Morgan Behrel
email: morgan.behrel@ensta-bretagne.org

Kostia Roncin

Damien Grelon

Fréderic Montel

Alain Nême

Jean-Baptiste Leroux

Christian Jochum

Yves Parlier

PRESENTATION OF AN EXPERIMENTAL CAMPAIGN FOR MEASURING PERFORMANCES AND EFFECTS OF A 50-SQUARE-METER KITE SET-UP ON A 13-METER TRAWLER

Cet article décrit une campagne de mesure en mer réalisée sur un chalutier de 13 mètres équipé d'un kite de 50 mètres carrés. Cette campagne s'est déroulée à Grande Rivière, Gaspésie, province de Québec, Canada, en octobre 2015. Le but était d'évaluer les performances du navire lorsque le kite est déployé. Pour cela, en plus des dispositifs de contrôle du kite, un ensemble de capteurs a été installé. Ainsi de nombreuses données ont été enregistrées comme la vitesse et le cap du navire, les efforts générés par le kite, la consommation du moteur, les mouvements du bateau, le couple et la vitesse de rotation de l'arbre d'hélice, l'angle du gouvernail ou la vitesse du vent.

Au cours de la campagne, des phases rectilignes avec le kite fonctionnant en vol statique ont été effectuées, avec environ 12 noeuds de vent réel. Le post-traitement des données qui est présenté permet d'estimer la finesse du kite en vol statique ainsi que son coefficient de portance. La valeur de la finesse obtenue est autour de 6, ce qui est cohérent avec d'autres résultats expérimentaux publiés.

Introduction

The use of kite to extract energy from wind is not a new idea, as it can be seen in Lloyd [START_REF] Loyd | Crosswind Kite Power (for large-scale wind power production)[END_REF]. However, the current growing shortage of fossil resources and the emergence of new ecological regulations force us to reconsider more renewable options, and the use of kite is one of them. The various ways to extract energy with kites have been properly summarize by Fagiano and Milanese in [START_REF] Fagiano | Airborne wind energy: an overview[END_REF], and Cherubini et al. [START_REF] Cherubini | Airborne Wind Energy Systems: A review of the technologies[END_REF] give a good oversight of possible technologies. The current research project, undertaken by the company beyond the sea®, and managed in partnership with ENSTA Bretagne, aims to develop kite as auxiliary propulsion system for ships. For this purpose, numerical models have been developed at ENSTA Bretagne, and so forces generated by kite and associated fuel savings can be predicted [START_REF] Leloup | A continuous and analytical modeling for kites as auxiliary propulsion devoted to merchant ships , including fuel saving estimation[END_REF]. Other models are also under development, in particular a parametrical maneuvering model to simulate the interaction between the kite and the ship [START_REF] Bigi | Course keeping of ship towed by kite[END_REF]. All these tools need to be validated, and experimental comparison is one of the best ways to do it. In this context, a sea trial and measurement campaign has been set up in partnership with a Canadian fishing vessel and the Merinov institute. This campaign held in Grande-Rivière, Gaspésie, Province of Québec, Canada in October 2015. The fishing vessel was a 13-meter trawler usually used for fishing shrimps on the Gulf of St. Lawrence. All specifications will be given in the first part. A set of winches and sensors has been installed on board, to control the 50-squaremeter kite, and measure the induced effects. All the experimental set up will be describe in Section 3.

First, mmaneuverability tests were carried out without kite, following as far as possible the ITTC guidelines [START_REF]ITTC -Recommended Procedures -Full Scale Measurements Manoeuvrability Full Scale Manoeuvring Trials Procedure[END_REF]. This aimed to get data to benchmark a maneuvering model based on work done by Yoshimura and Masumoto [START_REF] Yoshimura | Hydrodynamic Database and Manoeuvring Prediction Method With Medium High-Speed Merchant Ships and Fishing Vessels[END_REF]. Explanation on this model and validation method using data acquired with the presented experimental set-up are detailed by Bigi in [START_REF] Bigi | Course keeping of ship towed by kite[END_REF]. Secondly, runs with kite in static flight were carried out. Logged data were used to compute lift to drag ratio and lift coefficient of the kite, using a method presented in Section 4. This method is specifically adapted to the available data. Indeed, due to a lack of some information on kite orientation during flight and on wind along the altitude, strong assumptions and estimations have to be done, leading to results which must be interpreted with caution. However, the obtained lift to drag ratios and lift coefficients make sense with other experimental works like Dadd one [START_REF] Dadd | Kite Dynamics for Ship Propulsion[END_REF].

General presentation of experimental set up

Characteristics of the kite

The kite used during trials had an area of 50 square-meters (34 square-meters of projected area), with an inflatable leading edge, and 9 inflatable battens (see Fig. 1). With this architecture, the kite has his own shape without any aerodynamic load, so launching and recovering procedure are easier. The mass of the deflated kite with bridles is 21 kg.

