Cyril Legrand

Julie Beugin

Blaise Conrard

Juliette Marais

Marion Berbineau

El Miloudi

El Koursi

E.-M El-Koursi

Causal Analysis Methodology of Multisensor Systems based on GNSS

Keywords: GNSS-based localisation system, Dependability, Multisensor system, Railway safety applications

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Introduction

The first GNSS (Global Navigation Satellite Systems) -the GPS (Global Positioning System) -was designed by the DoD (Department of Defense) of United States for, first of all, aeronautics and military uses (missile guidance). Giving an unrivalled accuracy (in comparison with other positioning systems in this time) and a guaranteed independence to ground installations, a satellite system became a necessity to a military nation. That is why, Russia got equipped with the GLONASS (Global'naya Navigatsionnaya Sputnikovaya Sistema). In the mid of 1990s, the GPS became available to a civil use first in aeronautic domain and, nowadays, in ground transport (road and maritime transportation). In addition, face to the US monopoly, the European Union decided in 2002 to create their own GNSS, Galileo (full completion of the constellation expected by 2019). To more and more enhance the performances (especially in terms of accuracy and integrity (Ability of a system to timely alert the user of the unavailability of the service provided in the expected conditions)) of these technologies, overlay systems can be used in complementary of satellite systems like the EGNOS (European Geostationary Navigation Overlay Service) for an enhancement based on satellites or the DGPS (Differential GPS) based on ground installations.

These technologies show us their efficiency to provide an accurate position. However, the railway sector has been reluctant in the use of GPS in the past. Indeed, in a domain where the safety is more than required, a positioning service, which provide a fluctuated accuracy or can be interrupted (unavailability in a tunnel for example) is not acceptable. In addition, a step of certification is necessary based on a dependability assessment before putting into service a new positioning system based on GNSS.

Classically, the dependability is defined by the confidence on an expected service provided by a system. This confidence is addressed to the system designer but it is also useful for an user. The system will give a train position and an indicator, which indicates to him/her the confidence that can be placed into the position returned by the system. To determine the confidence, the knowledge of the error sensors is necessary. However, these data are lost after the fusion step. This is impossible to determine where the biases exactly come from in positioning system output and in which proportions.

According to the EN 50126 standard [START_REF]EN 50126 Railway Applications: The specification and demonstration of Reliability, Availability, Maintenability and Safety (RAMS)[END_REF], the services of a railway system have to generate an acceptable level of safety and its implementation does not lead to an unacceptable features like an excessive failure rate.

In so doing i.e. to evaluate the dependability of a system, it exists two categories of methods: predictive and operational. In the predictive analyse, two approaches are possible : inductive and deductive. This paper will not establish a list of all existing methods but the focus is placed on a reasoning based on truth tables and decision trees, which permit to identify all the state of a system based on binaries behaviours [START_REF] Rausand | System Reliability Theory: Models, Statistical Methods and Applications[END_REF]. These predictive methods require the perimeter definition of the qualitative/quantitative dependability analysis. This supposes that a physical hierarchy analysis has been done beforehand. In operational analyse, it is to characterise the function of a system and rank them. The interactions between sub-functions and the environment are also represented.

In a first part, a presentation of existing instrumentation system -sensors and techniques (proprioceptive and exteroceptive) -in railways will be made. These sensors architectures all of them are based on GNSS -are used in sereval projets (Rune [START_REF]Railway User Navigation Equipment (RUNE) project[END_REF], Grail-2 [START_REF]Grail-2 project[END_REF], Galoroi [START_REF]GaLoROI project[END_REF], etc). In the paper, the focus is placed on their fault modes and their impacts on the service returned by the system.

In a second part, an analysis will be made based on the combinatorial logic of sensors states. This step will be incremental. Indeed, different architectures will be considered with:

• an increased level of requirements in terms of RAMS attributes, accuracy or other indicators of performance

• a 1D and 3D context

• double or triple redundancies

In so doing, it proposes to begin with a simple theoretical case (three sensors : a GNSS receiver and two others) to a case closer to the reality. For each architectures and kind of faults leading to failure modes, expressions representing combination of logical states of sensors (nominal, acceptable or unacceptable) are computed. Furthermore, several detection techniques and combination strategies will be identified. Otherwise, the fusion step in multisensor positioning system will be supposed fault-free and correctly designed. The paper did not considered systematic errors.

This paper proposes to highlight behaviours of different multisensor systems with several architectures at the occurrence of one or several sensor errors. The main objective is to understand how a train positioning system is influenced by faults on its parts i.e. the sensors.

Existing localisation systems for safety-related applications 2.1 Railway positioning function

The railway positioning function is realised by several sensors not just one. Nowadays, it does not exist a simple and an unique method to determine an accurate and sure position and guarantee it during all the time of a mission. That is why, a combination of absolute positioning, the vehicle position is determined by natural or artificial reference in environment (balises) and dead reckoning, the position is provided by on-board sensors is generally implemented. The proprioceptive sensors (used in dead reckoning) have a weak point: they suffer of cumulative errors. A bias occurs due to a slide and increase all along the time of a travel. However, the use of sensor, which provide an absolute position i.e. exteroceptive sensors can compensate this phenomena.

The GNSS constitutes a absolute positioning solution and it is absolutely possible to integrate them in a railway domain. Nevertheless, are they reliable ? Is their contribution significant and do they enhance existing positioning systems ?

Evaluation of the localisation systems performances

In addition to RAMS parameters, it exists an other parameter class specific to positioning system based on satellite technologies [START_REF] Beugin | A dependability analysis for integrating a satellite positioning system in a rail freight application[END_REF]: accuracy, continuity, integrity and availability. The accuracy is defined by the degree of conformity between the estimated position and position its real position. The continuity refers to the probability that the system continues to operate during the time of the mission/task considered. The integrity concept is already seen in the introduction. Finally, the availability concerned the time proportion during which the services provided by the system are available and conform to the requirements. As this class is exclusive to GNSS, an extension for a multisensor system is required and can lead to futures works.

The idea to use GNSS in railways is not new and it is the subject of several researches. In the next subsection, some project will be introduced.

Projects about multisensor positioning system based on GNSS

In this decade, several projects have been led to evaluate the contribution of satellite technologies in train positioning:

APOLO (1998-2001) -Status: ended -

The main objective APOLO (Advanced POsition LOcator) was to develop a train position locator (cf Figure 1) based GNSS-1 (i.e. GPS with EGNOS) and INS (Inertial Navigation System composed of odometer, gyroscope, accelerometer and Dopplerinertial sensors commonly used in railways [START_REF] Liu | Integrity assurance of gnss-based train integrated positioning system[END_REF]) for proving its feasibility for railway safety applications.

Tests have been led with different experimental set-ups in order to validate the architecture's sensors in terms of accuracy and the error sources identification.

This architecture works with two sources of speed information in the same range of frequency. However, the odometer is the only proprioceptive sensor providing natively a position (even if, this information can be determined from speed but this operation The conclusion of APOLO project was that this kind of system is not adapted for safety-critical applications on weak SIS (Signal In Space) availability lines. Indeed, after collecting data provided during tests, the gyroscope drift proves to be critical for this kind of applications. The error resulting from this phenomena leads to a degradation related to the accuracy of several ten of meters -beyond RAMS requirements give by [START_REF]EN 50126 Railway Applications: The specification and demonstration of Reliability, Availability, Maintenability and Safety (RAMS)[END_REF] -.

The map-matching method (projection of position on precise map) has been given as a possibly reduction of this error.

DemoOrt (2004-2007) -Status: ended -

The DemOrt platform was built in order to evaluate the integration of different navigation systems especially innovative ones like GNSS on scenarios predefined (in open area and in a forest), applications where a safety responsibility is needed. Based on APOLO conclusions, GNSS can not be used alone for safety reasons. That is why, three parts compose this system (cf figure 2): 1) a GNSS (GPS) receiver, 2) RFID transponders and antennas, Doppler Radar and digital track map and 3) the software data processing with especially a fusion of information provided by the sensors except the GNSS part (qDemoOrt function). The fusion result is synchronised with GNSS receiver output.

In the dependability framework, the availability (A) and safety are evaluated quantitatively throughout tests (110352 samples with GNSS output frequency of 2 Hz) in The Doppler Radar constitutes the only on-board sensor. In consequence, its failure is critical. Indeed, in this case, the position would be determined by GNSS subsystem and the digital track map. In addition, this map does not represents an entire railways network (limited to the test tracks) and is determined a priori by GNSS services. The use of RFID technology [START_REF] Weis | Rfid (radio frequency identification): Principles and applications[END_REF] is interesting: it works at a range of frequencies bigger than railways balises.

As a proprioceptive sensor, Doppler Radar suffers of cumulative error and it represents a high drift. This is in this case that GNSS can be useful in readjusting the position.

Just after the first conclusions of the project, the ERTMS/ETCS compatibility had been discussed for futures works.

