L Léger

V Zmijanovic

M Sellam

A Chpoun

Experimental investigation of forced flow regime transition in a dual bell nozzle by secondary fluidic injection

Keywords: dual bell nozzle, flow control, supersonic flow, transition regime, nozzle flow

Experiments on an axisymmetric dual-bell nozzle were performed at EDITH nozzle test facility of CNRS in Orléans, France. The main purpose of the study was to explore the possibility of controlling the flow regime transition by a secondary fluidic injection in the dual bell nozzle. The main focus of the present paper is to investigate the impact of the secondary injection parameters on the flow regimes transition in such nozzles. Secondary injection has been found to effectively control the flow regime transition and consequently to increase the propulsive performance of the device. It has also been pointed out that even a very low injected secondary mass flow rate leads to the control of the transition and contributes to reducing the lateral loads which can exist, moreover, when transitions are operated without injection.

Introduction:

During the last two decades, a renewed interest in space propulsion can be observed. This surge in activity can be attributed to the emergence of new nations as well as private competitors in the civilian launch market. The main objective is to maximize the payloads and/or to reduce the amount of propellants for a given mission. Supersonic nozzles are crucial in propulsion systems based on gas acceleration in rocket engines. Thereby they offer a certain margin of progress in this regard, by optimizing the flow for the different ambient conditions of the flight. The Dual Bell Nozzle (DBN), as an altitude adaptive concept for rocket jet engines, can be an interesting alternative to the classical single-profile nozzles in use nowadays [1[2, [3]. Basically, the DBN operates without any moving mechanical parts, which would increase the engine weight and provide additional complexities, nor any additional film-cooling device. Hence, it provides a high reliability and simple straightforward optimization. A recent study carried out at the German DLR institute evaluated the performance gain of the DBN compared to a conventional nozzle, for the current European launcher Ariane 5, up to 490 kg of payload increase for a geostationary orbit mission [START_REF] Stark | Ariane 5 performance optimization using dual-bell nozzle extension[END_REF].

2

The proposed study is part of the development of innovative nozzles used in rocket engines. Basically, a dual bell nozzle profile is designed by two different contours joining at a junction point where a slope break is imposed. The first part, designed according to an ideal truncated contour (TIC) or a thrust optimized contour (TOC), generates a flow adapted for operation at low altitudes (in this phase the second part of the nozzle is inoperative). The second part, designed to produce a zero, negative or positive wall pressure gradients, is suitable for high altitude operation. The resulting DBN nozzle operates in these two altitude ranges. Swan first proposed the concept of step-nozzle] in his thesis work at CalTech JPL in the mid-1940s [START_REF] Swan | The Influence of Nozzle Design on the Flight Performance of Rocket Vehicles, with an Analysis of the Results of Jet Separation[END_REF]. The aim of the investigation was to explore the possibilities of increasing the performance of rocket vehicles by using multi-step conical nozzles. One of the conclusions of the study was that more than 2 steps nozzle do not significantly change the performance. Improvement of this performance was confirmed at JPL study reported by Foster and Cowles [START_REF] Foster | Experimental Study of Gas-Flow Separation in Overexpanded Exhaust Nozzles for Rocket Motors[END_REF]. The dual bell nozzle, as an altitude adaptive nozzle concept, was first patented by Rocketdyne in the 1960s [START_REF] Sutor | Rockwell International Corp. assignee, Step nozzle[END_REF]. The control of the transition between the two modes of operation of the nozzle remains, up today, a subject of investigation both theoretically and experimentally. Experimental studies show that the transition between these two regimes takes place before of the theoretical optimal transition point, is non-axisymmetric and results in loss of specific impulse (Figure 1). It also generates undesired side loads [8, [9]. The idea of bringing the transition toward the optimal point, by injecting a secondary fluid, is the main purpose of this study. The objective is therefore to control (to delay) the change of regime to reduce the specific impulse loss and to reduce side loads.

Over the past two decades, the dual bell flow field characteristics and the separation transition mechanism have been extensively investigated. The thermodynamic properties of the fluid and the geometric parameters of the nozzle and their influence on the transition from sea level to altitude modes have been discussed by Genin et al. [8[11], Verma et al [12[15] and Reijasse et al. [START_REF] Reijasse | Wall Pressure and Thrust of a Dual Bell Nozzle in a Cold Gas Facility[END_REF]. The influence of nozzle geometry and flow features on the transition has also been numerically investigated by Martelli et al. [START_REF] Martelli | Flow separation response to unsteady external disturbances in dual bell nozzles[END_REF] and Nasuti et al. [START_REF] Nasuti | Role of wall shape on the transition in axisymmetric dual-bell nozzles[END_REF]. These studies showed, as reported in DLR investigations, that the geometric features, particularly the inflection angle and the relative length of the extension part, can affect the behavior and speed of transition. Nurnberger-Genin and Stark [START_REF] Nurnberger-Genin | Experimental Study on Flow Transition in Dual Bell Nozzles[END_REF] proposed a transition prediction model and studied the separation transition mechanism by focusing on the oblique shock wave motion from the inflexion point to the extension section. The extension wall profile choice is also an important aspect of the problematic to achieve the desired transition and performance criteria: The favorable wall pressure gradient (Negative wall Pressure Gradient NPG) extension would theoretically deliver the higher thrust rate, however this design is prone to different flow separation instabilities ([1], [START_REF] Hagemann | Experimental and analytical design verification of the dual-bell concept[END_REF], [START_REF] Genin | Side loads in subscale dual bell nozzles[END_REF]), analogues to that of the single profile conventional nozzles [START_REF] Verma | Effect of ambient pressure fluctuations on dual-bell transition behavior[END_REF]. The constant wall pressure extension (CP) profile is generally better considered and adopted by researchers and engineers as the reference extension nozzle design due to its more stable separated flow feature. However, it also features the sneak transition. On the other hand, some studies as in Nasuti et al. [START_REF] Nasuti | Role of wall shape on the transition in axisymmetric dual-bell nozzles[END_REF] reported shorter transition times in the cases of positive wall pressure gradient (PPG) extension design. Contrary to the NPG extension design, adverse pressure gradient (APG) contour decrease the sneak transition phenomena but produces slightly lower thrust performance and could generate higher side loads ( [START_REF] Nasuti | Role of wall shape on the transition in axisymmetric dual-bell nozzles[END_REF], [START_REF] Kimura | Experimental and analytical study for design of dual-bell nozzles[END_REF]). The regime transition in DBN is also affected by the jet oscillations and buffeting phenomena. As studied by Verma et al. [START_REF] Verma | Unsteady flow conditions during dual-bell sneak transition[END_REF] Martelli et al. [START_REF] Martelli | Flow separation response to unsteady external disturbances in dual bell nozzles[END_REF], Proshchanka et al. [START_REF] Proshchanka | Jet oscillation at low-altitude operation mode in dual-bell nozzle[END_REF], external or internal sources of acoustic noise can be transmitted, through the separated subsonic flow region, toward the internal flows and can thus affect the transition. Recently a detailed study was conducted at the German DLR to assess the impact of sea-level transitioning dual bell on the payload mass for a multitude of nozzle contours [START_REF] Stark | Sea-level transitioning dual bell nozzles[END_REF]. The transition regime is taking place during the transient engine start-up at sea-level, such that the side loads are supported on the ground by the launch pad. This design may provide undeniable advantages concerning the side loads, which prevent the use of dual-bell nozzles, however the gain in performance, notably in terms of specific impulse for the two flow regimes, is yet to be achieved.

Transition control by a tangential fluidic injection is a concept that is already used in propulsion systems, equipped with a turbo pump exhaust gas, for the purpose of cooling the second part of the nozzle. The film cooling has been studied previously [START_REF] Martelli | Numerical analysis of film cooling in advanced rocket nozzles[END_REF], [START_REF] Stark | Hot flow testing of a film cooled dual bell nozzle[END_REF], [START_REF] Proshchanka | Control of operation mode transition in dual-bell nozzles with film cooling[END_REF] and potential benefit has been discussed. However, tangential injection adds complexity to the system and don't allow to solve the lateral loads apparition. In this experimental study, we propose to explore the possibility of controlling flow regime transition, inside a DBN nozzle, by radial fluid injection downstream of the contour inflection point.

Transversal secondary injection at the inflection location into the base nozzle in goal to control the separation and transition in DBN was investigated and reported by Tomita et al. [START_REF] Tomita | Control of transition between two work-ing modes of a dual-bell nozzle by gas injection[END_REF]. They have reported that use of secondary injection method of active control has a limited effect on the value of the transition NPR and also diminished the base nozzle thrust coefficient during the secondary injection operation. It was also reported that at least 4% of the injected mass flow rate is required to detect any positive effect of secondary injection on the separation sequence. With the same secondary injection setup, Ferrero et al. [START_REF] Ferro | Fluidic control of transition in a dual bell nozzle[END_REF] numerically showed that the transition is effectively delayed by injecting 7.7 % of nozzle mass flow rate.

