-C Baietto

V Georgenthum

T Jailin

N Tardif

P Chaudet

J Desquines

M Coret

M.-C Baietto

Measuring both thermal and kinematic full-fields using a single CMOS camera during high temperature tests

Keywords: Full-field measurements, DIC, Thermography, High temperatures

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

Introduction

Full-field measurement techniques have been increasingly used these last years in experimental mechanics. Access to full-field data is indeed of great interest for many purposes, such as model validation, identification, or even just to help evidence activated mechanisms during experiments. In numerous domains, both kinematic and thermal fields are sought to study temperature dependent phenomena (e.g. nuclear safety, manufacturing processes, aircraft engines, etc.). Digital image correlation (DIC) is a valuable method to measure the kinematic field. It is now a mature technique, used from micro scale [START_REF] Salvati | An analysis of macro-and microscale residual stresses of Type I, II and III using FIB-DIC microring-core milling and crystal plasticity FE modelling[END_REF][START_REF] Orozco-Caballero | Highresolution digital image correlation study of the strain localization during loading of a shot-peened RR1000 nickel-based superalloy[END_REF] to macro scale [START_REF] Jaafari | Effect of early age drying shrinkage on the seismic response of RC structures[END_REF] in many industrial fields. Regarding the thermal field, the most common way to measure it consists in using infrared (IR) cameras [START_REF] Artozoul | Extended infrared thermography applied to orthogonal cutting: Mechanical and thermal aspects[END_REF][START_REF] Harizi | Mechanical damage assessment of glass fiber-reinforced polymer composites using passive infrared thermography[END_REF]. Based on Planck's law, these devices convert the amount of radiation they capture in temperature data. Less common but very efficient to measure the temperature field above about 700 °C, the near-infrared (NIR) ther-20 mography technique [START_REF] Rotrou | Near infrared thermography with silicon FPA -Comparison to MWIR and LWIR thermography[END_REF][START_REF] Campello | Validation of a multimodal set-up for the study of zirconium alloys claddings' behaviour under simulated LOCA conditions[END_REF][START_REF] Zhang | An innovative technique for real-time adjusting exposure time of silicon-based camera to get stable gray level images with temperature evolution[END_REF] enables the thermal field to be measured using a standard CMOS or CCD camera.

Although IR thermography and DIC are two well mastered techniques, their combination is not straight-25 forward since they need radically different conditions to work efficiently: DIC requires a high random gradient of gray level in the images while IR thermography needs on the contrary a well-known and homogeneous emissivity on the sample surface. Furthermore, DIC 30 is based on the optical flow conservation assumption in the images, which may not be respected if the temperature varies during the test.

Several methods have been proposed by research

35 teams to overcome these difficulties. Some authors for instance used two different optical systems (i.e. a standard and a IR camera) to compute the kinematic and thermal fields [START_REF] Bodelot | Experimental setup for fully coupled kinematic and thermal measurements at the microstructure scale of an AISI 316L steel[END_REF][START_REF] Chrysochoos | Local energy analysis of high-cycle fatigue using digital image correlation and infrared thermography[END_REF]. But time and spatial interpolations remained necessary at the end to link the two fields, which are obtained from different devices. Furthermore, no efficient solution was proposed to measure these two fields at the exact same location without having the above mentioned emissivity issue. Other authors proposed to extend the DIC technique to infrared cameras [START_REF] Maynadier | One-shot Measurement of Thermal and Kinematic Fields : InfraRed Image Correlation (IRIC)[END_REF][START_REF] Charbal | Spatiotemporal regularization for digital image correlation: Application to infrared camera frames[END_REF]. However, this method needs a prior calibration step that one may want to avoid not to interfere with the material microstructure. Besides, IR cameras have often a poor spatial resolution and are relatively costly compared to standard CMOS or CCD cameras. Last but not least, this strategy needs to have both access to a DIC source code and to have some knowledge in DIC algorithms, which is not very convenient for a wide use in laboratories and industries.

An additional difficulty comes in case of fast thermal transients where the temperature is even more difficult to measure. Thermocouple measurements are not accurate during fast thermal transients due to their inertia effect [START_REF] Jailin | Experimental study and modelling of the phase transformation of Zircaloy-4 alloy under high thermal transients[END_REF]. The use of a pyrometer may also be complicated if a speckle pattern is present on the sample since the the emissivity will be dependent on the pattern. This paper proposes a procedure to measure both kinematic and thermal fields using a single standard camera. The thermal field is measured by NIR thermography, and the kinematic field by DIC. A procedure is detailed to overcome the aforementioned difficulties.

The key point lies in the calibration of the radiometric model used to compute the thermal field, and in a weak coupling between DIC and NIR thermography. A correction of contrast and brightness in the DIC algorithm was also used to manage the non conservation of the optical flow in the images. The method is validated on a virtual test case before being used on real experiments performed with a stereo setup [START_REF] Jailin | Thermo-mechanical behavior of Zircaloy-4 claddings under simulated post-DNB conditions[END_REF]. In the end, this method aims to be widely usable when dealing with high temperature mechanical testing since no specific DIC or thermography software are needed. Moreover, it solely requires one standard CMOS or CCD camera.

The paper is organized as follow: first, some reminders about DIC and NIR thermography are given and the proposed method is detailed. Then, a virtual test case is presented to validate the method. Section 4 details an application of the method to real experiments performed at high temperature and with high heating rates. As an example, the method is applied here using stereo-correlation measurements. The paper finishes with some discussions and conclusions.

Methods

Digital image correlation (DIC)

Widely used nowadays, the digital image correlation (DIC) technique enables the kinematic full-field to be measured on a sample surface [START_REF] Sutton | Determination of displacements using an improved digital correlation method[END_REF][START_REF] Lucas | An iterative image registration technique with an application to stereo vision[END_REF]. This method 95 is based on the optical flow conservation in the images during a test. When analyzing a reference image f and a deformed image g, the optical flow conservation equation reads:

f (x) = g(x + u(x)), (1)
where x denotes a pixel vector and u(x) the displace-100 ment field which enables g to match f . f and g are vectors containing the digital gray levels of the reference and deformed image, respectively.

The displacement field is usually found using a 105 Gauss-Newton algorithm, minimizing the following cost function:

η 2 (u) = ∑ Ω [ f (x) -g(x + u(x))] 2 , ( 2
)
where Ω is the region of interest (ROI).

This problem is ill-posed by nature and cannot be 110 solved without regularization. In this work we used a global (or finite element) approach [START_REF] Besnard | Finite-element" displacement fields analysis from digital images: application to Portevin-Le Châtelier bands[END_REF][START_REF] Réthoré | On the Use of NURBS Functions for Displacement Derivatives Measurement by Digital Image Correlation[END_REF], constraining the kinematic basis with finite element shape functions. The displacement field is thus described on a finite element mesh such as:

115 u(x) = ∑ n a n .Ψ n (x). (3)
where a n and Ψ n are the amplitudes and the shape functions, respectively. a n are the kinematic unknowns of the DIC problem.

It is worth reminding that 3D-surface displacement 120 field measurements are possible if more than one camera is used. This needs a prior calibration procedure of the optical system [START_REF] Sutton | Image Correlation for Shape, Motion and Deformation Measurements[END_REF][START_REF] Grédiac | Full-field measurements and identification in solid mechanics[END_REF], which will be discussed in the following.

DIC at high temperatures 125

Performing DIC at high temperatures is not straightforward because the optical flow may not remain constant due to changes in radiation intensity. To overcome this problem some authors proposed to use a combination of blue bandpass filters and blue LED 130 to avoid the influence of thermal radiations that are at higher wavelengths [START_REF] Grant | Hightemperature strain field measurement using digital image correlation[END_REF][START_REF] Archer | Global digital image correlation up to very high temperatures with grey level corrections[END_REF]. However, using this method precludes to perform both DIC and thermography with the same camera.

In this work the open source DIC software UFreckles [23] was used. In order to manage the non-conservation of the optical flow a correction of contrast and brightness is performed in all the elements at each time step of the DIC process. This correction is achieved using the Zero-Normalized Sum of Squared Differences (ZNSSD) matching criterion implemented in UFreckles. In all images, the gray levels in each element are affinely transformed so that they have a zero mean value and a unit standard deviation. The minimization problem being over-constrained by the finite element regularization, the DIC algorithm still succeeds to capture all the kinematic on the sample despite the additional degrees of freedom. This will be demonstrated on a virtual test case in the following 150 section.

Calibration of the optical system is required to derive 3D-surface full-field from stereo correlation. To avoid chromatic aberrations during the tests, the focus and the 155 calibration must be performed at the same wavelength as at high temperatures. To do so, the sample, which is used to calibrate the stereovision model, was lighted during the calibration process at room temperature with a near-infrared LED at a wavelength of 860 nm. Nearinfrared bandpass filters at the same wavelength were mounted on the cameras to take the calibration images. The 860 nm wavelength was chosen to be the most excited one on Zircaloy-4 around 850 °C, which are the conditions of our study. Doing so, the optical system was calibrated at room temperature prior to the test in optical conditions representative of high temperatures.

Near-infrared thermography (NIRT)

The near-infrared thermography (NIRT) technique [START_REF] Rotrou | Near infrared thermography with silicon FPA -Comparison to MWIR and LWIR thermography[END_REF][START_REF] Campello | Validation of a multimodal set-up for the study of zirconium alloys claddings' behaviour under simulated LOCA conditions[END_REF] is used to compute the temperature full-field on the surface of an object using a standard CMOS camera. This technique is based on the fact that each object emits a certain amount of spectral radiation depending on its temperature. Using Planck's law and Wien's approximation it is possible to obtain a direct relation between the digital level of an image I (i.e. the gray level of the pixels) and the temperature T such as:

T (x) = K 1 ln K 2 I(x) + 1 , (4)
where x denotes a pixel vector and K 1 et K 2 are two constants defined by:

K 1 = h.c k B .λ e (5)
and

K 2 = 2.ε(λ e ).c 2 .λ -4 e .W (λ e ).t i .K int , (6)
where h is the Planck's constant, c is the light speed, 180 k B is the Boltzmann's constant, λ e is the effective wavelength, ε is the emissivity, W is the spectral response of the acquisition chain, t i is the integration time and K int is an integration constant. More details on the construction of Equation ( 4) can be found in [START_REF] Rotrou | Near infrared thermography with silicon FPA -Comparison to MWIR and LWIR thermography[END_REF].

