Vincent T Renard

Michael Jublot

Patrice Gergaud

Peter Cherns

Denis Rouchon

Amal Chabli

Vincent Jousseaume

Catalyst preparation for CMOS-compatible silicon nanowire synthesis

Metallic contamination was key to the discovery of semiconductor nanowires 1 , but today it stands in the way of their adoption by the semiconductor industry. This is because many of the metallic catalysts required for nanowire growth are not compatible with standard CMOS (complementary metal oxide semiconductor) fabrication processes. Nanowire synthesis with those metals which are CMOS compatible, such as aluminium 2 and copper 3-5 , necessitate temperatures higher than 450 C, which is the maximum temperature allowed in CMOS processing.

Here, we demonstrate that the synthesis temperature of silicon nanowires using copper based catalysts is limited by catalyst preparation. We show that the appropriate catalyst can be produced by chemical means at temperatures as low as 400 C. This is achieved by oxidizing the catalyst precursor, contradicting the accepted wisdom that oxygen prevents metal-catalyzed nanowire growth. By simultaneously solving material compatibility and temperature issues, this catalyst synthesis could represent an important step towards real-world applications of semiconductor nanowires 6-11 .

Gold has been the historic catalyst 1 for silicon nanowire synthesis as it allows excellent yields of good quality nanowires at low temperatures by Chemical Vapour Deposition (CVD). Unfortunately, gold is prohibited in industrial clean rooms since it degrades the electrical properties of semiconductors. It is therefore necessary to find

another catalyst. However, compatible metals usually form a liquid alloy with silicon only at elevated temperatures, and as a consequence the standard Vapor-Liquid-Solid 1 (VLS) nanowire growth does not meet CMOS thermal budget (T < 450 °C). Recently the quest for low growth temperatures was fuelled by the discovery of the Vapour-Solid-Solid (VSS) regime where the metal rich catalyst remains in the solid state [START_REF] Kamins | Ticatalyzed Si nanowires by chemical vapor deposition: Microscopy and growth mechanisms[END_REF] (For a review see Ref. 13). However, the synthesis which includes catalyst preparation and nanowire growth itself is limited by thermally activated diffusion of silicon in a metalrich solid particle. Synthesis therefore necessitates high temperature [2][3][4][5] . Also, despite evidence for the coexistence of metal-catalyzed and oxide-assisted nanowire growth at high temperatures [START_REF] Wang | Nucleation and growth of Si nanowires from Silicon oxide[END_REF] , it is generally believed that the presence of oxygen prevents metalcatalyzed nanowire growth 2 . On the contrary, Figure 1 illustrates that oxidizing copper before CVD nanowire growth can have a dramatically positive effect on the production of nanowires at low temperatures. The initial oxidation state of the 20 nm copper layer was reproducibly controlled in-situ by a deoxidation/oxidation step at the same temperature as the following nanowire growth (see Methods). At T = 400°C nanowires are not produced using oxide-free copper while a sufficiently high oxygen pressure (1 Torr) during the oxidation step yields straight nanowires. At still higher oxygen pressures (5 Torr), worm-like structures are obtained (see supplementary section S1). This strong influence allows extremely low nanowire growth temperature (compared to eutectic temperature ~800°C) and raises question about the role of oxygen in this nanowire growth. In particular, is this regime a new type of metal-oxide assisted growth or does oxidation only assist the VSS growth?