RUNE (2001-2006) -Status: ended -

The RUNE (Rail User Navigation Equipment) project [START_REF]Railway User Navigation Equipment (RUNE) project[END_REF] is funded by ESA in order to determine that satellite-based system especially using EGNOS can be used to localise a train and improvement in train driver's situational awareness. RUNE equipment regroups a GNSS receiver, an IMU ((Inertial Measurement Unit) and odometer. These equipment uses a Virtual Balises Map with balises ID and their 3D-position. Their goal is to enhance the self-capability of the train to determine its own position in order to reduce trackside equipment as much as possible. The GRAIL [START_REF] Gsa | GNSS introduction in the RAIL sector: GNSS subsystem requirement specification for enhanced ETCS applications[END_REF] (and its extension GRAIL2 [START_REF]Grail-2 project[END_REF]) initiated by GSA in the Seventh Framework Programme, has as objectives to develop and validate a GNSS based ETCS (European Train Control System i.e.a component of ERTMS -(European Railway Traffic Management System)) application in High Speed Railway Lines. Among of the applications seen in the GRAIL project, GRAIL-2 focuses on Enhanced Odometry application with the use of GNSS subsystem as an additional sensor to compensate odometry problems in high speed runs (slip and slide phenomena). The project tried to validate this kind of system, which the composition is given in figure 3 (also used in GRAIL2 project) in a real environment with the constraints of urban area and the problems it generates. In 2000, Kayser-Threde and Bombardier Transportation develop a prototype system InteGRail (for INTElligent inteGration of RAILway) providing a train position, velocity and heading by use of GNSS to safety-critical system. In ERTMS context, InteGRail platform (cf figure 5) is composed of inertial sensors (Odometer, Fibre optic gyroscope -Ars module in the figure -and accelerometer) combined with GPS receiver (enhanced with EGNOS system).

InteGRail system has been shown its efficiency in various operational conditions on low density line (rural lines). The most important integrity risk was identified as the multipath and shadowing influence on GNSS SIS.

GaLoROI -Status: in progress (end: 2014) -

In order to make durable the railway, the GaLoROI (Galileo Localization for Railway Operation Innovation) project propose the use of GNSS particularly the future Galileo in a context of low density traffic lines. The aim in this project is to design a certifiable satellite-based localisation (cf Figure 6). It is proposed to use, in addition with GNSS data, a Eddy Current Sensor (ECS), which is slide/slip-free -main disadvantages of proprioceptive sensors like odometer -. A safety layer is added to this architecture, Figure 5: InteGRail platform [START_REF] Bedrich | GNSS-Based Sensor Fusion for Safety-Critical Applications in Rail Traffic[END_REF] which consists in the use of EGNOS Safety Of Life service providing timing and positioning signals (based on integrity data) for transport applications like railways. This combination is performed by a fusion based on Extended Kalman Filter, a filter used a lot in navigation.

Figure 6: System concept used in GaloROI project [START_REF] Nguyen | Dependability evaluation of a gnss & ecs based localisation unit for railway vehicles[END_REF] All these projects conclude in the same way: GNSS is useful in railway localisation but their performance cannot meet the requirements in particular environment (forest, urban area). However, a hybridised solution with GNSS and existing on-board localisation system can be conceivable.

3 Causal analysis methodology for dependability qualitative analysis of multisensor positioning systems

Methodology

In this paper, the idea is to find any combinations of faults at the level of sensors, which lead to a failure of the entire multisensor system and determine which one is more efficient in terms of availability or safety. In so doing, a five-steps approach has been defined:

• The first step is to define the different states of sensor. Beyond the nominal and the fail state, possible faults on the sensors have to be identified: the faults related to an incorrect position provided by a sensor and the faults linked to the lack of information (no data or no response) about a sensor. In the case of an incorrect position, the output of a faulty sensor can be acceptable or unacceptable in regard to the accuracy requirement of the output. This step is common with all architecture.

• The definition of the detectability constitutes the second step. The state of a sensor can be detected or not. Following the case, it is a degradation related to the availability or the safety. For the detection of an intermittent fault (inspired by [START_REF] Caspi | Threshold and Bounded-Delay Voting in Critical Control Systems[END_REF]), correlation between sensor measures are computed : 0 < r i2 and r i3 and ... and r ij < 0.5 (1) -0.5 < r i2 and r i3 and ... and r ij < 0 where, for the two sensors i and j, 0.5 and -0.5 respectively represents a weak correlation in positive and negative direction between measures coming from sensor i and j. It is computed by Eq 2.

r ij = σ ij σ i • σ j (2)
If a measure provided by a sensor i is not correlated with the others, i is considered faulty and it is removed from the fusion step.

For the detection and the treatment of the lack of information from a sensor has to be assessed. Three solutions can be found:

the first solution is to remove the faulty sensor from the fusion step. For a n-sensor system and at the instant of the fault, the system is, actually, considered like a (n-1)sensor system. This solution is not recommended in the case of several faulty sensors or if the faulty sensor has a critical role (sensor devoted to readjust the other)

at the occurrence of the fault on a sensor, the last available data provided by the same sensor can be used for fusion. The fault effect is more distributed in time (cf Table 1). However, if this situation lasts a long time (the time until which for the position becomes unacceptable), the entire system has degraded performances (cf the "critical" combination in the fifth step).

the available data of other sensor are used. This solution does not consist in the same thing than first one. Data are duplicated and replace incorrect one in fusion step.

In Table 1, these solutions are compared in regard to the accuracy on the final position after the fusion step. Data come from the first architecture in Section 3.2.

Solutions

Position error (m) 1st solution: GPS is remove from the fusion process 272,7085 2nd solution: Last available data from the same sensor is used 18,8046 3th solution: If last available data from an other sensor is used 5,4145

Table 1: Influence of different fault treatment solutions on the accuracy

• The third step is the choice of the sensor architecture and the combination strategy. This is inspired by the different systems or prototypes seen in the projects presented before. All the architecture have to be composed of a GNSS receiver at least. Their use is decided in following step: Are GNSS data used at the same moment than the other sensor (involves that all the devices run at the same frequency) or when it is unavailable? About the combination itself, a fusion operator will be used (for example, a weighted average).

• In order to compare with the different simulations, the fourth step is a formalised example. Some assumptions are given: a set of measures provided by a given sensor follows a normal distribution centred on µ value. 95% of the data M i are given in

[µ i -2 • σ i ; µ i + 2 • σ i ] = [M i ; M i ].
Whatever the kind of architecture, the multisensor system output is determined by the following interval (Eq. 3).

[S; S] = [f (C i ); f (C i )] (3)
Let us consider β, the accuracy specified as the position error at 95% confidence level and AlertLimit, the maximum allowable error in the measured position before an alarm is triggered (cf [START_REF]EN 50126 Railway Applications: The specification and demonstration of Reliability, Availability, Maintenability and Safety (RAMS)[END_REF] and [START_REF] Barbu | GNSS RAIL USER FORUM REQUIREMENTS OF RAIL APPLICATIONS[END_REF]. Knowing β < AlertLimit, the system output is considered as:

Acceptable ("N" state) if [S; S] ⊂ ]0; β[ Degraded but Acceptable ("DA" state) if [S; S] ⊂ ]β; AlertLimit[ or β ≥ S+S 2 Unacceptable ("U" state) if [S; S] ⊂ ]AlertLimit; +∞[ or AlertLimit ≥ S+S 2
An available position corresponding to a "N" or "DA" output state whatever this state is detected or not.

A safe position corresponding to a "N" output state knowing that this state is detected.

If the conclusions of theoretical example and simulations converge, the sensor models used in these simulations are valid. A contrario, if a difference is noticed, the sensor models have to refined.

• In addition, we focus on the different possible logical combinations. Indeed, their persistence in time is important and change the state of the system output. For example, a "(N ,DA,DA)" combination can lead to a "N " output but, if this situation stays valid for several second (a time called thereafter "critical" time t c ) this combination becomes "DA". This kind of combinations is called "critical" and represents output acceptability limit. Beyond this limit, the system is triggers a safe mode i.e. the train stops.

• For each sensor architecture, the steps three and four are realised. The last part of the analysis will be devoted to the comparison between the different systems and theoretical cases (cf fourth step) considered in regard to the efficiency of their fault tolerance technique i.e. the one, which impacts the least the accuracy.

In the next part, several architectures will be presented: sensors and fusion process.

Sensor architectures considered

This part begins with a simple theoretical case (cf Figure 7) already used in [START_REF] Legrand | Sensitivity assessment to analyse dependability of a multisensor localisation system based on gnss[END_REF]. It

α i = σ -2 i n j=1 σ -2 j (4)
where, α i is the weight of the sensor i, σ i is the standard deviation of the sensor i measures.

In multisensory fusion, this constitutes the easiest solution to use simultaneously all the position measures and does not need a long computing time. This kind of multisensor system considered that each sensor provides a continuous position. Even if it is still correct for proprioceptives (odometer and accelerometer in this case), GPS receiver provides discrete data. However, extrapolation is possible when there is no data from GPS receiver.