185

Assuming homogeneous and stable emissivity, and using the notion of effective wavelength K 1 and K 2 can be set as constants. Using a calibration point on the images (i.e. a point where the temperature is known 190 on the images) it becomes possible to identify the two constants and compute the thermal profile on the surface of the object of interest. Some points need nonetheless to be discussed: 195 (i) the temperature needs to be high enough to enable standard cameras to be sensitive to the spectral radiations. This minimal temperature value depends on the material and is about 700 °C for metals.

200

(ii) the radiometric model depends on the emissivity of the object. To avoid errors, the emissivity must remain constant during the whole test and be homogeneous in the region of interest (ROI) where the temperature full-field is computed. This may be an issue considering 205 a speckle pattern deposited over the sample to perform DIC. A method is proposed in the following to tackle this problem.

(iii) the radiometric model described in Equation ( 4) 210 is based on the notion of effective wavelength λ e , which may not be very accurate if a large range of temperature is measured. In this case it is possible to improve the result by linking the effective wavelength to the temperature, leading to the extended effective 215 wavelength, as proposed in [START_REF] Rotrou | Near infrared thermography with silicon FPA -Comparison to MWIR and LWIR thermography[END_REF][START_REF] Saunders | General interpolation equations for the calibration of radiation thermometers[END_REF]. It is also possible to use a bandpass filter to limit the wavelength spectrum measured by the camera. This latter option was chosen in the present work. discussed at the end of this work.

Manage the heterogeneous emissivity on the sam-

ple to compute the thermal field As already mentioned, the speckle pattern leads to a heterogeneous emissivity onto the sample surface. In order to use a single emissivity value in the radiometric model only one tint of paint must be used in the images to compute the thermal field. The tests are performed within an enclosure where the atmosphere is controlled with an argon flush to avoid oxidation of the sample. However, little oxidation can still occur at high temperature. An oxide layer can grow during the tests between the sample outer surface and the paint. Since the oxide layer is black, the emissivity of the white paint may be significantly impacted by the oxide growth. It is therefore preferable to perform the NIR 240 thermography on the black paint not to be affected by emissivity changes. A new parameter P is then defined to identify the pixels corresponding to the black paint. It is used as a threshold to select the brightest pixels in the images since the black paint has an highest 245 emissivity. Only these pixels were used to identify the radiometric model and compute the thermal field.

One may notice that if the temperature is not uniform on the sample using only the P% brightest pixel may not be sufficient to ensure that only the black paint is used through the radiometric model. Gray or white painted areas at a higher temperature may have a higher intensity in the images than black paint at lower temperatures. Errors would then appear because multiple emissivities would be used with the same radiometric model. To overcome this, a weak coupling between the DIC and the NIRT was developed. Two assumptions were first formulated: 260 (i) the temperature is averaged within each element of the correlation mesh;

(ii) the P% of the brightest pixels within an element correspond to the black paint (only these pixels are then used to compute the thermal field).

These two assumptions enable computing accurately the thermal field at each time step on the deformed correlation mesh without being affected by the heterogeneous emissivity of the sample surface. Yet, the value of the parameter P needs to be defined. If all the pixels in the elements are used, or if the parameter P is too high, the average emissivity of each element would depend on the fraction of black and white paints in each 275 element. This would induce errors in the temperature field since the radiometric model must be used with a single emissivity. On the contrary if not enough pixels are used, the influence of noise in the images becomes more important. In our case, a value ranging from 280 5% to 20% can be used without visible differences to obtain good results. This range depends on the number of pixels within the elements and of the speckle pattern distribution. A value of 10% was used in this study.

285

This method leads to a very smooth thermal field, because non-affected by the speckle pattern. It is also a good point to notice that no interpolation was necessary between the kinematic and thermal fields, both being computed on the same mesh.

Description of the test case

In order to validate the method, a virtual test case, reproducing a tensile experiment at high temperature, was setup. The flow chart presented in Figure1 details 295 the different steps of the virtual test case.

The reference image of the virtual sample (cf. Figure 2) was obtained from one of the real experiments presented in the next section.

Fifty deformed images were generated by adding a 300 virtual strain field and a virtual temperature field on the reference image. The tensile strain field was applied with strain values ranging from 0 to 18 %. The y-displacement field applied is given in Equation [START_REF] Campello | Validation of a multimodal set-up for the study of zirconium alloys claddings' behaviour under simulated LOCA conditions[END_REF]. The x-displacement field was applied using a Poisson's 305 ratio of 0.35. The y-displacement field at the last step can be observed in Figure 2.

U y (t, y) = y. exp t 10 , (7)
where t and y denote the time step and the y-coordinate of the pixel, respectively.

310

The virtual temperature evolution was applied onto the deformed images using the radiometric model described in Equation ( 4) and the constant values given in Table 1. The temperature evolution applied on the sample is described in Figure 3. An oscillation of 70 °C was

(x, y) = 1 - v u u u t (x -x 0 ) 2 + (y -y 0 ) 2 y M 2 2 + x M 2 2 , ( 9
)
where t is the time step, (x, y) is the pixel position, 320 (x 0 , y 0 ) is the center of the image, (x M , y M ) is the image resolution. T sample (t, x, y) is the temperature of the sample at the time t and pixel position (x, y). δ and ϕ are described in Equations ( 8) and ( 9), respectively.

After the generation of the virtual deformed images, a random noise (white noise with a standard deviation 325 of 1.8 gray level) was added in all the images to reproduce real experimental conditions. The values of the two constants used in the radiometric model were finally forgotten and will have to be re-identified.

330

The temperature is supposed to be known at a virtual thermocouple located at the boundary of the sample (cf. Figure 2). In some cases, pyrometric measurements are necessary no to be affected by the inertia effect of the thermocouples [START_REF] Jailin | Experimental study and modelling of the phase transformation of Zircaloy-4 alloy under high thermal transients[END_REF]. This requires to know the 335 emissivity of the spot area pointed by the pyrometer. If DIC is performed the emissivity may not be identical on each sample because of the speckle pattern (or of the natural texture of the surface). Depending on the material, it may be impossible to identify the sample 340 emissivity prior to the test without interfering with the metallurgical state of the material.

A procedure has been developed in this work to be able to use a pyrometer without knowing the emissivity 345 of the sample surface before the tests. The emissivity of the pyrometer must be first set to an arbitrary value, close to the expected value. It will have to be corrected after the test to obtain correct pyrometer measurements. Additionally to the two constants of the radiometric model, a third unknown ε pyro has then to be identify, being the true emissivity at the pyrometer pointing location. In order to validate the method in the most demanding case, a pyrometer pointing at the center of the sample is simulated in this virtual test case (cf. Figure 2). The true emissivity of the sample is set to 0.89. The pyrometer data are then generated with a wrong emissivity of 0.83 in order to be representative of the real experiments where the true emissivity is not known prior to the tests because of the speckle pattern.

Once all the data of the virtual test case is generated, the global DIC analysis is performed on the images using quadrilateral elements with linear shape functions. The element size was set to 35x35 Pix 2 (1 Pix ≃ 9 µm) to be representative of the real experiments presented in the nex section. The correlation mesh used can be seen in Figure 2.

Identification procedure

The pyrometer emissivity cannot directly be identified using the thermocouple data since the pyrometer and the thermocouple are not located at the same position. The NIR thermography is then used as a bond to link the different devices. The three parameters {K 1 , K 2 , ε pyro } are identified by minimizing the following costfunction:

er 2 = � T NIRT TC -T TC 2 + � T NIRT pyro -T pyro 2 , (10)
where T TC is the temperature measured by the thermocouple, T pyro is the temperature measured by the pyrometer, T NIRT TC is the temperature obtained by NIR thermography at the thermocouple welding vicinity, and T NIRT pyro is the temperature obtained by NIR thermography at the pyrometer pointed area. As a reminder, only the 10% brightest pixels were used to compute the temperature by NIR thermography at the pyrometer and thermocouple areas. This cost function gathers the temperature data of all the time steps in order to use the largest range of temperature and identify the three parameters as accurately as possible.

Results of the test case

Figure 4 presents a comparison of the y-displacement at the top of the correlation mesh between the values obtained by DIC and the ground truth (i.e. the well-known imposed displacement). The mean error is about 0.15 pixel, which is considered to be very good. Different residual maps are presented in Figure 5 for the last image of the test. The left map shows 400 the residual field obtained at the very beginning of the DIC process (i.e. without both the local contrast and brightness correction and the displacement field correction). The error is then a sum of the error due to the temperature variation and to the displacement. The 405 middle-left map presents the residual field obtained at the initial iteration of the DIC algorithm but with the local contrast and brightness correction. It can be observed that the error is now mainly due to the displacement in the images and that the effect of 410 temperature has been successfully removed. The middle-right map shows the residual field obtained at the converged iterations of the DIC algorithm. As a matter of comparison, the residual field solely caused by the noise in the images is also presented. To obtain 415 the latter, an image was generated without deformation and at the same temperature than the reference image. The differences between the two images are then only due to the noise added virtually onto the images at the end of the image generation process. A DIC analysis 420 was then performed between these two images. The displacement field obtained was as expected (almost) the null field, and the residual field obtained was (almost) similar to the noise field applied onto the images. Because of the nature of the noise, the best 425 residual field (i.e. with the smallest absolute values) that can be obtained with DIC is the noise residual map. It is then a very useful tool for comparison purposes. It can be observed in Figure 5 that the converged residual map is very low, almost equal to the residual map The two constants of the radiometric model as well as the corrected emissivity are then identified. The temperature field obtained at the 8 th time step is given in Figure 6 with the true applied field. This time step is the most demanding one since it is when the thermal gradient is the most important. The average error obtained is about 0.4 °C. It can be observed that the error is mostly positive. This is due to the noise added to the images and to the assumptions stated in section 2.4. Since only the brightest pixels are used to compute the thermal field, the temperature obtained by NIR thermography is slightly higher than the average one in the elements. When the thermal gradient is low (e.g. time step 16), the maximal error is about 0.1 °C.