The nanowires are non-epitaxial on the substrate since we used an amorphous metal diffusion barrier (TaN/Ta) on the wafer to meet CMOS standards. The worm-like wires present a lot of structural defects. They are therefore less interesting from the perspective of electrical applications than the straight ones which are single crystals as revealed by High Resolution Transmission Electron Microscope (HRTEM) images (see Figure 2a andb). In this publication, we will therefore concentrate on the results obtained at the optimal oxygen pressure for producing straight nanowires. These nanowires have a usual morphology with a catalyst at the tip. Their diameter is between 20 and 100 nm. The determination of the crystal structure of the nanowires necessitated careful investigation. Contrary to previous reports [START_REF] Morral | Synthesis of Silicon Nanowires with Wurtzite Crystalline Structure by Using Standard Chemical Vapor Deposition[END_REF][START_REF] Prades | Concerning the 506 cm-1 band in the Raman spectrum of silicon nanowires[END_REF] , the combination of Fast Fourrier Transform (FFT) of HRTEM images (Figure 2c) and Raman Scattering was not enough to discriminate between the usual cubic diamond Si I structure and the exotic hexagonal Lonsdaleite crystalline structure [START_REF] Wentorf | Two new forms of Silicon[END_REF][START_REF] Yeh | Zinc-blende-wurtzite polytypism in semiconductors[END_REF] (Also known as Si IV). This is due to the nanometre-scale thickness of the wires [START_REF] Bell | Imaging and Analysis of Nanowires[END_REF][START_REF] Zhao | Carrier-induced strain effect in Si and GaAs nanocrystals[END_REF] (see supplementary Section S2 for more details). X-ray diffraction measurements showed that the nanowires are in fact Silicon I (Supplementary section S2). Energy Dispersive X-ray (EDX) measurements (Figure 2d) revealed that the wires are free of copper in the detection limit of this technique, that the catalyst contains copper and silicon only and is free of oxygen.

Figure 1 illustrates that a certain amount of oxygen during catalyst preparation is necessary for the growth of nanowires, but its role is unclear since oxygen is not found in the catalyst after nanowire growth (Fig 2f). This excludes the possibility of an oxide assisted growth. Oxidation of copper is a long standing subject [START_REF] Cabrera | Theory of the oxidation of metals Rep[END_REF] as metal oxide is usually undesirable for good electrical contacts. Studies at the nanoscale were recently conducted since copper oxide may have interesting applications in photovoltaics and chemistry [START_REF] Gong | Atomic force microscopy and Raman spectroscopy studies on the oxidation of Cu thin films[END_REF][START_REF] Lampimäki | Nanoscale oxidation of Cu (100): Oxide morphology and surface reactivity[END_REF] . At low oxygen exposure, a Cuprous oxide (Cu 2 O) forms until, at higher exposure, a Cupric (CuO) phase nucleates [START_REF] Gong | Atomic force microscopy and Raman spectroscopy studies on the oxidation of Cu thin films[END_REF] and eventually forms nanowires [START_REF] Jiang | CuO Nanowires Can Be Synthesized by Heating Copper Substrates in Air[END_REF] . Figure 1d, 1e and Supplementary section S1 confirm this scenario. At the optimum oxygen pressure, the seed layer is completely oxidized to Cu 2 O (Fig 1e). Interestingly, the seed layer is still not properly dewetted until silane (SiH 4 ) is admitted in the chamber. We therefore propose the following mechanism for the nanowire synthesis in our case.

Upon exposure to SiH 4 , the very reactive Cuprous oxide [START_REF] Lampimäki | Nanoscale oxidation of Cu (100): Oxide morphology and surface reactivity[END_REF] chemically activates the formation of copper silicide particles following the reaction 2SiH 4 +3Cu 2 O→2Cu 3 Si+3H 2 O+H 2 (G=-43.8 kJ as determined with FactSage ® package) until the oxide is entirely reduced. Silicon nanowires then conventionally nucleate from crystalline silicide particles [3][4][5] . The size of the catalyst particle being larger than ten nanometres, a reduction to 400°C of their melting temperature is not expected [START_REF] Delogu | F Structural and energetic properties of unsupported Cu nanoparticles from room temperature to the melting point: Molecular dynamics simulations[END_REF] and nanowire growth should occur in the VSS regime. A lower limit of 500°C had been previously empirically predicted by Kalache et al. for VSS growth of Si nanowires with 3 nm thick copper seed layer based on measurements of the time delay before nanowire growth can occur (incubation time) 5 . They were able to determine an activation energy of E a =0.98 eV which permitted to relate incubation time to the formation of Cu 3 Si by diffusion of silicon in copper. Indeed, previous work [START_REF] Chromik | Thermodynamic and kinetic study of solid state reactions in the Cu-Si system[END_REF] showed that the growth of Cu 3 Si by diffusion is thermally activated with the same activation energy as measured by Kalache et al.. They predicted infinitely long incubation times, and therefore impossible nanowire growth, below 500°C. It had been established [START_REF] Chromik | Thermodynamic and kinetic study of solid state reactions in the Cu-Si system[END_REF] that the growth kinetics of the silicide phase by diffusion follows the law x 2 =k 2 t, where x is the silicide thickness, t is the time and

