C Hubert

K J Kubiak

M Bigerelle

L Dubar

IDENTIFICATION OF LOCAL LUBRICATION REGIMES ON TEXTURED SURFACES BY 3D ROUGHNESS CURVATURE RADIUS

Keywords: Roughness, Drawing, Lubrication, Texturation

This paper proposes a new method of 3D roughness peaks curvature radius calculation and its application to tribological contact analysis as a characteristic signature of tribological contact. This method is introduced through the classical approach of calculation of radius of asperity in 2D. Actually, the proposed approach provides a generalization of Nowicki's method [1], depending on horizontal lines intercepting the studied profile. Here, the basic idea consists in intercepting the rough surface by a horizontal plane and to calculate the cross section area without including "islands into islands", i.e. the small peaks enclosed in bigger ones. Then, taking into account the maximal value of the height amplitude of the roughness included into this area, an appropriate algorithm is proposed, without requiring the classical hypothesis of derivability, which may be unstable when applied to engineering surfaces. This methodology is validated on simulated surfaces, and applied to engineering surfaces created experimentally, with a laboratory aluminium strip drawing process. The regions of the textured and lubricated specimens surface are analysed, and the results gives interesting prospects to qualitatively identify the local lubrication regimes: regions with high curvature radii correspond to severe contact (boundary/mixed lubrication regime) while regions with low curvature radii correspond to hydrodynamic lubrication regime.

INTRODUCTION

The texturation of surfaces in metal forming processes has recently attracted considerable attention due to enhanced lubrication, resulting in lower friction and wear. Also, better control of the tool/workpiece interface can be achieved and the resulting surface finish can be controlled by adapting hydrodynamic and/or mixed lubrication regimes. The lubricant trapped in a texturation pocket can act as a fluid reservoir during the deformation process, intensifying the so called "Plasto Hydrodynamic Lubrication" (PHL) introduced by Mizuno and Okamoto [START_REF] Mizuno | Effects of lubricant viscosity at pressure and sliding velocity on lubricating conditions in the compression-friction test on sheet metals[END_REF]. Azushima et al. [START_REF] Azushima | An interpretation of the speed dependence of the coefficient of friction under the micro-PHL condition in sheet drawing[END_REF] investigated plane strip drawing with lubricant pockets, This is the pre-peer reviewed version of the following article: C. Hubert, K. J. Kubiak, M. Bigerelle, L. Dubar Identification of local lubrication regimes on textured surfaces by 3D roughness curvature radius, Advanced Materials Research Vols. 966-967 (2014) pp 120-125, doi: 10.4028/www.scientific.net/AMR.966-967.120, which has been published in final form at http://www.scientific.net/AMR.966-967.120 created by indentation and showed the phenomenon of fluid escape. Bech et al. [START_REF] Bech | A Study of Mechanisms of Liquid Lubrication in Metal Forming[END_REF] characterised the lubricant escape mode and direction as a function of process parameters such as the lubricant viscosity, the drawing speed, the thickness reduction ratio or the back tension. Based on the experiments carried out by Dubar et al. [START_REF] Dubar | Analysis of fluid lubrication mechanisms in metal forming at mesoscopic scale[END_REF], and using the methodology developed by Bigerelle et al. [START_REF] Bigerelle | Roughness Signature of Tribological Contact Calculated by a New Method of Peaks Curvature Radius Estimation on Fractal Surfaces[END_REF], Hubert et al. [START_REF] Hubert | Identification of lubrication regime on textured surfaces by multi-scale decomposition[END_REF] defined different zones of lubrication regimes inside on regularly textured surfaces. They also introduced the concept of roughness signature in tribological contact by estimation of peaks radii on 2D profiles.

The methodology developed by Bigerelle et al. [START_REF] Bigerelle | Roughness Signature of Tribological Contact Calculated by a New Method of Peaks Curvature Radius Estimation on Fractal Surfaces[END_REF] to estimate the peaks (and valleys) curvature radii was particularly reliable since it was based on the hypothesis of plane strain drawing, leading to analyse a 3D surface as a sequence of multiple 2D profiles. But in the case of a purely 3D lubrication phenomenon, such as specimens used by Bech et al. [START_REF] Bech | A Study of Mechanisms of Liquid Lubrication in Metal Forming[END_REF], where lubricant pockets were pyramidal indentations, this method cannot be applied.

In this paper, the method of peaks curvature radius estimation on textured surfaces is extended to 3D, where, for instance, the peaks orientation and radius distribution can be considered.

DEFINITION AND CALCULATION OF 3D PEAKS CURVATURE

In this study, the definition of the radius of 3D peaks is based on the method proposed by Nowicki [START_REF] Nowicki | Multiparameter representation of surface roughness[END_REF] and its extension presented in the Bigerelle et al. [START_REF] Bigerelle | Roughness Signature of Tribological Contact Calculated by a New Method of Peaks Curvature Radius Estimation on Fractal Surfaces[END_REF] work. It reads:

= + , (1)
where is the equivalent diameter of a peak calculated as the diameter of the circle with the same area as the ellipsoidal region, defined by its minor ( ) and major ( ) radii, resulting in the interception of a given peak by a plane . is thus calculated as

= 4 / . (2)
Finally, corresponds the distance between the centre of the equivalent circle and a crest of the profile, included in the region of the equivalent circle. These parameters are detailed in Figure 1.

The numerical algorithm to detect peaks curvature consists of the following steps:

• the vertical height of a given 3D surface is divided into an arbitrary number of levels (255 in this study, which has been found sufficient to obtain an acceptable number of peaks radii); • the surface is cut at each level by a plane (Figure 1), and only the top remaining part is considered;

• the peaks will be detected only if an enclosed region is found, and their radii will be calculated according to Equation 1. The numerical algorithm was written in MATLAB ® , and the function used to detect the enclosed regions was 'regionprops'.

To validate the developed algorithm, several surfaces have been created numerically with peaks simulated by spherical and sinusoidal caps with known radius. In the example below, illustrated in Figure 2, the surface has 3 3 peaks, their radius is = 80pt and the surface size is 512pt 512pt.

After application of the algorithm described above, the detected peaks radii can be plotted as a function of their equivalent diameter and the distance between the maximum height and the intercepting plane ( , Figure 1). It can be noted that the algorithm is more precise for large values of (or ), which is due to the discretization of the surface: if the value of (or ) decreases, it becomes closer to the sampling length, affecting the peak radius calculation. In the case presented in Figure 2, if ' 50pt (or ' 4pt), the error of peaks radii estimation is less than 1%.

EXPERIMENTAL PROCEDURE

The experimental testing device used in this study has been developed by Bech et al. [START_REF] Bech | A Study of Mechanisms of Liquid Lubrication in Metal Forming[END_REF] for thickness reduction of aluminium strips. The strips are drawn by means of a hydraulic jack between two dies. The upper die is made of hardened glass so the lubricant behaviour can be observed during thickness reduction. The lower die is made of steel, with an inclined plane with an angle ( 3° to apply the thickness reduction. The hardened glass is used as delivered in a shape of a circular disc with dimensions ø50mm 11mm. The lower die is 80mm long and 50mm wide, with a polished contacting surface to reduce friction. A high speed video camera is used to record the lubricant behaviour for subsequent analysis, i.e. identification of the occurrence of Micro Plasto Hydrodynamic Lubrication (MPHDL) and Micro Plasto Hydrostatic (MPHSL) [START_REF] Bech | A Study of Mechanisms of Liquid Lubrication in Metal Forming[END_REF], location of the onset and direction of escape or front wave speed. The testing device is illustrated on Figure 3.

Figure 3: Experimental testing device with its intrinsic parameters, after [START_REF] Dubar | Analysis of fluid lubrication mechanisms in metal forming at mesoscopic scale[END_REF].

The dimensions of the strips are + , + & + 450 20 2mm and their material is a semi-hardened aluminium AISI 1050 H24. Macro lubricant pockets are manufactured by Electro Discharge Machining to reduce the local residual stresses and to avoid the formation of banks on the indentation edges, that would change the tribological contact properties and fluid escape, as observed in a prior study [START_REF] Hubert | Numerical simulation of lubrication mechanisms at mesoscopic scale[END_REF]. The shape of the lubricant pockets is a groove normal to the drawing direction with triangular cross section. The grooves are 10mm long and 1mm wide, with an angle to the edge . 5°. The spacing between each cavity centre is 2mm, so that the plateaus are 1mm wide. The strips are obtained from cold rolling and their raw roughness is preserved all along the specimens preparation process. An illustration of the manufactured strips and an illustrative picture are given in Figure 4. This is the pre-peer reviewed version of the following article: C. Hubert, K. J. Kubiak, M. Bigerelle, L. Dubar Identification of local lubrication regimes on textured surfaces by 3D roughness curvature radius, Advanced Materials Research Vols. 966-967 (2014) pp 120-125, doi: 10.4028/www.scientific.net/AMR.966-967.120, which has been published in final form at http://www.scientific.net/AMR.966-967.120

The aluminium strips have been subjected to a reduction of about 15% during the drawing process. In this paper, we investigate the curvature radius of surfaces peaks to qualitatively correlate it with the lubrication regime. Thus, two conditions have been selected, one favouring a mixed lubrication regime (Testing Condition 1, TC1), and a second one favouring a hydrodynamic lubrication regime (Testing Condition 2, TC2). The strips surfaces resulting of these two drawing conditions will be compared with an initial surface. The drawing conditions were as follows:

• mixed lubrication regime (TC1): drawing speed / 0 = 5mm/s, lubricant viscosity (at

RESULTS AND DISCUSSION

The surface topography was acquired using a Zygo NewView 7300 profiler, which is a Vertical Scanning Interferometer (VSI). The measurement principle is based on unfiltered white light beam that is split in two: half of the beam is directed through a microscope lens and reflected from the surface, and the other half is reflected from the reference mirror. When the reflected beams recombine they produce interference fringes, where the best-contrast fringe occurs at best focus. In a VSI, the objective moves vertically to scan the surface at various heights. A 3D surface is then reconstructed by analysis of fringes at every pixel. A VSI uses an algorithm to process fringe modulation data from the intensity signal to calculate the surface heights. The obtained resolution will therefore depend on the precision of the zaxis positioning. In the case of the Zygo NewView 7300 instrument, a piezoelectric stage with range of 160 µm is used to refine the height resolution going down to 0.1nm.

The developed algorithm of 3D peaks detection and peaks curvature radius estimation described in Section 2 is applied to surfaces before and after a lubricated drawing process. Two examples of initial and processed surfaces with cavity pockets are presented in Figure 5.