The comparison between the real parameters and the identified ones is given in Table 1. Very low differences are found for the emissivity and K 1 . K 2 is a bit higher than the initial value because it adapts both the noise impact and the assumption stating that the temperature within an element is homogeneous. The comparison in the temperature data shows that the method is very effective to compute the thermal field onto speckle patterned samples at very high temperature.

Emissivity

Application to real experiments

Experimental setup

The method is now applied on real experiments which were presented in [START_REF] Jailin | Thermo-mechanical behavior of Zircaloy-4 claddings under simulated post-DNB conditions[END_REF]. The experimental setup ELLIE used in this work is first briefly described, more details can be found in the previous reference. It is worth noting that a stereo optical system was used in these experiments to reconstruct the 3D-surface fields. The present method can be used either for 2D DIC or stereo measurements, since the only difference between these two techniques is a 3D reconstruction step, which does not affect the method presented in this work.

The test bench ELLIE is composed of a 10 kN servo-hydraulic tensile machine (cf. Figure 7). The tubular sample is connected onto the test machine using custom grips [START_REF] Tardif | Experimental study of the fracture kinetics of a tubular 16MnNiMo5 steel specimen under biaxial loading at 900 and 1000°C . Application to the rupture of a vessel bottom head during a core meltdown accident in a pressurized water reactor[END_REF]. An internal pressure up to 70 bar can be imposed and controlled within the specimen during the whole tests using argon gas. A 30kW induction generator (ThermoInduzione TI3-30KW CT-110) is used to heat the samples with heating rates up to 1500 °C/s. The induction generator frequency 480 (92 kHz) was chosen so that the thermal loading is uniform across the wall-thickness of the sample. The temperature regulation is achieved using a pyrometer (CTLM-2HCF4-C3, wavelength of 1.6 µm). All the experiments were performed within an enclosure where 485 an argon flush was set up during the tests to prevent sample oxidation.

The specimens were cut from fuel claddings made of Zircaloy-4 alloy. They were 90 mm long with an outer diameter of 9.5 mm and a wall-thickness of 0.57 mm. In order to perform stereo-correlation measurements the specimens were painted with a black undercoating and a white speckle pattern. Three thermocouples (type K, wires of 79 µm diameter) were spot-welded on the sample outer surface in the region of interest (ROI) seen by the cameras (cf. Figure 7).

The test procedure aimed at reproducing simulated thermo-mechanical conditions of a reactivity initiated 500 accident (cf. Figure 8). The cladding was first internally pressurized with pressure levels ranging from 7 to 11 bar. Once the pressure was stabilized the sample was heated up in open loop with heating rate of about 1200 °C/s. The thermal regulation switched in closed loop control (i.e. with feedback) when the temperature measured by the pyrometer reached a value 20 °C lower than the target temperature. Temperatures of interest ranged from 840 to 1020 °C. A compressive force was also applied on the sample simultaneously to the inter-510 nal pressure to compensate the end caps effect. This force was calculated using the initial inner radius R i of the claddings and the internal pressure P i applied:

F z = P i .π.R 2 i . (11)
Two CMOS cameras, separated by an angle of 18 °, were used to record images of the sample during the 515 tests at a frequency of 55 Hz. An example of an image is given in Figure 9. It is worth pointing out that since the black paint has a higher emissivity than the white paint, the black paint appears in white in the images at high temperature and the white paint appears in black. The 520 image acquisition parameters and the DIC parameters are given in Tables 2 and3, respectively.

Identification procedure

As in the virtual test case, the parameters {K 1 , K 2 , ε pyro } need to be identified. The identification proce-525 dure is however slightly different than in the previous case because the thermocouples are not reliable during the thermal transient due to their inertia effect [START_REF] Jailin | Experimental study and modelling of the phase transformation of Zircaloy-4 alloy under high thermal transients[END_REF]. An identification procedure of the three parameters is proposed below. Two error functions are first defined:

530 er 2 1 = � T NIRT TC -T TC 2 , (12)
er 2 2 = � T NIRT pyro -T pyro 2 , (13)
where T TC is the temperature measured by the thermocouples, T pyro is the temperature measured by the pyrometer, T NIRT TC is the temperature obtained by NIR thermography at the thermocouple areas, and T NIRT pyro is the temperature obtained by NIR thermography at the 535 pyrometer area.

er 2 1 uses only the data during the thermal plateau once the thermocouples are stabilized. er 2 2 uses all the temperature data in the plateau and during the thermal tran-540 sient in order to cover a wide range of temperature. The three parameters are then identified by minimizing the sum of these two cost functions using a Gauss-Newton algorithm. This process enables the parameters to be identified without being affected by the inertia effect of 545 the thermocouples and using a large range of temperature. ROI is also given. Since a stereo setup was used, the kinematic and the thermal fields can both be obtained in 3 dimensions on the ROI. It can be observed that the two fields are smooth while no smoothing operations 555 were used. Since many pixels are used in each element to compute the NIR temperature and that the noise over signal ratio is low in the images, it follows that the uncertainty of the thermal field can be approached by the uncertainty of the calibration point. According to 560 the manufacturer documentation and in our application case, the thermocouple and pyrometer uncertainties are about 0.6% of the true temperature. Figure 11 shows the residual map obtained at the 565 initial and converged stage of the DIC algorithm. As a matter of comparison, the approximated residual map solely due to noise is also presented. To obtain the latter, a sample was heated to 1000 °C without internal pressure. Once the temperature was stabilized 570 50 images were recorded at 55 Hz. Since there was no mechanical loading and that the material was at equilibrium the differences between the images were only due to noise. A DIC analysis was then performed between these images to estimate the noise residual map. For each residual field, the standard deviation σ of the error is also given. It can be observed that the kinematic has been well captured by the DIC. Tiny stains of errors can be seen and are due to paint damaging after eyes checking. A noise sensitivity evidenced noise levels of about 1 µm on the in-plane displacement and of about 5 µm on the out-of-plane displacement. These values of noise result from the combined influence of several factors like the camera resolution, DIC algorithms, etc.

Results

In Figure 11 it can be observed that converged residual 585 map is finally closed to the noise map, having a factor 2 between their standard deviation values. Figures 12 shows the thermal results obtained for an experiment carried out at 970 °C. Only the results of the 590 first seconds of the test were sought here. The heating rate was too high to obtain a perfect temperature ramp and a sharp transition at the temperature plateau, but this is even more interesting to have this richer data for identification proposes [START_REF] Jailin | FEMU based identification of the creep behavior of Zircaloy-4 claddings under simulated 800 RIA thermo-mechanical conditions[END_REF]. It can be observed that the pyrometer data matches well the NIR thermography all test long. During the first seconds of the test a small difference can be noticed between the thermocouples and the thermography, which is due to the inertia effect of the thermocouples during the heating. This error finally decreases when the temperature stabilizes.

Discussion

Impact of the sample shape

The apparent emissivity of an object is dependent of the observation angle [START_REF] He | High emissivity coatings for high temperature application : Progress and prospect[END_REF] (i.e. the angle between the camera and the normal to the sample surface). Given the tubular geometry of the sample, the impact of the shape on the emissivity has been studied. A specimen was heated from room temperature to 1200 °C in a GLEEBLE-3500 device. Thanks to the grips and 610 to the thermal conduction across the sample, the temperature field obtains on the tubular specimen is axisymmetric [START_REF] Jailin | Experimental study and modelling of the phase transformation of Zircaloy-4 alloy under high thermal transients[END_REF]. Using a standard camera, the well known geometry of the specimen, and the Equation ( 4) it is possible to compute the variation of emissivity 615 on the sample as a function of the observation angle. As an example, the emissivity is shown as a function of the observation angle in Figure 13 for a specimen heated to 1000 °C. It can be seen that no significant impact is observed up to an observation angle of 60°. 620 Above this value the emissivity significantly decreases. If needed, corrections at the first order can be taken into account into the radiometric model to limit the errors. The authors remind that these results are dependent on the wavelength of the thermal radiations, 625 and of the roughness and surface of the specimens (i.e. painted, raw, oxidized...). Depending on these parameters it may be worth to study this effect when performing IR thermography with a stereo setup since at least one camera will have a non-null observation angle. 630 Impact of the heat haze effect One may also be careful to the haze effect, which can be observed when performing tests at high temperature [START_REF] Berny | On the analysis of heat haze effects with spacetime dic[END_REF]. In our case, the vertical position of the sample and 635 the argon flush during the tests enabled not to be impacted by the haze effect. Using a gas flush, a fan or positioning the sample vertically can contribute to reduce the haze effect.

Figure 13: Evolution of the emissivity with the observation angle. The emissivity at 0°is here arbitrary set equal to 0.9. These results were obtained using the black paint on a sample heated to 1000 °C.