Nonetheless, as reported by investigations on thrust vector control by secondary fluidic injection [START_REF] Zmijanovic | Experimental-numerical parametric investigation of a rocket nozzle secondary injection thrust vectoring[END_REF], [START_REF] Zmijanovic | Thrust shock vector control of an axisymmetric conical supersonic nozzle via secondary transverse gas injection[END_REF], [START_REF] Zmijanovic | Thrust vectoring effects of a transverse gas injection into a supersonic cross-flow of an axisymmetric C-D nozzle[END_REF], this method needs to be carefully optimized in a rocket nozzle in order to be used effectively. Indeed, the secondary injection in a supersonic cross-flow acts as an obstacle which leads to a bow shock and a wall separation of the oncoming flow, and consequently to an adverse wall-pressure gradient in the neighbor zone upstream of the injection port. It was shown [START_REF] Zmijanovic | Experimental-numerical parametric investigation of a rocket nozzle secondary injection thrust vectoring[END_REF] that the position of the secondary injection, the mass flow ratio, the heat capacities as well as the secondary to primary molar mass ratio have a strong influence on the effectiveness of the concept. Inspired from these investigations, the approach presented in the present work suggests setting the secondary injection downstream of the inflection point unlike the previous works. By this way, the main flow must undergo same shock-separation interaction mechanisms described above when meeting the fluidic obstacle. The adverse pressure gradient occurring upstream the injection orifice, coupled with the adverse pressure gradient (APG), dominating the downstream inflection region, must prevent the non-matured nozzle flow to early transit to the extension section, and then to delay the transition NPR towards the optimal point. This active method of transition control, first proposed and patented in [30, [31], is found to be innovative, very simple to implement and inexpensive.

In a precedent and recent study on controlled flow regime transition in a dual bell nozzle by secondary radial injection [START_REF] Léger | Controlled Flow Regime Transition in a Dual Bell Nozzle by Secondary Radial Injection[END_REF], we have investigated the effect of a constant mass flow rate secondary injection on the properties of flow regime transition. We have showed that the transition and retransition NPR are strongly impacted by a secondary radial injection placed in the extension nozzle. The transition and retransition NPR is increased, allowing a significant increase of the thrust. The side loads during the transition, when the secondary injection is applied, have been found to be significantly reduced for a secondary to primary mass flow rates ratio of 2.2%. In this study the regime transition was obtained by varying the ambient pressure. In the present study, the main nozzle NPR was kept constant and where the flow regime transition and retransition were obtained by varying the secondary injection masse flow rate.

1) Experimental setup and dual-bell nozzle design parameters

Experiments on axisymmetric dual-bell nozzle were performed in the EDITH nozzle test facility of CNRS in Orléans, France. EDITH, initially a blow-down type wind tunnel depicted in Figure 2, was arranged and set, by the authors of this study as a nozzle test facility. The wind-tunnel test chamber provides a low ambient pressure environment by means of two powerful vacuum pumps of 345 kW of combined power. The tested nozzle is mounted on a force-balance frame in the depressurized test section. Dry air from the storage tanks of a total 320 l at a pressure of 300 bars is regulated to 3.5 bars total pressure before being discharged through the dual-bell nozzle into the depressurized test section. The air is supplied via 8 mm pipeline system to the pressure regulator and after to a radial flow-splitter. Through the six 8 mm tubes and their evenly distributed injectors, air is supplied to the settling chamber of 160 mm diameter and 200 mm length before being exhausted through the nozzle into the depressurized test section of the tunnel. Pressures are measured by mean of Baumer TED6 transducers: in the range of 0 to 100 ± 0.1 kPa for the test section, while 100 to 600 ± 1 kPa range is used for pressure measurements in the settling chamber. The experimental setup also provides visual access to the nozzle exhaust jet for Schlieren visualization and camera recording. The force components exerted on the nozzle model were measured using a force balance system designed and constructed by the authors. The nozzle and its feeding chamber were mounted on the 3-frame force-balance system so that the assembly can move freely in the three orthogonal directions via frictionless bearings. Each frame is connected at one end of an HBM S2M S-shaped strain-gauge force transducer. The other ends of the transducers were fixed to the wind tunnel test section construction. The transducer signal was amplified to the 0-10 V range via an HBM RM4220 amplifier before being acquired by the National Instruments SCXI-1140 fast cards. Two symmetrically placed transducers of 200 N range were measuring the vertical (z-direction) force, one of 200 N range transducer was measuring the axial (Fx) force, while a smaller one of 20 N range transducer was installed to measure the (Fy) lateral force component. The force-balance with mounted test system was calibrated under static conditions using standard weights. From a large series of calibration measurement points, the transducer signal was treated and the standard deviations with maximum and relative error margins were evaluated. The balance was then qualified under static conditions and its reliability and repeatability were reported in [START_REF] Zmijanovic | Vectorisation fluidique de la poussée d'une tuyère axisymétrique supersonique par injection secondaire[END_REF]. In the current study, the standard deviation was found being in the range of 0.8%, while the absolute error was 3.5% for the measured side force. The dynamic calibration test was also carried out. It shows that the HBM resonance frequency is equal to 146Hz and that the sensor has a frequency pass-band range of 0.05 to 70 Hz.

Tests on the DBN flow regime transition were conducted imposing the constant inlet chamber pressure condition in one side and on the other side, by varying the test section ambient pressure. Roughly, it corresponds to reproducing 3 to 20 km of altitude, atmospheric flight conditions. In summary, employed diagnostics were composed of total pressure measurements in the settling chamber and in the test section, 3-axes force balance and Z-Schlieren visualizations. The NI (National Instruments TM ) acquisition system was programmed to collect the raw pressure and force data with 1 kHz sample rate. Flow visualizations were obtained via Z-schlieren system and recorded using a 50 Hz frequency Canon®EOS60D camera.

The DBN nozzle design parameters (dimensions, mass flow rate…) were selected according to the operating capabilities of the facility. It corresponds to settling chamber pressure and temperature of 350 kPa and 290 K respectively, which, for a nozzle throat diameter of 17 mm leads to a theoretical mass flow rate of 0.187 kg/s. The nozzle contour was designed using the method of characteristics [START_REF] Zucrow | Gas Dynamics[END_REF] [START_REF] Zucrow | Gas Dynamics -Multidimensional Flow[END_REF] with an in-house built code. In this work, the first contour (from the throat to the inflection section) is based on a truncated ideal contour (TIC) for a theoretical exit Mach number of Mtic=2.75 and the second one is constructed assuming a constant wall pressure (Figure 3, right) for an exit Mach design Me=3.16. Figure 3 (left) shows the test nozzle contour with secondary injection port and settling chamber geometries. The nozzle was manufactured in two parts (Figure 4). This allowed creating a single continuous annular injection slot (between the two parts) placed 8 mm downstream the profile inflection with a width of 0.2 mm. This technique permits creating a settling chamber for the secondary injection aimed to obtain a homogeneous axisymmetric injection. The secondary injection direction was normal to the dual bell axis. The secondary settling chamber was fed by four 8 mm diameter tubes evenly distributed. The injection pressure was recorded by a large bandwidth micro (1.5 mm diameter) Kulite® XCQ-062 pressure transducer of 0-100 kPa range which was positioned in the settling chamber. The injection slot dimension and position were optimized and chosen based on previous similar work about shock-induced thrust vectoring carried out in the same facility [27-[29]. It is well known that with a secondary flow injection, the main flow responds with a bow shock. Consequently, an adverse pressure gradient and flow detachment occur upstream of the injection slot. In our experimental case, the secondary injection was placed sufficiently downstream of the inflection point of the profile in the goal to add this effect with the APG dominated flow region. Injecting in this manner allowed decreasing the slot width in the goal to decrease the secondary mass flow rate. All the operating conditions as well as the geometrical characteristics of the nozzles are presented in Table 1.

2) Experimental results

One of the main originalities of the present experimental set-up is that it permitted the nozzle pressure ratio (NPR) variation by acting on the ambient pressure at the nozzle exit, while the nozzle feeding total pressure was kept constant. The ambient pressure variation is achieved by operating the main valve (partially opening or closing) located between the test chamber and the vacuum pumps (Figure 2).