The curvature radius of peaks for two selected surfaces obtained with the drawing conditions mentioned above (TC1 and TC2) are presented in Figure 6. It can be noted that in the case of This is the pre-peer reviewed version of the following article: C. Hubert, K. J. Kubiak, M. Bigerelle, L. Dubar Identification of local lubrication regimes on textured surfaces by 3D roughness curvature radius, Advanced Materials Research Vols. 966-967 (2014) pp 120-125, doi: 10.4028/www.scientific.net/AMR.966-967.120, which has been published in final form at http://www.scientific.net/AMR.966-967.120 the initial surface, the curvature radius of the surface peaks inside the cavities, obtained by means of Electro Discharge Machining, are very small in comparison to on the "plateaus", where the biggest detected radius is about 10 000μm. These regions correspond to almost flat surfaces (Figure 6b), which is consistent with surfaces obtained from a cold rolling process. Radii of peaks plotted as a function of the equivalent diameter (Figure 6c) for the initial surface and for testing condition TC2 shows distinctive lines where the same peak has been detected several times, at different interception levels. Concerning the initial surface, this shows that there are some large and relatively flat areas. These areas are conserved after the drawing process with testing condition TC2 (that favours a hydrodynamic lubrication This is the pre-peer reviewed version of the following article: C. Hubert, K. J. Kubiak, M. Bigerelle, L. Dubar Identification of local lubrication regimes on textured surfaces by 3D roughness curvature radius, Advanced Materials Research Vols. 966-967 (2014) pp 120-125, doi: 10.4028/www.scientific.net/AMR.966-967.120, which has been published in final form at http://www.scientific.net/AMR.966-967.120 regime), however they are removed in testing condition TC1 (that favours a mixed lubrication regime). For this testing condition, the contact conditions were more severe with a higher pressure of lubricant, which led to more local peaks deformation, and therefore the initial large flat areas are more localised.

In both cases, after the drawing process, the majority of detected peaks have higher curvature radius, which suggests that during the drawing process the peaks were flattened.

The proposed methodology can be used to analyse the lubrication regime of the surface subjected to plastic deformation by comparing initial and final surface morphology. However further analysis will be required to take full advantage of the developed method of 3D peaks curvature radii estimation, and to analyse the anisotropy of the detected peaks.

CONCLUSIONS

A new method of peaks curvature radii estimation of 3D surfaces was developed and applied to drawing process analysis. The 3D surface is intercepted by a flat surface and top part of the profile is analysed. If a peak is detected, the spherical cap with radius is fitted at point corresponding to the highest point inside the detected peak. Radius calculation is based on area of cross section of detected peak with cutting surface. This methodology allows to qualitatively analyse the effects of the lubrication regime in drawing process based only on measured surface. The proposed method shows clearly the differences in deformation in cavity pockets and on "plateaus" surfaces of aluminium strips used in this study. This method is also independent from the phenomena direction (2D or pseudo-2D), which means that the effect of lubricant escape could be analysed in any direction and can be linked to the surface anisotropy. This point, as well as the qualitative nature of the lubrication regimes identification, will be investigated in future works.

  This is the pre-peer reviewed version of the following article: C. Hubert, K. J. Kubiak, M. Bigerelle, L. Dubar Identification of local lubrication regimes on textured surfaces by 3D roughness curvature radius, Advanced Materials Research Vols. 966-967 (2014) pp 120-125, doi: 10.4028/www.scientific.net/AMR.966-967.120, which has been published in final form at http://www.scientific.net/AMR.966-967.120

Figure 1 :

 1 Figure 1: Schematic diagram of 3D roughness peak represented by an ellipsoidal cap.

Figure 2 :

 2 Figure 2: Curvature radii of peaks detected on a surface simulated numerically, with spherical caps with 80%& radius.

40°C) 3

 3 60cSt (3 0.0537Pa. s); • hydrodynamic lubrication regime (TC2): drawing speed / 0 50mm/s, lubricant viscosity (at 40°C) is 3 660cSt (3 0.5954Pa. s).

Figure 4 :

 4 Figure 4: Groove shaped pockets pattern (a) and picture (b) of an EDMed strip, after [5].

Figure 5 :

 5 Figure 5: Illustration of strips surfaces before (a) and after (b) drawing, after [7].

Figure 6 :

 6 Figure 6: Distribution of peaks curvature radii on an initial surface and after drawing process in mixed lubrication regime (TC1), and in hydrodynamic lubrication (TC2).

Published in Advanced Materials Research Vols. 966-967 (2014) pp 120-125 -Scientific Net http://dx.doi.org/10.4028/www.scientific.net/AMR.966-967.120

6. ACKNOWLEDGEMENTS K.J. Kubiak would like to thank all members of the Laboratory TEMPo for their hospitality and the University of Valenciennes for Invited Professorship position, making the research presented in this paper possible.


Martin Ragot
email: martin.ragot@b-com.com

Nicolas Martin
email: nicolas.martin@b-com.com

Salomé Cojean
email: salome.cojean@univ-grenoble-alpes.fr

AI-generated vs. Human Artworks. A Perception Bias Towards Artificial Intelligence?

Keywords: Perception Bias Towards Artificial Intelligence? Arts, Artificial Intelligence, AI, Authorship, Bias, CAN, Computational Creativity, GAN, Painting

HAL is

Introduction

For many years, technological progress has wrought profound changes in the creative industry, as in the fields of music (e.g., [START_REF] Cope | Computer models of musical creativity[END_REF][START_REF] Cope | Virtual music: computer synthesis of musical style[END_REF]), parametric design (e.g., [START_REF] Nagy | Project discover: an application of generative design for architectural space planning[END_REF]), generative fashion design (e.g., [START_REF] Kang | Visually-Aware Fashion Recommendation and Design with Generative Image Models[END_REF]), and paintings (e.g., [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF]). This has given rise to the emergence of new research fields: computational aesthetics [START_REF] Hoenig | Defining Computational Aesthetics[END_REF], attractiveness computing (see Chu [START_REF] Chu | A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia[END_REF] for a literature review), Creative Adversarial Networks (CAN) [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF] and computational creativity [START_REF] Colton | The Painting Fool: Stories from Building an Automated Painter[END_REF]. Computational creativity is a generic field of artificial intelligence (AI) that studies both artificial and human creativity [START_REF] Geraint | A preliminary framework for description, analysis and comparison of creative systems[END_REF] and has grown considerably. Indeed, in recent years, AI and especially its subfield machine learning (ML) have gained popularity [START_REF] Lee | Machine learning: Overview of the recent progresses and implications for the process systems engineering field[END_REF] thanks to research breakthroughs [START_REF] Lecun | Deep learning[END_REF]. In 2014, Goodfellow and collaborators [START_REF] Goodfellow | Generative Adversarial Networks[END_REF] proposed a new ML paradigm to generate synthetic data called a generative adversarial network (GAN) 1 . Artists have appropriated these models have been in recent years to the point that they have become a new art market [START_REF] Daniele | AI + Art = Human[END_REF]. Thus, a first art painting generated by a GAN was sold for $432,500 by Obvious [START_REF] Chu | A Survey on Multimedia Artworks Analysis and Attractiveness Computing in Multimedia[END_REF] 2 . With the increase in quality and ubiquity of creative systems such as GANs, studying these creativity support systems seems to be necessary [START_REF] Colton | Issues of Authenticity in Autonomously Creative Systems[END_REF][START_REF] Hennessey | Creativity[END_REF]. Evaluating the relevance of recently produced artistic works consists of assessing the perception of the public towards these artworks. To improve the algorithms, one approach is to try to understand user perceptions. There is also a need to understand the acceptance of works produced by AI [START_REF] Colton | The Painting Fool: Stories from Building an Automated Painter[END_REF]. Currently, this perception seems to be rather negative. Indeed, Gaut [START_REF] Gaut | The Philosophy of Creativity: Philosophy of Creativity[END_REF] has postulated a "negative value" towards machines or computers. Colton [START_REF] Colton | Creativity Versus the Perception of Creativity in Computational Systems[END_REF] is also concerned about the rejection of creative computers. Nevertheless, the evidence for such negative bias remains unclear. It seems crucial to study human perceptions of these AI-generated artworks. For this purpose, different methodologies exist. First, in many studies, a modified Turing test (TT) is used to analyze humans' capacity to distinguish human or AI artworks (e.g., [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF][START_REF] Pearce | Towards A Framework for the Evaluation of Machine Compositions[END_REF][START_REF] Schwartz | bot or not[END_REF]). However, the TT seems insufficient. Indeed, this type of test does not make it possible to study audience perceptions. This method has also been criticized for encouraging imitation and pastiches of existing human artworks [START_REF] Pease | On impact and evaluation in computational creativity: a discussion of the Turing Test and an alternative proposal[END_REF]. In parallel, to the best of our knowledge, few researchers have studied the differences in public perceptions between AI-generated or human artworks to understand the potential human bias. In their survey, Moffat and Kelly [START_REF] David | An investigation into people's bias against computational creativity in music composition[END_REF] found significant bias against computer-generated music. Other researchers have tried to reproduce these results with a small sample but without success [START_REF] Friedman | Exploring emotional responses to computationally-created music[END_REF][START_REF] Norton | Accounting for Bias in the Evaluation of Creative Computational Systems: An Assessment of DARCI[END_REF][START_REF] Pasquier | Investigating Listener Bias Against Musical Metacreativity[END_REF]. In addition, Hong [START_REF] Hong | Bias in Perception of Art Produced by Artificial Intelligence[END_REF] has analyzed the differences in perceptions of computer art within focus groups. For the same piece of art is more considered to be "art" when it is perceived to be of human origin than AI origin. In the field of art and paintings, Elgammal [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF] has found conflicting results with a positive bias towards machines. Despite this, the bias against computers is widespread and often cited [START_REF] Lamb | Evaluating Computational Creativity: An Interdisciplinary Tutorial[END_REF]: "This suggests that there is much more to investigate about our relation with artificially generated art" ( [START_REF] Daniele | AI + Art = Human[END_REF], p. 159).

With increasing exposure to "more high-quality computer generated artifacts," the following research questions have become "more pressing" ( [START_REF] Colton | Issues of Authenticity in Autonomously Creative Systems[END_REF], p. 276). Art is a symbol of an activity long regarded as a human task. Thus, AI raises important questions in terms of acceptability and human-andmachine relationships, which must be studied in the field of HCI. Can people distinguish computer-generated paintings? Do people prefer systems (or paintings) created by humans? Do people have a negative bias towards AI? Finally, we seek to learn how assumptions about the identity of the painters (AI or human) of the same artworks influences their evaluation. This paper presents a large-scale experiment to assess the existence of a potential human bias towards works generated by AI by using the technique of priming effect (e.g., [START_REF] Bargh | Automaticity of social behavior: Direct effects of trait construct and stereotype activation on action[END_REF][START_REF] Tulving | Priming effects in word-fragment completion are independent of recognition memory[END_REF]). The priming effect represents the influence of a first stimulus (the primer) on the processing of a subsequent stimulus (the target). Thus, our study evaluates the influence of the exposure of the primer (i.e., the declared identity of the painter, AI vs. human) on the perception of artistic targets (i.e., AI-generated paintings vs. paintings drawn by humans).