Summary and conclusions

640

A procedure to measure simultaneously kinematic and thermal fields with a single standard camera has been presented. It uses the global DIC method to measure the kinematic field and the NIR thermography method to compute the thermal field. Based on a 645 simple weak coupling, between DIC and thermography, a method is proposed to manage the presence of a speckle pattern when using thermography. The thermal field obtained is finally smooth and no interpolation is needed to match the data with the kinematic field. An identification strategy for the radiometric model has also been proposed to perform accurate temperature measurements during fast thermal transients.

The method was first validated on a virtual test case, 655 and was then applied in 3D using real experiments performed at high temperatures. Same levels of errors were obtained in the virtual test case and in the real experiments showing that the method can be applied accurately with a stereo setup. The proposed method showed a very good robustness even at temperatures greater than 1000 °C and with strain levels above 30%. Smooth 3D-surface thermal and kinematics full-fields were finally obtained, leading to very rich experiments where various temperature and loading conditions were 665 obtained at the same time.

The proposed method aims to be widely useful for any mechanical testing at above 700 °C. It does not require any specific software or device and showed a 670 very good robustness in severe conditions. If one wants to measure thermal fields with strong temporal evolution of the temperature he may nevertheless be impacted by over-saturated (or under-saturated) images. Using multi-exposure time or a PID regulation of the exposure 675 time of the cameras, as proposed in [START_REF] Zhang | An innovative technique for real-time adjusting exposure time of silicon-based camera to get stable gray level images with temperature evolution[END_REF], could be considered to overcome this issue.

290 3 .

 3 Application to a 2D virtual test case

Figure 1 :

 1 Figure 1: Flow chart presenting the different steps of the virtual test case.

Figure 2 :

 2 Figure 2: Example of an image obtained from a real experiment performed at 950 °C. The virtual pyrometer and thermocouple locations are shown in red and green, respectively. The deformed image generated for the last step of the virtual test case is also presented with the y-displacement field.

  ϕ

Figure 3 :

 3 Figure 3: Temperature impose to the sample during the virtual test.T sample (t, x, y) is the temperature of the sample at the time t and pixel position (x, y). δ and ϕ are described in Equations (8) and (9), respectively.

Figure 4 :

 4 Figure 4: Comparison of the vertical displacement U y at the blue cross between the ground truth and the displacement obtain by DIC.

Figure 5 :

 5 Figure 5: Residual maps obtained at the end of the virtual test case: (left map) at the beginning of the DIC process; (middle-left) at the initial iteration of the DIC algorithm with local and brightness correction; (middle-right) at the last iteration with local and brightness correction; (right) approximated residual map solely caused by noise. The error is expressed in gray level.

Figure 6 :

 6 Figure 6: Results of the NIR thermography for the virtual test case. (a) The left plot shows the temperature field computed on the DIC mesh at the 8 th time step. The pyrometer and the thermocouple location are shown in red and green, respectively. A comparison of the temperature field between the one measured by NIR thermography and the ground truth is also presented. (b) Difference in the temperature field between the thermal field obtained by NIR thermography and the ground truth.

Figure 10

 10 Figure 10 shows an example of a thermal and a kinematic field obtained during a test performed at 550

Figure 7 :

 7 Figure 7: ELLIE setup with a view of the sample within the enclosure -left: general overview of the setup, right: inside the enclosure view.

Figure 8 :

 8 Figure 8: Test procedure.

Figure 9 :

 9 Figure 9: Example of an image recorded by the right camera. The pyrometer is pointing at the center of the ROI (red point). The correlation mesh used to perform the global DIC is shown in blue.

Figure 10 :

 10 Figure 10: Example of results obtained during a test performed at 880 °C and 11 bar: (a) displacement field computed by stereocorrelation and normed by the initial radius of the sample R 0 ; (b) thermal field obtained by NIR thermography; (c) 3D reconstruction of the ROI.

Figure 11 :

 11 Figure 11: Residual maps obtained during the tests at the first and last iteration of the DIC algorithm (two left maps). The right map shows the approximated residual map solely caused by noise. The error is expressed in gray level.

Figure 12 :

 12 Figure 12: Comparison of the temperature measured by the pyrometer and thermocouples to the temperatures computed by NIR thermography at the same locations. The uncertainty bar comes from the thermocouple documentation at this level of temperature.

Table 1 :

 1 Comparison of the parameter values between the ground truth and the identified values.

			K 1	K 2
	Ground truth Identified Error (%)	0.89 0.8897 0.03	1.560 × 10 4 5.91 × 10 7 1.553 × 10 4 4.78 × 10 7 1.8 19.0

Table 2 :

 2 Image acquisition parameters.

Table 3 :

 3 DIC parameters.

(iv) finally, the shape of the object may have an impact on its apparent emissivity[START_REF] He | High emissivity coatings for high temperature application : Progress and prospect[END_REF]. This must be considered when dealing with non-planar objects. This will be

imposed with a hot-spot located at the center of the sample. The temporal evolution δ(t) was described using a sinus function and the hot spot shape ϕ(x, y) using the equation of a circle, as follow:δ(t) = 35. sin t 5 ,(8)

solely caused by noise. It shows that the DIC algorithm captured the kinematic on the sample very well, despite the non-conservation of the optical flow.

Acknowledgments

The authors acknowledge with thanks financial support of this work from EdF and Institut Carnot In-680 génierie@Lyon. The authors want also to express their thanks to J. Réthoré for allowing access to the UFreckles software and ATYS Consulting Group for helping on the induction problematic.


Doha Elalami

Mouna Aouine

Florian Monlau

Fabienne F Guillon

Claire Dumon

Guillermina Hernandez Raquet

Abdellatif Barakat
email: abdellatif.barakat@inrae.fr

Barakat Abdellatif

Guillermina Hernandez Raquet

Enhanced enzymatic hydrolysis of corn stover using twin-screw extrusion under mild conditions

Keywords: Biomass, Pretreatment, Biorefinery, Extrusion, Enzymes, Biofuel Abbreviations CrI crystallinity index, CS corn stover, XRD X-ray diffraction

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

Bioconversion of residual lignocellulosic biomass to bioethanol has shown these last decades environmental, economic and energetic advantages in comparison to bioethanol produced from starch or sugar derived from sugar cane and maize [START_REF] Mood | Lignocellulosic biomass to bioethanol, a comprehensive review with a focus on pretreatment[END_REF]. Lignocellulosic biomass is an abundant, cheap, and renewable organic resource with a heterogeneous structure mainly composed of cellulose, hemicelluloses, and lignin. After hydrolysis of cellulose and hemicellulose, the carbohydrate components (glucose and xylose) are fermentable, which makes lignocellulosic biomass an appropriate feedstock for bioenergy and biofuel production [START_REF] Vasić | Bioethanol Production by Enzymatic Hydrolysis from Different Lignocellulosic Sources[END_REF]. Nevertheless, the structural and chemica l properties of lignocellulosic biomass, make it extremely resistant to biodegradation by enzymes and microbial attacks [START_REF] Zheng | Pretreatment of lignocellulosic biomass for enhanced biogas production[END_REF].

In addition, enzymes can be used to biologically degrade the lignocellulose for bioethanol production. These enzymes can be produced in-situ by micro-organisms (4). Micro-organisms, mainly white and soft-rot fungi, actinomycetes, and bacteria can be used to pretreat lignocellulosic wastes. They can solubilize lignin due to the action of enzymes such as peroxidases and laccases.

However, the effectiveness of enzymes can be improved when breaking down the lignocellulosic structure first [START_REF] Zhao | Enzymatic Hydrolysis and Physiochemical Characterization of Corn Leaf after H-AFEX Pretreatment[END_REF]. In general, a suitable pretreatment procedure involves (i) cleaving cross-linked matrix in hemicelluloses and lignin, (ii) increasing the porosity and surface area of cellulose for subsequent enzymatic hydrolysis, and finally (iii) disrupting hydrogen bonds in crystalline cellulose [START_REF] Zhao | Enzymatic Hydrolysis and Physiochemical Characterization of Corn Leaf after H-AFEX Pretreatment[END_REF].

Various pretreatment approaches including physical, chemical, physicochemical, and biological methods have been developed to improve the enzymatic saccharification of biomass [START_REF] Zheng | Extrusion Pretreatment of Lignocellulosic Biomass: A Review[END_REF].