Figure 5 shows, during a test, typical evolutions of the total pressure Pt, the ambient pressure at the test section Pa and the pressure at the secondary injection settling chamber Pi. The nozzle pressures ratio NPR (ratio of Pt to Pa) is also presented. We can observe that the total pressure (Pt) was well regulated and remained almost constant while the pressure in the test section (Pa) was varied from 12 to 33 kPa. Consequently, the nozzle mass flow rate remained practically constant during the test case experimentation. Doing so, the NPR variation is only due to the test chamber pressure. This allowed a simple and direct force balance output recording versus the nozzle pressure ratio for a given nozzle mass flow rate. It's worth noting that, as long as the nozzle mass flow rate was kept constant, the variation of the force acting on the nozzle, recorded by the force balance device, was only a consequence of the variation of Pa. It should be noted, that this experiment was conducted without an effective fluid injection. The secondary settling chamber was closed upstream but remained open towards the nozzle primary flow. As the injection slot is located in the extension, logically as seen in Figure 5, the pressure in the cavity varied approximately between the ambient pressure (regime at sea level) and a pressure close to 7 kPa corresponding to an adapted flow in the extension (high altitudes regime). In other words, when the NPR increased, the secondary chamber was vacuumed and when the NPR decreased, the cavity was filled. Figure 6 shows side force component Fy (N), the specific impulse Isp (s), secondary pressures Pi and NPR variations versus time. In this case, the secondary injection was still not activated. As reported in [START_REF] Léger | Controlled Flow Regime Transition in a Dual Bell Nozzle by Secondary Radial Injection[END_REF], we can see that at each transition of regime, identified by the variation of the pressure in the secondary chamber, as expected the axial component of the force increased with increasing NPR. Additionally, at the expected transition, large peaks in the lateral force component (Fy) signal are clearly evidenced, while elsewhere, the average value of the signal is almost constant. Regarding the recorded axial thrust signal, reproducible "jumps" occurring at the same positions, corresponding to the flow transition and retransition respectively, are also observed. In our experimental conditions, it corresponds to a variation of about 4% of the nozzle thrust. This jump is downward when the NPR increases, and upward when it decreases. This is in accordance with the theoretical specific impulse curve depicted in Figure 1. Side loads are more intensive in the case of retransition (decreasing NPR, up to 4 N) compared to those observed at transition (increasing NPR, up to 1.5 N). This behavior is not reported in the literature. It can be noticed that lateral to axial forces ratio of about 1.5% is in the range of what is observed in the case of free shock separation in conventional nozzles. It can be considered as an acceptable level for a DBN transition. Although the re-transition introduces unexpected higher side loads of about 5%, it still remains within the range reported for a restricted shock separation flow situation. We think this is due to the presence of the secondary feeding chamber. Indeed, this volume seems to have an action on the transition properties. By emptying it, it must produce an additional transient injection jet (blowing effect) to the main nozzle flow which seems to have an impact on the forces. A second hypothesis is that this volume isolates or dampens all or part of the pressure fluctuations coming from downstream. The observed effect can also be induced by the slot injection itself perturbing the boundary layer development or delaying the movement of the separation shock front. The retransition does not seem to be affected by the presence of the secondary injection settling chamber. In the following experiments, the NPR was kept constant and the change in flow regime is forced by means of the secondary injection. a) Schlieren Visualisations Figure 7 shows different wave configurations observed during the experiments at the nozzle exit and by extension inside the nozzle. In under expanded flow case (a), the figure depicts the inflection expansion fan ( 5) and the subsequent recompression shock (3). This shock is induced directly behind the wall inflection and it compresses the expanding core flow to the desired wall pressure [START_REF] Hagemann | Experimental and analytical design verification of the dual-bell concept[END_REF]. The shock strength depends on the wall pressure profile prescribed within the nozzle design. As expected, the recompression shock reflects on the nozzle axis leading to the reflected shock (4). Downstream reflected shock interacts with expansion fan (2) issued from the nozzle lip. Finally, the jet boundary is also clearly visible [START_REF] Frey | Critical assessment of dual-bell nozzles[END_REF]. In over expanded flow case (b), in addition to the previous the case, as expected the reflection of the oblique shock wave (7) lead to a Mach disc [START_REF] Foster | Experimental Study of Gas-Flow Separation in Overexpanded Exhaust Nozzles for Rocket Motors[END_REF]. A reflected shock and a slip line [START_REF] Nurnberger-Genin | Flow transition in dual bell nozzles[END_REF] are issued from the triple point. The reflected shock is again reflected by the jet boundary [START_REF] Frey | Critical assessment of dual-bell nozzles[END_REF]. Also, the recompression shock (3) interacts with the oblique shock (7) of the same family forming, as expected, a single shock and a slip line is issued from the [START_REF] Génin | Experimental and Numerical Study of Heat Flux in Dual Bell Nozzles[END_REF], a separation zone upstream of the injection slot is produced. The induced separation shock merge with the bow shock [START_REF] Genin | Side loads in subscale dual bell nozzles[END_REF], formed by the fluidic obstacle, and coalescing into a single shock [START_REF] Nurnberger-Genin | Experimental Study on Flow Transition in Dual Bell Nozzles[END_REF]. This configuration has been described in previous work [START_REF] Zmijanovic | Experimental-numerical parametric investigation of a rocket nozzle secondary injection thrust vectoring[END_REF]. Downstream of fluidic obstacle, the flow is redirected toward the nozzle wall and creates a recompression shock (3). Figure 8 shows Schlieren visualizations of the forced transition and retransition by injection pressure variation. In this experiment, the NPR is kept constant close to 16. This value is slightly higher than the transition NPR, so that the flow regime configuration is in high altitude mode, i.e. with the flow attached to the base and to the extension nozzles. Then, first, the injection is activated by increasing the pressure in the secondary chamber and, thereafter, the flow is returned to its initial configuration by decreasing the injection pressure. It is observed in Figure 8 (a) that the increase in the secondary injection pressure induces the separation of the flow ahead of the injection slot and subsequently favoring the detachment of flow in the whole extension section, thus forcing the retransition of the flow. The whole sequence of Schlieren visualization was recorded by a 50 Hz camera which had shown no instability (no flip-flopping) during the retransition. With this acquisition frequency, the images do not show the presence of high frequency instability. If there is flow instability, it occurs in maximum of 0.02 s duration time. The secondary injection induces a shock which allows the reduction of the Mach disc, typical of over expanded nozzle flows, then a retransition which is produced without upstream-downstream motion of the detachment point along the extension profile and with an axisymmetric flow behavior. It should be noted that flow regime transition is obtained for a low secondary injection pressure, around 33 kPa. When the injection pressure is reduced (Figure 8 (b)), the flow returns to its previous configuration characterized by an attached flow to the base and to the extension profiles. The transition takes place in an axisymmetric manner, without or with less observed instability which means a potentially lower side force. Flow transition also takes place when the secondary injection pressure drops below 33 kPa. It can be observed that this pressure value is close to that observed inside the settling chamber in low altitude mode flow regime (Figure 6). This confirms the hypothesis that the blowing effect due to the emptying of the cavity can explain the reduction of the observed lateral load at the transition. a) Force measurement For the experiment described in the previous section, Figure 9 shows the evolution of the nozzle specific impulse ISP, the pressure ratio NPR, the secondary injection pressure Pi and the lateral force component Fy as function of time. The specific impulse was calculated considering both the main and the secondary mass flow rates contributions. In this test-case of experiment, the nozzle pressure ratio was set constant (NPR ≈16). It can be observed that when the secondary pressure increases, the change of regime occurs and induces an ISP jump of about 2 seconds. The increase in ISP is due to the fact that the injection forces the flow to return to its optimal functioning regime (low altitude mode, Figure 1). Conversely, the overserved ISP decrease, when the injection pressure decreases, is due to the reattachment of the flow to the extension nozzle profile. The analysis of the measured lateral force component (Fy) shows that the strong peak intensity became undetectable during the forced transition and retransition. It confirms somehow the axisymmetric character of the flow as observed by the optical visualizations. It should be noted, that the retransition is obtained for a secondary to main nozzles mass flow rates ratio of about 0.5% for a given NPR=16.5. For this NPR, the flow configuration is for the high-altitude mode. This NPR is close to the natural retransition (without injection) which is reached for NPR close to 14.8. The low value of secondary mass flow rate is obtained thanks to the position of the secondary injection downstream of the inflection point of the profiles. Figure 10 shows the evolution of the DBN specific impulse versus the secondary mass flow rate ratio fm during a sequence of increasing and then decreasing the secondary injection pressure. The transition and retransition occur at approximately 33 kPa for the injection pressure, i.e. for a ratio of the injected to the primary masse flow rates of approximately 0.52%. A small hysteresis during the transition-retransition cycle can, however, be observed.

As observed in the previous works on fluidic trust vectorization [START_REF] Zmijanovic | Thrust vectoring effects of a transverse gas injection into a supersonic cross-flow of an axisymmetric C-D nozzle[END_REF], the secondary injection into the supersonic nozzle flow induces a wall pressure increase upstream of the injection port due to the separation shock induced by the fluidic obstacle. This effect is then coupled with the adverse pressure gradient (APG) dominated inflection region and enhances the flow detachment from the extension nozzle by mean of such low secondary mass flow rate. The effect of the secondary injection is therefore to avoid the attachment of the flow at the extension wall. This coupling effect is made possible by injecting downstream of the inflexion region (8 mm in our case). Compared to the works of Tomita et al. [START_REF] Tomita | Control of transition between two work-ing modes of a dual-bell nozzle by gas injection[END_REF] and Ferro et al. [START_REF] Ferro | Fluidic control of transition in a dual bell nozzle[END_REF] where the radial injection was placed at the inflection point, the downstream secondary injection port position may have a strong influence on the flow regime transition with low secondary mass flow rate (2% compared to 4 -7.7%). We think that the wall pressure in the area between the injection port and the profile inflection is an important parameter controlling the detachment. It was confirmed by Takahashi et al. [START_REF] Takahashi | LOX∕CH4 Hot-Flow Testing of Dual-Bell Nozzles with Rapid-Expansion Extension[END_REF] in hot-flow testing of dual-bell nozzle experiment. It indicates that the separation transition condition is strongly related to the wall pressure in the inflection region. The secondary injection also induces an additional shock wave which increases the flow pressure inside the extension nozzle. The difference between the jet pressure and the ambient pressure is therefore reduced and high over expanded flow regime configurations are avoided or limited.