Method

Participants 565 participants (M = 32.80 years; S.D. = 9.91; 234 females and 331 males) were involved. The participants were recruited without specific criteria on Amazon Mechanical Turk (AMT) in order to select more diverse profiles and obtain a larger sample [START_REF] Buhrmester | Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality[END_REF]. The reward paid to each worker was $0.75.

Measures

As Amirshahi [START_REF] Ali Amirshahi | JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores[END_REF] has recommended with lead subjective tests, many proprieties related to the paintings were studied. Inspired by the works of Berlyne [START_REF] Berlyne | Aesthetics and psychobiology[END_REF], Jordanous [START_REF] Jordanous | Stepping Back to Progress Forwards: Setting Standards for Meta-Evaluation of Computational Creativity[END_REF], and Elgammal [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF], many dimensions were evaluated: liking ("I like this painting"), beauty ("This painting looks beautiful"), novelty ("This painting seems novel"), and meaning ("I perceive the meaning of the painting"). To evaluate subjective dimensions, questionnaires with Likert scales are used in this survey. The optimal number of rating bars seems to be seven [START_REF] Cai | Study of the optimal number of rating bars in the likert scale[END_REF]. For these four dimensions, participants were asked to indicate how much they agreed according to seven-point Likert scales (1 = totally disagree; 7 = totally agree). Each Likert had antagonist anchors without labels between.

Material

Following Pasquier [START_REF] Pasquier | Investigating Listener Bias Against Musical Metacreativity[END_REF], the selection of pieces of art was influenced by their similarity in order to conceal their authorship. For this purpose, 40 impressionist-style paintings made by Piet Mondrian, Claude Monet, Robbie Barrat, and the collective Obvious were selected 3 (see Erreur ! Source du renvoi introuvable. for examples).

Protocol

An online survey was used to control the experimental conditions in this type of study [START_REF] Ali Amirshahi | JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores[END_REF]. The participants who accepted the task on AMT were redirected to the SurveyMonkey platform. A mixed-subject design was used; see Erreur ! Source du renvoi introuvable.. Similar to [START_REF] Friedman | Exploring emotional responses to computationally-created music[END_REF], 3 Due to space limitation, the complete list of paintings is not presented. Please contact the first author for details.

Portrait Human

Portrait AI participants were randomly assigned to one of the two conditions (i.e., Human or AI condition, the between-subject variable). Thus, participants saw one of the two primings and instructions presented in Erreur ! Source du renvoi introuvable.. All participants saw portraits and landscapes actually made by humans and AI (within-subject design). The artworks were randomly selected from 40 paintings (10 portraits by AI, 10 landscapes by AI, 10 portraits by humans, and 10 landscapes by humans) to improve the generalization of the results, to reduce experiment duration, and to avoid the repetition of painting styles. Next, for each painting, participants had to complete several items (i.e., liking, perceived beauty, novelty, and meaning). Finally, in order to ensure the effectiveness of the priming of the declared author type (i.e., AI or human), and following previous studies (e.g., [START_REF] Norton | Accounting for Bias in the Evaluation of Creative Computational Systems: An Assessment of DARCI[END_REF]), an item of manipulation check was introduced (i.e., "At the beginning of the study, the identity of the painters was defined. Do you remember the identity of the painters?"). Afterward, it was explained that the origin of the painting had been manipulated. Participants then had to guess the origin of four paintings (one painting was randomly selected from each category). This modified TT was proposed at the end of the study to avoid any bias in evaluation and the priming effect. Lastly, two demographic questions about age and gender were asked.

Results

Mixed model analyses were used for each dependent variable. Thus, the participant and the paintings 4 were considered random factors. The induction condition was considered a between-subject factor. The type of paintings and the real authors were considered within-subject factors. The statistical analysis was performed using R [START_REF] Core | R: A Language and Environment for Statistical Computing[END_REF], lme4 [START_REF] Bates | Fitting Linear Mixed-Effects Models Using lme4[END_REF], and afex [START_REF] Singmann | afex: Analysis of Factorial Experiments[END_REF]. The effect sizes were based on [START_REF] Brysbaert | Power Analysis and Effect Size in Mixed Effects Models: A Tutorial[END_REF] (measured as d).

Participants who responded "I don't know" to the manipulation check were excluded (i.e., 79 out of 565 participants, or 13.98% of the sample). Moreover, for "Human condition" and "AI condition," 82% and 62%, respectively, of participants remembered their type of induction. Thus, to control the effect of this variable, answering correctly or incorrectly on this item was introduced as a random factor similar to [START_REF] Ali Amirshahi | JenAesthetics Subjective Dataset: Analyzing Paintings by Subjective Scores[END_REF].

The effects of the induction condition, the type of paintings, and the real authors were evaluated on declared liking, perceived beauty, perceived novelty, and meaning. All descriptive statistics are presented in Erreur ! Source du renvoi introuvable., Erreur ! Source du renvoi introuvable., Table 4, and Table 5.

Concerning declared liking (see Erreur ! Source du renvoi introuvable.), the analyses show a main effect of induction (F1,481. Concerning perceived meaning (see Table 5), the analyses showed a main effect of induction (F1,481.34 = 15.09, p < .001, d = 0.13), the type of painting (F1,28.81 = 19.61, p < .001, d = 0.10), and the real authors (F1,28.81 = 52.96, p < .001, d = 0.16).

rate close to chance (50%), people have difficulty distinguishing the type of authors. These results may suggest that the technical proprieties of AIgenerated paintings are currently more advanced for the generation of landscapes than portraits. A possible explanation of the existing bias against AI-generated paintings could be that participants evaluated the pieces of art with an intergroup bias. AI may be anthropomorphized and thus considered as the out-group [START_REF] Edwards | Evaluations of an artificial intelligence instructor's voice: Social Identity Theory in human-robot interactions[END_REF]. Intergroup bias can be defined as the tendency people have to evaluate their own group (i.e., the in-group) in a more positive manner than another group (i.e., the out-group) [START_REF] Billig | Social categorization and similarity in intergroup behaviour[END_REF][START_REF] Hewstone | Intergroup bias[END_REF]. Social changes relative to the out-group may bring a sense of threat and negative feelings that reinforce the intergroup bias [START_REF] Brewer | Ingroup identification and intergroup conflict. Social identity, intergroup conflict, and conflict reduction[END_REF][START_REF] Victoria M Esses | Intergroup competition and attitudes toward immigrants and immigration: An instrumental model of group conflict[END_REF][START_REF] Hewstone | Intergroup bias[END_REF]. The aim of the out-group devaluation is to maintain a positive social identity and superiority in some areas of competence [START_REF] Tajfel | An integrative theory of intergroup conflict[END_REF]. To further explain the existence of a negative perception bias towards AI, several concepts can be cited, such as technophobia ( [START_REF] Oh | Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match[END_REF]; see [START_REF] Rosen | Myths and realities of computerphobia: A meta-analysis[END_REF] for meta-analysis), anxiety towards machines, and reactive devaluation [START_REF] Ross | Reactive Devaluation in Negotiation and Conflict Resolution[END_REF]. AI can be seen as a potential source of danger because it could replace humans in many areas [START_REF] Oh | Us vs. Them: Understanding Artificial Intelligence Technophobia over the Google DeepMind Challenge Match[END_REF]. To combat this bias, some authors have raised the question of framing information on user perceptions [START_REF] Binns | Perceptions of Justice in Algorithmic Decisions[END_REF][START_REF] Colton | Creativity Versus the Perception of Creativity in Computational Systems[END_REF][START_REF] René | How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface[END_REF][START_REF] Rader | Explanations as Mechanisms for Supporting Algorithmic Transparency[END_REF][START_REF] Sinha | The role of transparency in recommender systems[END_REF]. Indeed, "[f]raming information might be useful to help humans understand an agent who is unlike them; but this remains to be tested" ( [START_REF] Lamb | Evaluating Computational Creativity: An Interdisciplinary Tutorial[END_REF], p. 28:24), even if the experimental results seem to be unclear [START_REF] Mcgregor | Process Based Evaluation of Computer Generated Poetry[END_REF]. Thus, for Lamb ([36], p. 28:24), "a more subtle question is if humans are biased toward familiar and humanlike forms of creativity," which could be linked to the previously mentioned intergroup bias.

Future works

Within this paper, a general bias against AI-generated artwork can be highlighted. In future works, the interaction effects across different parameters (e.g., induction and painting type) may be explored. Moreover, it may be interesting to include a measure of self-reported expertise in Art. Hekkert and Wieringen [START_REF] Hekkert | Beauty in the Eye of Expert and Nonexpert Beholders: A Study in the Appraisal of Art[END_REF] have analyzed the differences in the art evaluation among experts and non-experts; they found a correlation between perceived originality and quality that was significantly higher among experts than non-experts. In the same way, Moffat and Kelly [START_REF] David | An investigation into people's bias against computational creativity in music composition[END_REF] have found a bias against computers that was significantly higher in musicians than non-musicians. Nevertheless, in a modified version of the TT, non-musicians were surprisingly better than musicians at recognizing music generated by computers or humans. In parallel, in order to make our results even more generic, the evaluation of a general bias against AI-generated artworks could be made with different styles of paintings (e.g., [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF]) or types of art (e.g., music, poem). In this paper, public perception bias is questioned based on dichotomic human or AI priming. However, the frontiers between AI and human-generated artworks may appear more and more faint. For Elgammal [START_REF] Elgammal | AI Is Blurring the Definition of Artist[END_REF], AI has even blurred the definition of artists. More generally, this raises the issue of authorship: "Can an Artificial Intelligence make Art without artists?" [START_REF] Audry | Can Artificial Intelligence Make Art without Artists? Ask the Viewer[END_REF]. An initial answer could be in the eye or mind of the viewer.

Conclusion

The idea of a negative perception bias against machines, computers, and/or AI is widely shared, particularly in computational creativity. However, there has been a lack of experimental proof that demonstrates evidence of this bias. Unlike previous studies, the current large-scale experiment with 565 participants focused on AI and AI-generated paintings with different methodological precautions (large sample, randomization, manipulation check as random factor, etc.). In addition to the large sample, the main contributions of this paper are the methodological precautions and the robustness of the results, which seem to represent significant progress in the field of HCI and computational creativity to demonstrate the existence of a negative perception bias towards AI at the expense of human-made systems. Thus, depending on the perceived identity of the author (Human vs. AI), the same artworks were evaluated differently. Moreover, the results show that real artworks made by humans are also evaluated more highly than real AIgenerated artworks. Alongside technological advances in AI, this paper paves the way for other studies to further consider perception bias in computational creativity and to analyze the impact of AI on human representations.

Figure 1 .

 1 Figure 1. Examples of used art paintings (Human: The Stroller -Claude Monet; AI: Madame De Belamy -@obvious_art)

Figure 2 .

 2 Figure 2. Experimental design

Table 1 :

 1 by some Artificial Intelligence will be presented. You will be asked to rate them. There are no right or wrong answers. Only your opinion counts. Please respond spontaneously according to your feelings. Instructions

		Induction
		Between-subject
		variable
		AI	Human
		Real author:
		AI
		Landscape
	Randomized order Within-subject variables	Portrait Landscape Real author: Human
		Portrait

Table 2 .