An other architecture can be imagined with the same sensors (cf Figure 8). Here, the proprioceptive sensors are readjusted by GPS data. In the case of GPS SIS unavailability, only odometry and accelerometer measures are treated because the readjustment is not available. This solution is classically used in navigation [START_REF] Mcnail | Multi-rate sensor fusion for gps navigation using kalman filtering[END_REF]. Data in Figure 8 are considered to be in the same format (conversion or other transformation are implicit -operation is located inside sensor boxes in the figure). For the transition between 1D-context and 3D-context, the accelerometer is replaced by an IMU. Classically, it is just three accelerometers -one for each coordinate in Cartesian space -. Therefore, to have a correct position, all its coordinates have to be correct.

To enhance the reliability of a system, the redundancy is often used. In a railway localisation, the redundancy can be applied at different levels: the multi-constellation (cf 3InSat project) or the multiplication of on-board sensors. It is very current to find an odometer on several wheels or axles to mitigate the slip/slide effect. However, some redundancies can not be realistic especially in terms of costs. Some sensor like INS are very expensive. In consequence, for a fourth architecture, only a double redundancy and is considered.

Causal analysis of each systems

Each path of the tree (Figure 9) represents a logical combination of sensor states. Green, Orange, Red paths represent respectively logical combinations leading to a "N ", "DA" and "U " output state.

These results correspond to the theoretical case (cf Section 3.1 in fourth step) with high requirements in regards to level of accuracy. This means that the β and AlertLimit have been fixed respectively to 1 and 2, 5 meters (requirements for train control on high density line [START_REF] Lu | Rams evaluation of gnss for railway localisation[END_REF]). Among to the "DA" combination (20 in total), 15 combinations are considered as "critical". Indeed, in these cases, if the middle of [S; S] i.e. S+S 2 is upper to AlertLimit value, this last becomes unacceptable.

For the sake of size of the paper, this decision tree is not shown for each architecture but for the formal example presented in Section 3.1 step four.

Insofar as possible, a mathematical model is programming to simulate the behaviour of each sensor. For example, accelerometer model is given in [START_REF] Kubrak | Hybridisation of a GPS Receiver with Low-Cost Sensors for Personal Positioning in Urban Environment[END_REF] and use in the simulation. The other sensors model (for odometer and GPS receiver) are explained in [START_REF] Legrand | Sensitivity assessment to analyse dependability of a multisensor localisation system based on gnss[END_REF].

In the case of sensor hardware failures, the distribution for the time t of a failure occurrence is approximated by an exponential law.

Architecture 1: Accelerometer + Odometer + GPS receiver

The architecture considered here is presented in Figure 7. Eight combinations (cf Table 2) are identified as "critical" i.e. their persistence in time (after t c second(s)) can change the output system from "DA" to "U " state.

In addition to U ones, these combinations are to keep under surveillance. They represent the limit of position acceptability. As an example, for this architecture, if

C1 C2 U C3 U O27 → U U O26 → DA DA O25 → DA N U C3 DA O24 → DA U O23 → DA DA O22 → DA N DA C3 N O21 → DA U O20 → DA DA O19 → DA N N U C2 DA C3 U O18 → DA U O17 → DA DA O16 → DA N U C3 DA O15 → DA U O14 → DA DA O13 → N N DA C3 N O10 → DA U O11 → N DA O10 → N N N DA C2 N C3 U O9 → DA U O8 → DA DA O7 → DA N U C3 DA O6 → N U O5 → N DA O4 → DA N DA C3 N O3 → DA U O2 → N DA O1 → N N N N Figure 9
: Decision tree for a 3-sensors architecture (U ,U ,N ) combination of sensors state are still the same after t c = 2 seconds, the output state changes from "DA" to "U ".

The (N ,N ,N ), (N ,N ,DA) and (N ,DA,N ) combinations, lead well to a N . So, they respect the safety theorem (no "U " in the combinations). 7 combinations lead to a "U " output whatever happens. In this architecture, the GPS receiver is used for the readjustment accelerometer and odometer data. The last sensors follow the same models than the first multisensor system.

The principle of the Extended Kalman Filter (EKF) is not explained in this paper. However, to understand the GPS contribution, the Kalman filter has two main steps: prediction and update. GPS receiver data are used in this last step. The EKF is considered as fault-free.

The "critical" combinations are the ones where the GPS receiver state is to "U ". In consequence, it concerns (φ,φ,U ) with φ = {N ,DA,U }. Indeed, if the readjustment is strongly degraded, the step of update will be biased. the t c time is 1 to 3 seconds depending on the importance of the degradation.

Finally, this architecture is very dependent on the performances of GPS receiver. In real case, degradations related to GPS SIS are correlated to the environment configuration like urban or forest. These situation correspond to (φ,φ,U ) combinations.

Architecture 1 ter: Accelerometer + Odometer x2 + GPS receiver

Here, the odometer is duplicated to implement an active redundancy. This choice is explained in Section 3.2. The two odometers have the same features (resolution, noises, etc... (cf [START_REF] Legrand | Sensitivity assessment to analyse dependability of a multisensor localisation system based on gnss[END_REF])).

In this architecture (a 4-sensor system), there are, in consequence, 3 4 so 81 possible logical combinations of sensor states. They are not all occurred during the simulation. Table 3 shows the combinations that occurred during a simulation of the architecture (100 seconds). Table 3: "Critical" combinations of architecture 1 ter First conclusions, the "critical" combinations where the odometer provides an unacceptable position are not critical if the second odometer give a nominal value.

Architecture 2: IMU + Odometer + GPS receiver

The IMU is considered as 3 accelerometers, one on each axis. The features of IMU depend on the accelerometers seen in previous architectures. In addition, each component of the position vector (x, y and z in Cartesian coordinate system) have to be correct to provide a 3D correct position. This consideration is taking into account in IMU model for the simulation via its parameters. The first observation is that the (U ,U ,U ) combination does not immediately lead to a U output. However, in this case, t c is very low i.e. 1 second. A precaution must be taken: here, the time step is 1 second. For the worst "critical" combinations i.e. the combinations with a t c time very short (t c ≤ 1), a time step must be more adequate i.e. less than the shortest t c time.

Conclusions and prospects

In this paper, a causal analysis of several multisensor systems based on GNSS has been attempted. These lasts are inspired by the different platforms already used in projects presented at the beginnings of the paper. "Critical" combinations have been identified for several sensor architecture, combinations, which if they persist after a time (called t c ) became unacceptable.

The time determined for a "critical" combinations can be seen as a maximum Time To Alarm (quantity used to evaluate the integrity, attribute only valid for the GNSS performances). This constitutes a possible prospect for an integrity concept extended to an entire multisensor system in order to assess its performances.

All the times are not measured yet. The immediate pursuance of this paper is to determined the times t c for each "critical" combinations.

In future works, an analyse of multisensor system behaviour will be made in existing environment with data (true measures of different sensors seen in this paper).

A potential perspective is to use importance [START_REF] Rausand | System Reliability Theory: Models, Statistical Methods and Applications[END_REF] and sensitivity measures [START_REF] Legrand | Sensitivity assessment to analyse dependability of a multisensor localisation system based on gnss[END_REF] in order to quantify the sensor error influences.

Figure 1 :

 1 Figure 1: APOLO equipment

Figure 2 :

 2 Figure 2: DemoOrt Structure [2]

2. 3

 3 .4 GRAIL (2005-2007) -Status: ended -and GRAIL2 -Status: in progress (end: 12/2013) -

Figure 3 :

 3 Figure 3: GRAIL prototype

Figure 4 :

 4 Figure 4: 3InSat Reference Architecture

Figure 7 :

 7 Figure 7: Accelerometer/Odometer/GPS receiver architecture

Figure 8 :

 8 Figure 8: Accelerometer/Odometer/GPS receiver enhanced architecture with Extended Kalman Filter

  Combination format : (state(Odo),state(Odo2), state(Acc),state(GPS)) (U ,U ,N ,DA) (U ,U ,DA,DA) (U ,U ,U ,N ) (DA,DA,DA,DA) (DA,DA,U ,N ) (DA,DA,N ,U ) (N ,N ,DA,U ) (N ,N ,U ,DA)

  Combination format : (state(Odo), state(IMU),state(GPS)) (U ,U ,N ) (U ,U ,DA) (U ,U ,U ) (DA,DA,DA) (DA,DA,U ) (DA,DA,N ) (N ,N ,DA) (N ,N ,U )Table 4: "Critical" combinations of architecture 2

Table 2 :

 2 "Critical" combinations of architecture 1 3.3.2 Architecture 1 bis: Accelerometer + Odometer + GPS receiver + EKF

Railways 2014 The Second International Conference on Railway Technology: Research, Development and Maintenance Ajaccio, Corsica, France 8-11 April 2014 http://www.civil-comp.com/conf/railways2014.htm

Railways 2014 The Second International Conference on Railway Technology: Research, Development and Maintenance Ajaccio, Corsica, France 8-11 April 2014

Acknowledgments

The authors would like to thank the Technological Research Institute Railenium and the IFSTTAR for their support in this work and the International Campus on Safety and Intermodality in Transportation (CISIT) and the European Commission via the European Regional Development Fund (ERDF) for making possible this work.