3) Summary and conclusion

The current study further investigates and analyses the proposed method of active flow regime transition control in a dual-bell rocket nozzle reported previously in [START_REF] Léger | Contrôle de transition de régime et de vectorisation de poussée dans une tuyère à galbe multiple par injection secondaire[END_REF][START_REF] Léger | Controlled Flow Regime Transition in a Dual Bell Nozzle by Secondary Radial Injection[END_REF]. The main focus of the present work was the influence of secondary injection characteristics on the flow transition in DBN. In the studied nozzle model, the first profile is an ideal contour and the second one is constructed hypothesizing a constant pressure profile. The nozzle is manufactured in two parts. This allows creating a single continuous annular injection slot placed on 8 mm downstream the profile inflection with a width size of 0.2 mm and a secondary settling chamber. Pressure measurement, Schlieren visualization and thrust and side loads have been reached.

Without secondary injection, experimental results show lower lateral load peak during the transition (when the NPR increases) compared to the retransition case (when the NPR decreases). The secondary settling chamber volume seems to have an action on the properties of the transition. It must produce a small injection jet (blowing effect) which may have an impact on transition phenomenology and consequently on lateral load. A second hypothesis is that this volume isolates or dampens all or part of the pressure fluctuations coming from downstream.

We show that that a secondary fluidic injection placed downstream of the inflection region can force the flow to retransition, from high altitude regime to sea level altitude regime. This forced retransition was reached without measurable side loads, for low secondary injection mass flow rate (0.5%) and was characterized by an ISP jump. When the injection pressure is reduced, the flow returns to its natural configuration (high altitude flow regime). The transition from the low altitude forced regime to the high-altitude regime is then observed. This transition is characterized by an ISP fall and is also achieved without measurable side loads. This flow control technique allows overcoming the main problem preventing the use of these nozzles and can be implemented for transition and retransition control that could be interesting in reusable satellite launchers development context. These initial results are very encouraging and the technique has been the subject of a patent application. In an increasingly competitive sector, the performance increase made by the possible use of dual bell nozzles could reduce the access cost to space.

Figure 1 :

 1 Figure 1 : Performance characteristics of a dual bell nozzle compared with two baseline bell-type nozzles as a function of flight altitude. (derived from [3]).

Figure 2 :

 2 Figure 2 : The wind-tunnel EDITH test facility: Flow chart, test section photography (left), pumping group and storage tanks (right).

Figure 3 :

 3 Figure 3 : Design of Dual-Bell Nozzle contour and secondary settling chamber (left), MOC wall pressure along the nozzle (right).

Figure 4 :

 4 Figure 4 : Three dimensional Design of the DBN nozzle (left); two parts of the manufactured DBN nozzle (right).

Figure 5 :

 5 Figure 5 : Pressure and NPR evolutions versus time : Pa the ambient pressure (in the test section), Pt the total pressure ( upstream of the dual bell), Pi the pressure inside the secondary stilling chamber, NPR the Nozzle Pressure Ratio (Pt/Pa)

Figure 6 :

 6 Figure 6 : ISP (s), lateral load (Fy, N), Secondary pressure (Pi) and NPR variation versus time, no pressure injection

  injection jet a) NPR=38.5, no injection, under expanded flow b) NPR=16.2, no injection, over expanded flow c) NPR=16.2, Pi=22.16 kPa, over expended flow with injection

Figure 7 :

 7 Figure 7 : Waves structure configurations for NPR =38.5 (a), NPR=16.2 (b) and NPR =16.2 with secondary injection (c). Dot line : slip line, dash line : jet boundary; line : shock wave

  , Pi=22.1 kPa, T=0 s NPR=16.54, Pi=40.7 kPa, T=0 s NPR=16.46, Pi=25.3 kPa, T=1.82 s NPR=16.47, Pi=36.8 kPa, T=0.36 s NPR=16.19, Pi=30.3 kPa, T= 3.54 s NPR=16.53, Pi=33.2 kPa, T=0.78 s NPR=16.50, Pi=32.9 kPa, T= 4.52 s NPR=16.53, Pi=33.0 kPa, T=0.80 s NPR=16.51, Pi=32.9 kPa, T=4.54 s NPR=16.53, Pi=32.9 kPa, T=0.82 s NPR=16.51, Pi=32.9, T=4.56 s NPR=16.53, Pi=30.1, T=1.04 s NPR=16.52, Pi=32.9, T=4.58 s NPR=16.53, Pi=27.0, T=1.24 s

Figure 8 :

 8 Figure 8 : Schlieren visualization of the forced transition and retransition by injection pressure variation. The NPR is kept constant

Figure 9 :

 9 Figure 9 : Isp (s), axial load (Fy, N), Secondary pressure (Pi) and NPR variation versus time.

Figure 10 :

 10 Figure 10 : Evolution of the specific impulse Isp versus the secondary mass flow rate ratio fm during a sequence of increasing and then decreasing the secondary injection pressure

Table 1 : Main and secondary flow conditions and Nozzle geometric characteristics

 1

	Stagnation conditions	Nozzle geometry	Nozzle Flow conditions	Injection chamber: Geometry and injected flow conditions
	Pt= 350 kPa T0= 293 K ρ0= 4.16 kg/m 3	TIC Base nozzle: -Length: Lb= 50.06 mm -Sonic throat radius: rt= 8.5 mm -Exit diameter: detic= 32.16 mm CP Extension Nozzle:	-Mach number at inflection: Mtic=2.75 -Exit Mach number: Me=3.16 -Pressure at inflection: 13717 Pa -Exit Pressure: 7418 Pa	-Injection slot width: 0.2 mm -Injection axial position: 8 mm from the inflection point -Injection direction: normal to the main axis -Settling chamber volume: 89.8 cm 3 -Settling chamber condition
		-Length: Le= 36.72 mm -Exit diameter:	-Nozzle main mass flow rate: 187 g/s	Pi: up to 77 kPa Tinj = 293 k
		de= 48,20 mm
		-Inflexion angle : αi = 8°	-Operating NPR range:
		-Exit angle : αe = 1.2°	from 10 to 100

ACKNOWLEDGELENTS

We would like to gratefully acknowledge the laboratory of Excellence CAPRYSSES framework and the financial support of this study from grant No. ANR-11-LABX-0006-01 of the "Investissements d'Avenir LabEx CAPRYSSES". We would also like to thank Nicolas Gouillon of ICARE/CNRS and Michael Burman of LMEE/Univ-Evry-Paris-Saclay for their technical assistance and Matthieu Sammut of CNRS for his assistance during patent filing.


Ibtissem Daoudi
email: ibtissem.daoudi@ensi-uma.tn

Raoudha Chebil

Erwan Tranvouez

Wided Lejouad Chaari

Bernard Espinasse

Introduction

Crises and risks such as natural disasters and industrial accidents represent a real and major threat for the life and well-being of people causing injuries, diseases and deaths [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. In this context, Crisis Management (CM) aims to reduce injuries and to increase survival under stressful conditions [START_REF] Solinska-Nowak | An overview of serious games for disaster risk management -prospects and limitations for informing actions to arrest increasing risk[END_REF]. Crisis Management is the application of strategies designed to help stakeholders with various skills or roles, either from different organizations (medical unit, police etc.) or the general public, deal with unexpected negative events in order to limit the severity of its consequences [START_REF] Walker | Training and learning for crisis management using a virtual simulation/gaming environment[END_REF]. As a way to define and to test those strategies, training (based on "battlefield"-born experience) and simulation are useful but also highly complex, costly, and time-consuming tasks [START_REF] Oulhaci | Improving players' assessment in crisis management serious games: The simfor project, Information Systems for Crisis Response and Management in Mediterranean Countries[END_REF]. Indeed, one of the major characteristics of the field exercises, is the variability of unpredictable events stemming from human actions and interactions as well as technology's shortcomings. Such uncertainty can result from scarce or false information, events judged too improbable to really prepare for, or taking into account people with disabilities [START_REF]European Network for Psychosocial Crisis Management Recommendations for Preparedness, Response and Recovery, Federal Office of Civil Protection and Disaster Assistance[END_REF]. In the former case, each disability situation may require specific procedures.

As a computer-based solution, Serious Games (SG) in CM can be programmed with various scenarios allowing learners to encounter such situations before they really occur [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. In particular, collaborative Crisis Management Serious Games (CMSG) represent a complementary training tool that can improve individuals' quality of life and behavior by focusing on operational aspects of CM processes. CMSGs have proved their potential for teaching learners both technical and soft skills related to solve different types of crisis problems such as natural disasters (earthquakes, floods), man-made disasters (terrorist attacks), and technological crises (cyber attacks) in an engaging way while reducing training cost and saving time [START_REF] Sauvagnargues | Concepts, Tools and Methods for Crisis Management training[END_REF]. Soft skills, including emotional and social skills like communication, sense of responsibility and stress management, are necessary in order to make a rapid and efficient response to the crisis. Despite its obvious interest, the exploitation of the CMSG concept in training processes is not always a guarantee of its usability. As with any learning systems, CMSGs rely on the implicit alignment of the learning outcomes (knowledge or skill) and the game experience (motivation, engagement). In particular, the usability of collaborative CMSGs depends on different learners' characteristics including cognitive, emotional, and social aspects [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. Consequently, several works propose to evaluate learners' behavior during their interaction with a CMSG.