 2 Descriptive statistics for declared linking. Land = landscape Regarding perceived beauty (see Erreur ! Source du renvoi introuvable.), the analyses show a main effect of induction (F1,481.23 = 17.74, p < .001, d = 0.05), the type of painting (F1,32.14 = 22.18, p < .001, d = 0.16), and the real authors (F1,32.14 = 64.81, p < .001, d = 0.27).

		Human	AI
	Induction	5.12 (1.53)	4.81 (1.67)
		Human	AI
	Real Authors	5.20 (1.38)	4.71 (1.78)
		Land	Portrait
	Painting type	5.37 (1.36)	4.54 (1.73)

Table 3 .

 3 Descriptive statistics for perceived beauty. Land = landscape In terms of perceived novelty (see Table4), the analyses show a main effect of induction (F1,481.28 = 13.31, p < .001, d = 0.11), the type of painting (F1,25.72 = 16.33, p < .001, d = 0.08), and the real authors (F1,25.72 = 3.47, p = .074, d = 0.04).

	Human	AI

Table 4 .

 4 Descriptive statistics for perceived novelty. Land = landscape

Generative Adversarial Network (GAN)[START_REF] Friedman | Exploring emotional responses to computationally-created music[END_REF] consists in train jointly two separate models: a generator and a discriminator. The generator is train to generate the most realistic data (e.g., images, text, signal) and to fool the discriminator. The discriminator is train to discriminate real data (i.e., data from the training dataset) and fake data (i.e., data from the generator).

https://obvious-art.com/

To consider the variability between paintings, the painting is included as a random factor. Thus, the variability in perception between the paintings is absorbed in the model fitting.

Acknowledgement

This study was carried out within b<>com, an institute of research and technology dedicated to digital technologies. It received support from the Future Investments program of the French National Research Agency (grant no. ANR-07-A0-AIRT).

Discussion

A phase with a manipulation check was proposed to participants to verify the relevance of the proposed induction. The responses suggest that a large majority of participants believed and remembered their type of induction (i.e., AI or human condition). These results seem consistent with those of Friedman [START_REF] Friedman | Exploring emotional responses to computationally-created music[END_REF]. In their first experiment, 83.87% and 65.27%, respectively, believed in the assertion in "Human Condition" and "AI condition." Unlike in the previous studies, here, the use of a large sample (565 participants), combined with the introduction of the answers in the manipulation check as a random factor, resulted in precise statistical analyses. Indeed, the results show significant effects of both the induced and real author. More specifically, the artworks presented as AI-generated paintings were significantly less liked and were perceived as less beautiful, novel, and meaningful than paintings presented as drawn by a human. The same pattern, with significant effect, is also apparent for the real type of authors of the artworks presented. Indeed, AI-generated paintings were less well evaluated (in terms of liking, beauty, novelty, and meaning) than paintings made by humans. These results support the first results obtained by Moffat and Kelly [START_REF] David | An investigation into people's bias against computational creativity in music composition[END_REF] on the perception bias towards computer-generated music. To the best of our knowledge, these results had never been replicated on a large sample, with the previous methodological precautions presented. Moreover, the modified TT, in which participants have to guess the real author of the paintings, showed a better recognition of human paintings (66%) than AIgenerated paintings (56%). These results are consistent with Burnett [START_REF] Burnett | Validation of Harmonic Progression Generator Using Classical Music[END_REF] for composed-computer music and are opposed to results of Moffat and Kelly [START_REF] David | An investigation into people's bias against computational creativity in music composition[END_REF]. In Elgammal [START_REF] Elgammal | CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms[END_REF], 75% of respondents thought that the AI-generated paintings were made by humans. These differences could be explained by the improvement of the techniques in computational creativity, especially with the emergence of GAN. In parallel, it should be noted that participants are better able to identify the origin of the author for portraits (69%) than for landscapes (53%). Similar to Boden [START_REF] Boden | The Turing test and artistic creativity[END_REF], with a recognition


Fabien Stark
email: fabien.stark@inrae.fr

Julie Ryschawy

Myriam Grillot

Romane Mettauer

Guillaume Martin

Andréa Cassagnes

Yolande Bertin

Marc Moraine

© J Ryschawy

© G Jacquemin

© R Mettauer

© Y Bertin

Combining biotechnical and organisational levers to engage agroecological transition of farming systems: A case-study of sheep integration into organic

published or not. The documents may come   L'archive ouverte pluridisciplinaire



  Negative impacts of specialization and concentration of agricultural systems  Specialization of both farms and regions  Integrated crop-livestock systems (ICLS) as a relevant lever for agroecological transition …but wide range of • possible crop-livestock integration practices • and types of organisation Which Spatial distribution of surfaces = 3,000 ha (20 % of the territory's agricultural surface)  Feed resources estimation (legume fodder, moors, interrow vineyard, …)  Farming systems and CLI practices calibration: semistructured interviews (n=22) simulation plan consistent with local actors  settings consistent with farming system structures and functioning Results: Baseline scenario  Herd of 1,000 ewes feed on the territory (shepherd)  Diversity of feed resources  Feed ressources vary with seasons  Quantity of available ressources (and consequently number of animals fed) depend also of seasons Consider ICLS all over the territory  Increase number of ewes (> 8,000)  Interest in combining dynamic analysis (seasonality) and spatial analysis (heterogenity of land use) to design future landscapes  Issues of reintroducing livestock farming in specialised territories :  Services provided to crops: weeding, fertilization, outlet  Services provided to landscape: Shrub encroachment, fire, … biodiversity  Multicriteria assesment of benefits associated to these ICLS (realised but not presented)  Agro-environmental dimension: soil fertility, energy consumption & emission  Socioeconomical dimension: Gross Margin, Need to take into account organizational issues and relationships between actors  Consultation meetings with farmers, stakeholders, inhabitants, associations…  Importance of local « leaders »  Ongoing project (funding achieve for the next 3 years !)  Not yet published (ASD special issue ;-)  Thanks to Romane's master thesis © F. Stark


Chrystelle Lasica

Ronaldo De

Carvalho Augusto

Hélène Moné

Gabriel Mouahid

Cristian Chaparro

Anne-Clemence Veillard

Agnieszka Zelisko-Schmidt

Christoph Grunau
email: christoph.grunau@univ-perp.fr

Ronaldo De Carvalho Augusto

Anne-Clémence Veillard

Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni

Keywords: ChIPmentation, ChIP-seq, N-ChIP, limited biological material, Epigenetics, Schistosoma mansoni Data Curation, Formal Analysis, Investigation, Methodology, Validation, Writing -Original Draft Preparation, Writing -Review Investigation, Methodology, Writing -Review Conceptualization, Funding Acquisition, Investigation, Project Administration, Supervision, Writing -Review & Editing, Mouahid G: Conceptualization, Funding Acquisition, Investigation, Project Administration, Supervision, Writing -Review Curation, Methodology, Resources, Software, Supervision, Writing -Review Methodology, Supervision, Visualization, Writing -Review Conceptualization, Methodology, Supervision, Visualization, Writing -Review & Editing, Conceptualization, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Supervision, Writing -Original

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Schistosoma mansoni is a human parasite with a complex life cycle that shows strong developmental phenotypic plasticity, with intra-molluscal and intra-vertebrate stages, and two free-swimming larvae stages (miracidium and cercariae). We had shown by native chromatin immunoprecipitation (N-ChIP) that the different life cycle stages also show strong histone modification plasticity [START_REF] Rdc | Histone Methylome of the Human Parasite Schistosoma Mansoni. The DNA RNA, and Histone Methylomes[END_REF][START_REF] Cosseau | Native chromatin immunoprecipitation (N-ChIP) and ChIP-Seq of Schistosoma mansoni: Critical experimental parameters[END_REF][START_REF] Roquis | Histone methylation changes are required for life cycle progression in the human parasite Schistosoma mansoni[END_REF]. While N-ChIP has been successfully used, we found that it is associated two challenges: one is the high hands-on time with the N-ChIP, and the other is obtaining enough biological material to perform several ChIP experiments with different antibodies. We therefore explored and benchmarked an automated ChIP procedure (Figure 1). ChIPmentation is a ChIP-sequencing (ChIP-seq) technology which uses a transposase to add the sequencing adaptors to the DNA of interest instead of the classical multi-step processing, including end repair, A-tailing, adaptor ligation and size-selection [START_REF] Schmidl | ChIPmentation: fast, robust, lowinput ChIP-seq for histones and transcription factors[END_REF]. Thanks to the action of the transposase, loaded with sequencing adaptors, the library preparation is performed in only one step, which reduces hands-on time and material loss. Moreover, in the ChIPmentation approach, this tagmentation process is performed directly on chromatin during the immunoprecipitation process instead of naked DNA after purification. This workflow allows for a more reproducible tagmentation.

The combined facts that ChIPmentation has been automated on Diagenode's IP-Star Compact Automated System and that this technology has been validated on low amounts of human cells [START_REF] Roels | Distinct and temporary-restricted epigenetic mechanisms regulate human αβ and γδ T cell development[END_REF][START_REF] Schmidl | ChIPmentation: fast, robust, lowinput ChIP-seq for histones and transcription factors[END_REF] make it a perfect candidate for ChIP-seq on limited material of other, non-model species.

Here we addressed the questions of whether ChIPmentation, which was originally developed for human cell cultures, (i) can be used with schistosomes, (ii) whether it can be automated on a pipetting robot, and finally (iii) what the lowest schistosome cell number would be to obtain robust results with this procedure. We show here that the method is almost as sensitive as N-ChIP, but is about two times faster and can be carried out on the IP-Star pipetting robot, reducing experimenter hands-on and, more importantly, training time.

Methods

Production of biological material S. mansoni NMRI eggs were extracted from livers of golden hamsters (donated by ParaDev) 42 days post-infection. Nocturnal S. mansoni from Oman [START_REF] Mouahid | A new chronotype of Schistosoma mansoni: adaptive significance[END_REF] were extracted from livers of two swiss OF1 mice eight months post-infection. Miracidia were allowed to hatch for two hours in spring water, collected by pipetting and sedimented on ice for 30 min. Miracidia were counted under a microscope, aliquoted and stored at -80°C.

Ethical considerations

Experiments on animals permits The laboratory received the permit number A 66040 for experiments on animals, from both the French Ministère de l'Agriculture et de Ethical approval number: we obtained approval from the CEEA -036 Comité d'éthique en expérimentation animale Languedoc Roussillon (CEEA-LR)", which is the registration code of our ethical committee within the Comité national de réflexion éthique sur l'expérimentation animale (CNREEA), under the agreement number C66-136-01. The CNREEA is part of the French Ministry of Higher Education, Research and Innovation.