Four 60-meter-tethers link the kite to the boat. The two main ones, so-called front tethers, are linked to the bridle system attached on the leading edge, and resume about 80% of forces generated by kite. Their lengths are constant. The 20% remaining are taken by the back tethers, which are linked to another bridle system attached on the battens. Back tether are used to control kite flight, and for this purpose their lengths can vary. The attachment point of the tethers was located just ahead the forward bulkhead of the wheelhouse, as it can be seen in Fig. 2. The mass of the 4 tethers is 2 kg. For launching and recovering procedures, all tethers are wound on the same winch. For control purpose, each back tethers goes through a specific pulley system, whose length is adjustable using electric winches. Each one is powered with 24 V DC, and can deliver 700W of nominal power. Optical encoders are fixed on each motor to ensure a feedback to the control system. The latter is running Windows 7, and the software controlling motors operates in LabVIEW (National Instruments). This one allows two mode of control: automatic and manual. The automatic one maintains the kite on a specified stationary position, using a small Inertial Measurement Unit (IMU) attached on the kite to get a feedback on kite position and orientation. The IMU uses a wireless connection to send data to the control system. This autopilot can only ensure static flight, which means the kite can only fly on the wind window edge. It has been developed by engineers of beyond the sea®, but this paper does not aim to give more details about it. The manual mode allows the operator to control directly the motor, using two joysticks. The first joystick acts on the differential length between the two back tethers, and so on the direction of the kite. The second joystick is used to trim simultaneously the length of the two back tethers, that means adjust the global angle of incidence of the kite. The kite control system is fully independent from the data acquisition system that will be described later.

Characteristics of the vessel

The trials were conducted on a 13-meter fishing vessel, called Steven Paul, used to fish shrimps with trawl on Saint Laurence gulf during summer season. General information and dimensions are given in Table I; a side view plan is given in Fig. 2 and a picture in Fig. 3. The Steven Paul, like the majority of the fishing vessels of the area, is equipped with lateral stabilizers to reduce roll motion, and so, improve working comfort of the crew. Internal structure modifications have been carried out to allow the use of a towing kite, especially by reinforcing the foredeck on which kite tethers were attached. A Caterpillar 480 horse power engine ensures the propulsion of the vessel (model Marine 3408). At full power (during trawling operation or maximum transit speed), the fuel consumption is about 80 liters per hour. A reduction gear, with a transmission ratio of 1:4.48 transfers power to the propeller (Fig. 5). The latter is a 4 blades ducted propeller, with diameter of 1.26 meter. A grid is protecting propeller from unidentified floating objects or from the fishing ropes in case of problem during trawling operation. The characteristics of the propeller are given in Table II.

The rudder is a flat plate (Fig. 5), 1.45 meter high, for 0.84 meter chord, driven by a hydraulic actuator.

Data acquisition system

The whole data acquisition system is based on a National Instruments CompactRIO platform. It consists of 3 main parts: a set of I/O modules depending on sensor technology, a Field-Programmable Gate Array (FPGA, NI CRIO-9114) and a Real-Time processor (NI CRIO-9024). All I/O modules are connected to the FPGA, and the very accurate clock of the FPGA ensures a good synchronization between the channels, and precise acquisition frequencies. The Real-Time processor logs all data coming from sensors through the FPGA on a non-volatile memory. Table III gives the details of I/O modules which have been used for this campaign. All acquisition programs have been developed with LabVIEW. Raw data were all stored in a single file, using a National Instruments proprietary format (TDMS file). These files were then converted into MATLAB® format files, and all calibration procedures and data processing were done using this software.

Sensors

Kinematics of the vessel

To measure motions and velocities of the boat, an IMU coupled with a GPS was set up (Xsens MTi-G-700). This Unit includes a microprocessor able to realize data fusion, based on an extended Kalman filter providing roll and pitch information. Manufacturer ensures dynamic error for roll and pitch under 1° with a 1σ RMS error of 0.1°. The acquisition frequencies of the Xsens were 50Hz for inertial sensors (gyroscope and accelerometer), 20 Hz for roll and pitch, and 5 Hz for all data regarding GPS technology (position and velocity). The Xsens was directly linked to a serial port of the CompactRIO. It was fixed in the wheelhouse, just behind the forward bulkhead, a few centimeters behind the attachment point of the kite, located ahead this bulkhead.

To avoid complex and unreliable calibration procedure of magnetometers, it has been decided to use an existing onboard sensor to get yaw information, based on dual antenna GPS, instead of yaw information provided by the magnetometers of the Xsens. The sensor was a Si-Tex Vector Pro, with 1σ RMS error under 0.3°. This sensor, providing the true heading of the boat regardless the boat velocity, is used by the onboard autopilot. Therefore, a serial link was done between the autopilot and the CompactRIO to recover this information. The maximum update rate of the Si-Tex Vector Pro is up to 20 Hz, according to the manufacturer. However, because the information goes through the autopilot before reaching the data acquisition system, the final update rate was 1Hz. This could not be changed during the campaign.