However by studying the state of the art, we have noticed that researchers often neglect a crucial characteristic of CMSGs namely "emotions" [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. In fact, CM with new training technologies involves not only teaching cognitive procedures related to managing crisis situations but also emotional processes which can significantly impact actors' learning behavior. Indeed, positive affective states such as engagement/Flow can contribute to increase learning gains, while negative ones like frustration and boredom can produce misconceptions and difficulties [START_REF] Reis | Analysis of permanence time in emotional states: A case study using educational software[END_REF].

According to our findings, we focus on the detection and analysis of learners' emotions expressed in textual and visual data to infer Flow game-play experience (engagement) indicator in CMSGs [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF]. These two types of modalities are selected because they allow us to detect emotions in an objective and implicit manner without interrupting players from game-play [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF]. To the best of our knowledge, players' engagement measurement and its impact on learning using Artificial Intelligence (AI) techniques have not been investigated in such context. Hence, our objective is to propose an emotion-based Educational Data Mining (EDM) approach to assess learners' affective dynamics during a collaborative game session for CM training, and to evaluate their final mental states (at an individual and collective level) based on two different interaction modes: facial expressions and text chat data. Such a proposal would allow a more comprehensive evaluation in and of CMSGs.

The remaining parts of this article are organized as follows. In Section 2, we present works and concepts related to our proposition. In Section 3, we present our main contribution consisting in an EDM-based method for evaluating learn-ers' affective states in CMSGs. In Section 4, we present the game-based evacuation scenario. In section 5, we show the obtained results from the application of our method on this game scenario, and we discuss our major findings. Finally, in section 6 we share our conclusion and our future works.

Related Works

This section presents the most important works on evaluation of CMSGs, and gives an outline of the key concepts used in this article.

Literature Review

As the number of CMSGs increases, varying in terms of crisis situation, number of players or learners' characterization, numerous researchers have worked on evaluation of and in game-based learning environments [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. Many of these works focus on a summative evaluation, conducted at the end of the learning process to validate the overall learners' achievements [START_REF] Haferkamp | Training disaster communication by means of serious games in virtual environments[END_REF] [11] [START_REF] Silva | Stop disasters: serious games with elementary school students in rio de janeiro[END_REF], whereas others consider a formative evaluation (often known as assessment [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]) realized during the learning process by observing learners' behaviors [START_REF] Oulhaci | Improving players' assessment in crisis management serious games: The simfor project, Information Systems for Crisis Response and Management in Mediterranean Countries[END_REF].

For instance, the work referenced by [START_REF] Haferkamp | Training disaster communication by means of serious games in virtual environments[END_REF] and [START_REF] Silva | Stop disasters: serious games with elementary school students in rio de janeiro[END_REF] presented respectively an evaluation of the "Dread-Ed" and "Stop Disasters" games using questionnaires.

More precisely, "Dread-Ed" training objectives involve soft skills in a crisis situation such as communication and group decision making [START_REF] Haferkamp | Training disaster communication by means of serious games in virtual environments[END_REF]. Evaluation was then performed via a questionnaire using a 5-point Likert scale to conclude whether the game contributes to the learning of social skills, completed with a debriefing session at the end of the training process. Dealing with a more general learning outcome, "Stop Disasters" embrace building a safety culture for emergencies [START_REF] Silva | Stop disasters: serious games with elementary school students in rio de janeiro[END_REF]. To verify if the game really improves the awareness of risky situations, the participants answered questionnaires, before and after playing the game, about three aspects : game-play, missions, and game scenarios.

All these works use explicit evaluation techniques like questionnaires, interviews and debriefing sessions proposed before, during or after the game session.

Despite their frequent use, these techniques represent a subjective evaluation and rely on non-exhaustive players' opinions. For example, as with all questionnaires, some learners may understand or answer incorrectly the questions, impacting thus negatively the reliability of the game data and its analysis [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. In addition, using questionnaires during the game session is too intrusive and thus impacts learners' engagement by disrupting the game dynamics. Players' engagement and accuracy of evaluation results can be improved with non-intrusive AI techniques known as "stealth assessment" [START_REF] Shute | Stealth assessment in computer-based games to support learning[END_REF]. For example, the work referenced by [START_REF] Oulhaci | Improving players' assessment in crisis management serious games: The simfor project, Information Systems for Crisis Response and Management in Mediterranean Countries[END_REF] presented a multi-criteria and distributed "stealth" assessment approach of learners in a collaborative CMSG called "SimFor". To implement this approach, they have developed a multi-agents system which produces both individual and collective learners' evaluation (social and cognitive states).

Although CM training is an emotionally rich experience (stress, surprise, fear), a literature study shows a lack of works focusing on evaluating affective states like frustration and engagement in CMSGs. Moreover, most of these works produce a summative evaluation at the end of the game session, which can not be very useful for adapting the game to their current emotional states compared to formative evaluation [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. In this article, our objective is to explore the potential of an affective computing approach by producing both formative and summative evaluation of learners' emotions within a collaborative CMSG.

The following subsection explores what such objective entails.

Background

Having identified the role of emotions in improving CMSG's usability, this subsection presents how affective computing provides sound conceptual background allowing to improve players' engagement as defined by the Flow theory.

Affective Computing and Emotions

One of the earliest mentions of the term "Affective Computing" (AC) was introduced by Picard in 1997. According to Picard, AC is "the ability of a com-puting device to recognize, interpret and simulate human affective state" [START_REF] Picard | Affective Computing[END_REF]. This affective state is a complex phenomenon including emotions, sentiments, personality traits, needs and subjective well-being [START_REF] Reis | Affective states in computer supported collaborative learning: Studying the past to drive the future[END_REF]. However, emotion remains the most relevant affective state since it plays a crucial role in our daily lives especially in decision making, learning, and situation awareness [START_REF] Guthier | Affective Computing in Games[END_REF]. In the literature, there are three main categories of models aiming to represent emotions namely the discrete model [START_REF]Handbook of cognition and emotion[END_REF], the dimensional model [START_REF] Russell | Emotion, core affect, and psychological construction[END_REF], and the cognitive appraisal theory [START_REF] Ortony | The Cognitive Structure of Emotions[END_REF].

AC aims to gather information about users' affective states from a variety of data sources including verbal sources like textual data and speech patterns as well as non-verbal sources such as behavioral features and physiological features [START_REF] Poria | A review of affective computing: From unimodal analysis to multimodal fusion[END_REF]. Generally, the tools that support emotion detection are based on sensors devices, cameras, and microphones. Then, emotional data are analyzed by applying various AI methods such as machine/deep learning algorithms and data mining techniques.

A series of recent studies have proved that basic emotions [START_REF]Handbook of cognition and emotion[END_REF] namely anger, joy, fear, surprise, disgust, and sadness do not play a significant role in computerbased learning [START_REF] Reis | Affective states in computer supported collaborative learning: Studying the past to drive the future[END_REF] [START_REF] Kory | A review and meta-analysis of multimodal affect detection systems[END_REF]. In e-learning processes, we study a subset of cognitiveaffective states that are the most relevant to learning. These affective states are a combination of basic emotions and include Flow/engagement, frustration, confusion, and boredom [START_REF] Mello | Dynamics of affective states during complex learning[END_REF]. For this reason we present, in what follows, the Flow theory [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF] as well as the cognitive disequilibrium model [START_REF] Mello | Modeling cognitive-affective dynamics with hidden markov models[END_REF] that investigates the dynamics of affective states over time during learning activities.

Flow Theory and Cognitive Disequilibrium Model

Introduced by Csíkszentmihályi, the Flow Theory is defined as: "rewarding, subjective, emotional state of optimal pleasure that arises when an individual is absorbed in either work or leisure activities that are perceived as valuable" [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF]. Learners can experience boredom when the material does not attract them. This state occurs when their skills level is high and the game challenge is low.

Conversely, learners can experience anxiety (or frustration) when they make mistakes and get stuck. The state of anxiety occurs when their skills level is low and the game challenge is high as shown in Figure 1. Flow, also known as concentrated engagement, is a mental state in which the involvement and enjoyment in the learning process are so strong that time perception and fatigue disappear. This state occurs when both the levels of learners' skills and game challenge are high. However, confusion state is a state of uncertainty experienced when learners have difficulty of comprehending the material and are unsure on how to proceed. The confusion state (or apathy) occurs when both the levels of learners' skills and game challenge are low [START_REF] Cruz | Player-centered game ai from a flow perspective: Towards a better understanding of past trends and future directions[END_REF]. in relatively short learning sessions [START_REF] Mello | Dynamics of affective states during complex learning[END_REF]. This model assumes that learners are in a base state of engagement until they face errors, uncertainties and others obstacles. Confusion is a key signature of the cognitive disequilibrium that occurs when an impasse is detected [START_REF] Mello | Modeling cognitive-affective dynamics with hidden markov models[END_REF]. Learners revert into the engagement state if equilibrium is restored through reflection and problem solving. However, when the source of the confusion is never resolved, the learner gets stuck and important goals are blocked. In this situation, the learner will become frustrated, and then bored if the frustration persists. Boredom state represents a critical point at which the learner disengages totally from the learning process [START_REF] Mello | Modeling cognitive-affective dynamics with hidden markov models[END_REF].