        kT E k a exp 2
is the reaction constant. We therefore expect two orders of magnitude larger incubation times, at a given temperature, than Kalache et al. since our Copper layer is about 10 times thicker.

Nevertheless, we achieved nanowire growth at 400°C, because the formation of Cu 3 Si no longer relies on diffusion but is chemically activated. With consideration of these two independent incubation methods, we conclude that the synthesis temperature of silicon nanowires with Cu is in fact limited (at least down to 400°C) by catalyst preparation rather than by the other limiting steps expected during nanowire growth itself [START_REF] Kamins | Ticatalyzed Si nanowires by chemical vapor deposition: Microscopy and growth mechanisms[END_REF] (gas-phase transport of the Si-containing gas to the wire, precursor decomposition, surface/volume diffusion of Si in the silicide or incorporation of Si to the growing wire).

Finally, the importance of oxygen in the chemistry of the obtained nanowires is further evidenced by the substantial morphological evolution of their tips during conservation in ambient atmosphere. TEM images (Figure 3a) reveal that after several hours in atmosphere the catalyst has evolved to a dense-particle array included in an amorphous matrix. In addition the nanowires have a dark plate at the interface between the amorphous region and the silicon (see Arrow in Fig. 3a). A video from High Angle Annular Dark Field tomographic reconstruction is provided as supplementary information for a better understanding of the tip morphology after exposure to oxygen (See also Supplementary Section 3). Elemental mapping (Figure 3b, c andd) from Energy Filtered TEM reveal that the copper rich region is surrounded by a silicon and oxygen rich region and that the plate is copper rich. Electron Energy Loss Spectroscopy experiments show that the Si L 2,3 -edge, acquired from the amorphous region, is typical of SiO 2 (See supplementary Section S4). This stack is characteristic of copper-silicideassisted oxidation of silicon reported in bulk systems [START_REF] Harper | Roomtemperature oxidation of Silicon catalysed by Cu 3 Si[END_REF] . The oxidation proceeds as a cyclic decomposition (Cu 3 Si+O 2 →SiO 2 +3Cu) and formation (3Cu+Si→Cu 3 Si) of Cu 3 Si at the SiO 2 /Si interface as long as oxygen is provided at this interface [START_REF] Harper | Roomtemperature oxidation of Silicon catalysed by Cu 3 Si[END_REF] (Figure 3a).

Silicon is consumed on one side and SiO 2 is rejected on the other side of the interface.

Together with EDX measurements, this result confirms indirectly that the nanowires nucleate from Cu 3 Si. This oxidation could potentially be used to produce SiO 2 nanowires. Also, this issue may become important in the perspective of fabricating devices. Indeed, unless a proper encapsulation is used, their properties may be affected by this phenomenon.