Engine and rudder system

A double flowmeter had been installed previously on the engine, to measure the fuel feed and fuel return, and so provide fuel consumption (Maretron M2RSP-2R-E8). This sensor is connected to the on board NMEA 2000 network. A conversion device on the NMEA2000 network provides a serial output with NMEA183 protocol. This output was connected to another serial port of the CompactRIO, and so the fuel flow was logged at 1Hz.

A device measuring the torque on the propeller shaft, developed by the company UpDaq, had also been installed previously on board. A strain gauge had been stuck on the shaft and is linked to an amplifier, sending data wirelessly to a receiver in the wheelhouse. The latter was connected to the acquisition system through a serial link. The torque on the shaft was logged at 20 Hz.

The measurement of rotational speed of the shaft propeller was carried out thank to a binary sensor, going from 0 V to 5 V each time the shaft completes a revolution. The sensor was directly linked to the Digital Input module (NI 9411) of the CompactRIO system.

A rudder angle sensor was part of the autopilot system to provide feedback. This information had been retrieved using the existing serial link presented part 3.1, with a resolution of 1°, and a frequency of 1 Hz.

Kite

To get forces on tethers, load cells had been used during trials. A three dimension load cell (Michigan Scientific TR3D-4K) had been rigidly fixed on the foredeck, just in front the forward bulkhead of the wheelhouse. The location in S  reference frame is (3.1 m, 0 m, -1.7 m). This load cell has a full scale load of 17,800 N for each axis, with a safe overload of 300%. The selection process of the load cell range was done using simulation tools developed by Leloup et al. [START_REF] Leloup | A continuous and analytical modeling for kites as auxiliary propulsion devoted to merchant ships , including fuel saving estimation[END_REF]. During measurement phases, i.e. excluding launching and recovering procedures, the front tethers of the kite were directly connected to this load cell, without any idler pulleys. So, this sensor provided information of force vector generated by the kite, expressed into the load cell axis system in a first time, and into the boat axis system after post processing (the position and orientation of the load cell implantation had been carefully measured). Knowing the length of tethers, and considering tethers are straight, the position of the kite in the wind window can be recovered. The non-linearity error specified by manufacturer is under 0.5% of full scale, and hysteresis and repeatability errors are under 0.05% of full scale each. It was not possible on the boat to carry out a complete calibration of measurement chain, so sensibilities provided by manufacturer had been used. Measurement of forces in back tethers was different due to the variable length of these tethers to ensure kite control. Two one dimension load cell (Futek LCM200) were installed into the pulleys system, and each one measured twice the load passing through each back tether. These load cells have a full scale load of 4,500 N, with a specified non-linearity error under 0.5% of full scale, a hysteresis error under 0.5% of full scale and repeatability error under 0.1% of full scale. Here again, full calibration procedure was difficult to carry out onboard, so sensibilities given on calibration certificate of each load cell has been used. The fact that measurement of back tether forces were done into a pulley circuit adds incertitude in measurement. The used pulleys were high-class ones with ball bearing design (Harken Carbo 57 mm), so it is sensible to expect limited effects, although no test in laboratory was carried out.

Each load cells and each axis of the 3D load cell were connected to one of the bridge analogical input of the CompactRIO system. The frequency of acquisition of all channels was 1 kHz.

Wind

Measurement of relative wind was done using an ultrasonic anemometer (LCJ Capteurs CV7) fixed at 9.01 m from the water line, and 0.3 m aft the mid-ship. The update rate of the sensor is 2 Hz, with a direction resolution of 1° and wind module resolution of 0.05 m/s. The sensor was linked to one of the serial port of the CompactRIO platform, using NMEA183 protocol.

The roll and pitch motion of the boat were small (maximum ±1°), with averaged periods of 2.9 s for pitch and 10 s for roll. These motions have induced a maximum velocity at the anemometer level of 0.2 m/s (3% of the averaged wind measured), and it has been decided to neglect these effects.

Post-processing of kite flight data

During the campaign, due to availability issues with the boat and inoperable weather, only one day of exploitable measurements was able to be achieved. During this day, runs with kite in static flight have been done, with about 12 knots of true wind speed. The aim of the following part is to process data to retrieve lift to drag ratio of the system {kite + tethers}. However, to compute the lift to drag ratio, the wind direction at the altitude of the kite needs to be known since it directly affects the orientation of the wind window, and the associated reference frame . As it will be shown in Eq. (4) all the data must be expressed in . In the same way, to compute the lift coefficient, the wind intensity at the kite altitude needs to be known. Unfortunately, there was no reliable mean to measure it. Indeed only the relative wind over the boat, at 9 m over the sea, is measured (see Fig. 2). The true wind at the same location can be easily retrieved using the ship speed and heading information (see Eq. ( 1)), but the true wind at other altitude can only be estimated. Indeed, altitude impacts true wind both in strength and direction. With no possibility of getting accurate modelling of the twist of the flow along the altitude, it has been decided to neglect the effect of twist, and only take account the 2D shear stress distribution calculated using the Eq. ( 2), from the ITTC 2014 recommendations [START_REF]IITTC Symbols and Terminology List[END_REF]. Equations ( 1) to (3) present the process to go from relative wind velocity and boat velocity measurement to relative wind velocity for any altitude z.