For instance, enzymatic hydrolysis of wheat straw was enhanced after hot water pretreatment under 180°C for 10 min [START_REF] Rosgaard | Comparison of Different Pretreatment Strategies for Enzymatic Hydrolysis of Wheat and Barley Straw[END_REF]. Moreover, the addition of dilute acid (1% of H2SO4, 1 h at 220°C) to steam explosion pretreatment increased arabinose and xylose content by 200% and 61% compared to untreated barley straw [START_REF] Rosgaard | Comparison of Different Pretreatment Strategies for Enzymatic Hydrolysis of Wheat and Barley Straw[END_REF]. Acid pretreatment can lead to high solubilization of hemicelluloses and cellulose, but at high temperatures and high acid concentration, inhibitory compounds (furfural, 5-HMF) can be generated [START_REF] Monlau | Do furanic and phenolic compounds of lignocellulosic and algae biomass hydrolyzate inhibit anaerobic mixed cultures? A comprehensive review[END_REF]. Furthermore, alkaline pretreatment favors lignin degradation and alteration, while increasing the accessible surface area [START_REF] Kim | A review on alkaline pretreatment technology for bioconversion of lignocellulosic biomass[END_REF]. Ammonia fiber explosion has been shown to increase pore size in corn stover leading to a higher ethanol production compared to dilute acid pretreatment [START_REF] Mathew | An evaluation of dilute acid and ammonia fiber explosion pretreatment for cellulosic ethanol production[END_REF]. Biological pretreatments using fungus can also be applied to lignocellulosic biomasses for delignification to enhance enzymatic hydrolysis [START_REF] Saha | Biological pretreatment of corn stover with white-rot fungus for improved enzymatic hydrolysis[END_REF]. However, these methods require the use of acids, bases, salts, solvents, and micro-organisms, which introduce additional costs [START_REF] Bensah | African perspective on cellulosic ethanol production[END_REF] and may have a negative environmental impact. Ultrasonic and microwave pretreatments were found to enhance sugar solubilization and their efficiency depended on the energy supplied. In the last decade, the extrusion process has been widely used in the food, feed, and plastic industries. As a result, extrusion has been identified as an interesting alternative method for fractionation of lignocellulosic biomass based on its ability to provide high shear, rapid heat transfer, and effective and fast mixing [START_REF] Duque | Extrusion as a pretreatment for lignocellulosic biomass: Fundamentals and applications[END_REF]. It is a highly versatile pretreatment that can be used alone as a physical pretreatment [START_REF] Karunanithy | Influence of Extruder Temperature and Screw Speed on Pretreatment of Corn Stover while Varying Enzymes and Their Ratios[END_REF][START_REF] Lee | Enzymatic saccharification of woody biomass micro/nanofibrillated by continuous extrusion process II: Effect of hot-compressed water treatment[END_REF] or combined with chemicals and other pretreatment methods [START_REF] Da Silva | Continuous pretreatment of sugarcane bagasse at high loading in an ionic liquid using a twin-screw extruder[END_REF][START_REF] Karunanithy | Optimization of switchgrass and extruder parameters for enzymatic hydrolysis using response surface methodology[END_REF]. In addition, as a continuous pretreatment process, extrusion has a higher processing capacity when compared to batch systems [START_REF] Cha | Continuous alkaline pretreatment of Miscanthus sacchariflorus using a bench-scale single screw reactor[END_REF]. Furthermore, extrusion pretreatment has the advantage of avoiding the generation of inhibitors, except when the extrusion is carried out in combination with chemical pretreatments [START_REF] Moro | Continuous pretreatment of sugarcane biomass using a twin-screw extruder[END_REF]. Furthermore, compared to traditional physical methods such as chopping, grinding, and milling, extrusion not only reduces the particle size but also provides a thermochemical pretreatment [START_REF] Gu | Effect of Ca(OH)2 pretreatment on extruded rice straw anaerobic digestion[END_REF][START_REF] Kratky | Biomass Size Reduction Machines for Enhancing Biogas Production[END_REF]. The extrusion process has many advantages like the ability to provide high shear, rapid heat transfer, and effective mixing [START_REF] Kratky | Biomass Size Reduction Machines for Enhancing Biogas Production[END_REF], which leads to an increase in the surface area of the cellulose available to the enzymes [START_REF] Bensah | African perspective on cellulosic ethanol production[END_REF]. Structural changes are mainly promoted in the mixing zones of the screws formed by the successive kneading elements connected with a slight deviation angle between them, which depends on the extrusion purpose.

Previous studies have shown the interest of extrusion pretreatment on enzymatic hydrolysis of biomass. Myat and Ryu (2014) obtained 28% higher ethanol production due to twin-screw extrusion [START_REF] Myat | Characteristics of destarched corn fiber extrudates for ethanol production[END_REF], while [START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF] and [START_REF] Kang | Optimization of alkali-extrusion pretreatment with twin-screw for bioethanol production from Miscanthus[END_REF] reported that, around 20% and 25% of solid to liquid ratio, ethanol content was at its maximal value [START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF][START_REF] Kang | Optimization of alkali-extrusion pretreatment with twin-screw for bioethanol production from Miscanthus[END_REF]. Although extrusion had a positive impact on sugar release, the effect of operating conditions was not significant in contrast to enzyme source and dose [START_REF] Cha | Continuous alkaline pretreatment of Miscanthus sacchariflorus using a bench-scale single screw reactor[END_REF]. Indeed, several studies have been conducted on the effect of extrusion operating conditions. However, the evaluation of the energy-based efficiency is missing in these studies. In addition, most of the studies are carried out at high temperatures, as well as in the presence of chemical reagents.

Therefore, the present study assesses the effect of extrusion on the enzymatic hydrolysis under mild conditions ensuring environmentally friendly solubilization of corn stover. The effect of two different enzymes cocktails on untreated and pretreated corn stover hydrolysis was investigated. The physicochemical properties changes, enzymatic digestibility, energy consumption, and efficiency were evaluated and compared to select the suitable conditions for the enzymatic hydrolysis of pretreated corn stover.

Materials and Methods

2.1.Biomass preparation

Corn stover was obtained from (the Occitanie department, Toulouse, France). Before pretreatment, CS was chopped to a particle size of approximately 10-20 cm using a vegetable-milling machine.

The chopped samples were then ground using knife milling (Retsch SM 100, Germany) with a screen size of 4 mm. The substrate was dried and stored at room temperature. Raw CS contained 37.5% (TS basis -w/w) of cellulose, 22.3% (TS basis) of hemicelluloses, and 25.2% (TS basis) of lignin. In addition, carbon and oxygen contents were 46% and 48% (TS basis) respectively. Therefore, CS can be considered as a biomass of interest for conversion into biofuels (25).

2.2.Continuous pretreatment using a twin-screw extruder

The extrusion pretreatment was conducted using a twin-screw extruder (Eurolab 16, Thermo Scientific, USA). The barrel had a diameter of 16 mm and the length to diameter ratio (L/D) of the screws was 25. The experimental setup used in this study is illustrated in Fig. 1.

Insert Figure 1 The extruder contains three different elements: forward screw elements, which enable the continuous movement of the material from inlet to outlet; kneading screw elements, which exert an effective mixing and shearing effect on the biomass; and reverse screw elements, which move the material backward and increase its residence time to improve the pulverization effect [START_REF] Moro | Continuous pretreatment of sugarcane biomass using a twin-screw extruder[END_REF].

Table 1 presents the operating conditions used in this study. The ground biomass was impregnated with water using a spray system to reach the different solid/liquid ratios. To reduce the energy demand of the pretreatment, the temperature was maintained at 37°C and the two parameters considered were extrusion speed and solid load. However, it should be noted that other factors are also important such as extruder design, reagent addition, feed rate, and also the size of the biomass at the process inlet. It was chosen to vary only the rotation speed and the solid/liquid ratio which, in addition to the temperature, are reported as the most influencing parameters (26).

2.3.Enzymatic hydrolysis of CS biomass

The efficiency of extrusion for the CS pretreatment was evaluated through the enzymatic hydrolys is using different enzyme cocktails. Cellulose and hemicelluloses, which represent two-thirds of cell wall dry matter, can be degraded into sugars. For this purpose, the enzymatic hydrolysis of untreated and pretreated CS by different enzymatic cocktails was evaluated. Cellulase from T. longibrachiatum ≥ 1mg/unit (C9748), and T. reesei (C2730) were obtained from Sigma Aldrich and were added under the same conditions (with a dose of 30 U/g TS). Enzymatic hydrolysis was carried out at a solid concentration of 5% (w/v) in a 50 mM sodium acetate buffer (pH 5.0) at 37 °C for 72 h with agitatio n (100 rpm). After enzymatic hydrolysis, the supernatant was filtered (0.45 µm) and the enzymatic digestibility was assessed by the amount of sugar released (i.e. glucose, xylose, arabinose in mg g -1 CS) determined according to the procedure described previously [START_REF] Amamou | Mechano-Enzymatic Deconstruction with a New Enzymatic Cocktail to Enhance Enzymatic Hydrolysis and Bioethanol Fermentation of Two Macroalgae Species[END_REF]. The sugars contained in the enzymatic hydrolysates were analyzed by high-performance liquid chromatography (HPLC) using the BioRad HPX-87P and HPX87H columns [START_REF] Barakat | Eco-friendly dry chemo-mechanical pretreatments of lignocellulosic biomass: Impact on energy and yield of the enzymatic hydrolysis[END_REF]. All the experiments were operated in triplicate, and the average value was reported.

The results were compared in pairs using the Student's t-test, assuming equal variance, normality, and independence of repetitions.

2.4.Biochemical and structural analysis of substrates

The carbohydrate and lignin concentrations in lignocellulose samples (untreated and pretreated CS) were determined using the Klason method described in [START_REF] Barakat | Eco-friendly dry chemo-mechanical pretreatments of lignocellulosic biomass: Impact on energy and yield of the enzymatic hydrolysis[END_REF] and [START_REF] Monlau | Predictive Models of Biohydrogen and Biomethane Production Based on the Compositional and Structural Features of Lignocellulosic Materials[END_REF]. All the values reported here were duplicate results. The crystallinity of untreated and pretreated CS was measured using an X-ray diffractometer (XRD, Bruker diffractometer D8 Advance). The measurements were carried out on compacted powder on small mats. X-ray diffraction (XRD) data were collected from 2θ = 5° to 50° with a step interval of 0.02°. The crystallinity index (CrI) was defined as follows (Eq.1): CrI =(I 002 -I am )/I 002 ×100 (Eq.1)

Where I002 and Iam are the peak intensities of crystalline and amorphous cellulose, respectively.