Cell lysis and chromatin shearing

Chromatin preparation was performed using the Diagenode ChIPmentation Kit for Histones, Cat. No. C01011009 and protocol with minor modifications. A total of 10,000 miracidia (1,000,000 cells based on the observation that one miracidium is composed of 100-120 cells) were resuspended in 1 mL 1x Hank's balanced salt solution (HBSS), split into 2x500 µL and crushed with a plastic pestle in an Eppendorf tube on ice during ~1 min. For cross-linking, 13.5 µL of formaldehyde were added and tubes were incubated for 10 min at room temperature with occasional inversion. To stop cross-linking fixation, 57 µL of glycine were added and samples were incubated for 5 min at room temperature. Samples were centrifuged at 500xg, at 4°C, for 5 min. The pellet was resuspended in 2x1 mL of ice-cold Lysis Buffer iL1, combined and homogenized in a Dounce (pestle A) on ice for 5 min. After another centrifugation (500g, 5 min, 4°C), the supernatant was discarded and the pellet was resuspended in 1 mL of ice-cold Lysis Buffer IL2 and centrifuged (500xg, 5 min, 4°C). The supernatant was discarded and the pellet was resuspended in 100 µL of complete Shearing Buffer iS1 for each tube. Samples were sonicated with the Bioruptor Pico (Diagenode, Cat. No. B01080010) for 5 cycles (30 s ON and 30 s OFF). After transfer into new tubes, samples were centrifuged (16,000g, 10 min, 4°C). The supernatants were transferred into a new single tube (200 µl total) and 20 µl iS1 were added, yielding a total volume of 220 µl. The procedure was done in duplicate (named L and R in the following). Serial dilutions were done in iS1 to produce 100 µl equivalents of 10,000 miracidia (10 6 cells), 1,000 miracidia (10 5 cells), 100 miracidia (10 4 cells), 50 miracidia (5,000 cells), 10 miracidia (10 3 cells), five miracidia (500 cells) and one miracidium (100 cells) or 100 µl iS1 as negative control.

Magnetic immunoprecipitation and tagmentation

Immunoprecipitation (IP) was performed on the Diagenode IP-Star Compact Automated System (Cat. No. B03000002) according to the ChIPmentation Kit for Histones User Guide and by following the manufacturer's on-screen instructions. Antibody (Ab) coating time was set to 3 h, IP reaction to 13 h, washes to 10 min, and tagmentation to 5 min. For each sample the Ab coating mix was done with 4 µl anti-H3K4me3 (Diagenode, Cat. No. C15410003; mixture of lot A1051D and A1052D; raised in rabbit). Stripping, end repair and reverse cross-linking were done as indicated in the User Guide.

Input library tagmentation

In the ChIPmentation Kit for Histones (Diagenode, Cat. No. C01011009) the suggested strategy is to sequence one immunoprecipitated sample with a control immunoglobulin G (IgG) and to use it for sequencing normalization instead of the traditional input, which cannot be treated in exactly the same way as the immunoprecipitated samples. However, IgG are negative control samples, and the generation of such samples in low-amount approaches involves in our experience the use of a high number of amplification cycles that can induce some biases. A protocol for the tagmentation of the input sample was therefore set-up as follows.

For each immunoprecipitated sample, 1µL of sheared chromatin was kept aside before IP in the IP-Star. 1 µL of MgCl 2 (Diagenode ChIPmentation kit for Histones, Cat. No. C01011009), 8 µL of molecular biology grade water, 10 µL 2xTagment DNA buffer and 1 µL of 100-fold in molecular-grade water diluted DNA tagmentation enzyme (Illumina 20034197, lot 20464427) were added to each 1 µL input. The tagmentation reaction was performed in a thermocycler for five minutes at 55°C. Then, 25 µL of 2xPCR NEB master mix (New England Biolabs M0541L, lot 10067165) was added to each input. The end-repair and de-cross-link were performed in a thermocycler for five minutes at 72°C followed by 10 minutes at 95°C. An aliquot of 2 µL was taken from each input and added to 8 µL of quantification mix. For each reaction, this quantification mix was composed of 0.3 µL of forward and reverse ATAC-seq primers (25 µM) (Table 1, [START_REF] Buenrostro | ATAC-seq: A Method for Assaying Chromatin Accessibility Genome-Wide[END_REF], 1 µL SYBR Green 10X (Diagenode kit), 1.3 µL of molecular biology grade water and 5 µL of 2xPCR NEB master mix. While not formally tested, leftover primers of the ChIPmentation kit could probably also be used, but the volume must be adjusted as the primer pairs in the kit were 10 µM instead of 25 µM. Library amplification, purification, quality checking and sequencing steps were performed as for the immunoprecipitated samples (see below).

Library amplification

To determine optimal number of library amplification cycles we proceeded as described in steps 5.1 to 5.5 of the ChIPmentation User Guide with modifications detailed below.

We determined the number of amplification cycles for each library by using the number of cycles that corresponded to 1/3 of the qPCR amplification curve slope during the exponential phase. Results are shown in Table 6.

Amplification was done in step 5.7. After PCR, 48 µl of library amplification mix were AMPure-purified on the IP-Star using 86 µl of AMPure beads (1.8x). Instead of a resuspension buffer, we used ChIP grade water for a final volume of 20 µl.

Library check and sequencing

Library fragment size and concentration was checked on an Agilent 2100 Bioanalyzer with a High Sensitivity DNA Assay v1.03. Paired-end sequencing (2x75 cycles) was performed at the Bio-Environnement platform (University of Perpignan, France) on a NextSeq 550 instrument (Illumina, USA).

Bioinformatics analysis

Reads were quality-checked with FastQC, Galaxy version 0.72 and Galaxy 1 (Smith, 2019). Adapters were detected in less than 4% of reads. Reads were aligned with the S. mansoni v7 reference genomes using Bowtie2 Galaxy version 2.3.4.3 [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF]., the default sensitive settings.

Uniquely aligned reads were filtered from the BAM files using the XS: tag of Bowtie2, Galaxy version 2.3.4.3 and Galaxy 0 [START_REF] Langmead | Fast gapped-read alignment with Bowtie 2[END_REF]. PCR duplicates were removed with samtools rmdup Galaxy version 2.0.1 [START_REF] Li | The Sequence Alignment/Map format and SAMtools[END_REF]. BAM files were subsampled to 3.8 M uniquely aligned reads with Picard DownsampleSam Galaxy version 2.18.2.1 (Broad Institute 2022, February 23) . Peakcalling was done with Peakranger (v1.17) Galaxy version 1.0.0 [START_REF] Feng | PeakRanger: a cloud-enabled peak caller for ChIP-seq data[END_REF], with P value cut-off: 0.0001, false discovery rate (FDR) cutoff: 0.05, Read extension length 100 -2,000 bp, Smoothing bandwidth: 99. Delta: 0.8, Detection mode: region. ChromstaR, Galaxy version 0.99.0 [START_REF] Taudt | chromstaR: Tracking combinatorial chromatin state dynamics in space and time[END_REF] was used with a bin size same as Peakranger extension lengths and a step size of half a bin size. Miracidia genomic DNA libraries served as input. In ChromstaR postprocessing, the maximum posterior probabilities to adjust sensitivity of peak detection was set to 0.999. This kept broad peaks intact. We reasoned that gaps between peaks that were larger than a nucleosome are not biologically meaningful and peaks were merged with BEDTOOLS, Galaxy version 2.29.2 [START_REF] Quinlan | BEDTools: a flexible suite of utilities for comparing genomic features[END_REF] when they were ≤150 bp apart (the average length of DNA in a nucleosome).

Enrichment plots over metagenes were produced over 5,073 genes on the positive strand based on the canonical annotation v7 of S. mansoni.

Results

ChIPmentation has a sensitivity that is comparable to N-ChIP We used S. mansoni miracidia which are composed of 100-120 cells as starting material, allowing for a good estimate of cell numbers. After the chromatin fragmentation and library amplification, 10 6 to 10 4 cell equivalents gave comparable Bioanalyzer profiles with peaks around 1kb. For 5,000 cells equivalents and below, fragments of smaller size became clearly visible. No high-molecular fragments were observed in the negative control without chromatin (Figure 2 and Table 2).

Alignments to the genome gave expected results (~50% uniquely aligned reads) for 10 6 to 10 4 cell equivalents, but dropped to ~35% with 10 3 cells equivalent and were <20% for 100 cells equivalent. No contaminating DNA was detected (Table 3).

In order to compare ChIPmentation results to N-ChIP we re-analysed earlier data obtained by N-ChIP (Augusto et al., 2019) using the same data-cleaning, alignment and peak-calling parameters as for ChIPmentation (Table 4). For ChIPmentation, peakcalling with Peakranger was highly robust for 10 6 cells and delivered the expected values (based on earlier N-ChIP results). Below this cell equivalent, peak-calling became dependent on bin size (Table 5).

We iteratively identified 1,000 bp as best-read extension length. Using the HMM-based ChromstaR improved peak calling for ChIPmentation 10 4 cell equivalent, but not for 10 3 or 100 cells. We obtained comparable results for ChIPmentation of 10 6 , 10 4 cells and N-ChIP ~10 6 , 10 4 and 10 3 cells (Table 6).

ChromstaR metagene profiles showed consistent profiles for ChIPmentation 10 6 , 10 4 and 10 3 cells, and all N-ChIP, but not for ChIPmentation on 100 cell equivalents (Figure 3).

ChromstaR allows for estimating the correlation of chromatin profiles based on read counts (Figure 4), and indicates high correlations between ChIPmentation 10 6 , and N-ChIP ~10 6 , 10 4 .

All other overall chromatin profiles were below 0.75 correlation coefficient. This is surprising given the high similarity of metagene profiles. Visual inspection of peaks and profiles showed that peaks were actually correctly identified by ChromstaR (but much less by Peakranger) in ChIPmentation until 10 4 cells, but there was higher background than in N-ChIP which probably decreased the correlation for lower cell equivalents (Figure 5).

BEDTools intersect with default parameters identified 5,948 common peaks between the 6,682 ChIPmentation peaks and the 6,186 N-ChIP peaks obtained with 10 6 cells. This corresponds to an empirical FDR of 0.89-0.96%. This FDR cannot distinguish between biological and technical variation.

ChIPmentation input library can rapidly be produced in parallel to the automated procedure After having formally established that automated ChIPmentation had a comparable sensitivity to our routine N-ChIP procedure, we aimed to identify the optimal way to produce input libraries for control of unspecific enrichment. The production of input chromatin is "built-in" the N-ChIP protocol [START_REF] Cosseau | Native chromatin immunoprecipitation (N-ChIP) and ChIP-Seq of Schistosoma mansoni: Critical experimental parameters[END_REF][START_REF] De Carvalho Augusto | Measuring Histone Modifications in the Human Parasite Schistosoma mansoni[END_REF][START_REF] Roquis | Histone methylation changes are required for life cycle progression in the human parasite Schistosoma mansoni[END_REF] and needed to be adapted to the automated ChIPmentation procedure. During a ChIPmentation experiment three types of input can be considered (Figure 6): (i) 1 µL of chromatin before immunoprecipitation, (ii) chromatin that binds non-specifically to any support, and (iii) chromatin truly available for IP. In (i), an aliquot of 1 µL is taken from the sample before immunoprecipitation. The two other types (input library ii and iii) need a supplementary sample in which mock IP is done without antibody. After IP, this supplementary sample contains magnetic beads with the non-specifically bound chromatin and the supernatant, which is the available chromatin for IP.