Where Z 0 is the altitude measurement (m) and z is the altitude above sea level (m). Rigorously, S V has no reason to be permanently contained in the ( 0 X , 0 Y ) plane. Indeed, boat motions induced a vertical velocity. However, considering the low level of boat motions during trials (lower than ±1°), the vertical component of the ship velocity is under 2% of the ship velocity magnitude. Hence, vertical speed of the ship has been neglected, and so S V and WR V are assumed to be contained in ( 0 X , 0 Y ). With the relative wind vector defined for any altitude, we can create a new axis system based on it. The Relative Wind reference frame is defined with the X-axis along the relative wind vector at the kite altitude, the Z-axis vertical pointing down and the Y-axis completing the coordinate system to create a direct one. This reference frame is noted with the subscript WR.

Because kite position measurement was done through a 3D load cell rigidly attached to the boat, boat motions affects kite position measurement. This is visible in Fig. 6 where elevation angle θ of the kite, resulting from the basic transformation of Cartesian position coordinates of the kite into spherical ones, according to boat axis system, evolves in line with the pitch of the boat. To remove these effects, position coordinates of the kite have been expressed into the   axis system, independent from the boat pitch and roll motion. The transformation matrix is created from the first two Euler angles (roll and pitch) provided by the inertial measurement unit Xsens, according to Xsens User Manual [START_REF]Xsens Technologies[END_REF]. Result of the transformation is given in Fig. 6, showing a few seconds of a starboard run with kite in static flight. To process the lift to drag ratio and lift coefficient, the apparent wind on the kite needs to be known. This one is the vectorial difference between the relative wind and the kite velocity in the   reference frame. For the specific case of static flight, with the ship moving a constant velocity, the kite velocity is theoretically zero in   . However kite in static flight undergoes small but permanent displacement around a middle position, but these displacements are mainly sideslip motion. With the used experimental set up, the yaw orientation of the kite was not measured, so the kite velocity vector cannot be known in the K  reference frame. To deal with this issue, it has been decided to consider the kite in perfect static flight at all times, i.e. the kite velocity in the   reference frame is taken equal to 0. In other words, the kite is considered as continuously located on the wind window edge as defined by Leloup et al. [START_REF] Leloup | A continuous and analytical modeling for kites as auxiliary propulsion devoted to merchant ships , including fuel saving estimation[END_REF], and the apparent wind vector is equal to the relative wind vector and is included in the symmetry plane of the kite assumed as a rigid body.

WR  WR  S WR WT V Z V Z V   ) ( ) ( 0 0 (1) ) ( ) ( 0 7 / 1 0 Z V Z z z V WT WT          (2) S WT WR V z V z V   ) ( ) ( (3)

Lift to drag ratio estimation

From there, it becomes easy to compute lift to drag ratio by expressing kite forces in the WR  reference frame. Indeed, the component of the force along XWR-axis is the total drag, and the projection into the (Y WR , Z WR ) plane is the lift. The component of force along the Z WR -axis is the sum of the vertical aerodynamic force generated by kite and the weight P of the kite and tethers (equals to 226 N). The latter is then subtracted to the vertical component of force to get only aerodynamic force. Finally the lift to drag ratio is achieved by processing the following equation:

WR WR WR Fx P Fz Fy D L 2 2 ) (    (4)

Lift Coefficient Estimation

The previous part has shown the identification of lift and drag component of kite force, from the measured force expressed in the relative wind axis system. From there, and with the same assumptions, lift coefficient can be processed using Eq. ( 5).

WR k a WR WR a k a L V A P Fz Fy V A L C             (5)
A k is the projected kite area (34 m 2 ), ρ a is the density of the air, estimated to 1.22 kg•m -3 during the measurement day (air temperature 15°C, atmospheric pressure 1012 hPa, relative humidity 70%), V a is the kite apparent wind speed, equal to the relative wind speed V RW in case of static flight.

Results

The Eq. ( 4) and Eq. ( 5) have been processed for 3 runs with kite in static flight. Results are presented in Table IV. No kite setting was modified during or between periods, which means the global lengths of back tethers were maintained constant (no change in global angle of incidence of the kite). Only differential variations of tether lengths were done for control purpose, and to keep the kite on a static defined position. The instantaneous post processed signals for the lift to drag ratio and the lift coefficient, corresponding to Period 2 are plotted in Fig. 7 and Fig. 8. Results are varying a lot, but the average lift to drag ratio during the considered period is equal to 6.06, and the average lift coefficient is equal to 0.76. This seems consistent with other experimental data published, like Dadd ones [START_REF] Dadd | Kite Dynamics for Ship Propulsion[END_REF]. Indeed, for a 3-square-meter kite with an aspect ratio of 4.9, Dadd got a lift coefficient of 0.78 and a lift to drag ratio of 6.07. To estimate drag coefficient and so lift to drag ratio of other kite with other aspect ratio, Dadd uses Prandtl formula as presented in Abbott and Von Doenhoff [START_REF] Abbott | Theory of Wing Sections[END_REF] and given in Eq. ( 6), assuming both kite are trimmed to produce same lift coefficient.