Particle size was analyzed by laser granulometric (Mastersizer 2000, Malvern Instrument, USA) and the surface area of untreated and pretreated CS samples was determined using the BET (Micromeritics, ASAP2020, USA) method. The gas adsorption data was collected using a Micrometrics (France) 3Flex Surface characterization analyzer using N2. Before N2 sorption, the samples were degassed at 50°C overnight. The specific surface areas were determined from the nitrogen adsorption/desorption isotherms (at -196°C). The swelling capacity of the powdered samples was measured using the bed volume technique [START_REF] Robertson | Hydration Properties of Dietary Fibre and Resistant Starch: a European Collaborative Study[END_REF]. 100 mg of sample was dispersed in 10 mL of distilled water and left overnight at room temperature. The volume of the swollen sample was then measured and expressed as mL of swollen sample per g of original dry sample [START_REF] Robertson | Hydration Properties of Dietary Fibre and Resistant Starch: a European Collaborative Study[END_REF]. The conversion yields of cellulose and xylose after enzymatic hydrolysis are calculated using the following equations (Eq.2 and Eq.3):

𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑦𝑦𝑦𝑦𝐺𝐺𝐺𝐺𝑦𝑦 (%) = 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖 𝐺𝐺𝑖𝑖𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖 𝐺𝐺 ℎ𝑒𝑒𝑦𝑦𝑦𝑦𝐺𝐺𝐺𝐺𝑒𝑒𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺(𝑔𝑔/100𝑔𝑔𝑔𝑔𝑔𝑔) 𝐶𝐶𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖 𝑏𝑏𝑖𝑖𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺𝐺𝐺 (𝑝𝑝𝑦𝑦𝐺𝐺𝑒𝑒𝑦𝑦𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺𝑦𝑦 𝐺𝐺𝑦𝑦 𝑖𝑖𝐺𝐺𝑒𝑒)(𝑔𝑔/100𝑔𝑔𝑔𝑔𝑔𝑔) 𝑥𝑥100 (𝐸𝐸𝐸𝐸. 2) 𝑋𝑋𝑦𝑦𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑦𝑦𝑦𝑦𝐺𝐺𝐺𝐺𝑦𝑦 (%) = 𝑋𝑋𝑒𝑒𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖 𝐺𝐺𝑖𝑖𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑖𝑖 𝐺𝐺 ℎ𝑒𝑒𝑦𝑦𝑦𝑦𝐺𝐺𝐺𝐺𝑒𝑒𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺(𝑔𝑔/100𝑔𝑔𝑔𝑔𝑔𝑔) 𝐻𝐻𝐺𝐺𝑒𝑒𝑖𝑖𝐺𝐺𝐺𝐺𝐺𝐺 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺 𝑖𝑖𝑖𝑖 𝑏𝑏𝑖𝑖𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺𝐺𝐺 (𝑝𝑝𝑦𝑦𝐺𝐺𝑒𝑒𝑦𝑦𝐺𝐺𝑒𝑒𝑒𝑒𝐺𝐺𝑦𝑦 𝐺𝐺𝑦𝑦 𝑖𝑖𝐺𝐺𝑒𝑒)(𝑔𝑔/100𝑔𝑔𝑔𝑔𝑔𝑔) 𝑥𝑥100 (𝐸𝐸𝐸𝐸. 3)
After the extrusion, the liquid and solid fractions were separated by filtration. The liquid was then analyzed to assess the solubilization effect of extrusion by determining sugar content using the HPLC.

2.5.Specific energy consumption and pretreatment efficiency

To evaluate the energy efficiency of the extrusion pretreatment, the electric energy consumed by the extruder was determined based on the torque factor. Torque indicates the power consumption of the electric motor while specific mechanical energy (SME) is the amount of power that is being input by the motor into each kilogram of material being processed (kWh kg -1 TS). Torque (τ) was measured directly by the extruder system (Nm). The total energy requirement (SME) of different extrusion experiments was calculated using Eq.4 [START_REF] Duque | Extrusion as a pretreatment for lignocellulosic biomass: Fundamentals and applications[END_REF]:

𝑆𝑆𝑆𝑆𝐸𝐸 = τ * 2π * speed * 60 𝑚𝑚 (𝐸𝐸𝐸𝐸. 4)
Torque and SME of different extrusion pretreatments depend on the physicochemical properties of biomass and also on the initial particle size, which also depends strongly on the extrusion parameters and conditions (speed, screw profile, solid loading, temperature…).

The efficiency of the pretreatment was given by the following equation (Eq.5):

� = 𝑆𝑆𝐺𝐺𝑆𝑆𝑆𝑆𝑆𝑆𝐺𝐺 𝑦𝑦𝑦𝑦𝐺𝐺𝐺𝐺𝑦𝑦(𝐺𝐺𝑆𝑆 𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺𝐺) (𝑆𝑆/𝑘𝑘𝑆𝑆 𝐶𝐶𝑆𝑆) 𝑆𝑆𝑆𝑆𝐸𝐸 (𝑘𝑘𝑘𝑘ℎ/𝑘𝑘𝑆𝑆 𝐶𝐶𝑆𝑆) (𝐸𝐸𝐸𝐸. 5)

Results and discussion

3.1.Effect of twin-screw extrusion on CS properties and composition

Table 2 presents the main physicochemical properties and composition of raw and pretreated CS under different extrusion conditions. Extrusion increased surface area and reduced CS swelling. In addition, the soluble sugar content was not significantly increased after extrusion. However, in [START_REF] Huang | The effect of extrusion processing on the physiochemical properties of extruded orange pomace[END_REF] swelling property of orange pomace was increased by 85% after extrusion under 125°C, 290 rpm, and 14% of moisture [START_REF] Huang | The effect of extrusion processing on the physiochemical properties of extruded orange pomace[END_REF]. In this study, any modification of the CS fiber compositions may be due to the lignocellulosic CS complex matrix and to the low applied temperature of 37°C compared to 125°C [START_REF] Huang | The effect of extrusion processing on the physiochemical properties of extruded orange pomace[END_REF]. Furthermore, an increase in solid loading and speed of extruder was advantageous in reducing the particle size of pretreated CS, except for extrusion under 400 rpm and 300 gTS/L, which may be explained by CS lignocellulosic matrix heterogeneity. The particle size of raw CS was around 4 mm and decreased to 0.42 mm when the sample's loading achieved 600 gTS /L at a rotation screw speed of 300 rpm. In general, the available surface area is closely related to the particle size and the volume of the pores generated by the pretreatment.

Extrusion has been reported to cause physical disruption of cell wall assembly due to the combination of thermal and mechanical energy (shearing forces), leading to a partial deconstruction of biomass structure. In addition, extrusion reduced the crystallinity of CS from 46 to 30 %.

Table 3 presents the impact of extrusion on the solubilization of different biomasses reported in the literature. Previously, Aktas-Akyildiz et al. (2020) reported that the extrusion speed affected the solubilization of sugars in wheat bran. Under the same temperature and solid loading conditions, the soluble sugar content achieved the double, when the screw speed was increased from 100 to 200 rpm [START_REF] Aktas-Akyildiz | Effect of extrusion treatment on enzymatic hydrolysis of wheat bran[END_REF]. In the same manner, Zhang et al. ( 2012) found that glucose yield from CS increased with screw speed up to 100 rpm when using two temperature zones (50-140°C). However, above 100 rpm, the glucose yield was decreased. Moreover, Zhang et al. 2012 reported that, below 100 rpm, moisture content affected positively the glucose and xylose yields [START_REF] Zhang | Pretreatment of Corn Stover with Twin-Screw Extrusion Followed by Enzymatic Saccharification[END_REF]. In contrast, Haghighi-Manesh and Azizi (2018) reported that moisture above 50% reduced the solubilization of sugars [START_REF] Haghighi-Manesh | Integrated extrusion-enzymatic treatment of corn bran for production of functional cake[END_REF]. However, this negative effect of moisture was not observed in our study, which may be due to the low temperature used. Zhang et al. (2020) studied the effect of extrusion operating parameters on the degree of expansion that is related to the release of sugars from the rice husk. Biomass size, moisture, temperature, and screw speed can highly impact the solubilization of sugars [START_REF] Zhang | Extrusion followed by ultrasound as a chemicalfree pretreatment method to enhance enzymatic hydrolysis of rice hull for fermentable sugars production[END_REF]. Based on sugar release yields, the optimum screw speed and biomass humidity at temperatures around 60°C

will not be the same as at high temperatures (> 100°C). However, from an economic point of view, low-temperature pretreatment is more advantageous if the other parameters are well optimized. An increase of glucose of 38% was obtained from corn starch after extrusion under 120 rpm with a moisture of 33% at 70°C [START_REF] Wu | Effects of Extrusion Technology Combined with Enzymatic Hydrolysis on the Structural and Physicochemical Properties of Porous Corn Starch[END_REF]. Although, Zeng et al. (2017) did not report any solubilization improvement compared to control when pretreating brown rice at 60°C, 100 rpm, and 30% of humidity [START_REF] Zeng | The relationship between reducing sugars and phenolic retention of brown rice after enzymatic extrusion[END_REF]. This finding was in accord with the results of this study. To increase the efficiency of the extrusion, chemicals can be added which enhance the degradation of lignocellulose and thus, improve soluble sugar content [START_REF] Coimbra | Sugar production from wheat straw biomass by alkaline extrusion and enzymatic hydrolysis[END_REF][START_REF] Han | Extrusion process to enhance the pretreatment effect of ionic liquid for improving enzymatic hydrolysis of lignocellulosic biomass[END_REF][START_REF] Pérez-Rodríguez | Extrusion and enzymatic hydrolysis as pretreatments on corn cob for biogas production[END_REF]. Extrusion can also be followed by ultrasonic for further solubilization of biomass fibers (35).

3.2.Enzymatic hydrolysis of untreated and pretreated CS

The main effects of screw speed and solid load (Table 1) were evaluated using different enzymes by determining the total sugars, glucose, and xylose released after hydrolysis as illustrated in Fig. 2. The release of glucose and xylose by T. reesei or T. longibrachiatum cellulases was enhanced by extrusion pretreatment. Extrusion enhanced glucose release by 21-48 % for T. reesei cellulase while T. longibranchiatum enabled an increase of 139-209% due to extrusion (Fig. 2b). Xylose content in the hydrolysate was increased by 20-59% and 220-286 % when using T. reesei and T.

longibranchiatum respectively. Therefore, extrusion had a positive effect on enzymatic hydrolysis.