Ion Candel
email: ion.candel@gipsa-lab.grenoble-inp.fr

Cindy Bernard
email: cindy.bernard@gipsa-lab.grenoble-inp.fr

Ioana Cornel
email: cornel.ioana@gipsa-lab.grenoble-inp.fr

Bertrand Reeb
email: bertrand.reeb@edf.fr

Didier Boldo
email: didier.boldo@edf.fr

SPARSE REPRESENTATION OF CHANNEL'S IMPULSE RESPONSE FOR UNDERWATER INHOMOGENEITIES TRACKING

Keywords: sparse representation, underwater acoustics, inhomogeneity, decomposition

In this paper, we propose a method for underwater inhomogeneities characterization using sparse representation of channel's impulse response. We consider the case of moving vortices created naturally or artificially that do not conserve their physical properties when observed at two distinct positions in space. Existing amplitude-based techniques fail to provide an accurate representation when the physical properties of the dynamic inhomogeneity are altered, but it can be achieved using the decomposition of the inhomogeneity's impulse response, based on a physically driven decomposition basis. Tests carried out in a reduced scale experimental facility show, on real data, the efficiency of the inhomogeneities tracking.

INTRODUCTION

The dynamics of underwater phenomena can be understood as the propagation of vortices produced either naturally (natural inhomogeneity embedded in water flow, marine life such as fish and mammals) or artificially (underwater obstacles, submarines, vessels). These vortices are detected and analyzed using pairs of acoustic transducers. If a vortex intersects the path between two pairs of active transducers, the signatures observed on the received signal's amplitudes are quantifiable. However, specific phenomena associated to the context alter the signature when the inhomogeneity travels to another pair of transducers and thus the similarity between the two signatures is poor.

An alternative is to decompose the impulse responses corresponding to the two transducers pairs on basis of elementary functions. In order to adapt the basis to the context of our application, two concepts are studied in this paper. First, we propose to design a decomposition basis inspired by the physical characteristics of the vortex "seen' by the impulse response of two propagating acoustic paths.

The second concept is the one of the sparse representation [START_REF] Candés | Decoding by Linear Programming[END_REF]: the analyzed phenomena can be recovered only using some of the decomposition coefficients that are relevant with respect of the minimization criterion, thus obtaining a sparse representation of the phenomena. At this level, we show the interest of the L 1 norm.

The article is structured as follows: section 2 presents the theoretical concepts used in our work: waveform decomposition and sparse representation. Section 3 presents the main results and remarks of our work and section 4 presents the concluding remarks and future work.

DECOMPOSITION BY SPARSE REPRESENATION

The general layout of the analyzed phenomena is illustrated in figure 1. Wide band signals are transmitted using two pairs of sensors placed on the Upstream and Downstream sides.

In the absence of inhomogeneity (such as a vortex), there are no changes in the shape of the impulse responses of the channels defined by the two pairs of transducers. The apparition of the inhomogeneity between the transducers introduces modifications on the amplitudes of the impulse responses, calculated for each transmission, a fact that can be seen as signatures of the inhomogeneity's source (figure 2).

However, the vortex can change the characteristics while moving from one pair of sensors to the other, the similarity of the two signatures is low and can be considered as belonging to two different phenomena. This drawback can be eliminated using a decomposition of the two signatures based on a physically driven basis. That is, the decomposition attempts to highlight the common elements that will allow us to identify the inhomogeneity.

Considering the emitted signals from figure 1, s e1 (t) and s e2 (t), as two linear frequency modulations, described by the following equation: where 0 + γt is the linear frequency variation law. The received signals s r1 (t) and s r2 (t) for each transducer can be expressed, ignoring the noise, as:

, ( 2.1)
, ( 2.2)
where τ is the propagation time of the vortex between the emission and reception transducers. The envelopes A r1 and A r2 in equations (2.2) contain the information about the inhomogeneity evolution. We calculate the impulse response of the system for each transmission by correlating the received signal with the emitted one [START_REF] Bell | Information Theory and Radar Waveform Design[END_REF]:

(2.3)
As it can be seen from figure 2 (envelopes of impulse responses are shown for a better understanding of the phenomena), the signature left by a vortex is localized just after the main peak of the impulse response. However, the extraction of this signature must take into account the fact that due to occurring reflection of echoes, a secondary much smaller peak appears (figure 3). This representation of the signature (amplitude data) cannot reveal accurate amount of information regarding the physical properties of the vortex. Therefore, we propose the analysis via a decomposition using an appropriate basis function constructed from an initial function having an amplitude variation similar to the one introduced by the vortex (see figure 3).

The basic principle of the decomposition is illustrated, using real data, in figure 3.

Step one of the sparse representation consists in constructing the elements of the initial basis which is the same for both h r1 (t) and h r2 (t), with a zero mean and a rapid decrease. The basis starts from an initial waveform 0 , which is a modulated sine wave centered on the resonance frequency of the transducers as illustrated in equation (2.4):

, ( 2.4)
where 0 corresponds to the resonance frequency of the transducers. The term A(t) represents the amplitude modulation produced by the dynamic vortex over the impulse responses. The choice of this term is based on the physics of the vortex: from one path to another, the envelopes are similar, but due to the heterogeneity induced by the vortex the shape of the envelope suffers a contraction in time (figure 4). Thus, the choice of A(t) is made from the envelopes of the impulse responses that are affected the most by the inhomogeneity, in order to provide an physically adapted basis for the decomposition. As the inhomogeineity progresses through water, it keeps on enlarging due to its inertial character [START_REF] Jimenez | The Contributions of A.N. KOMOGOROV to the Theory of Turbulence[END_REF] and its energy decreases with distance.

Having defined the initial waveform we construct a set of functions that are shifted in time and scale, according to the continuous wavelet analysis, thus creating a dictionary of elementary functions, at different scales, shifted in time:

(2.5)
where N is the maximum decomposition resolution, s and u are, respectively, the scale and shift parameters of the initial waveform 0 . The envelopes of the impulse responses from the Upstream side will be more contracted in time than the ones from the Downstream side. The reason for selecting the initial waveform among the impulse responses where the envelope's contraction is the highest resides in the decomposition process. The impulse responses will match the initial waveform 0 at different scales highlighted by the s parameter in (2.5).

The representation that we wish to obtain for h r1 (t) and h r2 (t) can be written, using the resulting coefficients, as follows [START_REF] Baraniuk | Model-Based Compressive Sensing[END_REF] :

where and are the N resulting coefficients for the decomposition with the best i basis for the two impulse responses. The and coefficients represent the projection of the impulse function h r (t) on the i functions described in (2.4):

(2.7)

The second step of the sparse optimization consists in minimizing the number of coefficients obtained from the decomposition. This is defined as a problem of minimization [START_REF] Candés | Decoding by Linear Programming[END_REF] with solutions derived from convex optimization [START_REF] Boyd | Complex Optimization[END_REF].

In the case of the inhomogeneity estimation, a possible solution to the problem can be formulated as follows [START_REF] Chen | Atomic Decomposition by Basis Pursuit[END_REF]: quantify the impact of each coefficient -basis pair between the two estimated inhomogeneitis, in our context, and minimize the number of coefficients needed to construct two signatures h * r1 and h * r2 with minimum errors. Based on (2.6), we calculate a residual for each one of the N coefficients (C i,j s,u ) and its corresponding basis:

where R P j (t) is the residual calculated for the P th coefficients of the decomposition.

In order to quantify the impact of each residual with respect to the decomposition and minimize the number of coefficients, we can use the L 1 or L 2 norm for each of the N residuals and select only the K coefficients corresponding to the number of coefficients for which the norm is minimum [START_REF] Candés | Stale Signal Recovery from Incomplete and Inaccurate Measurements[END_REF]. We choose to use the L 1 norm over the L 2 norm because the former leads to fewer coefficients than the latter (figure 5). The minima points of the L 1 norm correspond to the lowest number of coefficients used to represent sparsely the two signatures and :

Since the basis of the decomposition is the same for both and , the two signatures will have a very strong resemblance, as it will be shown in the next sections.

RESULTS

The tests were carried out in our reduced scale experimental facility (figure 6). For our experiment, we used two pairs of 1MHz transducers placed on the outer walls of the tank. The length of the acoustic path was 1 meter at a depth of 40 cm.

Wide band signals were generated with a linear frequency modulation between 800 kHz and 1.2 MHz, and were downloaded into a signal generator for transmission. The repetition rate of the signals was 1msec and the duration of the wide band signal is of 200 microseconds in order to avoid the overlap of echoes over the received signals. First, the pairs of transducers had to be set apart by a certain distance because of the occurrence of crosstalk caused by the wide beam angle of each transducer.