Applying this method to the kite used for the present study (aspect ratio of 5.5), the expected lift to drag ratio should be 6.36. The solid red line is the mean of the lift to drag ratio during the considered period, and is equal to 6.06. The associated standard deviation is 2.45. Fig. 8: Lift coefficient for each point using Eq. ( 12) (dotted), during a 569s run with kite in static flight. The solid line is the mean of the lift coefficient during the considered period, and is equal to 0.76. The associated standard deviation is 0.26.

Discussion

The averaged results of the 3 periods are close, with also a good agreement with published data, as it has been shown previously. However, the point to point data analysis in Fig. 7 and Fig. 8 shows extreme values of lift to drag ratio and lift coefficient that are not realistic. This demonstrates the limits of the various assumptions which have been done. One of the most important is probably the consideration of a kite velocity equal to 0 into the LF  reference frame, but the straight line assumption could also be a source of uncertainty. The decision to disregard the twist wind flow along the altitude due to a lack of data and models could be also detrimental. Indeed, a fourth period with kite in static flight has been logged, but it had to be discarded due to inconsistent data. One possible explanation is a significant difference between the wind orientation at the measurement point and the wind orientation at the kite position. This eventuality leads us to consider, for future experimental campaign, a duplication of wind measurement systems. Moreover, the installation of multiple anemometers at various altitudes could be also a good way to improve wind estimation with altitude. This approach has already been undertaken during another experimental campaign held in Brittany in June 2016. This campaign took place on shore, with the kite control system solidly fixed to the ground. A SOnic Detection And Ranging (SODAR) wind profiler was used to get the wind intensity and direction along altitude. The used kites were commercial ones, with area of 5-sqaremeters, and length of tethers from 25 meters to 80 meters were tested. An automatic pilot based on Fagiano work [START_REF] Fagiano | Automatic crosswind flight of tethered wings for airborne wind energy: modeling, control design and experimental results[END_REF] was implemented to generate repeatable eight-pattern trajectories. An example of multiple eight-pattern trajectories during a ten minutes record is presented in Fig. 9. More than 15 hours of trials were logged, and data are currently under post-processing. Results will be presented and discussed in a future paper. Another goal of the on board measurement campaign was initially to benchmark the fuel savings prediction tool developed by Leloup et al. [START_REF] Leloup | A continuous and analytical modeling for kites as auxiliary propulsion devoted to merchant ships , including fuel saving estimation[END_REF], using the flowmeter installed on the engine. However, due to an unexpected unavailability of the boat and unsuitable weather, it has been impossible in only one day to carry out enough measurements alternating runs with kite and then runs without kite with identical environmental conditions. Nevertheless, a first comparison can be done between power supplied by the kite, and power delivered by the engine to the propeller thanks to the sensors that were installed on-board. For example for the Period 2, on the engine side, the average rotation speed of the propeller shaft is 3.4 revolutions per second, and the average torque on the propeller shaft is 1800 Nm. The total power provided by the engine to the propeller is then 38000 W. On the kite side, the average propulsive force generated by the kite during the 569 s is 505 N, the average speed of the boat is 2.4 m/s, so the average power is 1212W. This leads to a kite providing 4.5% of the total power, with only 6.1 m/s of true wind speed, and in static flight condition. Extrapolation of this case to a true wind speed of 12m/s leads then to a kite providing 17% of the total power required. However this basic estimation needs to be treated very carefully. Indeed this is only one particular case, and it does not reflect all operational conditions of the boat. Moreover this extrapolation does not fully consider secondary effects, such as the impact of the kite on the drift of the boat. This could for instance induce bigger rudder angles in order to counteract the effects of kite, and so would affect fuel savings. The investigation of these possible issues would be done in a future work, using in particular the manoeuvrability model presented and validated by Bigi in [START_REF] Bigi | Course keeping of ship towed by kite[END_REF]. On another hand, in the present study the kite flight tested was not optimized. Much better results should be achieved in dynamic mode eventually.

Conclusion

A full scale kite experimental set up was installed on a13-meter fishing vessel, equipped with a 50-square-meter kite. Boat motions, engine parameters, kite forces, kite positions and wind data were recorded during one day of measurements. Multiple runs with kite in static flight were done with various durations, from 3 to 10 minutes. From data which were logged during these runs, a method for estimating lift to drag ratio and lift coefficient has been carried out. This method was computed, and showed results making sense. Thus, a lift to drag ratio about 5.9 and a lift coefficient about 0.7 could be retained (average on the 3 periods). These results are very close of those obtained by Dadd [START_REF] Dadd | Kite Dynamics for Ship Propulsion[END_REF], even if strong assumptions have been done to get them. These assumptions have been discussed and improvements in the experimental set up for future work are under consideration.

Fig. 1 :

 1 Fig. 1: Kite used during measurement. C-shape with inflatable leading edge and battens, 50m², 23 kg (deflated, with bridles and tethers).