Even if extrusion had no drastic change in biomass composition, it resulted in particle size decrease, morphology, and surface aspect changes [START_REF] Banvillet | Cellulose fibers deconstruction by twin-screw extrusion with in situ enzymatic hydrolysis via bioextrusion[END_REF]. The biomass structure became more accessible for the enzymes therefore easier to hydrolyze which improved the sugars yield. Glucose and xylose yields were also reported in Fig. 2. It can be shown that for all conditions, extrusion enhanced cellulose and hemicelluloses conversion into glucose and xylose. The highest glucose yields were achieved with cellulase from T. longibrachiatum with up to 200 % improvement over untreated CS. Overall, the glucose conversion yield remained below 36% which was quite low compared to wood chips (45%) [START_REF] Liu | Effects of combined pretreatment of dilute acid pre-extraction and chemical-assisted mechanical refining on enzymatic hydrolysis of lignocellulosic biomass[END_REF] and banana peel (65%) [START_REF] Pereira | Deconstruction of banana peel for carbohydrate fractionation[END_REF]. Thus, the conversion yield depends on biomass and enzymatic hydrolysis conditions.

Moreover, according to Fig. 2, cellobiose release was obtained using cellulase from T. reesei cocktail. Indeed, T. reesei cellulase is composed of two cellobiohydrolases ( 44) and low β-glucosidase activity which explains the release of cellobiose. On the other hand, T. longibrachiatum contains different xylanase and accessory activities [START_REF] Morgavi | Synergy Between Ruminal Fibrolytic Enzymes and Enzymes from Trichoderma Longibrachiatum[END_REF] which may explain the high effectiveness of this cocktail. In addition, there is a slight difference between the molecular weight of these enzyme cocktails (68 kDa and 57.250 kDa for T. reesei and T. longibrachiatum cellulases respectively) which may affect the hydrolysis yield [START_REF] Kunamneni | Trichoderma Enzymes for Food Industries[END_REF]. However, no difference was noticed between the results obtained from T.

reesei and T. longibrachiatum cellulases on the untreated CS. This can be explained by a reduction of the effect of T. longibrachiatum on the control due to untreated CS size, which decreased the contact surface. In addition, it should be noticed that, in the case of enzymatic hydrolysis with T.

reesei cocktail, the best sugar solubilization was observed for the extrudate sample with 600 gTS /L of solid load and at a rotation screw speed of 300 rpm. In these operating conditions, the extrudate CS presented the highest surface area (about 1.697 m 2 g -1 ) which improved the contact surface of the enzymes with the substrate during conversion [START_REF] Licari | Comparison of various milling modes combined to the enzymatic hydrolysis of lignocellulosic biomass for bioenergy production: Glucose yield and energy efficiency[END_REF]. Regarding the cocktail from T. longibrachiatum, the best sugar release was obtained after the extrusion of CS at 400 g/L and 200 rpm. As this enzyme source was the most effective in sugar hydrolysis, it was retained for energy efficiency calculations.

As seen previously, extrusion can lead to physical changes in biomass matrix and cellulose crystallinity, which allows for enhanced accessibility and thus, improved sugar release after enzymatic hydrolysis. Particle size was found to highly affect the hydrolysis rate (48). Kapoor et al.

2019 reported that a reduction of rice straw size from 20 to 5 mm increased glucan solubilization from 8.5 to 17.7% [START_REF] Kapoor | The impact of particle size of cellulosic residue and solid loadings on enzymatic hydrolysis with a mass balance[END_REF]. Cellulose crystallinity and lignin content were also reported as key factors of enzymatic hydrolysis efficiency [START_REF] Vandenbossche | A new lignocellulosic biomass deconstruction process combining thermo-mechano chemical action and bio-catalytic enzymatic hydrolysis in a twin-screw extruder[END_REF]. The crystalline cellulose is characterized by its high density, strong intramolecular bonding, and compact arrangement. The reduction of crystallinity was found to be significantly related to enzymatic hydrolysis efficiency [START_REF] Xu | Effect of crystallinity on pretreatment and enzymatic hydrolysis of lignocellulosic biomass based on multivariate analysis[END_REF]. In addition, a high lignin content can inhibit enzymatic hydrolysis as cellulase can be adsorbed onto lignin and thus can be deactivated [START_REF] Yuan | Recent advances in understanding the effects of lignin structural characteristics on enzymatic hydrolysis[END_REF]. However, in the case of this study, enzymatic hydrolysis of extruded corn stover resulted in higher soluble sugars even if fiber content was not affected by extrusion. [START_REF] Myat | Characteristics of destarched corn fiber extrudates for ethanol production[END_REF] reported an enhancement of 20% of xylose after enzymatic hydrolysis, due to extrusion under 140°C with 0.75% NaOH added [START_REF] Myat | Characteristics of destarched corn fiber extrudates for ethanol production[END_REF]. The operating conditions of extrusion can affect the enzymatic hydrolysis results as shown by [START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF][START_REF] Choi | The influence of screw configuration on the pretreatment performance of a continuous twin screw-driven reactor (CTSR)[END_REF].

The screw speed was found to positively affect the enzymatic hydrolysis efficiency through glucose release enhancement (53), while Han et al. (2013) reported a negative impact of temperature increase in glucose content after enzymatic hydrolysis [START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF]. Extrusion pretreatment can be combined with acid or alkaline reagents addition under relatively high temperatures, promoting thus, the solubilization of biomass [START_REF] Cha | Continuous alkaline pretreatment of Miscanthus sacchariflorus using a bench-scale single screw reactor[END_REF][START_REF] Kang | Optimization of alkali-extrusion pretreatment with twin-screw for bioethanol production from Miscanthus[END_REF]. However, the energy consumption of the process must be taken into account, as well as the profitability of the recovery of ethanol after fermentation [START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF].

Insert Figure 2

For efficient enzymatic hydrolysis, a synergistic enzyme combination is recommended [START_REF] Merino | Progress and Challenges in Enzyme Development for Biomass Utilization[END_REF]. Fig. 3 shows the amount of released sugars from T. longibrachiatum cellulose completed with xylanase.

For both untreated CS and CS (400/200), the addition of xylanase was effective in enhancing the release of xylose and glucose. Indeed, xylanase hydrolyzes the xylan bound around the cellulose and therefore the cellulose becomes more accessible to the cellulase, whereas the cellulase allows better accessibility of the xylan. Thus, it has been shown that there is a real synergistic effect between xylanase and cellulase [START_REF] Hu | The enhancement of enzymatic hydrolysis of lignocellulosic substrates by the addition of accessory enzymes such as xylanase: is it an additive or synergistic effect?[END_REF]. Overall, the highest sugar yield was obtained from CS (400/200) using T. longibrachiatum cellulase in synergy with xylanase which released both glucose and xylose.

Insert Figure 3 3.3.Pretreatment efficiency Fig. 4 presents the effect of extrusion conditions on torque and specific energy requirement.

Interestingly, the torque decreased when screw speed and moisture content increased, in accord with [START_REF] Aktas-Akyildiz | Effect of extrusion treatment on enzymatic hydrolysis of wheat bran[END_REF]. Torque decreased also when temperature increased [START_REF] Duque | Extrusion as a pretreatment for lignocellulosic biomass: Fundamentals and applications[END_REF]. Moreover, torque is mainly affected by screw speed, degree of filling of the barrel, and viscosity of the biomass [START_REF] Akdogan | Pressure, torque, and energy responses of a twin screw extruder at high moisture contents[END_REF]. [START_REF] Chen | Pretreatment of rice straw using an extrusion/extraction process at bench-scale for producing cellulosic ethanol[END_REF] reported torque values ranging from 107 Nm (150 rpm at 500 g/L) to 202 Nm (30 rpm at 500 g/L)

during twin-screw extrusion of rice straw [START_REF] Chen | Pretreatment of rice straw using an extrusion/extraction process at bench-scale for producing cellulosic ethanol[END_REF]. As temperature and moisture impact the viscosity, torque is therefore affected. Mass flow and particle size are also affecting the torque. Lignocellulosic biomass nature can also modify the torque. Karunanithy and Muthukumarappan (2010) reported that torque was 100 Nm and 70 Nm for switchgrass and corn stover (155 rpm and 500 g/L), respectively [START_REF] Karunanithy | Influence of Extruder Temperature and Screw Speed on Pretreatment of Corn Stover while Varying Enzymes and Their Ratios[END_REF]. Overall, among the extruder conditions, the temperature was the most influencing parameter both on torque and specific mechanical energy (SME) [START_REF] Akdogan | Pressure, torque, and energy responses of a twin screw extruder at high moisture contents[END_REF]. In the present study, specific mechanical energy was not significantly affected by either the screw speed or the moisture content of CS, suggesting that the pretreatment efficiency of each experiment will depend mainly on sugar release.

Insert Figure 4 Fig. 5 shows the impact of extrusion conditions on pretreatment energy efficiency. Pretreatments under 400 gTS/L and 200 rpm and 600 gTS/L and 100 rpm resulted in the highest energy efficiency.

It should be pointed out that these results are only relevant at 37°C. At high temperatures, thermal energy should be added to the specific mechanical energy to calculate the pretreatment efficiency. To enhance the energy recovery from corn stover, anaerobic digestion of the solid residue can be considered after enzymatic hydrolysis [START_REF] Pérez-Rodríguez | Extrusion and enzymatic hydrolysis as pretreatments on corn cob for biogas production[END_REF].

