document image database where many defects are present. This complete database is finally useful for precise performance evaluation or to provide multiple cases for retraining processes (in algorithms embedding a learning step). [START_REF] Kieu | A Character Degradation Model for Grayscale Ancient Document Images[END_REF]) simulates the apparition of ink spots or the disappearance of ink pixels within the characters. It simulates either a bad print process or an age-related wear. See Figure 3.a for an ink degradation example.

B. Degradation models 1) Ink degradation: this model (presented in

2) 3D deformation: this model reproduces common paper distortions: small or big curvature, rotation, fold, etc. In [START_REF] Kieu | Semisynthetic Document Image Generation Using Texture Mapping on Scanned 3D Document Shapes[END_REF] we detail how several real books have been digitized with a 3D scanner and how our algorithm is able to map any 2D document image on these 3D meshes. See Figure 3.b for results.

3) Adaptive blur: generating blur on image is quite easy. In DocCreator the originality is that the user provides himself a real image with blur. Using an adaptation of [START_REF] Lelégard | Motion blur detection in aerial images shot with channel-dependent exposure time[END_REF], DocCreator compute a "blur intensity" value. Then, using a dichotomic algorithm, we compute the size of the kernel of a Gaussian blur that once applied on the input image produces a blur similar to the chosen real blur image.

4) Bleed through: In [START_REF] Moghaddam | Low Quality Document Image Modeling and Enhancement[END_REF] the authors proposed a method to erase the show-through defect (step by step ink from verso sheet appears on the recto side). We decided to adapt their model. By just giving an input recto image, an input verso image and the amount of wished degradation, DocCreator can reproduce the natural bleed-through defect. See Figure 3.c for an example.

5) Phantom Character: When a document is manually printed using wooden or metal character, after a long time, the characters are usually damaged. The printing process is thus not as clean as it should be: small ink parts are unintentionally printed when the character is pressed up against a sheet of paper. To simulate this defect, we have manually extracted many different phantom characters form real document images. The degradation algorithm is an adaptation of a patch algorithm [START_REF] Shakhnarovich | Learning task-specific similarity[END_REF] where a zone from another part of the document image is selected and copied within the patch (place where the phantom character is pseudo-randomly set). See DocCreator is an open-source and multi-platform software. DocCreator is available as a stand alone software or as an online demonstrator. The global architecture is detailed in Figure 2.

The user can choose between a desktop or online version. The desktop version comes with a Qt GUI, and has dependencies on OpenCV and Tesseract libraries. It can be compiled/used on Linux, Apple OSX and Microsoft Windows. The desktop version allows to work locally on (potentially copyrighted) data. The online version is accessible through a web browser (tested on Google Chrome and Firefox). It does not need any installation, but requires to upload images to a server. The main part of the C++ source code is shared between the two versions. The core C++ is executed via libnavajo (C++ web server) for the online version. Some resources for creating a synthetic image (fonts, background, 3D models, etc.) are embedded with the desktop version and downloaded when required for the online version. With both versions, the user can complete his own database.

Source code, video presentation, installation instructions, online demonstrators are available on our website http://doc-creator.labri.fr/.

IV. Conclusion

With DocCreator any DIAR researcher can create complete groundtruthed images and increase the size of its document image database. After 5 years of several collaborations and test campaigns, DocCreator (or databases created with DocCreator) have been tested by different researchers and used to publish [START_REF] Mehri | Old Document Image Segmentation Using the Autocorrelation Function and Multiresolution Analysis[END_REF], [START_REF] Calvo-Zaragoza | Music staff removal with supervised pixel classification[END_REF], [START_REF] Montagner | A Machine Learning based method for Staff Removal[END_REF], [START_REF] Wei | Evaluation of SVM, MLP, and GMM Classifiers for Layout Analysis of Historical Documents[END_REF] proving its utility for performance evaluation or retraining tasks. DocCreator is on an open source ongoing project. We already plan to integrate new degradation models and another standard output format (TEI). The central windows allows to input text to compose interactively synthetic document images using aivalable fonts and backgrounds. On the left, the original text is present. On the right, one can edit the properties of a character of the font. On the bottom, the mapping of the font onto the physical keyboard is presented via a virtual keyboard. One can also edit global properties of the document. Defect models are available in the top menu. Batch generation is available through "the factory" icon on the toolbar. An online version is also available at http://doc-creator.labri.fr/. The different features available on online/desktop latests versions are listed on the web site.

Fig. 1 .

 1 Fig. 1. An original ancient document image (left) and its synthetic version created using the font, background and layout of the original image. The text has been replaced by Lorem Ipsum. During the font extraction process, several instances of each character are extracted.Thus, when the font is used to write a character, DocCreator will randomly choose an image among different versions. This will make the final output look more realistic by reducing the strict uniformity between similar letters. A font extracted by the user can be used later to compose a new document

Figure 3 .

 3 d for an example. 6) Paper holes: Due to poor book storage conditions, holes may appear on documents. These holes might have different shapes, sizes and locations. DocCreator provides the possibility to add different kinds of holes in a real document image. As with the phantom defect, we manually collected many different hole examples from document images. According to the user wishes, black holes from the database are applied on a given document image. See Figure 3.e for an example.C. Architecture

Fig. 2 .

 2 Fig. 2. Architecture of DocCreator.

Fig. 3 .

 3 Fig. 3. Example of degradation model available in DocCreator

http://www.fki.inf.unibe.ch/databases/iam-historical-documentdatabase/washington-database

2 https://www.lrde.epita.fr/wiki/Olena/

DatasetDBD 3 http://ciir.cs.umass.edu/downloads/ocr

-evaluation/ 4 https://sites.google.com/site/

paradiitproject/ 5 http://www.fki.inf.unibe.ch/databases/iam-

handwriting-database 6 http://www.a2ialab.com/doku.php?id=rimes_database:start

http://www.cvc.uab.es/ marcal/GREC-SEG/index.html

http://www.cvc.uab.es/cvcmuscima/index_database.html

http://tc11.cvc.uab.es/datasets/

http://doc-creator.labri.fr

http://docs.opencv.org/2.4/modules/photo/doc/inpainting.html