Fig. 2 :

 2 Fig. 2: Side view of the Steven Paul with the waterline as it was during trials. Marker 1 denotes the kite attachment point as well as the position of the 3D load cell described part 3.3. Marker 2 shows the position of the anemometer described part 3.4. Marker 3 notes the launching mast used for launching and recovering procedures. Marker 4 shows the mid-ship of the boat and the origin of the reference frame. Kite size and tether length are not true to scale.

Fig. 3 : 4 :

 34 Fig. 3: Picture of the Steven Paul. Fig. 4: Onboard picture of the kite in static flight during one of the starboard run.

Fig. 5 :

 5 Fig. 5:Pictures of the Steven Paul propeller and rudder.

Fig. 6 :

 6 Fig. 6: Influence of pitch angle of the boat (solid red line) on kite position measurement. The dotted line is the expression of kite elevation angle in S  , and the dash-dotted in   , during one of the runs with kite in static

Fig. 7 :

 7 Fig.7: Lift to drag ratio for each point using Eq. (11) (dotted), during a 569s run with kite in static flight.

Fig. 9 :

 9 Fig. 9: Position of the kite during a ten minute record of the new experimental campaign held in Brittany in June 2016. The total force at each point is given according to the color bar.

Table I :

 I General characteristics of the fishing vessel Steven Paul

	Length Overall	13.39 m
	Length of waterline (trials loading conditions)	12.89 m
	Length between perpendiculars	12.28 m
	Beam of the hull	5.61 m
	Displacement (trials loading conditions)	64 T
	Displacement (full load)	90 T
	Maximum Draft (trials loading conditions)	2.66 m
	Draft (full load)	3.23 m
	Motorization	Caterpillar 3408 -480hp -
	Propulsion	Ducted Propeller -
	Crew for fishing operations	Captain with 2 seamen -

S  TRIALS WATERLINE

Table II :

 II Propeller characteristics of the fishing vessel Steven Paul

	Propeller Diameter	1.26 m
	Number of blades	4 -
	Pitch Ratio	1.015 -
	Blade Area Ratio	0.55 -

Table III :

 III Summary of National Instrument I/O module used for data acquisition

	Module	Description	Total number of channels
	2 x NI 9870	Serial Port	8
	1 x NI 9411	Digital Input	6
	2 x NI 9237	Bridge Analog Input	8

Table IV :

 IV Compiled results of four periods with kite in static flight

			Averaged	Averaged	Lift to drag ratio L/D	Lift coefficient C L
		Dura-	True Wind	True Wind		Standard		Standard
		tion (s)	Speed (m/s)	Angle (°)*	Mean	deviation	Mean	deviation
	Period 1	399	5.7	88	5.9	2.3	0.68	0.21
	Period 2	569	6.1	95	6.1	2.5	0.76	0.26
	Period 3	209	5.7	304	5.3	1.5	0.59	0.17
	*According to boat axis; for example 90° means cross wind, starboard tack

Acknowledgements

The authors wish to express their deep appreciation for the full support of the Steven Paul's master-owner Guy Leblanc, and his great motivation to continuously innovate. The authors are also grateful to the French agency for energy development and control (ADEME) for the funding of this study.


Ahmad Ali-Ahmad

Olivier Bornet

Firas Fadel

Yves Bourne

Florence Vincent

Christophe Bordi

• Franc ¸oise Guerlesquin

Corinne Sebban-Kreuzer

NMR assignments of the GacS histidine-kinase periplasmic detection domain from Pseudomonas aeruginosa PAO1

Keywords: Pseudomonas, Two-component system, GacS, Protein, NMR

Pseudomonas aeruginosa is a highly adaptable opportunistic pathogen. It can infect vulnerable patients such as those with cystic fibrosis or hospitalized in intensive care units where it is responsible for both acute and chronic infection. The switch between these infections is controlled by a complex regulatory system involving the central GacS/GacA two-component system that activates the production of two small non-coding RNAs. GacS is a histidine kinase harboring one periplasmic detection domain, two inner-membrane helices and three H1/D1/H2 cytoplasmic domains. By detecting a yet unknown signal, the GacS histidine-kinase periplasmic detection domain (GacSp) is predicted to play a key role in activating the GacS/GacA pathway. Here, we present the chemical shift assignment of 96 % of backbone atoms (HN, N, C, Ca, Cb and Ha), 88 % aliphatic hydrogen atoms and 90 % of aliphatic carbon atoms of this domain. The NMR-chemical shift data, on the basis of Talos server secondary structure predictions, reveal that GacSp consists of 3 b-strands, 3 ahelices and a major loop devoid of secondary structures.