Although sugar yields are still lower (27-36%) than what can be obtained by chemical pretreatment (up to 90%) (57), this yield is higher than that obtained after ball milling, with a glucose yield of 25% for corn stover [START_REF] Yang | Effect of combined wet alkaline mechanical pretreatment on enzymatic hydrolysis of corn stover and its mechanism[END_REF]. The most important advantage of extrusion is the use of a high solid load and no chemical reagent was added. This suggests that liquid pretreatment management costs should not be assumed contrary to the case of chemical pretreatment. It should be noted that the cost of treating a pretreatment liquid is around 20% of the bioethanol production unit costs [START_REF] Humbird | Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover[END_REF][START_REF] Kim | Evaluation of the wastewater generated during alkaline pretreatment of biomass for feasibility of recycling and reusing[END_REF].

Insert Figure 5 4.

Conclusions

In this study, an innovative treatment using twin-extrusion without chemical addition was studied at low temperature and with high solid loading. No effect was observed on chemical and biochemical composition, however, extrusion affected significantly the physical structure of CS biomass while increasing cellulose and xylan enzymatic accessibility. Although the sugar yield obtained in this work was low compared to severe pretreatments, the use of mild conditions saves energy while not adversely affecting the environment. The highest sugar content was obtained from CS extruded under 400 gTS/L and 200 rpm. The energy efficiency of the pretreatment was also assessed and was found to be maximal at 400 gTS/L and 200 rpm. Twin-screw extrusion was a suitable pretreatment prior to enzymatic hydrolysis of biomass in particular corn stover. Therefore, twin-extrusion can be considered as a clean way to pretreat the biomass and therefore enhance subsequent sugar release and thus ethanol production.

Accepted Article

This article is protected by copyright. All rights reserved.

Accepted Article

This article is protected by copyright. All rights reserved.

Accepted Article

This article is protected by copyright. All rights reserved.

Accepted Article

This article is protected by copyright. All rights reserved.

Accepted Article

This article is protected by copyright. All rights reserved.

FiguresFig. 1

 1 Figures

Fig. 2

 2 Fig.2Released sugars and their yields after different extrusion conditions: a) using cellulase from T. reesei; b) using cellulase from T. longibrachiatum. The coding of pretreated corn stover refers to CS (solid loading/screw speed).

Fig. 3

 3 Fig.3 Released sugars from untreated and pretreated corn stover (under 400 gTS/kg and 200 rpm) with xylanase addition

Fig. 4

 4 Fig.4 Impact of extrusion conditions on: a) Torque and b) specific mechanical energy consumption

Fig. 5

 5 Fig.5 Energy efficiency (g. kWh-1) of sugars production after hydrolysis enzymatic of CS biomass of different TSE experimental conditions. The coding of pretreated corn stover refers to CS (solid loading/screw speed).

Table 2

 2 Main Physico-chemical properties of untreated and extruded corn stover. The coding of pretreated corn stover refers to CS (solid loading/screw speed). For each column, identical letters refer to insignificant differences (p> 0.05).

		Size D50 (μm)	Surface area (m 2 /g)	CrI (% )	Swelling (mL/g)	CS composition (g/100g TS) Lignin Cellulose Hemicelluloses	Soluble sugars (mg/gTS)
	Untreated	Under detection limit	17.1±0.6 a	25.2±0.2 a	37.5±0.5 a	22.3±0.7 a	27.2±1.5 a
	CS (200/100)	720.6	1.573	46	10.4±0.9 b	25.0±0.1 a	36.9±1.4 a	21.5±0.4 a	25.0±3.7 a
	CS (200/200)	566.9	1.575	31	12.1±0.9 c	24.2±0.1 a	37.8±0.6 a	22.3±1.8 a	29.7±2.0 a
	CS (200/300)	533.7	1.578	30	12.6±1.1 c	24.7±1.9 a	38.5±0.4 ab	21.6±0.1 a	29.1±2.3 a
	CS (400/100)	464.9	1.697	35	9.4±1.2 b	24.8±0.9 a	37.1±2.9 a	21.4±0.3 a	19.3±0.7 a
	CS (400/200)	708.4	1.698	38	9.7±1.1 b	24.9±0.1 a	37.4±4.4 a	21.9±0.4 a	25.0±1.4 a
	CS (400/300)	940.6	1.606	35	9.7±0.4 b	23.1±0.4 a	38.3±0.7 a	21.9±1.1 a	29.2±1.7 a
	CS (600/100)	675.3	1.661	31	8.7±1.2 b	24.3±0.8 a	39.7±0.1 a	22.1±0.7 a	25.5±2.4 a
	CS (600/200)	559.4	1.681	34	8.2±0.4 b	23.8±0.8 a	39.9±0.8 b	22.7±0.0 a	25.8±0.9 a
	CS (600/300)	423.4	1.671	35	8.2±0.4 b	24.5±1.3 a	36.4±4.9 a	21.5±0.6 b	22.1±2.0 a

Acknowledgments

This work was supported by ANR Hi Solid. The authors would like to thank Dr. Bruno PONTOIRE from INRAE-Nantes for conducting the XRD analysis.

Conflict of interest

The authors declare that they have no conflict of interest.

Tables Table 1 Twin


Tony Zaouter

Karl Vulliez

Benjamin Michel

S Gazotti

J.-P Friconneau

Jean-Pierre Martins

Assessment of a bolted-flange connection for ITER Test Blanket Module

teaching and research institutions in France or abroad, or from public or private research centers.

Temperature reaches unreasonable levels if a single cooling channel is used. o Two cooling channels are necessary Cooling heat flux of 3.5 kW, a rather high value but within the design acceptance margins.

Monitoring of the effect of water velocity (through ℎ 𝑐 1 ) on the cooling power: o still important even for low ℎ 𝑐 1 due to high convection on the gaseous helium side o can be explained by the simple following model

𝜑 𝜑 ∞ = Bi 1 + Bi 1 Bi = 𝑅 1 -𝑅 0 ln 𝑅 1 -ln 𝑅 0 1 𝑅 0 Bi 0 + 1 𝑅 1 Bi 1 𝐵𝑖 𝑗 = ൗ ℎ 𝑐 𝑗 𝑅 1 -𝑅 0 𝜆 𝑠 , 𝑗 = 0,1
Harmonic dependence of 𝜑 on Bi 1  difficult to decrease 𝜑 Geometrical optimisation of the channels remains a possibility to reduce the cooling power.

Assessment of a bolted-flange connection for ITER Test Blanket Module

Problem position

Mechanical design & Numerical model

Results

Conclusions

The Test Blanket Module (TBM) are core components of ITER.

Several designs of tritium Breeding Blankets are proposed. All these concepts share a common feature: they have to be connected to a network of pipes, referred to as `Pipe Forest (PF)'. The PF connects the AEU to the TBM and will provide fluids for tritium process and coolant (gas/water).

Lithium-Lead Water Helium

Pipe size DN25 DN65 DN80 To comply with ALARA approach, the studies continue considering design of flange mounted with Quick Disconnecting System (QDS) to replace the fasteners.

𝒉 𝒄 𝟎 [W•m -2 •K -1 ] 𝒉 𝒄 𝟏 [W•m -2 •K -1 ] 𝝋 [kW]

Tab. 2: Results at particular locations in the assemblies

Tab. 1: Design conditions

Water & Lithium-Lead flanges

Helium flange

Tab. 3: Cooling heat fluxes

The PF design is strongly constrained. In addition to stringent definition requirements, the PF has to be conceived under ALARA principles. In maintenance phase, the hands-on operational time required for connection/disconnection of the PF shall be minimized and Remote Handling (RH) operation will be considered when possible.

Two technical solutions are considered at the TBM/PF interface: welded and bolted flange connection. The present study focuses on the latter.

  20-25 th September 2020 Commissariat à l'énergie atomique et aux énergies alternatives 31 st Symposium On Fusion Technology Temperature remains low in the bolts and the seals with two cooling channels, allowing potential thermal design margins.

o

  Implementation of industrial standards for water and lithium-lead bolted flanges. o Custom design for helium pipe with water cooled flanges. o FEM thermomechanical analysis demonstrate the feasibility of flange connections.

Fig. 1 :o

 1 Fig. 1: View of the TBS in the tokamak environment

Fig. 3 :

 3 Fig. 3: Load-compression curve of the different seals

Fig. 6 :

 6 Fig. 6: Tresca equivalent stress (in MPa) in the flanges a. water b. lithium-lead

FigFig. 4 :

 4 Fig. 7: Computed fields in the helium flange

  T. Zaouter 1 , K. Vulliez 1 , B. Michel 1 , J.-P. Martins 2 1. CEA, DES, ISEC, DE2D, SEAD, Laboratoire d'Étanchéité, Univ. Montpellier, Marcoule, France.2. ITER Organization, Route de Vinon-sur-Verdon, 13067 Saint-Paul-lez-Durance, France.

Lead Water Helium (2 cool. ch.) Helium (1 cool. ch.)

				𝜎 max [MPa]	250	276	1,340	1,097
				𝜎 bolt [MPa]	212	81	383	521
				𝜎 pipe [MPa]	107	109	202	203
				𝑇 max [°C]	340	345	525	525
				𝑇 bolt [°C]	-	-	221	361
				𝑇 seal [°C]	-	-	64	417
				𝑇 channel [°C]		62	417
	Pipe material	Eurofer	SS-316L	SS-316L
	Outer diam. [mm]	33.7	76.1	88.9
	Thickness [mm]	5	12.5	8.8
	Flange `Class'	300	2,500	2,500
	Bolts	4 × M16	8 × M30	18 × M24
	Bolt tension [kN]	20	30	60
	Temperature [°C]	340	345	525
	Pressure [MPa]	0.9	18.8	9.8
	Traction [N]	1,000	3,025	1,930

Bending [N•m] 325 2,975 7,175 Torsion [N•m] 100 850 2,685 Leak rate criterion `bubble tight' `bubble tight' `helium tight' Lithium-

Acknowledgements

This project was conducted under the `SSA-51' working agreement between the CEA and ITER Organization.