Reverberation was not an issue because water attenuation for 1MHz is high and the signal processing algorithms are capable to distinguish the received pings from echoes.

Water flow was created using the recirculation pump in order to provide a background inhomogeneity noise that would eventually superimpose over the simulated phenomena. Air was pumped through a tube submerged in the flow and the created inhomogeneity intersected the two acoustic paths.

The scale factor of s (decomposition basis parameter) was set to 0.01 due to resolution considerations and the initial waveform was selected among the zones extracted from the impulse response where the inhomogeneity had the strongest effect, thus creating a physically driven basis. Each received signal yielded a decomposition and a number of K coefficients was calculated using the L 1 norm. It was no surprise that except for the moments where inhomogeneity was at its strongest, the number of coefficients used for decomposition remained constant.

We compare our results in two ways: first, we show a significant improvement with a typical representation technique consisting in calculating the maxima values of the impulse response corresponding to each transmission as illustrated in figure 7. This technique is used extensively in certain flow metering applications presented in [START_REF] Candel | Using Wide Band Signals for Obstacle Path Correction in Acoustic Scintillation Flow Meters[END_REF] and [START_REF] Reeb | Investigation of the Performance of Acoustic Scintillation Flow Meter when Turbulence Levels are Low[END_REF] and is sensible to interference induced by the measurement conditions.

Secondly, we use the results presented in figure 7 to calculate the average flow velocity between the acoustic paths. The accuracy of estimating the average velocity depends on the precision of estimating the time delay between the two acoustic paths. This is done by computing the cross correlation of the two signatures (Upstream and Downstream), as illustrated in figure 8 (a zoomed region displaying the main peaks of the two cross correlations).

In the second subplot of figure 8, we see an enhanced similarity between the signatures for the two acoustic paths, which describes an initial dynamic and random vortex. This result is possible only because of the physically driven basis that was used in the decomposition.

The second value (the real value calculated after the decomposition, highlighted on the red dotted trace with the vertical black line) corresponds to the correct orientation of the flow and a more pertinent 1.47 meters/second flow velocity is computed. Using the average flow velocities from multiple levels over a square section output the values of flow on that section. This is a very sensitive problem as results from the existing methods are biased by unfavorable conditions.

It is very important to keep in mind that the simulated conditions aimed also at testing the limits of the algorithm in terms of robustness.

Figure 8 shows that, by comparing with the initial estimation in figure 7, the sparse representation manages to provide a correct estimation of a inhomogeneity translated in space.

The purpose of sparse representation, as shown in [START_REF] Candés | Stale Signal Recovery from Incomplete and Inaccurate Measurements[END_REF], was to recover a signal using minimum number of coefficients from incomplete and altered measurements.

The use of wide band signals in inhomogeneities tracking was introduced previously in [START_REF] Candel | Using Wide Band Signals for Obstacle Path Correction in Acoustic Scintillation Flow Meters[END_REF] as an alternative to transmitting pulses in flow metering applications for obstacle path correction. It has been shown at the time that transmitting large band signals with a bandwidth around the central frequency of the transducers can improve the inhomogeneity derived flow metering estimation in case of large obstacles passing between the transducers. Additionally, in [START_REF] Reeb | Investigation of the Performance of Acoustic Scintillation Flow Meter when Turbulence Levels are Low[END_REF] we concluded that the inhomogeneity analysis would benefit from wide band signals in the case of low inhomogeneity levels and for this purpose, appropriate representation spaces are required in order to get invariant information about the inhomogeneity when "seen" from two (or more) spaced separated pints of view.

CONCLUSIONS

In this paper, we proceeded to demonstrate that sparse representation and adaptive decomposition techniques can be successfully used to recover a complete representation of inhomogeneity from incomplete and contaminated measurements (sparse representation).

A small scale experiment consisted in generating inhomogeneity was carried out and wide band signals were used to highlight the passage of the inhomogeneity at two separated acoustic paths, all in the least favorable conditions.

Results obtained with our technique prove the theoretical concepts of sparse representation and adaptive waveform using large band signals.

Our work will focus in the future on combining adaptive waveform techniques with sparse representations aiming at finding a signal that is appropriate to underwater inhomogeneity estimation using an orthonormal decomposition basis.

Figure 1 .Figure 2 .

 12 Figure 1. Vortex travelling with the flow of water

Figure 3 .

 3 Figure 3. Decomposition principle of an inhomogeneity extracted from an impulse response.

Figure 4 .

 4 Figure 4. The contraction of the impulse response envelope due to the dynamic vortex.

Figure 5 .

 5 Figure 5. Variation of the L 1 and the L 2 norms.

Figure 6 .

 6 Figure 6. Reduced scale experiment test bench.

Figure 7 .

 7 Figure 7. Inhomogeneity representation before and after the sparse decomposition.

Figure 8 .

 8 Figure 8. Flow velocity calculation using the cross correlation between the acoustic paths.


Experimental assessment of a PCM to air heat exchanger for heating peak loads shaving

Keywords: Phase Change Material, heat storage, experimental tests

INTRODUCTION

Nowadays, thermal energy storage systems (TESS) are essential to rationalize the energy management and the use of environmental energy potential. Concepts including phase change material (PCM) are technically mature for building applications and innovations. Among the existing concepts of TESS, PCM to air heat exchanger can directly be integrated in building ventilation systems in order to store energy for heating peak loads shaving [START_REF] Virgone | Peak power reduction using a PCM storage coupled with a heat pump in a ventilation system[END_REF]. A similar system could also be used for cooling purposes, as presented in [START_REF] Lazaro | PCM air heat exchangers for free cooling application in buildings: Experimental results of two real-scale prototypes[END_REF]. Here, during the off-peak period, the air is heated by a heat pump and passes through the heat exchanger to store energy. This energy is released during the peak period to heat the building and reduce the required heating load. The objective is to stop the heat pump without affecting the thermal indoor comfort. Quantitatively, the heat exchanger is designed so it can replace about a 1 kW heating system during 2 hours.

The second issue of this work is to provide detailed results suitable with the validation of numerical models. On this subject, data from several experiments are collected and presented in [START_REF] Lopez | Numerical modelling and experimental validation of a PCM to air heat exchanger, Energy and Buildings[END_REF] and the authors are underlining the lack of data. For this reason, the geometry of the exchanger will be detailed here and the uncertainty associated with the heating power measurement will be given.

In this paper, the design and the instrumentation of the heat exchanger is first presented. Next, the tests simulating a ventilation system, and realized in our laboratory setup, are presented and discussed. In the conclusions, design guidelines are given in order to meet the storage unit specification requirements.

EXPERIMENTAL FACILITY

The heat exchanger was designed so it contains almost 30 kg of PCM. First, the PCM properties will be presented. Then, a short description of the heat exchanger and of the air supply system will be given. Finally, this section will focus on the instrumentation.

PCM properties

The selected material is paraffin based. Its commercial name is Microtek 37D1 and its thermal properties were measured in laboratory (EDF -R&D). Results are presented in Table 1.

Precisely, the melting heat is measured on a 0.40505 g sample, using the DSC method (Differential Scanning Calorimertry) at a 0.15°C.min -1 heating rate. Phase change is observed when the temperature is growing from 30 to 38°C and a temperature peak is observed at 37.2°C. The same method is applied to measure the solidification heat and a very good agreement is obtained with the measured melting heat (0.1 %). However, the temperature peak is different (31.7°C).

Table 1: Thermal properties of the material

Solid state

Liquid state Density (kg.m -3 ) 870 779

Thermal conductivity (W.m -1 .K -1 ) 0.24 0.18

Heat capacity (kJ.kg -1 .K -1 ) 2,360 (20°C) 2.453 (45°C)

Melting heat (kJ.kg -1 .K -1 ) 230

Design of the PCM containers

The PCM is distributed into 34 plane-parallel aluminium containers (dimensions: 0.018 m × 0.200 m × 1.000 m). Each container is made of two identical cavities which are filled with PCM. Each cavity is sealed with aluminium sidings (0.016 m × 0.016 m × 0.048 m) and silicon.

However, the melting of PCM results in density change (see Table 1). To prevent PCM leakages, the cavities are partially filled and air vents (achieved by the simple mean of copper tubes) are placed at the top of the aluminium sides as presented in Figure 1.

During the experiments, no mass losses have been identified. The total mass of empty containers is 18.53 kg, and the total mass of PCM is 27.88 kg. Storage, June 19-21, Trinity College Dublin, Ireland Figure 1: Scheme of a single container filled with PCM

Design of the heat exchanger

The heat exchanger is made of the 34 containers and cooling fins (the total mass of the cooling fins is 4.69 kg). Containers are pilled up by two, which results in 17 blocks (0.400 m × 0.018 m × 1.000 m). The blocks are separated by fins of identical crosssection (see Figure 2). The fins are designed so that the air layer which is separating two blocks is split into 50 channels (0.080 m × 0.018 m × 1.000 m).