Pseudomonas aeruginosa (PA) is a major opportunistic pathogen, responsible for nosocomial infections in immunocompromised patients. It can be involved in both acute and chronic infections [START_REF] Gooderham | Regulation of virulence and antibiotic resistance by two-component regulatory systems in Pseudomonas aeruginosa[END_REF]. During acute infection, PA activates genes responsible for bacterial motility and excessive production of toxins that will be liberated in the extracellular medium or directly injected in host cells via type III secretion system. In contrast, chronic infections are characterized by formation of persistent bacterial communities enclosed by polysaccharide-rich matrix (biofilm) that provides high level of resistance against host innate immune response and antibacterial treatments [START_REF] Furukawa | Keeping their options open: acute versus persistent infections[END_REF]. The switch between chronic and acute infection is regulated by a complex regulatory network involving two-component systems (TCSs), di-GMPc level and quorum sensing (QS). In this regard, environmental stresses and unknown signals detected by PA trigger expression of genes responsible for one infection type or another [START_REF] Rasamiravaka | The formation of biofilms by Pseudomonas aeruginosa: a review of the natural and synthetic compounds interfering with control mechanisms[END_REF].

TCSs are ubiquitous regulatory systems found in both prokaryotes and eukaryotes but absent from mammalians. They play essential role for the adaptation and survival of the organism by modulating cellular functions in response to environmental changes. TCSs comprise classically a membrane embedded histidine kinase (HK) sensor, which acts as a dimer, and a cognate response regulator (RR). Detection of environmental stimuli by the HK sensor leads to the autophosphorylation of its histidine kinase domains. The phosphate group is afterward transmitted to the cognate RR. Consequently, the activated RR triggers expression of different target genes [START_REF] Mitrophanov | Signal integration in bacterial two-component regulatory systems[END_REF]. Comparing to 30 HKs and 34 RRs encoded by pET21a-gacSp were cultured overnight at 37 °C in 100 mL of LB medium containing 50 lg/mL kanamycin and 34 lg/ mL chloramphenicol. The cells were harvested by centrifugation and resuspended in 1 L of M9 growth medium containing 4 g/L ( 13 C)-D-glucose, 1 g/L ( 15 N)-NH 4 Cl (Cambridge isotope Laboratories), 50 lg/mL kanamycin and 34 lg/mL chloramphenicol. The culture was grown at 37 °C to an OD 600 of 0.6. IPTG (0.2 mM) was added and the cells were incubated overnight at 20 °C for GacSp overproduction. Cells were harvested by centrifugation (5000 g, 15 min, 10 °C) and the pellet was resuspended in 50 mL of lysis buffer (50 mM Tris/HCl, 300 mM NaCl, 10 mM imidazole, 5 % glycerol, 1 tablet of SIGMA-FAST TM EDTA-free protease inhibitor cocktail and lysosyme pH 8) and disrupted by sonication on ice after the addition of 20 mM MgSO 4 and DNase. The lysate was clarified by centrifugation (13,0009g, 40 min, 4 °C) and the supernatant was applied onto nickel-chelate affinity resin column (5 mL) using an A ¨ktaXpress (GE-Healthcare). The column was washed with five volumes of 50 mM Tris-HCl, 300 mM NaCl, and 40 mM imidazole pH 8 and the protein was eluted with 50 mM Tris-HCl, 300 mM NaCl, and 250 mM imidazole pH 8. GacSp was concentrated and further purified by gel filtration (GF) on a Superdex75 16/60 column (GE-Healthcare) equilibrated with 50 mM sodium phosaphate pH 7 and 150 mM NaCl (GF buffer). Pure fractions of GacSp, as analyzed by SDS-PAGE electrophoresis, were pooled and concentrated to 12.5 mg/mL using a 10 kDa cut-off ultracentrifugation membrane (Thermoscientific).

NMR spectroscopy

NMR data were collected at 298 K on a 0.8 mM sample of 13 C, 15 N-labeled GacSp in GF buffer containing 10 % D 2 O. All the NMR experiments required for backbone and sidechain assignment were recorded on Bruker Avance III 600 MHz spectrometer (IMM NMR platform, Marseille) and on a 950 MHz Bruker Avance III HD spectrometer (IR-RMN, Grenoble), respectively. Both spectrometers were equipped with a TCI cryoprobe. Data were processed using Topspin 2.1 (Brucker) and analysed by using the program CARA (keller 2004). Backbone chemical shifts were assigned using HNCA, HN(CO)CA, CBCANH, CBCA(CO)NH, HNCO and HN(CA)CO. These values were used to calculate backbone torsion angles (U and w) using TALOS ? server [START_REF] Shen | TALOS?: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts[END_REF]. Aliphatic side chain carbons were identified using HNHA, HCCH-TOCSY, 2D HSQC-TOCSY, 15 N-HSQC-TOCSY, 15 N-HSQC-NOESY. Aromatic side chain resonances were assigned using the 2D HSQC-NOESY and the three-dimensional 13 C-HSQC-NOESY spectra.

Escherichia. coli, PA uses 10 % of its genes to encode for 64 HK and 72 RR, which reflects the adaptation capacities of this bacterium [START_REF] Rodrigue | Two-component systems in Pseudomonas aeruginosa: why so many?[END_REF].