Figure 2 illustrates the cognitive disequilibrium model. Transitions (red links) have been confirmed by experimentations described in [START_REF] Mello | Modeling cognitive-affective dynamics with hidden markov models[END_REF]. However, other authors have proposed that other transitions from frustration to confusion or to engagement can be possible in other scenarios. In addition, the work referenced by [START_REF] Mello | Dynamics of affective states during complex learning[END_REF] observed boredom ⇒ frustration and frustration ⇒ confusion oscillations in a complex learning context (green links). The next section presents how the concepts and methods previously described, can be combined to enhance a more comprehensive learners' assessment and evaluation in collaborative CMSGs.

Contribution : An Emotion-based EDM Method for Learners' Evaluation

The aim of the article is to answer our global research question :"How to evaluate the usability of collaborative CMSGs ?" To this end, we propose an emotion-based Educational Data Mining (EDM) method for learners' affective states evaluation. Such investigation in a domain like CM training is a distinctive aspect of our work. This domain seems likely to evoke rich affective experiences that would differ from those typically observed during learning in traditional academic subjects. In this section, we start by presenting a general overview of our method, after that we describe its composing tasks.

Extending Learners' Assessment and Evaluation in Collaborative CMSGs

As discussed in previous sections, the usability of CMSGs can be improved by a more extensive evaluation capacity based on a deep analysis of different learners' characteristics [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. Extending such capacity does not mean that no SGs fulfill part of the assessment needs, but rather it is possible to complement such games by considering all various criteria needed for a complete evaluation of a particular CMSG. Thus, we consider how each criterion, either qualitative (social interactions, emotions) or quantitative (duration, human and material impacts), and data on which they are based on, can be regrouped and globally analyzed. In this article, we propose a method that can automatically evaluate learners' behavior at two levels : individual, which focuses mainly on the emotional aspect, and collective dealing with the overall evaluation at a group level using results obtained from the first level. Therefore, the proposed method is composed of three main steps as follows:

1. Trace recovery: this step collects players' data produced during a CMSG session. These data cover visual data (real-time video recordings from webcams) to capture facial expressions, and textual data (exchanged messages during playing) to detect emotions from text.

2. Basic emotions and stress detection: the previously collected data are analyzed according to the discrete model [START_REF]Handbook of cognition and emotion[END_REF] to classify learners' basic emotions. Given the sensibility of crisis context to stress factor, we also detect the learners' stress state (which is not considered as a basic emotion) to study its impact on their actions during playing.

3. Learners' profiles construction: the detected basic emotions are then analyzed to produce individuals formative and summative evaluations of learners' emotional profiles as well as a collective emotional profile (group emotion) using a decision tree model. These three steps correspond to specific tasks namely: data collection and annotation, data fusion, data analysis, and data visualization as illustrated in Figure 3. These four different tasks are performed using specific AI algorithms presented in the following parts.

Data Collection and Annotation Task

In order to collect data in a way that is less intrusive compared to physiological measurements [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF], we extract visual data from facial expressions and textual data from exchanged messages during playing. These data sources are annotated as follows:

• Text annotation: the textual content of these messages represents a rich source to detect their emotions that are revealed by deep learning algorithms. In doing so, we utilize an automated predictive analytics tool classified as one of the top AI APIs for emotion detection from raw text strings (shorter instances of text like conversations) with 93.5% of accuracy namely Indico1 . This annotator gives as an output the probability that the text reflects the Ekman' basic emotions.

• Video annotation: the collected visual data are annotated using machine learning and computer vision algorithms to recognize facial expressions. Basing on these methods, we adopt Openface 2.02 : an automatic facial behavior analysis and understanding toolkit. Openface is a powerful toolkit that provides highly accurate facial landmark detection, head pose tracking, eye gaze estimation and Facial Action Unit recognition (FAU) [START_REF] Baltrusaitis | Openface 2.0: Facial behavior analysis toolkit[END_REF]. It is possible to save the outputs of the processed data as CSV files containing 18 FAU (codes describing specific facial muscle activations) listed in terms of presence (0 as not present and 1 as present in the face) and intensity (how intense is the FAU on a 5-points scale). Table 1 In our work, we exploit the output of FAU recognition system since it expresses human emotions according to Ekman [START_REF]Handbook of cognition and emotion[END_REF]. In order to recognize the basic emotions, we need to perform a rules matching between FAU outputs and basic emotions based on the EMFACS (Emotional Facial Action

Coding System) [START_REF] Friesen | Emfacs-7: Emotional facial action coding system, university of California[END_REF] shown in

Data Fusion Task

In this task, we perform a bi-modal fusion at the decision-level which refers to the process of combining data collected from the two modalities after being pre-classified independently to obtain the final classification. Each classified modality, using the previous annotators, provides one hypothesis on labeled emotion categories; and this integration method gives a global estimate of labeled emotion categories based on partial results [START_REF] Poria | A review of affective computing: From unimodal analysis to multimodal fusion[END_REF].

In our case, we consider the face modality as a "major" mode from a qualitative point of view but also from a quantitative point of view as, for now, this interaction mode produces more data than textual mode. So, we rely on the classification provided by the face classifier "Openface". For this reason, we adopt the averaging rule using weights as a combination rule for the fusion task.

Hence, we assign weights as follows: µ T =0.3 for the text modality and µ F =0.7

for the face modality. We adopt this weighting proposed and validated by works referenced by [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF] and [START_REF] Tan | A feasibility study in using facial expressions analysis to evaluate player experiences[END_REF]. These works are similar to our study in terms of multimodal emotion detection. In what follows, we describe the adopted approach of decision-level fusion of the individual annotators' outputs:

X all = ( X T , X F ) represents the global feature vector consisting of the text feature vector, X T and the face feature vector, X F . The two separate classifiers provide the posterior probabilities P(ei |X T ) and P(ei |X F ) for text and face, respectively, having to be combined into a single posterior probability P(ei |X all ) where ei represents one of six possible classes of basic emotions (e1=joy, e2=sadness, e3=surprise, e4=anger, e5=fear and e6=disgust). Then, we apply the averaging formula using these weights to compute the average probability of the two modalities defined as follows [START_REF] Kuncheva | A theoretical study on six classifier fusion strategies[END_REF]:

P(ei |X all =X T and X F )= (µ T *P(ei |X T )+ µ F *P(ei |X F ))/2

Data Analysis Task

In this task, we start by detecting some specific FAU to distinguish stressed from non-stressed participants and to study the impact of stress on affective transitions. Then, we propose a new mapping between basic emotions and affective states.

• Stress detection: stress is one of the most frequently occurring emotions inherent to CM since it affects the actors' way to manage crises [START_REF] Daoudi | Towards a grid for characterizing and evaluating crisis management serious games: A survey of the current state of art[END_REF]. Stress can be defined as: "the emotional, cognitive, behavioral and physiological reaction regarding to a negative event" [START_REF] Alberdi | Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review[END_REF]. In general, stress can be split into the following three categories depending on the time of exposure to stressors: acute stress, episodic acute stress, and chronic stress. The acute stress factor is characterized by a short period of time where the body returns to its normal state after the stress factor has passed and it is not considered harmful [START_REF] Alberdi | Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review[END_REF]. It is this category of stress that lies within the scope of our research. We believe that acute stress has an important impact on learners' engagement and transitions between different affective states. Therefore, we focus in what follows on detecting stress mentalstate.

Given stress is related to emotions; facial expressions have been used to detect acute stress by linking some of basic emotions as features. Many works have proved that, in different contexts like driving and working environments, stress is detected if either anger, fear or a combination of these two negative emotions is detected constantly within a fixed time interval [29] [30]. In particular, they focus on some specific FAU and their activation level extracted in each video frame, described as an indicator for fear and/or anger including AU2, AU4, AU5, AU7 and AU20.

• Mapping between affective states and basic emotions: affective states including engagement, anxiety and boredom are particular combi-nations of basic emotions as shown in [START_REF] Mello | Dynamics of affective states during complex learning[END_REF]. In our study, we adopt the existing mapping as described in [START_REF] Mello | Dynamics of affective states during complex learning[END_REF] and [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF]; and we propose other combinations of basic emotions to deduce learners' affective states based on existing emotions theories [START_REF]Handbook of cognition and emotion[END_REF] [18] [START_REF] Ortony | The Cognitive Structure of Emotions[END_REF] and the Flow definition [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF].

Flow or engagement is defined by a high level of surprise and a low sadness level [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF]. Since joy and sadness are opposite emotions as validated by

Ekman [START_REF]Handbook of cognition and emotion[END_REF] and Flow is characterized by a full involvement and enjoyment in the activity [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF], we can affirm that flow can be defined also by a high level of surprise and a high joy level.

Anxiety or frustration is detected at the presence of a high degree of anger and a low degree of joy [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF]. Likewise, anxiety can be defined by a high level of anger and a high sadness level. Moreover, basing on the definition of anxiety affective state [START_REF] Csikszentmihalyi | Flow: The psychology of optimal experience[END_REF], this state can be mapped to a high level of fear as well as a high level of sadness. In the same way, anxiety can be defined by a high level of fear and a low level of joy.

Boredom can be mapped to a high level of disgust as well as a low level of joy. In the same manner, boredom can be defined by a high level of disgust and a high sadness level [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF].