Only (i), i.e. before IP chromatin and (iii) i.e. free chromatin available for IP give ideal library sizes (Figure 6). We decided to optimize the input protocol for option (i), non-immunoprecipitated chromatin because it does not occupy a slot in the IP-Star. In addition, during the preliminary test, 1 µL chromatin input showed a lower Ct compared to option (iii) input, which means that it requires fewer amplification cycles (data on Zenodo).

For the preparation of the 1 µL chromatin input libraries, we identified two critical parameters. The first one was the dilution of the tagmentation enzyme. Using undiluted Tn5 caused complete over-tagmentation. Between 10-and 100-fold dilutions in water delivered optimal results (Figure 7A).

Secondly, we found that, in our hands, there was no need to add tagmentation neutralizer (0.2% sodium dodecyl sulfate [SDS]) after tagmentation (Figure 7-B and Figure 8). This actually inhibits the PCR amplification step [START_REF] Picelli | Tn5 transposase and tagmentation procedures for massively scaled sequencing projects[END_REF]. Interestingly, parameters like tagmentation temperature (37°C-55°C) (Figure 8), tagmentation time (2-10 min) and addition of MgCl 2 did not have a critical effect on input library generation.

Discussion

As many methods, ChIPmentation had been developed using readily available but highly artificial cell cultures [START_REF] Schmidl | ChIPmentation: fast, robust, lowinput ChIP-seq for histones and transcription factors[END_REF]. The transition from such model systems to non-model species and ecologically realistic conditions is sometimes very difficult or even impossible. Here we showed that the automated ChIPmentation can be done with a parasitic flatworm and delivers results comparable to N-ChIP, the current method of choice for this species. However, the method is roughly two times faster and requires roughly six times less hands-on time. Since the procedure is done on a pipetting robot with on-screen instructions for the researcher, in our experience, training time was reduced to about a week.

We empirically define the limit for the robust detection of peaks with ChIPmentation to be 100,000 cells equivalent per antibody reaction, with 10,000 being the absolute limit if background is acceptable. Operational limit for N-ChIP is 10,000 cells equivalent, confirming our previous results. To avoid variations that might be introduced by small errors in the estimation of cell numbers, we arbitrarily doubled this lower cell limit and established ≥20,000 cells as the lower limit for the routine ChIPmentation-seq procedures in S. mansoni. This is a little higher than what has been described in Human cells where good results have been obtained with as little as 5,000 cells.

To improve signal to noise ratio and reduce background in ChIPmentation, it could be useful to increase the washing time (currently 10 min) and speed (currently medium) but we recommend increasing cell number rather than to invest in washing optimization. Using 1 µL of non-immunoprecipitated chromatin for the reference input library production is the best compromise to save space in the IP-Star, experiment time and biological materials when one is restricted by quantity.

This ChIPmentation protocol is not limited to miracidia cells. We also performed this protocol on S. mansoni adult worms and sporocysts. It should be noted that the number of sonication cycles needs to be experimentally determined and adapted for each sample type before proceeding to ChIPmentation experiments.

A new version of the ChIPmentation solution, called µChIPmentation for Histones (Diagenode, Cat. No. C01011011), has also been released recently in order to improve the quality for low-amount samples. This relies on a reduced number of steps, especially during chromatin preparation, and reduced number of tube transfers, in order to avoid DNA loss. It also contains a new protocol to process the non-immunoprecipitated chromatin input samples up to the sequencing step. This new version of µChIPmentation may be a good alternative for experiments on very low cell numbers in the future.

  la Pêche (Ministry of Agriculture and Fisheries) and the French Ministère de l'Education Nationale de la Recherche et de la Technologie (Ministry of Education, Research and Technology, Décret n° 2001-464 du 29 mai 2001 modifiant le décret n° 87-848 du 19 octobre 1987). This includes housing, breeding and care of the mice and hamsters, and animal experimentation. HM holds the official certificate for animal experimentation N° C661101 delivered by the Direction Départementale de la Protection des Populations (Articles R 214-87 à R 214-122 du Code Rural et article R 215-10 ; Arrêté du 19 avril 1988).

Figure 1 .

 1 Figure 1. Comparison of Native-ChIP and ChIPmentation workflows. Biological sampling time depends on the biological model used. Native-ChIP protocol lasts three to four days. A sucrose cushion is used for cell lysis and MNase digestion for the fragmentation step. The immunoprecipitation is done by centrifugation. The whole process is done manually. ChIPmentation protocol lasts two days. Cross-linking is used for cell lysis and sonication for the fragmentation step. The immunoprecipitation, tagmentation and library cleaning are done with the IP-Star.

Figure 2 .

 2 Figure 2. ChIPmentation library profiles for two technical replicates (L and R). Electrophoresis performed on BioAnalyser. Lanes 1-8 from left to right: 10 6 , 10 5 , 10 4 , 5x10 3 , 10 3 , 500, 100, and 0 cells. Libraries used in lanes L1, L3, L5 and L7, and R1, R3, R5 and R7 were subsequently sequenced. Size indicated on the left in base pairs.

Figure 3 .

 3 Figure3. Average metagene profiles over 5,073 plus strand genes for ChIPmentation and N-ChIP. ChIPmentation samples were 10 6 (C1), 10 4 (C3), 10 3 (C5) and 100 cell equivalents (C7). N-ChIP samples were 0.8x10 6 cells (NA), 10 4 (NB) and 10 3 (NC). X-axis: bp upstream, within and downstream of genes. TSS/TES for transcription start and end sites. Y-axis: log(observed/expected). Not all genes contributed to the profiles as only roughly half of the genes show a H3K4me3 peak at the TSS.

Figure 4 .

 4 Figure 4. ChromstaR read count correlations between libraries (lowest 0, highest 1). L and R samples were considered as replicates 1 and 2 for ChromstaR analysis. ChIPmentation 10 6 (C1), 10 4 (C3), 10 3 (C5) and 100 cell equivalent (C7). N-ChIP 0.8x10 6 cells (NA), 10 4 (NB) and 10 3 (NC).

Figure 5 .

 5 Figure 5. Genome browser screen shot of a typical region of the S. mansoni genome with Peakranger chromatin profiles for visual inspection and HMM model-based ChromstaR peak regions (grey underlay). ChIPmentation 10 6 (L1, R1), 10 4 (L3, R3), 10 3 (L5, R5) and 100 cell equivalent (L7, R7), and N-ChIP 0.8x10 6 cells (N-A), 10 4 (N-B) and 10 3 (N-C). Color codes as in previous figures : ChIPmentation 10 6 (L1, R1) in dark red, N-ChIP 0.8x10 6 cells (N-A) in magenta, ChIPmentation 10 4 (L3, R3) in orange, N-ChIP 10 4 (N-B) in dark orange, ChIPmentation 10 3 (L5, R5) in green, N-ChIP 10 3 (N-C) in light green and ChIPmentation 100 cell equivalent (L7, R7) in light blue The region circled in red illustrates higher background for 10 4 cells equivalent in ChIPmentation C3 (orange, replicates L3, R3) than in N-ChIP B (dark orange).

Figure 6 .

 6 Figure 6. Types of inputs generated during ChIPmentation and their associated bioanalyzer profiles. X axis represents the size in base pair, and Y axis represents the fluorescence intensity. Ideal library size is between 150 bp and 500 bp (blue rectangle). (i) 1µL aliquot of one sample chromatin taken before its immunoprecipitation. (ii) and (iii) are from a supplementary sample where immunoprecipitation in the IP-Star is done without antibody. After immunoprecipitation, this supplementary sample contains magnetic beads with the (ii) non-specifically binding chromatin and the supernatant which is the (iii) available chromatin for immunoprecipitation. (ii) No specific binding chromatin which did not deliver a usable library.

Figure 7 .

 7 Figure 7. Bioanalyzer profiles for input libraries performed with different Tn5 and SDS conditions. a) with undiluted Tn5 (Tn5), 10-fold diluted Tn5 (1/10 Tn5) and 100-fold diluted Tn5 (1/100 Tn5). b) 10-fold diluted Tn5 with 0.2% SDS (S55) and 10-fold diluted Tn5 without 0.2% SDS (E55). X axis represents the size in base pair, and Y axis represents the fluorescence intensity. Ideal library size is between 150 bp and 500 bp (blue rectangle).

Figure 8 .

 8 Figure 8. Picture of size separation of PCR products after qPCR for input library quantification by electrophoresis. Electrophoresis was performed through an agarose gel stained with ethidium bromide. Tagmentation was performed at 37°C and 55°C with 10-fold diluted Tn5. Sample legend: (S) SDS, (E) No SDS, (TS) no Tn5 enzyme with 0.2% SDS, (TE) no Tn5 enzyme and no SDS, (T-) qPCR negative control without chromatin. Ideal library size is between 150 bp and 500 bp. Only E samples had the right library size.

Table 1 . ATAC-seq primer indexes and sequences (Buenrostro et al., 2015).