In PA, the GacS/GacA TCS plays a key role in the transition between acute and chronic infection that is antagonistically modulated by the two other hybrid histidine kinase sensors, LadS and RetS [START_REF] Goodman | A signaling network reciprocally regulates genes associated with acute infection and chronic persistence in Pseudomonas aeruginosa[END_REF][START_REF] Ventre | Multiple sensors control reciprocal expression of Pseudomonas aeruginosa regulatory RNA and virulence genes[END_REF]. The activation of GacS/GacA pathway induces expression of non-coding small RNA (sRNAs), RsmY and RsmZ [START_REF] Brencic | The GacS/GacA signal transduction system of Pseudomonas aeruginosa acts exclusively through its control over the transcription of the RsmY and RsmZ regulatory small RNAs[END_REF]). Subsequently, an increased level of intracellular sRNAs leads to sequestrate the RNA-binding protein, RsmA. RsmA is a translational repressor that binds to mRNAs and modulates expression of more than 500 genes, including QS and T6SS gene. The GacS/GacA TCS positively regulates expression of genes required for biofilm formation, like psl and pel operons responsible for the production of Psl and Pel expolysaccharides that represent major components of the biofilm matrix. Conversely, it down-regulates expression of T3SS and motility factors (flagellum) [START_REF] Bordi | Regulatory RNAs and the HptB/RetS signalling pathways finetune Pseudomonas aeruginosa pathogenesis[END_REF][START_REF] Goodman | A signaling network reciprocally regulates genes associated with acute infection and chronic persistence in Pseudomonas aeruginosa[END_REF].

GacS is a histidine kinase harboring one periplasmic detection domain, two inner-membrane helices and three H1/D1/H2 cytoplasmic domains. Compared to a classical HK, the GacS HK is an unorthodox sensor that possesses a second receiver domain (D1) and an alternative histidinephosphotransfer domain (H2). In this case, the phosphorelay mechanism requires a four-step pathway. Autophosphorylation of a conserved His residue on the H1 domain initiates a series of phosphotransfer reactions involving HisH1 ? AspD1 ? HisH2. Afterward, the cognate response regulator GacA is activated by receiving a phosphate group from the GacS H2 domain [START_REF] Chambonnier | The hybrid histidine kinase LadS forms a multicomponent signal transduction system with the GacS/GacA two-component system in pseudomonas aeruginosa[END_REF]In this system, GacSp is predicted to play a decisive sensing role. Whereas, post-binding conformational changes might engender structural rearrangements in the transmembrane helices leading to the activation of the H1 domain phosphorylation [START_REF] Heeb | Regulatory roles of the GacS/GacA twocomponent system in plant-associated and other gram-negative bacteria[END_REF]. Moreover, the nature of the signal that binds to GacSp is still unknown. To date, there is no structural information for this domain which limits our understanding regarding its mode of action. Here we report the backbone and side-chain resonance chemical shift assignments ( 1 H, 13 C, 15 N) and identify the secondary structure elements of the periplasmic detection domain of the GacS HK sensor from PA.

Expression and purification of GacSp

The DNA sequence, coding for the periplasmic domain of , was cloned into pET21a plasmid containing an oligohistidine tag (MGHHHHHHSSGVDLGTEN-LYFQS). E. coli BL21 (DE3) cells (Novagen) harboring

Assignments and data deposition

The 1 H, 15 N HSQC spectrum of GacSp, recorded at 298 K, contains 153 resonances of which 18 correspond to amine groups of glutamine and asparagine and seven correspond to NHe group of arginine side chains (Fig. 1). The first nine residues of the His-tag, except G2 and H8, and S23, R25, S87, H99 and L116 were not assigned. In total, greater than 96 % of backbone atoms (HN, N, C, Ca, Cb and Ha), 88 % aliphatic hydrogen atoms and 90 % of aliphatic carbons atoms were assigned. Roughly 50 % of the aromatic side chains atoms were attributed, probably due to unfavourable exchange dynamics and GacSp unstable conformation.

The results of the secondary structure prediction, carried out on TALOS ? server, are shown in (Fig. 2). GacS is predicted to harbor three N-terminal a-helices (a1, a2 and a3) followed by two b-strands (b1, b2), one a-helix (a4) and a b-strand (b3). A major loop (32 residues) is predicted to link a4 and b3. Chemical shift assignments for GacSp have been deposited in the BioMagResBank (http://www. bmrb.wisc.edu) under the accession number 26840.

Fig. 1

 1 Fig. 1 1 H, 15 N-HSQC spectrum of GacSp in 150 mM NaCl, 50 mM KPO 4 buffer pH7, 10 % D 2 O, at 298 K on a Bruker Avance III 600 MHz spectrometer. The backbone 1 H, 15 N correlations are labeled

Fig. 2

 2 Fig.2GacSp secondary structure predictions and the confidence scores (0-10) as derived from a Talos? analysis (H for a-helix, E for b-strand an L for loop)

Acknowledgments The authors thank Drs. A. Favier at the Institut de Biologie Structurale and the TGIR-RMN-THC FR3050 CNRS (Grenoble, France) for data collection.