In conclusion, chemically assisted incubation of catalysts appears to be a step towards silicon nanowire growth with full CMOS compatibility. The technique could be used to add new functionality (such as nanoelectromechanical systems and sensors) above integrated circuits. We used harder oxidation conditions to confirm the crucial role of Cu 2 O in the synthesis of silicon nanowires at low temperature. After native oxide removal (see methods), the copper layer was oxidized for 180 s with 5 Torr of pure oxygen. The growth was performed using 30 Torr of pure SiH 4 for 40 minutes. Supplementary figures 1a and 1b

show SEM micrograph of top view and side view of the yields. The nanowires obtained are worm-like instead of the straight ones seen in Fig. 1b andc. We performed morphological and chemical characterization of the seed to confirm that this change in nanowire morphology is due to the appearance of CuO. The plasma cleaning does not modify the surface morphology of the seed while oxidation strongly reshapes the surface (Suppl. Fig. 1c). XRD measurements (Suppl. Fig. 1d) confirm that Cupric oxide phase appears during oxidation at such high pressure (compared to that used in Fig. 1). This is also illustrated by the nucleation of CuO nanowires (dashed circle in Suppl. Fig. 1c) in agreement with the literature. In turn, the quality of the produced silicon nanowires degrades which offers further evidence that the incubation is controlled by Cu 2 O.

S2 Determining the crystal structure of the nanowires

The Fast Fourrier Transform (FFT) of HRTEM image (Suppl. Fig. 2a and2b) produces a surprising result. Three planes with nearly the same interplanar distance (about 0.32 nm) and the same angle (60°) between them are identified. This does not relate to any known zone axis of the cubic diamond Si I structure (space group Fd3m).

the unusual Lonsdaleite (hexagonal) crystalline structure 2 , also known as silicon IV (space group P6 3 mc). This metastable phase of silicon, discovered 3 in the 1960's, was recently claimed to have been observed in nanowires by VLS with a gold catalyst 4 .

Following previous investigation 4,5 we proceeded to use Raman scattering measurements to determine the crystal structure (Suppl Fig 2c) and methods). The resonances at 223 cm -1 , 288 cm -1 , 503 cm -1 and even 419 cm -1 suggest that our nanowires are Si IV 4,5 . However, this set of resonances is also characteristic of 2TA(L), 2TA(X), TO(L) and LO() modes of nanocrystalline Si I 6 . Raman scattering is therefore also unable to distinguish between Si I and Si IV. X-Ray-Diffraction shows that Si I and Method:

Electron tomography was carried out on an FEI Titan S/TEM operating at 300kV. A tilt series was acquired consisting of 141 high angle annular dark field (HAADF) images.

The sample was tilted between +/-70°, using a Fischione 2020 high tilt sample holder.

The tilt series was reconstructed using the simultaneous iterative reconstruction technique (SIRT) implemented in the FEI Inspect 3D software. Visualisation is carried out using Chimera (software distributed by UCSF).

Figure 1 :

 1 Figure 1: Silicon nanowire yield at T=400°C with oxide-free and oxidized copper seed layer.

Figure 2 :

 2 Figure 2: Structural and chemical characterization of nanowires. a, HRTEM image of a straight nanowire with the catalyst at the tip. b, Magnified HRTEM image (dashed rectangular region in a) c, FFT performed in the dotted square area shown in a. d, EDX measurements showing the chemical nature of both the catalyst and the nanowire. The nanowire is free of copper in the detection limit. The catalyst is composed of silicon and copper.

Figure 3 :

 3 Figure 3: Oxidation at the tip of the nanowire in ambient atmosphere. a, TEM image of an oxidized nanowire tip. b-d, Location of the different elements illustrated by Energy Filtered EFTEM with bright contrast variation: b, Silicon, c, copper, d, oxygen (see Methods). After a few hours in ambient atmosphere, the copper catalyst region has been transformed in an amorphous matrix containing dense particles. A plate of copper is well distinguished between the SiO x region and the silicon nanowire. Cyclic reactions described in a lead to the displacement of the copper rich interface consuming Si on one side and producing SiO 2 on the other side.