Figure 2 : Picture of the front of the heat exchanger

The whole assembly is surrounded with aluminium plates and the overall cross-section is 0.200 m × 0.630 m. These aluminium plates are maintained together with aluminium rails, screws and nuts, located on the external side of the heat exchanger. Finally, the exchanger is insulated using a 0.04 m thick layer of polystyrene and mineral wool.

Setting the heat exchanger in the air-supply system

The heat exchanger is placed into a closed-loop wind tunnel (the exhaust air is recycled). The air supply system includes a complete HVAC system (Heating, Ventilation Air Conditioning) and a 2.50 m long testing section within its upper part.

The exchanger is placed in the test section as presented in Figure 3. However, its cross-section is smaller than the one of the air inlet (1.00 m × 1.00 m). In order to control the airflow rate crossing the exchanger, both an upstream and a downstream section are added.

The whole system is designed in order to be as airtight as possible. For example, fastening techniques are preferred to drilling techniques to gather the different part of the system. Moreover, each juncture is sealed with aluminium tape.

The upstream section is made of two parts. In the first one, the cross-section is gradually reduced to the targeted cross-section (slope: 30°). In the second part, the cross-section remains constant so that the airflow can stabilize. The overall length of the upstream section is 1.000 m.

Figure 3: Scheme of the air supply system

To limit air leakage, the exchanger has to be tightened on the downstream section. This can be achieved by adding 0.030 m wide aluminium corners at the edges of the heat exchanger (see Figure 2). Next, a rubber strip with identical dimensions is screwed on these corners (not visible in Figure 2). Finally, two fastening straps are passing through the aluminium rails (the ones which are maintaining the aluminium plates altogether). These straps are wrapped around the downstream section and stretched (see Figure 3).

The upstream section is much simple. It is a 0.400 m long constant cross-section element. Its cross-section is slightly greater than the one of the heat exchanger. The air-tightness is achieved by the mean of a plastic foil maintained with aluminium tape. Finally, polystyrene elements are added inside in order to extend the cross-section homogeneously. A picture of the heat exchanger installed in the testing section of the air-supply system is given in Figure 4.

Instrumentation

The instrumentation is selected in order to estimate the heating power, which can be calculated with equation ( 1):

( ) ( )

A A P V A T C Q T P ∆ ⋅ ⋅ ⋅ = , & ρ (1)
Where: P is the heating power (W); ρ A is the air density (kg.m -3 ); Q V is the airflow rate (m 3 .s -1 ); C P,A is the heat capacity of air (= 1004 J.kg -1 .K -1 );

) ( 15 . 273 293 . 1 K T A A ⋅ = ρ (2)
Therefore, it is necessary to give precise estimations of both the airflow rate and ∆T A . The airflow rate is monitored by the airsupply system. Because of air leakages however, the airflow rate crossing the heat exchanger might be smaller and still have to be estimated. For this reason, a unidirectional hot wire anemometer is set inside the downstream section. It is sensitive to air speeds ranging from 0.05 to 2.50 m.s -1 and is 4 % but not to the airflow direction.

To estimate the temperature difference, 5 differential thermocouples are installed, both in the upstream and downstream sections. They are placed in cross-sections located 0.15 m away from the heat exchanger and fixed on nylon ropes as presented in Figure 5. One sensor is located in the middle of the cross section, while four others are located 0.05 m away from the walls. These thermocouples were all calibrated in laboratory, which allows achieving 0.3°C accurate measurements. Three classical T-type thermocouples are added at these locations (middle, top and right positions) and measure the air temperature. This system will be integrated in an air distribution system. The induced pressure difference is then a major issue and has to be estimated. Here, a pressure difference device is connected both to the upstream and the downstream sections, close to the position of the temperature sensors (not visible in Figure 4). It is installed so it measures the static pressure.

Finally, more than thirty thermocouples are used to measure temperatures at various locations inside the heat exchanger. For the sake of clarity, only a few will be presented and discussed in this paper. Later, temperature measurements achieved in the container located in the middle of the heat exchanger will be presented. There are three thermocouples installed in the middle of the cavities filled with PCM; two are positioned 0.05 m away from the edges and the third one is located at middle-length.

The number and accuracy of the sensors are summarized in Table 2.

RESULTS AND DISCUSSION

The airflow rate is set to 900 m 3 .h -1 during the whole experiment. The objective is to observe the complete MCP melting and solidification cycle. The inlet air temperature evolution is composed of two (20 and 45°C) over 4 hours. In this chapter, both temperature and airflow measurements will be presented. Finally, the heat power and the cumulative energy will be estimated and discussed.

Temperature measurements

The MCP temperature measurements achieved during the melting and the solidification periods are presented in Figure 6 and in Figure 7 respectively. The time reference (t =0 h) corresponds to the temperature change. The air temperature at the inlet is the average value of the three measurements achieved in the downstream section. It is faster for the temperature change from 45 to 20°C (18 minutes), yet a lower temperature is measured and ± 0.6°C variations are observed. This is probably due to the air-supply controlling system, which was calibrated when the testing section was empty. As this calibration was not achieved at the laboratory, it can be hardly modified and this assumption can not be verified. Still, temperature measurements achieved directly in the PCM are showing a regular behaviour. Heating rates are calculated for different temperature ranges and presented in Table 3. During the heat storage (see Figure 6), the temperature increases slowly from 30 to 34°C (the heating rate is approximately 0.037°C.min -1 ), which corresponds to the melting of the material. During this period, temperature is 1°C homogeneous within the PCM, which corresponds to the uncertainty range. When the PCM is melted, temperature increases quickly to meet the inlet air temperature (0.91°C.min - 1 ). This quick temperature increase does not occur simultaneously over the whole container's length; it is 13 minutes delayed between the inlet and the middle of the container, 17 minutes between the inlet and the outlet.

A similar behaviour is observed during the heat release period (see Figure 7). After a quick decrease from 45 to 35°C (0.91°C.min -1 ), an 18 minutes long stable-temperature-period is observed. Then, temperature starts to decrease with a slower rate, meaning the PCM solidification is not complete.

Measured temperatures in the PCM are equal to the inlet temperature 1.5 hours after the temperature change. This means the release of heat is achieved over this period. Therefore, it is obvious the 1 kWh during 2 hours goal is not meet during this experiment.

Temperature difference measurements

To give an idea of the heating power, it is interesting to study temperature difference measurements during the solidification period (see Figure 8). Although constant temperatures are measured in the material for a while, this is not visible when looking at the temperature difference over the whole heat exchanger. It remains higher than 8°C during the beginning of the experiment (18 minutes approximately), then it decreases rather linearly to 2°C over 50 minutes. However, the temperature difference is not homogeneous over the cross-section; an averaged 3°C difference is observed between the different sensors (the standard deviation is 1.2°C). Lower values are measured close to the sidings while the highest temperature differences are measured in the middle of the cross section and at the bottom. However, the standard deviation is lower than 0.1°C when the exchanger is neither storing nor releasing heat. Observed difference could be the consequence of non-uniform velocities over the cross-section. As a consequence, it seems necessary to estimate the airflow rate Q V accurately to compute the heating power.

Airflow measurements and comparison with CFD results

The hot-wire anemometer was used to achieve airspeed measurements in the downstream section when the heat exchanger was not installed in the test-section. It is placed 0.1 m away from the end of the downstream section, and moved successively at nine different locations; in the middle of the cross section, close to the position of the temperature sensors, and close to the corners.

Measurements are averaged over 90 seconds and results are presented in Figure 9. Relative coordinates are used to identify the sensor positioning. It is defined on a [-1; 1] range, where x*=0 corresponds to the middle-length, and z*=0 to the middleheight. Under a lower airflow rate, a constant heating power results in a higher a temperature difference. This could explain why temperature difference measurements are different from the bottom to other sidings (see Figure 8).

To assess this statement, a commercial CFD software (Computational Fluid Dynamics) is used to simulate the airflow in the whole downstream section. An unstructured mesh is selected and the air volume is meshed with more than 500 000 polyhedral cells. This mesh is refined close to the sidings, so the 0.01 m boundary layer is composed of 10 layers, as presented in Figure 10.

The boundary conditions are:

• An homogeneous 900 m 3 .h -1 airflow at the inlet;

• A zero-pressure condition at the outlet;

• Wall conditions for other boundaries.

Reynolds number is higher than 35 000, meaning the airflow is turbulent. Therefore, a classical k-epsilon turbulence model is selected. As the main goal is to give a general idea of the airflow properties, no detailed study is achieved on the influence of the simulation parameters. Finally, the air velocity is observed in the cross-section located 0.100 m before the heat exchanger (the exchanger is not simulated). Differences are computed using equation ( 3) and results are presented in Table 4.

Table 4: Velocity differences between simulation results and measurements

x*= -0.85

x*= 0 x*= 0.85 z* = 0.4 -0.6% -4.7% -15.6% z* = 0 -4.6% -6.5% -10.4% z* = -0.8 -3.8% -8.9% -10.3%

Sim Sim Meas U U U U - = ∆ (3)
Where: ∆U is the velocity difference (-); U Meas is the measured velocity (m.s -1 ); U Sim is the computed velocity (m.s -1 ).