In our work, we suppose that the state in which all the levels of six basic emotions are low will represent the confusion (apathy) affective state [START_REF] Tadayon | Real-time stealth intervention for motor learning using player flow-state[END_REF].

Data Visualization Task

The final task concerns visualization of our analysis results at two levels:

individual and collective. On the one hand, we visualize the summative individuals emotional profiles by selecting the dominant and the most pronounced emotion [START_REF] Reis | Affective states in computer supported collaborative learning: Studying the past to drive the future[END_REF]. On the other hand, we visualize the aggregation of all individuals emotional profiles based on a decision tree algorithm to decide on the polarity of group emotion (positive or negative) and then to constitute the collective emotional profile [START_REF] Feidakis | A Review of Emotion-Aware Systems for e-Learning in Virtual Environments[END_REF]. Tree-based methods are easy to explain and to understand since its graphical representation is very intuitive for decision making [START_REF] Drazin | Decision tree analysis using weka, machine Learning -Project II[END_REF]. In addition, decision trees perform well when they are applied on the educational data for classifying and predicting the learners' behavior [START_REF] Kumar | Efficiency of decision trees in predicting student's academic performance[END_REF].

So, we apply J48 decision tree classifier [START_REF] Drazin | Decision tree analysis using weka, machine Learning -Project II[END_REF] to generate a decision on the group emotion based on individual emotions with a default confidence value = 0.25.

The principle is to decide the class label of group emotion (positive or negative) by learning decision rules inferred from prior training data. For example, the following instance (Engagement=40%, Apathy=50%, Boredom=5%, Anx-iety=5%) would be sorted down the leftmost branch of this decision tree and would therefore be classified as a positive instance.

Figure 4 shows the decision tree model of group emotion. Basing on the following accuracy formula and according to our experimental results, this tree-based method reaches an accuracy of 81% using 5-fold crossvalidation.

Accuracy = (correctly predicted class/total testing class)

The group emotion helps instructors to understand the group performance.

Indeed, when the group emotion was positive, it was positively related to the team's performance and vice-versa [START_REF] Feidakis | A Review of Emotion-Aware Systems for e-Learning in Virtual Environments[END_REF]. Moreover, the group emotion helps researchers to have a better understanding how learners interact with educational environments and to make decisions to improve their effectiveness [START_REF] Feidakis | A Review of Emotion-Aware Systems for e-Learning in Virtual Environments[END_REF].

In the following section, we report the development of a collaborative evacuation scenario based on an existing simulation game platform.

Experiment: Development of a Game-based Evacuation Scenario

In order to test and to improve the proposed method, we consider an evacuation scenario. This scenario is developed in a SG implemented with the commercialized iScen platform. First of all, we introduce this platform, then we describe the developed scenario. Finally, we present our experimental protocol.

Game Scenario and Experimental Protocol

In order to test our method, we have built a proof of concept evacuation scenario. In what follows, we first describe the scenario, then we detail the experimental protocol based on this scenario. Finally, we present the postevaluation questionnaire used to prove the obtained results.

Game Scenario Description

We have implemented a collaborative game prototype for building evacuation training in case of a fire emergency situation as shown in figure 5. In fact, such scenario aims to respond to a real training need that does not exist in the cited university. Moreover, the developed evacuation scenario constitutes a low-cost solution for building evacuation training in a difficult economic context compared to real exercises.

In order to make safe and orderly the evacuation process, all the learners need to perform the appropriate actions that can be taken using the keyboard and mouse. The evacuation exercise involves a group of participants (including player characters and non-player characters) having different roles namely coordinator, security responsible, warden and deputy who must collaborate and coordinate their actions to manage an emergency evacuation procedure.

• The coordinator: he/she decides to evacuate or to abandon the floors at any time, and assures the overall coordination of actions related with emergency evacuation procedure. The coordinator is responsible for ensuring that all stakeholders are aware of their responsibilities and missions, passing on the result of the evacuation to the fire service and other emergency services, as well as reporting back to evacuees.

• The security responsible: his/her role consists in assisting warden and deputy in the emergency procedure by checking that all occupants have proceeded to the designated assembly area. The security responsible should also help people who require special assistance or micro-missions during evacuation in particular vulnerable people like people with mental and/or physical disabilities as well as panicked and injured people. These micro-missions are for example: providing adequate medical care and special equipment or calling an ambulance if needed, reassuring the crowd, assisting the disabled person to a predetermined safe area, bringing the wheelchair if possible etc. Such micro-missions will stimulate the sense of responsibility among participants and will help them in dealing with real-life situations.

• The wardens and deputy wardens: they are in charge of supervising the evacuation of the building when the fire alarm system sounds, especially guiding rapidly people to a safe area (generally known as assembly point)

and searching for missing people using some evacuation drills (extinguishers, fire hose reels etc).

Experimental Protocol Description

The experimentation is carried out in the context of a training session for university building evacuation where learners are a group of 30 computer science engineering students. In order to characterize more precisely the task of protocol construction in our context, we conducted an approach that consists of the following experimental actions :

1. Information collection on participants: it is a qualitative pre-evaluation via an interview session. It contains questions about personal data, such as whether the participants have already used some computer-based learning software before, especially educational games, as well as their prior knowledge on CM. In this way, we separate learners into 3 categories as follows:

(1) novice, (2) intermediate and ( 3) expert. This information is useful to explain the obtained results during the debriefing session since learners' evaluation depends strongly on their profile before playing.

2. Brief presentation of the game: the purpose of this step is to put the participants in the context by explaining the rules/instructions of the game, and showing them how to use and interact with the platform.

3. Launching the game session: participants were invited to play a network session created by the moderator in iScen. The game session lasted on average 20 minutes. Videos of the participants' faces were captured while they interacted with the game using webcams equipments.

Collection of interaction traces after the end of the experiment:

the collected traces are the video recordings captured during the session as well as the log files automatically generated by iScen especially the exchanged messages using the text chatting system.

Performing a post-evaluation of participants' emotional states:

through a statistical analysis of the responses obtained via a post-evaluation questionnaire to measure the level of engagement for a proof purpose. This questionnaire will be described in the following subsection.

6.

A human debriefing session: this step aims to analyze the players' learning: their performances, and their acquired knowledge but also their missing skills.

Post-Evaluation Questionnaire Description

The Game Engagement Questionnaire (GEQ), proposed by [START_REF] Brockmyer | The development of the game engagement questionnaire: A measure of engagement in video game-playing[END_REF] and validated by [START_REF] Johnson | Validation of two game experience scales: The player experience of need satisfaction (pens) and game ex-730 perience questionnaire (geq)[END_REF], is one of the most commonly used self-report questionnaires in the player experience field for measuring engagement specifically elicited while playing games. The GEQ has a modular structure consisting of: the core module, the social presence module, and the post-game module. All three modules are meant to be administered immediately after the game session has finished.

From the perspective of our study, only the core module is useful since it allows to measure players' engagement. As shown in the online available link, the core module5 is a 33-item scale which is designed to measure game players' experience across seven dimensions namely Immersion, Flow, Competence, Positive Affect, Negative Affect, Tension, and Challenge. Dimension scores are computed as the average value of their items. Each item consists of a statement on a five-point Likert scale ranging from 1 (not at all) to 5 (extremely) [START_REF] Johnson | Validation of two game experience scales: The player experience of need satisfaction (pens) and game ex-730 perience questionnaire (geq)[END_REF].

Examples of statements of the dimensions are: "I was interested in the game's story" (Immersion), "I was deeply concentrated in the game" (Flow), "I felt competent" (Competence), "I felt happy" (Positive Affect), "I felt bored" (Negative Affect), "I felt frustrated" (Tension), and "I felt pressured" (Challenge).

The following section aims to show and to discuss our major findings.

Results Analysis

In this section, we show the obtained results from the method application on the developed scenario. Then, we discuss our findings by comparing evaluation results to learners' responses of the GEQ as well as the limitations of our study.

Obtained Results

As mentioned in the Contribution section, our study aims to answer the following research question: "How to evaluate the usability of collaborative CMSGs?".

In doing so, we need to perform an evaluation of learners' affective states at two levels: individual and collective as proposed by our method. Therefore, we divide our results analysis into two main parts as follow:

• Individual evaluation of learners' affective states: at this level, we study the impact of stress on affective dynamics to produce both formative and sommative evaluation of learners' affective states. In fact, we exploited the proposed mapping to perform a fine-grained analysis (every hard to evaluate because they often mix different emotional states [START_REF] Mello | Dynamics of affective states during complex learning[END_REF].

Comparing to several predictions proposed by the CDM (shown in figure 2), some of these predictions have been confirmed while others not addressed by the model are identified by our method. The supported predictions include the transitions from the state of engagement into confusion, confusion into frustration, and frustration into boredom which naturally occurred. The two predictions that have been identified, but were unexpected in the model, include the transitions from frustration to confusion and boredom to frustration. First, even the transition from frustration into confusion occurred rarely, we believe that some frustrated participants, could view the situation as a challenge and become more energized;

and ultimately enter the confusion state while trying to resolve the current misunderstanding. Second, the transition from boredom into frustration occurred significantly when we detect a high activation level of some FAU characterizing the stress emotion.