Cu 3

 3 Si only are present on the substrate (Suppl Fig2e). We therefore conclude that our nanowires have the usual cubic diamond structure, though further investigation on a large amount of material will be necessary to eliminate the possibility of a tiny fraction of Si IV nanowires. It follows that it is now possible to correctly index the FFT of the HRTEM image (Fig.2c). Our observations illustrate that extreme care is needed for nanoscale characterization and raises some concern about previous claims of the observation of Si IV nanowires.MethodAn Ar+ laser with 514.5 nm as the excitations wavelength was used as the photon source during Raman measurements. A 100× objective was used to focus the laser spot and to collect the emitted radiation. A low excitation power (20 kW/cm2) was used to avoid sample heating. The signal was dispersed through a grating spectrometer and projected to a liquid N2-cooled charged coupled device (CCD). S3 Nanowire's tip after oxidation: a tomographic reconstruction.The supplementary video shows an electron tomogram acquired from a nanowire. The video shows first a volume view of the tomogram. A series of vertical slices through the 3D volume are then displayed in sequence. In the isosurface representations, the first threshold used corresponds to the intensity level of the Si, hence showing the outer surface of the lower region of the wire in blue. A second threshold is defined, corresponding to the Cu-rich material in top region of the wire. This is displayed in red, and can be seen when the outer surface is transparent. These representations allow observation of the distribution of the Cu-rich material.

Methods

Nanowire Growth

Silicon nanowires were prepared by Chemical Vapor Deposition (CVD) in a production 200 mm Applied Materials Centura 5200. Accessible temperatures in the CVD chamber were below 425°C. Standard silicon {100} wafers were used in the experiments. The substrates were first coated with a 10 nm TaN/Ta metal diffusion barrier to avoid metallic diffusion to the substrate (similar results are obtained without this diffusion barrier). A 10 to 50 nm Copper layer was then deposited by a standard PVD technique.

The native Copper oxide was first cleaned using hydrogenated plasma [START_REF] Baklanov | K Characterization, of Cu surface cleaning by hydrogen plasma[END_REF] in the growth chamber. Copper was then re-oxidized in a controllable way by a flow of pure O 2 at pressures between 0.5 and 5 Torr during 180 s. Pure Silane (SiH 4 ) was used as the vapor source of silicon during the growth. Silane pressures were kept between 1 and 50 Torr and growth lasted between one minute and one hour.

Characterisation

The obtained nanowires were characterized using the Hitachi 5500a scanning electron microscope operating at 30 kV and a JEOL 2010-FEF Transmission Electron Microscope operating at 200kV, equipped with an Omega filter. The nanowires were sonicated in Ethanol and transferred onto carbon grids supported by copper or gold grids for TEM observation. The transfer to TEM was made in less than 20 min to avoid oxidation of the nanowires. Before FFT analysis of the HTREM images, the microscope have been previously calibrated using bulk crystalline Si I samples. The EDX measurements were obtained from nanowires deposited on gold TEM grids. This


Gilles Montagne
email: gilles.montagne@univmed.fr

Julien Bastin

David M Jacobs

What is visual anticipation and how much does it rely on the dorsal stream ?

Keywords:

The present commentary addresses the function of visual anticipation in ball sports, redefines the concept of anticipation, and examines the theoretical consequences of the redefinition.

Visual anticipation has traditionally been defined as the ability to predict a forthcoming event from incomplete cues. In this commentary, however, anticipation is defined as the ability to use prospective information scaled in units of maximum action capabilities. It is argued that the latter definition more faithfully reflects the challenges faced by sportsmen in situ. Also, the definition proposed in this commentary emphasizes the importance of dorsal steam functioning, whereas the traditional definition has often led visual anticipation to be related to ventral stream functioning. In line with this redefinition, it is argued that part of the functioning generally attributed to the ventral stream should in fact be attributed to the dorsal stream, even though expertise in ball sports should eventually be characterized by the interacting contributions of the dorsal and ventral streams.