Simulation results are consistently higher than measured velocities (the average difference is 7.3 %). This could results from air leakages, and means the airflow crossing the exchanger is lower than the programmed value. More, differences are not homogenous over the whole cross-section; higher differences are observed on the right side (x*=0.85) than on the left (x*=-0.85), while average differences are observed at middle-length (x*=0). As the simulated geometry is symmetric, so are the numerical results. The computed differences are resulting from the values of U Meas , which are varying from one side to another (6 to 16 %). Considering the measurement uncertainties however, it would be rather risky to take into account these variations in the calculation the heating power.

Therefore, it is better to use a single measurement to estimate the total airflow. It can be quickly estimated with reducing the airflow rate according to the comparison of the velocities as presented in (4).

( )

U S U Q Meas V ∆ - ⋅ ⋅ = 1 & (4)
Where: S is the cross-section (m 2 ); Finally, the pressure difference across the heat exchanger remains the same during the whole experiment (92.0 ± 1.3 Pa).

Estimation of the heating power and of the energy released by the system

The heating power P can now be calculated as presented in [START_REF] Virgone | Peak power reduction using a PCM storage coupled with a heat pump in a ventilation system[END_REF]. The temperature difference is the average value of the 5 measurements achieved both in the upstream and in the downstream section as presented in 3.1. The airflow rate is estimated from airspeed measurements as presented in [START_REF] Eurachem/Citac | Quantifier les incertitudes dans les mesures analytiques[END_REF]. Results are presented in Figure 11. The uncertainty on the measured heat power u(P) is estimated with the general equation (5) given in [START_REF] Eurachem/Citac | Quantifier les incertitudes dans les mesures analytiques[END_REF].

∑         ⋅ ∂ ∂ = 2 ) ( i i x x P P u σ (5)
Where: x i is any of the variables used to compute P; σ(x i ) is the standard deviation associated with x i .

The heating power can be integrated over the heat release period. The result represents the cumulative energy released by the heat exchanger. In this case, the uncertainty can also be estimated with using the same approach as presented in (5). Results are presented in Figure 12. As the airflow rate remains constant over the experiment, the heating power follows closely the measured temperature difference. As a consequence, it rises quickly to (2770 ± 180) W for 15 minutes, and then it decreases regularly to (500 ± 120) W in almost 1 hour. The initial goal (obtaining a minimum 1 kW heating power during 2 hours) is no more reached 1 hour after the temperature change.

However, the cumulative energy curve indicates the total energy released from the exchanger is (2.16 ± 0.16) kWh. This means the exchanger has stored enough energy to meet the heating demand.

Moreover, the theoretical value can be estimated by considering the measured properties of PCM (see Table 1) and the general properties of the aluminium (thermal capacity = 897 J.kg.K -1 , density = 2700 kg.m -3 ). Under a 25 K temperature difference, 2.6 kWh should be stored in the heat exchanger. The relative difference with the experimental value is close to 17 %. This difference can be partially explained as PCM losses are observed during the experiment. This may result from leakages at the sidings of the containers. It was also observed the heat exchanger was not exactly horizontally installed (the slope is close to 0.5 %), which can lead to mass loss through the air vents.

Outlook

Other tests should be conducted soon to observe the influence of the airflow rate on the heating power. On a theoretical point of view, the cumulative energy should not depend on the airflow rate. Verifying this point would allow concluding on the reliability of both the method and the instrumentation.

Even if stable temperature is observed in the PCM during the solidification period, the heating power decreases regularly during this period. Then, it seems rather hard to obtain a constant heating power under such conditions, and so as to meet the objectives. However, the heat exchanger is designed so enough energy can be released. An alternative could be to consider a variable airflow rate rather than a constant value. To do this, a control device should be implemented in order to get a constant heating power.

CONCLUSION

In this paper, a heat exchanger containing more than 27 kg of PCM is presented and tested within an air supply system. It is designed so it can store enough energy to replace a 1 kW heat pump during 2 hours.

The heat exchanger is composed of aluminium containers and combined with cooling fins. The assembly is insulated and special care is taken to limit air leakages and mass losses resulting from density change during the melting of PCM.

The air supply system is equipped with specially designed downstream and upstream sections. It is used to produce a constant airflow rate with temperature changes, selected so the PCM is allowed to melt, then to solidify.

Temperatures measurements achieved within the PCM allowed to observe a stable temperature during the solidification. However, the evolution of the heating power is quite different. Even if the heat exchanger is releasing enough energy, the initial objective could not be met: the heating power reaches as much as (2770 ± 180) W at the beginning of the experiment and is lower than (500 ± 120) W after 90 minutes. Further tests should be conducted under various airflow rates to assess the results presented here. However, it seems hard to meet the actual goal with considering constant airflow rates, so one should focus on controlling the airflow rate.

Figure 4 :

 4 Figure 4: Picture of exchanger installed in the air-supply system

Figure 5 :

 5 Figure 5: Picture of the sensors installed in the downstream section

Figure 6 :

 6 Figure 6: Temperature measurements during the melting period

Figure 8 :

 8 Figure 8: Temperature difference measurement during the solidification period

Figure 9 :

 9 Figure 9: Airspeed measurements achieved in the downstream section The velocities are rather similar for four locations (z*= [0; 04], x*= [-0.85; 0]). The average value is 1.84 m.s -1 and the

Figure 10 :

 10 Figure 10: Right part of the meshed cross-section located 0.1 m before the end of the downstream section

Figure 11 :

 11 Figure 11: Measured heating power after the temperature change (from 45 to 20°C).

Figure 12 :

 12 Figure 12: Cumulative energy released by the system after the temperature change (from 45 to 20°C).

nd International Conference on Sustainable Energy Storage, June 19-21, Trinity College Dublin, Ireland ∆T

  A is the temperature difference between the inlet and the outlet sections of the heat exchanger (K).ρ A can be estimated with equation (2):

		2
	PCM container	Cooling fins
	Aluminium corner	Fastening strap
		Heat exchanger
		Downstream section	Upstream section

Table 2 : Description of the instrumentation

 2

	Physical parameter	Number	Accuracy
	Temperature difference (°C)	5	± 0.3°C
	Air velocity (m.s -1 )	1	± 0.08 m.s -1
	Pressure difference (Pa)	1	± 0.5 %
	Temperature (°C)	32	± 0.5°C

Table 3 : Measured heating rate during the melting and solidification periods for typical temperature ranges.

 3

	Period	Temperature range °C	Heating rate °C.min -1
		[20 ; 30]	0.44
	Heat storage	[30 ; 35]	0.037
		[35 ; 40]	0.93
	Heat release	[45 ; 40] [30 ; 25]	0.91 0.60

nd International Conference on Sustainable Energy Storage, June 19-21, Trinity College Dublin, Ireland

	2 ( )
	Polyhedral cells
	Mesh refinement

http://www.microteklabs.com/pdfs/MPCM-37D%20Product%20Data%20Sheet.pdf

ACKNOLEDGMENTS

This work has been made with the support of the French ANR project STOCKAIR2.


ULg Serge Schmitz

Sarai De Graef

IEP), Luc Scheers (VormingPlus Michel Ericx

Luc Scheers

Stéphane Noirhomme

ULg Isabelle Dalimier

Chris Verheyen

Marc Philippot

Christine Partoune

Etienne Van Hecke

Territorial intelligence is also networking! Which strategies could be adopted to create a learning community regarding a public place?

Keywords: Networking, Public place, Action Research, Community of learning, recruiting participants, territorial intelligence Networking, Lieu public, Recherche-action, Communauté d'apprentissage, recrutement des participants, intelligence territoriale

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

1.INTRODUCTION

The Topozym action research (www.topozym.be) aims at helping develop concepts and practices in projects of management and use of public places and spaces with regard to sustainability. As a part of the Topozym project (2007)(2008), the Topozym team followed five running projects in public places in Belgium: Park Spoor Noord (Antwerp), RAVeL (Charleroi), Christmas Village (Liège), Muntstraat (Leuven), Bathing place in the river Ourthe (Noiseux). For each case study an analysis of the sustainability of the project was followed by training sessions led by trainers of the Topozym team. In other words, in order to improve the sustainability of public places and spaces, the Topozym project opted for a methodology combining research, training and action, in which a partnership with stakeholders of five running projects in defined public places was created. The idea was to constitute five learning communities for the five projects in question in order to rethink concepts and practices form a sustainable point of view.