 1

	Index ID	Sequence
	Ad1_noMX:	AATGATACGGCGACCACCGAGATCTACACTCGTCGGCAGCGTCAGATGTG
	Ad2.1_TAAGGCGA	CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGGAGATGT
	Ad2.2_CGTACTAG	CAAGCAGAAGACGGCATACGAGATCTAGTACGGTCTCGTGGGCTCGGAGATGT
	Ad2.3_AGGCAGAA	CAAGCAGAAGACGGCATACGAGATTTCTGCCTGTCTCGTGGGCTCGGAGATGT
	Ad2.4_TCCTGAGC	CAAGCAGAAGACGGCATACGAGATGCTCAGGAGTCTCGTGGGCTCGGAGATGT
	Ad2.5_GGACTCCT	CAAGCAGAAGACGGCATACGAGATAGGAGTCCGTCTCGTGGGCTCGGAGATGT
	Ad2.6_TAGGCATG	CAAGCAGAAGACGGCATACGAGATCATGCCTAGTCTCGTGGGCTCGGAGATGT
	Ad2.7_CTCTCTAC	CAAGCAGAAGACGGCATACGAGATGTAGAGAGGTCTCGTGGGCTCGGAGATGT
	Ad2.8_CAGAGAGG	CAAGCAGAAGACGGCATACGAGATCCTCTCTGGTCTCGTGGGCTCGGAGATGT
	Ad2.9_GCTACGCT	CAAGCAGAAGACGGCATACGAGATAGCGTAGCGTCTCGTGGGCTCGGAGATGT
	Ad2.10_CGAGGCTG CAAGCAGAAGACGGCATACGAGATCAGCCTCGGTCTCGTGGGCTCGGAGATGT
	Ad2.11_AAGAGGCA CAAGCAGAAGACGGCATACGAGATTGCCTCTTGTCTCGTGGGCTCGGAGATGT
	Ad2.12_GTAGAGGA CAAGCAGAAGACGGCATACGAGATTCCTCTACGTCTCGTGGGCTCGGAGATGT
	Ad2.13_GTCGTGAT	CAAGCAGAAGACGGCATACGAGATATCACGACGTCTCGTGGGCTCGGAGATGT
	Ad2.14_ACCACTGT	CAAGCAGAAGACGGCATACGAGATACAGTGGTGTCTCGTGGGCTCGGAGATGT
	Ad2.15_TGGATCTG	CAAGCAGAAGACGGCATACGAGATCAGATCCAGTCTCGTGGGCTCGGAGATGT
	Ad2.16_CCGTTTGT	CAAGCAGAAGACGGCATACGAGATACAAACGGGTCTCGTGGGCTCGGAGATGT
	Ad2.17_TGCTGGGT CAAGCAGAAGACGGCATACGAGATACCCAGCAGTCTCGTGGGCTCGGAGATGT
	Ad2.18_GAGGGGTT CAAGCAGAAGACGGCATACGAGATAACCCCTCGTCTCGTGGGCTCGGAGATGT
	Ad2.19_AGGTTGGG CAAGCAGAAGACGGCATACGAGATCCCAACCTGTCTCGTGGGCTCGGAGATGT
	Ad2.20_GTGTGGTG CAAGCAGAAGACGGCATACGAGATCACCACACGTCTCGTGGGCTCGGAGATGT
	Ad2.21_TGGGTTTC	CAAGCAGAAGACGGCATACGAGATGAAACCCAGTCTCGTGGGCTCGGAGATGT
	Ad2.22_TGGTCACA	CAAGCAGAAGACGGCATACGAGATTGTGACCAGTCTCGTGGGCTCGGAGATGT
	Ad2.23_TTGACCCT	CAAGCAGAAGACGGCATACGAGATAGGGTCAAGTCTCGTGGGCTCGGAGATGT
	Ad2.24_CCACTCCT	CAAGCAGAAGACGGCATACGAGATAGGAGTGGGTCTCGTGGGCTCGGAGATGT

Table 3 . General statistics on ChIPmentation libraries.

 3 All libraries were downsampled to 3.8 M uniquely aligned reads.

	Pos Equivalent	Equivalent	Amplification	qBit	Read pairs Uniquely	After	%	% to
		miracidia	cells	cycles	DNA HS		aligned	deduplication	unique	keep for
					ng/µl				non-dup	3.8 M
	L1	10,000	1,000,000	17	23.2	5,728,173	7,969,894	6,775,525	0	0
	R1	10,000	1,000,000	18	21.2	8,388,53	13,469,770	10,007,402	0	0
	L3	100	10,000	18	25.2	12,537,243 19,157,585	14,417,614	0	0
	R3	100	10,000	19	30.4	8,665,837 13,838,684	8,409,793	0	0
	L5	10	1,000	19	13.8	11,436,716 17,920,411	8,664,467	0	0
	R5	10	1,000	20	19.3	11,109,380 17,854,360	7,214,153	0	0
	L7	1	100	22	22.0	11,662,120 15,737,202	4,095,477	0	0
	R7	1	100	22	11.9	10,566,906 15,221,913	3,827,034	0	0

Table 2 . Cell number equivalents and library amplification cycles for ChIPmentation. Position Equivalent Miracidia Equivalent cells Amplification cycles L1-8

 2

	Amplification cycles	Sequenced
	R1-8

Table 6 . Peakcalling with HMM-based ChromstaR before and after merging adjacent peaks (in bold). Cell equivalents 1,000,000 10,000 1,000 100 ChromstaR bin 1000, step 500, post prob 0.999 All merged All Merged All Merged All Merged

 6

	ChIPmentation	13,565	6,682 7,194	5,504 3,137	2,876 10,665	10,522
	N-ChIP	6,262	6,186 7,058	6,922 5,296	5,129

Table 5 .

 5 Optimization of peakcalling with Peakranger. Number of peaks identified for each condition. ChIPmentation on top. In bold: 1000 bp was selected as the best extension length and applied to N-ChIP data below. N-ChIP A is for 0.8x10 6 cells.

	cell	1,000,000		10,000		1,000			100
	equivalents
		ChIPmentation		ChIPmentation	ChIPmentation		ChIPmentation
	Peakranger	L1	R1	N-ChIP A L3	R3	N-ChIP B L5	R5		N-ChIP C L7	R7
	read
	extension
	length in bp
	100	6,871	6,767		625	424		200	370		4,732	5,683
	300 10,369	9,545		2,125	1,211		499	635		3,944	5,264
	600 10,581	9,924		3,513	2,823		610	813		3,197	3,816
	1,000	9,632	9,835	8,320	4,033	2,749	3,888	695	811	131	3,098	3,416
	1,500	9,901	9,426		3,315	2,925		847	837		4,019	3,526
	2,000	9,067	8,353		3,052	2,631		626	693		4,336	3,897

Table 4 .

 4 General statistics on N-ChIP libraries. When possible, libraries were downsampled to 3.8 M uniquely aligned reads.

	Pos Equivalent	Equivalent	Amplification	Read pairs Uniquely	After	% unique	%
		Miracidia	cells	cycles		aligned	deduplication	non-dup	downsampled
	A	8,000	800,000	14 13,129,021 11,299,195	10,763,378	41 %	35 %
	B	100	10,000	14 21,415,343	5,409,960	2,477,749	6 %	100 %
	C	10	1,000	14 29,857,139 12,170,093	6,718,570	11 %	57 %

Wellcome OpenResearch 2022, 7:133 Last updated: 12 APR 2022

Acknowledgements

This study is set within the framework of the Laboratoire d'Excellence (LabEx) TULIP (ANR-10-LABX-41), with the support of LabEx CeMEB, an ANR Investissements d'avenir program (ANR-10-LABX-04-01) and the Environmental Epigenomics Core Service at IHPE. We thank S.A.S. ParaDev and Julien Portela for livers of infected hamsters. We also thank the Bio-Environment platform (University of Perpignan Via Domitia) and Jean-François Allienne for support in library preparation and sequencing. We have obtained permission from these colleagues to be acknowledged.

Grant information: This work was supported by Wellcome [107475/Z/15/Z]. This research was also funded by the French National Agency for Research (ANR) [grant ANR-17-CE12-0005-01] CHRONOGET and the CNRS (French National Centre for Research). CL is a PhD candidate who receives funding from the Occitanie Region (France).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

Underlying data NCBI SRA: Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni. Accession number: PRJNA816041, https://identifiers.org/ ncbiprotein:PRJNA816041

This project contains the Fastq data of the ChIPmentation libraries.

Zenodo: Supporting information for "Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni", https://doi.org/10.5281/zenodo. 6375548 [START_REF] Grunau | Supporting information for "Automated ChIPmentation procedure on limited biological material of the human blood fluke Schistosoma mansoni[END_REF] This project contains the following underlying data:

-20200612-1_Report.pdf (qPCR report for inputs 1µL and available chromatin ("Row7"), see Figure 6)

-20200919-1_Report.pdf (qPCR report for testing SDS after Tn5, see Figure 7B and Figure 8) -20200921-1_Report.pdf (qPCR report for comparing inputs with enzyme of Diagenode kit and our protocol with other enzyme Tn5) -CG_Ro_1_High Sensitivity DNA Assay_DE13805677_2019-06-20_09-10-48.pdf (DNA assay file underlying Figure 2) -CG_Ro_2_High Sensitivity DNA Assay_DE13805677_2019-06-20_10-12-12.pdf (DNA assay file underlying Figure 2) -gel qpcr test input sds_01.Tif Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).


Identification paramétrique des propriétés thermophysiques d'un mur en brique ancienne : Approche expérimentale et numérique

Emilio SASSINE 1,2

RÉSUMÉ

L'objectif de ce travail est d'améliorer les connaissances sur la caractérisation thermophysique de l'enveloppe de bâtiment en particulier les murs en brique utilisés dans les constructions anciennes du Nord de l'Europe. Une double démarche à la fois expérimentale et numérique est réalisée pour identifier les propriétés thermophysiques du mur (à savoir la capacité thermique ρCp et la résistance thermique R). L'optimisation de ces paramètres est basée sur l'algorithme de type Levenberg-Marquardt via l'outil de simulation Comsol multiphysics. La validation de l'algorithme a été réalisée ainsi que son utilisation dans différentes conditions d'ambiances (régime harmonique, et régime aléatoire quelconque).

Mots Clés: mur, brique, thermophysique, expérimentale, numérique, optimisation..

NOMENCLATURE

Symboles

Cp

chaleur spécifique [J.kg -1 .K

INTRODUCTION

Pour évaluer la performance thermique globale des bâtiments et fournir un outil de décision précieux pour la conception de bâtiments économes en énergie, certains attributs de construction doivent être pris en considération. Ces attributs comprennent le coefficient de transmission thermique U, l'absorptivité des surfaces extérieures, le coefficient d'ombrage (SC) des parois vitrées, le coefficient d'ombrage extérieur des dispositifs d'ombrage, le coefficient gain de chaleur solaire (SHGC) des parois vitrées, l'orientation du bâtiment, et le ratio des fenêtres par rapport aux murs (WWR) [1].

De nombreuses formulations de transfert de chaleur utilisent l'état d'équilibre des modèles de réseau thermique qui ne nécessitent que les conductivités thermiques efficaces ou bien les valeurs associées de U et R de la paroi. Cette approche est toujours adoptée dans de nombreuses études de recherche en utilisant soit des dispositifs expérimentaux de laboratoire ou bien des mesures in situ. Le thermo-fluxmètre est utilisé dans des conditions de laboratoire en régime permanent pour déterminer la conductivité thermique des matériaux de construction [2]. La norme ISO 9869 nécessite un capteur de flux de chaleur et deux thermocouples pour déterminer la résistance thermique des murs à l'aide de mesures in situ. Une certaine amélioration a été proposée par Rasooli et al. [3] qui a suggéré d'utiliser un capteur de flux de chaleur supplémentaire, opposée au premier pour réduire la durée des mesures et améliorer la précision des résultats. Cet article examine l'utilisation de la méthode inverse pour l'identification paramétrique des propriétés thermophysiques d'un mur expérimental de briques (à savoir la conductivité thermique équivalente λ et la capacité thermique équivalente ρCp), en comparant et en minimisant la différence entre les résultats issus des essais expérimentaux et ceux qui résultent du modèle numérique. La paroi en a été particulièrement choisie car elle constitue l'une des particularités architecturales des maisons anciennes (avant 1948) du Nord de l'Europe. D'un point de vue littérature, de nombreux travaux ont étudié la performance thermique [4-6] l'isolation potentielle de ces parois [7]. Walker et al. [8] ont étudié le comportement thermique et les transferts d'humidité dans le cas de l'isolation thermique appliquée à l'intérieur sur des murs en briques historiques en utilisant des sondes de température et d'humidité. Plusieurs études ont également évalué les performances thermiques des briques de construction. Les résultats montrent que les propriétés thermiques des briques présentent des variations importantes au sein d'une même zone géographique. Cela peut être clairement observé dans les travaux de Touré et al. [9] qui ont étudié les briques de terre stabilisée sous pression (CSEB) produites par huit briqueteries au Sénégal et ont constaté que la conductivité thermique varie entre 0.66 et 0.85 W.m -1 .K -1 et que la capacité thermique varie entre 939 et 1170 J.kg -1 .K -1 . Certaines études ont étudié l'effet de modifier la composition de la brique sur ses performances thermiques telles que le travail de Kazmi et al.