To resume, our findings suggest that some aspects of the model might need refinement to be adapted in the context of CMSGs. When aggregated across the all participants at the end of training process, our results indicated that 25% of learners felt engagement, 50% expressed boredom, 25% felt frustration, and 0% experienced confusion as shown in figure 6. The generated results aim to help learners as well as researchers and instructors to understand how participants behave during a session based on a CMSG by analyzing learners' emotions. In addition, such results are of great interest since they can be exploited by researchers to adapt some game aspects (especially the game challenge) according to the detected emotional profiles of learners; and thus to enhance their learning experiences.

Discussion

In order to test our method, the obtained summative evaluation results are compared to self-reported subjective descriptions based on the GEQ. After the collaborative iScen game session, the GEQ core module is presented to learners as an electronic questionnaire to complete. The descriptive statistics obtained from learners responses are reported in Table 5.

As shown in Table 5, positive feelings are much less severe and less frequently experienced compared to negative feelings (lower than the mid-value of the scale).

In fact, participants reported the level of positive affect to be low (2.00). More specifically, results analysis shows that immersion (reflecting how players felt strongly connected with the game) and Flow (indicating whether players lost track of their own effort and/or the passage of time during the game) receive respectively average degrees (2.13 and 2.34).

The dimension negative affect receives the highest value of all (4.28). This result indicates that playing the game engendered some negative emotional experiences in particular boredom. In addition, participants experience a certain high degree of tension (3.96) Our findings showed that the final affect annotations obtained via our method correlate well with subjective responses to the GEQ. Indeed, our method focuses on evaluating learners' affective states using unobtrusive techniques of collecting and analyzing data without affecting the high level of engagement provided by the game. Compared to the GEQ, our method represents an objective and rapid manner to analyze learners' emotions and thus to infer their affective states without the need of physiological sensors or complex algorithms.

However, our research study includes some limitations. In fact, during the use of the game-based training environment, it appeared that learners experienced more negative affective states, such as boredom and frustration, rather than transited into positive states of engagement, or even confusion. This can be explained by the fact that the change from an emotional state to another one strongly depends on several factors not addressed in this study, including personality traits, social interactions and prior knowledge on the studied subject [START_REF] Reis | Affective states in computer supported collaborative learning: Studying the past to drive the future[END_REF].

These factors should be taken into account in order to more deeply understand the dynamics of affective states over time during a game session. Moreover, our experimental protocol can not validate totally our contribution since the experimentation involves few human participants and generates small data sets. So, we need to extend this study to a larger sample of participants and to perform many advanced experiments in order to draw generalized conclusions about the usability of the considered CMSG.

Conclusion and Future Works

Serious games are proved to be an efficient tool to incentivize people to improve their lifestyle and especially to help people with special needs [START_REF] Wattanasoontorn | Serious games for health[END_REF]. In this article, we use AI technologies to investigate the role of emotions in improving the usability of CMSGs. In fact, this global objective is composed of three sub- Our experimental results confirm the major predictions of the cognitive disequilibrium model to assess learners' affective dynamics during learning. Our findings also suggest refinements for the model during crisis management training due the stress impact on affective transitions, especially the presence of boredom⇒frustration and frustration⇒confusion oscillations. These findings confirm that it is possible to detect emotions in a less invasive way and without the need of physiological sensors or complex algorithms.

In future works, we plan to test the generalizability of our contribution on new students' populations within another multi-players CMSG which is currently under development using Unity3D game engine. In particular, we will develop a game scenario that takes into account the needs of people with mental and physical disabilities in case of disaster, and we want to extend this study to a larger sample of participants based on this scenario. For example, we can simulate a behavior of a player character with a special need who is supposed to manage his/her situation in case of emergency. Furthermore, we plan to exploit our evaluation results to adapt various aspects of the game to the detected learners' affective profiles to regulate their emotions when they are feeling some negative emotions. In fact, recognizing the players' affective states allows the researcher/instructor to adjust the challenge level of the game according to their competencies level basing on their current emotional states (engaged, confused, bored or anxious) [START_REF] Stein | Eeg-triggered 735 dynamic difficulty adjustment for multiplayer games[END_REF]. For example when players feel anxious, a decrease of the challenge level seems to be effective in helping them to transit into the Flow state again.

Acknowledgment

The authors would like to thank EVERSIM (Simulation & Serious Games) society for providing the iScen platform and assisting in its use.

Figure 1 :

 1 Figure 1: Flow State Diagram (reproduced from [8])

Figure 2 :

 2 Figure 2: Cognitive Disequilibrium Model [23]

Figure 3 :

 3 Figure 3: General Overview of the Proposed Method

Figure 4 :

 4 Figure 4: Decision Tree Model of Group Emotion

4. 1

 1 . iScen Platform Iscen 3 is a software platform aiming to create interactive scenarios and use them in multi-players game sessions on a local network or on the Internet. Developed by Eversim 4 society, this platform is specifically intended for crisis simulation, management, and training. It relies on the Client-Server architecture. The server is used by the moderator who creates and administrates the scenario and the game session. All the learners (participants) connect to the server through their client machines to join the session. IScen has several communication systems consisting of voice chatting, messaging, and a system of shared notes. The messaging system let us incorporate different types of content like text, images, videos, and questionnaires. IScen requires defining scenarios which the game session instantiates. These scenarios are script-based via a graphical interface. A script is a combination of several steps linked together by paths, and manipulates a finite set of game objects. However, the current version of iScen does not allow more than six human players to participate to a game session simultaneously.

Figure 5 :

 5 Figure 5: Screen Capture of Crisis Situation Simulated in iScen Platform

  20 seconds) of the dynamics of learners' affective states during an approximately 20 minutes training session based on facial features. We evaluated what affective states were present in each 20-second interval. This interval length is chosen after a number of tests showing that shorter clips (or periods) do not provide sufficient and reliable data, and longer clips are

  515

Figure 6 :

 6 Figure 6: Global View of Individual Affective States of Players

  objectives. The first is to evaluate the temporal dynamics of learners' affective states during playing at fine-grained level (every 20 seconds) in order to adapt the game challenge to their current profiles. The evaluation of this affective dynamics relies on detecting basic emotions from facial features and studying the impact of stress, a specificity of CM domain, on transitions between affective states. Our second sub-objective is to evaluate their final affective states at the end of training process by selecting the dominant emotion based on a new mapping between affective states and basic emotions. The third sub-objective aims to explore the final individuals' affective profiles to generate the group emotion at a collective level.

Table 1 :

 1 lists some examples of the facial Action Units (AU) used as features and provides Intensity information (I) or Presence information (P). List of some examples of AUs and their Descriptions[START_REF] Baltrusaitis | Openface 2.0: Facial behavior analysis toolkit[END_REF]

	Action Unit Number Description	Prediction
	AU1	Inner brow raiser	I
	AU7	Lid tightener	P
	AU17	Chin raiser	I
	AU45 ...	Blink	P

Table 2 .

 2 This table represents a commonstandard for characterizing the six basic emotions from the detected FAU that are well defined and universal.

	Action Units	Basic Emotion
	AU6+AU12	Joy
	AU1+AU4+AU15	Sadness
	AU1+AU2+AU5+AU26 ...	Surprise

Table 2 :

 2 Examples of Rules Extracted from the EMFACS[START_REF] Friesen | Emfacs-7: Emotional facial action coding system, university of California[END_REF]

Table 3

 3

	resumes our novel interpretations.
	Affective State	Basic Emotions Combination
	Engagement/Flow	high level of surprise + high level of joy
	Anxiety (Frustration) high level of anger + high level of sadness
		or high level of fear + high level of sadness
		or high level of fear + low level of joy
	Boredom	high level of disgust + high level of sadness
	Confusion (Apathy)	all the levels of six basic emotions are low

Table 3 :

 3 Our novel interpretations of basic emotions combinations to deduce affective states

Table 4

 4 resumes the possible affective transitions proposed by our method.

	510	Several significant transitions from an affective state at time Ti to another
		affective state at time Ti+1 were identified.
					Time Ti+1
		Time Ti
			Boredom	Engagement Confusion	Frustration
		Boredom		-	-
					++
		Engagement -		+	-
		Confusion	-	+	+
		Frustration +	-
					++

Table 4 :

 4 Affective Transitions. (+) shows that the transition occurred as predicted by the CDM. (-) shows that the transition is highly unlikely. (++) shows our discovered transitions.

  in the form of specific negative emotions like frustration. Moreover, in terms of challenge, participants report that the game environment is difficult and challenging (3.43) according to their level of competence (2.43). All these results confirm the negative group emotion detected after the application of the proposed method on the same CM scenario.

	Dimension	Mean	Standard	Max	Min
			Deviation
	Immersion	2.13	0.60	4.00	1.00
	Flow	2.34	0.62	3.55	1.13
	Positive affect	2.00	0.50	3.00	1.00
	Negative affect	4.28	0.85	5.00	2.00
	Tension	3.96	0.77	5.00	1.65
	Challenge	3.43	0.68	5.00	2.00
	Competence	2.43	0.56	3.00	1.20

Table 5 :

 5 Descriptive statistics for dimensions of the core module of GEQ

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https://github.com/TadasBaltrusaitis/OpenFace

http://www.i-scen.com/home.php?langue=en

http://www.eversim.com/ns/en/home.php

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