The question of who to include in the partnership and process was of major importance for the success of the Topozym project. Following the stakeholder theory [START_REF]Strategic Management: A stakeholder approach[END_REF], the Topozym research took into account a wide variety of points of view to grasp the complexity of public place. Participants to the trainings, who would ideally become members of a stable learning community, should thus be stakeholders with diverse functions, skills and intelligences [START_REF] Gardner | Multiple Intelligences: New Horizons in Theory and Practice[END_REF]. We postulate that sustainable development of public places needs to encompass both a broad series of vocational experts, as well as representatives of users, including those who are usually forgotten or ignored. Going beyond improving group dynamics of existing teams, the Topozym action research bridges [START_REF] Gittel R | Community Organizing: Building Social Capital as a Development Strategy[END_REF] people who usually do not work together. This paper analyses how people were joined and stimulated to work together before and during the training sessions of each case study. It focuses on the balance between research, actions, and training in an action research. It compares the different recruiting strategies used in the Topozym project, from an efficiency, and an ethical point of view. Although tools and methods are of major importance for territorial intelligence, each action research in territorial intelligence begins with networking. Curiously enough finding advice on this crucial question in literature is difficult. The answer to the following question can thus only be answered based on experience: which strategies are the most efficient and ethically acceptable?

The only two incentives offered to the stakeholders to motivate their participation in the learning community were (1) the opportunity to learn a new way of working and (2) taking the time to think about a project with the help of trainers and researchers. A more concrete investment of the Topozym team in the project or public place in question or a specific retribution for each participant would have probably changed the recruitment and the participation in the action research.

As was already discussed in Huelva [START_REF] Schmitz | A self-critical analysis of a running research project to improve the sustainability of public place management, Papers on Territorial Intelligence and governance, participatory action-research and territorial development[END_REF], we recognize it would have been better to negotiate the research project with the partners when writing the proposition to obtain a grant, but it was difficult to motivate so many partners from different backgrounds to work together on a project without financing or security to get financed. Moreover, it is not usual for actors to candidate research grants.

The Topozym project was a one shot action aiming at small changes of practices. The Topozym action research must be seen as an enzyme that accelerates the awareness of people for another way of working that is more sustainable, and more participative. However, it will be an unexpected success if the work done during this short time by the five groups may have positively influenced the sustainability of their action and if the five groups of participants really became or become a learning community.

IN THEORY

An internal discussion within the Topozym team tried to find answers to the question of which strategies of networking are the most efficient and ethically acceptable. When asking Topozym researchers and trainers what the three most relevant issues one has to be aware of when recruiting people to join a group are to them, several issues came up that for different reasons had not at all or not fully been taken into account during the research.

A first group of recommendations that came up during the discussion within the Topozym team deals with the composition of the group. The group has to be varied (representing different skills and intelligences) in order to allow rich exchanges going beyond expert discussions on a specific topic. Nevertheless, the Topozym researchers and trainers underline that the members of the group should share the same aspirations and share the will to engage in the process of finding a way to reach these aspirations together. We prefer focusing on aspirations because they are often easier to share than needs, aims, projects or common history. Focusing on aspirations also seems more effective when trying to find innovative ways to solve problems.

Other recommendations were oriented on practical issues, but appear necessary to make the networking successful: the clarification or negotiation of the work rules, the transparency and easy access of information, the equal opportunity to speech and influence the group. Finally, it seems important to lessen time and space constrains to be able to work in depth, to know and work better together. For instance, making certain choices concerning timing and location of the meetings clearly influences the kind of stakeholders that can and want to participate.

According to the literature on social capital the key features of groups are not only the network of people but especially the confidence between the partners and the sharing of norms (Colemans, 1990). The same line of thought can be found in the two mechanisms pointed out by [START_REF] Ruuskanen | Social Capital and Innovations in small and medium sized entreprises[END_REF] who insists that the increasing of the trust inside the community and the good communication within the group are the two main ways to empower a community. These theoretical statements were confirmed during the internal discussion based on the Topozym experiences with networking.

3.IN PRACTICE

The way we recruited the different persons to participate in the training, differed for many reasons from our own theoretical recommendations. The five projects were so diverse that adaptations were necessary. The researchers did use a common protocol in order to be able to compare the different case studies. The main reason for the divergence between theory and practice was the difficulty to combine research, training and action objectives (see [START_REF] Schmitz | A self-critical analysis of a running research project to improve the sustainability of public place management, Papers on Territorial Intelligence and governance, participatory action-research and territorial development[END_REF]. Due to the research background of the project and the time constraints, the researchers hurried to find partners and wanted to impose their timing to the community of stakeholders. For instance in the Muntstraat, the economic relevance of the realization of the renovation works influenced the projects' timing in that way that it no longer allowed a real training phase. In the case of Park Spoor Noord the project was highly influenced by important switches of the staff, that led to a necessary renegotiation of the collaboration with the Topozym team.

Reflecting on networking also raises the discussion of the need for an indicator to evaluate the networking process. What is "good networking"? Is the number of participants that important? Is it the diversity or the representativeness of the different stakeholders that counts? Does one has to look forward and acknowledge that the progress made by the group is more significant that the groups composition? Or does one has to focus on the results in the projects actions? Of course, we believe that the means need to be in accordance with the goal and that the different levels -from the personal to the global one -have to be taken into account in the evolution of the action research.

The protocol used by the researchers to recruit participants advised starting with a key stakeholder, asking him or her who the other stakeholders were, and who -from his point of view -had to take part in the action research. Based on a snowballing process, a core group was thus constituted. Concurrently, on the basis of the analysis of the project and of visits to the place by the researchers, the researchers defined the missing stakeholders and asked them to join the group. This second path aimed at enriching the core group constituted via the snowballing process. In addition, two different strategies were tested for the different case studies, in the Topozym research: individual interviews before a synchronisation meeting with all the potential participants or a synchronisation meeting without preceding interviews.. Based on the protocol and the Topozym experience, three main issues need to be addressed. Who to choose as a key person to begin the snowballing process? Is it right to include people from outside the snowballing process? Is it better to start with a plenary meeting or to take off with personal interviews before the first meeting?

RESULTS AND DISCUSSION

According to the five Topozym case studies, it appears to be important that the key person is relatively stable in his position, works close to or in the field, shares the aspirations of the project and is not too high in the hierarchy. For instance, in one case in a rural area, the Mayor was involved to soon and several other participants felt obliged to take part in the group. This situation was difficult to manage during the training sessions. We learned from the case studies -including the aborted ones -that it is very important to obtain the agreement of the hierarchy, especially the agreement of the public authority responsible for the public place in question, as soon as possible.

In all the cases we chose to include people selected from outside the snowballing process. The idea was to enrich the group with new people, new points of view and new skills. These inclusions were not easy and requested a specific attention from the part of the trainer. We have to confess that we did not clarify as clearly as we could have to the group how the participants were recruited and who decided to invite these particular persons. This was particularly problematic in the scenario were no preceding interviews had had place and people were joined immediately for a synchronisation meeting. In Noiseux, several people invited directly to a synchronisation meeting did not understand the reason of their presence. Some of them made a mixture between the Topozym research and the local project that Topozym proposed to follow. Due to this lack of explanation on the Topozym goal, some of the participants were irritated and reacted strongly against the Topozym initiative.

Another problem was the difference in aspirations between the participants. Some stakeholders really invested in the place in question, but they did not have the same aspirations as the core group. This was, for example, the case for the Christmas village of Liege. The key persons were the organisers of the event, who were perceived as private actors organising a private event on the central public square. They wanted to balance the economic, the environmental and the social pillars. However, the public square is relevant for the other stakeholders too, who we tried to include in the process. These stakeholders felt out of scoop because the Christmas village was "not their project". The presence of the organisers as key persons was also problematic because the training process strengthened their central position within the group.

Starting the process with a synchronisation meeting appears interesting from an ethical point of view, because everyone receives the same information and may decide to continue after a first meeting with the group. However, it demands moderators with high skills in group dynamics and a huge effort of communication in accordance with the diversity of the public. The moderator has to put everyone on the same level and has to be attentive to the distribution of power amongst the group. Beginning with a face to face meeting may be more personal and more motivating, because the future participants are given the opportunity to explain their motivations and their fears individually. This process needs more time and investment from the researchers and trainers of course. However, in the Topozym experience it seemed to be of importance especially because of the few meeting sessions that would later on be organised and because the people were not used to the tools and methods used during the training.

CONCLUSION

Recruiting people to work together is always a challenge. When this work concerns public places within a participative approach, this challenge is even bigger, because the process has to be opened up to a wide variety of stakeholders. The elaboration of the network needs to take into account the diversity of stakeholders, in particular the diversity of their needs and aspirations. A protocol is useful but needs to be adapted to the local context. As the Topozym experience suggests: researchers and trainers need to experiment with a new path each time. A key principle seems to be finding a way to project the stakeholders in the future, and to focus on their aspirations instead to get bogged down in the diversity of present needs.

The second issue we underlined seems trivial, but experience teaches us that it cannot be emphasised enough: partnerships require time. The duration of the Topozym research was too short to really establish strong learning communities. Because establishing a community is more than join people, it is as well a work on the truth and the circulation of information within the group. Nevertheless, some members of the network constituted in the Topozym action research are still working together and have adopted small changes in their way of working.