[10] qui a évalué les briques écologiques incorporant des boues de déchets de verre et Raefat et al.

[11] qui a démontré l'intérêt de substituer les briques de terre cuites par des briques de liège granulaire.

Démarche expérimentale

Le mur expérimental étudié est un mur de briques massives semblables aux murs des vieilles maisons du Nord de la France ; il a une épaisseur totale de 34 cm. Les blocs de briques de pleines de dimensions (6 cm x 11 cm x 22 cm) sont reliés par des joints de mortier [4][5][6][7].

La paroi est soumise à une ambiance contrôlée de 1m 3 environ, sur une face, régulée par un radiateur alimenté par un fluide à température contrôlée variant dans une plage allant de 5 °C à 60 °C.

Hypothèses : paroi homogène, transfert de chaleur unidirectionnel

Le mur en maçonnerie constitué de blocs de briques et les joints de mortier sont supposés former une paroi homogène ayant une conductivité thermique équivalente λ et une capacité calorifique équivalente ρCp [9].

Les surfaces latérales de la paroi ont été isolées par 20 cm de laine de roche, ce qui permet de supposer un transfert de chaleur unidirectionnel. Le caisson thermique a également été isolé pour réduire les pertes de chaleur. Le problème se réduit donc à un transfert de chaleur unidirectionnel à travers une paroi homogène équivalente ayant des propriétés thermiques équivalentes λ et ρCp.

Conditions aux limites des profils de température

Deux types de conditions aux limites ont été étudiés : le cas harmonique et le cas du profil de température aléatoire.

Le profil de température du fluide frigorigène circulant à travers le radiateur est prédéfini par un ensemble de valeurs générées par le programme LabVIEW. Ceci génère dans la zone de chauffage une température Ti, sur la face interne de la paroi une température Twi, et sur la face extérieure de la paroi, une température Two. Ces valeurs ont été utilisées comme conditions aux limites dans le modèle numérique.

Fi et Fo sont respectivement les flux de chaleur intérieur et extérieur au niveau des parois intérieure et extérieure.

Dans l'étude expérimentale (Fig. 2), l'intérieur du caisson thermique représente l'environnement extérieur, qui varie de façon aléatoire, et l'ambiance du laboratoire (en dehors du caisson) représente l'ambiance intérieure où la température varie légèrement environ autour de 22°C comme représenté sur les figures 3 et 4.

Figure 1-Conditions aux limites du mur

Le dispositif de chauffage impose dans le caisson une température Ti(t) qui sollicite la surface intérieure de la paroi pour atteindre une température Twi(t) par un transfert de chaleur convectif, cette chaleur traverse la paroi par conduction et, en complément des sollicitations ambiantes, la surface externe de la paroi est à une température Two(t). La température de l'air ambiant dans le laboratoire a une valeur To(t). Les quatre profils de températures Ti(t), Twi(t), Two(t) et To(t) sont représentés sur les figures 3-4 pour les deux cas étudiés.

Dans le cas sinusoïdal (signal 1), le profil de température du fluide caloporteur a une forme sinusoïdale de période 24 heures, une température moyenne de 40 °C et une amplitude de 20 °C, les données de température et de flux de chaleur ont été enregistrées à un intervalle de temps régulier de 10 minutes grâce à une centrale d'acquisition de données (Fig. 3).

Pour le profil de température aléatoire, deux profils de température différents ont été générés et les données de température et de flux de chaleur ont été enregistrées à des intervalles de temps réguliers (10 mn pour le signal 2) comme représenté sur la Fig. 4.

                                  i wi o wo F T pRC ch pRC sh R pC pRC sh pC R pRC ch F T
Où, R est la résistance thermique de la couche considérée et C sa capacité thermique surfacique.

En prenant en considération les coefficients d'échanges convectifs hi sur la face intérieure de la paroi et ho sur la face extérieure de la paroi, la matrice de transfert globale reliant [To, Fo]

à [Ti, Fint] devient:                                       1 0 1 1 1 0 1 1 e i h pRC ch pRC sh R pC pRC sh R pRC ch h
Dans les problèmes de transfert de chaleur directs, les paramètres R et C sont connus et les deux conditions aux limites (températures et/ou flux de chaleur) sont les inconnues à déterminer.

Description de la méthode d'optimisation (méthode inverse)

Trois paramètres physiques ont été optimisés dans deux calculs d'optimisation différents en utilisant l'algorithme de Levenberg-Marquardt (LMA). Dans un premier temps, les propriétés thermiques paroi (λ et ρCp) ont été optimisées, et ensuite le coefficient d'échange de chaleur par convection interne hi a été optimisé séparément.

L'algorithme LMA est normalement utilisé pour résoudre des problèmes de moindres carrés non-linéaires. Il a été choisi pour son temps de simulation rapide et sa fiabilité.

a-Méthodologie pour λ et ρCp

Trois paramètres ont été ciblés en utilisant la méthode inverse. Dans un premier temps, les propriétés thermiques spécifiques à la paroi expérimentée ont été déterminés en utilisant comme conditions aux limites les températures des surfaces intérieure (Twi) et extérieure (Two) de la paroi et en minimisant la différence entre le flux de chaleur mesuré sur la face interne de la paroi Fi_exp et le flux de chaleur obtenu numériquement sur la même face Fi_num pour la fonction objectif Fi. Dans ce cas, deux variables de contrôle ont été optimisés simultanément grâce à un algorithme inverse d'optimisation utilisé dans le logiciel COMSOL Multiphysics, la conductivité thermique λ et la capacité calorifique ρCp.

Les paramètres inconnus λ et ρCp sont liés à la résistance thermique équivalente R et la capacité thermique surfacique équivalente C par les équations suivantes:

;

. .

b-Méthodologie pour hi

Par la suite, la détermination du coefficient d'échange de chaleur convectif interne hi est effectuée en utilisant les propriétés de la paroi déjà déterminées dans la première phase, et en utilisant comme conditions aux limites de la température ambiante du caisson Ti et la température de la face interne de la paroi (Twi). Le flux de chaleur convectif est donc donné par:

  wi i i i T T h F  
La valeur optimale de hi est donc la valeur qui minimise la différence entre le flux de chaleur mesuré sur la face interne de la paroi et le flux de chaleur obtenu numériquement sur cette même face.

RÉSULTATS Caractérisation thermique des parois et détermination du coefficient d'échange convectif

Les résultats sont obtenus par méthode inverse, en fonction des différents régimes étudiés. Trois profils de flux de chaleur ont été comparés dans les figures. 6-8; a) le flux de chaleur expérimental qui a été mesuré par le fluxmètre thermique interne : Fi_exp, b) le flux de chaleur interne numérique pour déterminer les propriétés thermiques optimisées de la paroi (λ et ρCp) : Fi_num (λ, ρCp), c) le flux de chaleur interne numérique pour déterminer le coefficient d'échange de chaleur interne optimisé hi : Fi_num (hi). Cela signifie que Fi_num(λ, ρCp) et Fi_num(hi) proviennent de deux simulations différentes, où la première simulation est effectuée pour déterminer les propriétés thermiques optimales de la paroi (λ et ρCp), et la deuxième pour déterminer le coefficient d'échange convectif hi, à l'intérieur du caisson thermique de chauffage.

Les résultats (Fig. 56) montrent que les graphiques numériques et expérimentaux de la fonction objectif Fi correspondent parfaitement dans les deux cas étudiés (le cas sinusoïdal, et les cas aléatoire) et pour les deux cas d'optimisation (Fi_num (λ, ρCp) et Fi_num (hi)).

CONCLUSION ET REMARQUES

Une méthode de détermination des propriétés thermophysiques des éléments de construction à partir des mesures de température et de flux de chaleur en laboratoire en utilisant la méthode inverse a été développée pour deux types de conditions aux limites différentes : le cas harmonique et le cas du profil de température aléatoire.

Figure 1 :

 1 Figure 1 : montage expérimental. La figure 1 illustre le dispositif expérimental (mur et caisson thermique), ainsi qu'une vue en coupe transversale schématique du dispositif et de ses composants principaux. Deux thermocouples (type T) fournissent les températures de surfaces intérieure et extérieure de la paroi et deux fluxmètres mesurent le flux traversant la paroi au niveau de sa surface intérieure et extérieure. Tous les capteurs sont reliés à une centrale d'acquisition de données.

Figure 2 -Figure 3 -

 23 Figure 2-Conditions aux limites de température pour le signal harmonique 1

  l'épaisseur totale de la paroi.

Figure 5 -

 5 Figure 5-Flux de chaleur internes numérique et expérimental dans le cas du signal 1 pour les propriétés thermiques optimisées λ et ρCp du mur et pour le coefficient d'échange de chaleur interne optimisé hi

Figure 7 -

 7 Figure 7-Flux de chaleur externe numérique et expérimental du signal 1 pour les propriétés thermiques optimisées λ et ρCp

  , Yassine CHERIF 1 , Emmanuel ANTCZAK 1

	¹ Univ. Artois, IMT Lille Douai, Junia, Univ. Lille, ULR 4515, Laboratoire de Génie Civil et géo-Environnement
	(LGCgE), F-62400 Béthune, France
	² Université Libanaise, Département d'Architecture, Beyrouth, Liban

Une identification paramétrique des propriétés thermophysiques d'un mur expérimental en brique (à savoir la conductivité thermique équivalente λ et la capacité thermique équivalente ρCp) a été réalisée en comparant et en minimisant la différence entre les résultats issus des essais expérimentaux et ceux résultants du modèle numérique. L'étude réalisée a montré des résultats satisfaisants pour les deux paramètres λ et ρCp. Le coefficient d'échange convectif interne hi a également été déterminé avec succès pour les deux essais avec des valeurs proches (6,13 ; 6,03 W / m 2 .K).

Les résultats de λ et ρCp ont également été validés en comparant les flux de chaleur externes mesurés et numériques pour les propriétés thermiques optimisées.

On peut ainsi conclure que la méthode inverse peut apporter des résultats satisfaisants dans la détermination des paramètres thermiques statiques (λ), mais aussi dynamiques (ρCp) pour des conditions aux limites aléatoires. Par la suite, on peut envisager d'utiliser cette approche sur des murs existants à partir d'un dispositif expérimental assez simple composé de deux thermocouples et d'un fluxmètre.

RÉFÉRENCES