Introduction

 [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF]have recently introduced an ecologically-inspired framework for visual anticipation in ball sports. Their framework emphasizes the interacting contributions of the dorsal and ventral systems (e.g., [START_REF] Milner | The visual brain in action[END_REF]. The framework is based on the idea that the ventral system is involved in perceiving which actions a particular situation affords and the dorsal system in the visual guidance of the action. In this commentary we further discuss the function of visual anticipation in ball sports. More specifically, we redefine the concept of visual anticipation and examine the theoretical consequences of the redefinition.

Visual anticipation and predictive information

The majority of studies designed to study visual anticipation have adhered (more or less explicitly) to the definition proposed by [START_REF] Poulton | On prediction in skilled movements[END_REF]. According to this definition, visual anticipation is the ability to make accurate predictions from partial or incomplete sources of visual information. An example of such an incomplete source of information could be the direction of gaze of a soccer player that is about to take a penalty kick. Poulton's definition has led to the use of paradigms in which the ability to accurately predict a forthcoming event is carefully examined by occluding candidate incomplete sources of information. Although several studies have been successful in showing that high levels of expertise go together with the ability to predict forthcoming events rapidly and accurately (e.g., Abernethy & Russel, 1987;[START_REF] Williams | Advance cue utilization in soccer[END_REF], [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF] raised an interesting issue: The magnitude of predictive errors, even for experts, often contrasts with the accuracy of related actions.

How can actions be so accurate while the perception of properties that are crucial to the actions are perceived so poorly? A first possibility is that the large perceptual errors were merely due to technological limitations of the considered experiments. For instance, the use of video recordings of the opponent's movements from the player's perspective might not allow a good immersion of the participants into the environment, with the result that important (predictive) information could be missing. However, similar results have been reported in field settings [START_REF] Starkes | A new technology and field test of advance cue usage in volleyball[END_REF].

A second possibility is that action studies do not show the same errors because of the perceptual-motor mechanisms involved in anticipation. Would it be possible that visual anticipation does not rely on predictive information? An affirmative answer to this perhaps counterintuitive question is suggested by the results of studies reviewed by van der [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF], and also by recent psychophysical studies. [START_REF] Welchman | Human observers are biased in judging the angular approach of a projectile[END_REF] revealed systematic errors in the perception of approaching objects, leading them to question the accuracy of human 3-D perception in general. In addition, [START_REF] Harris | Using visual direction in three-dimensional motion perception[END_REF] showed that a perceptual mechanism that is possibly involved in predicting where a projectile is going is not used in ongoing behaviour. Moreover, a recent study by [START_REF] Craig | Judging where a ball will go: The case of curved free-kicks in football[END_REF] showed that judgments of the place of arrival of a free kick in soccer in the presence of spin are biased, and that the bias exists for all levels of expertise (i.e., expert goalkeepers, expert players, novices). These different results indicate that the ability to accurately predict a forthcoming event does not necessarily reflect a high level of expertise with regard to related actions.

As a consequence, one might wonder to what extent mechanisms analyzed in studies in which observers have to predict a forthcoming event are representative of mechanisms that are relied on by sportsmen in situ. Most probably, perceptual mechanisms that are used in situ are often not predictive. Furthermore, as also mentioned by van der [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF], the perceptual and motor mechanisms involved in fast ball sports cannot be studied in isolation.

Numerous studies have shown, for example, that affordances are perceived more accurately if perceivers engage in ongoing action (e.g., [START_REF] Oudejans | To cross or not to cross: the effect of locomotion on street-crossing behavior[END_REF][START_REF] Oudejans | The relevance of action in perceiving affordances: perception of catchableness of fly balls[END_REF][START_REF] Cornus | Perception of a stepping-across affordance[END_REF].

According to [START_REF] Heft | A methodological on overestimates of reaching distance: distinguishing between perceptual and analytical judgments[END_REF], a major problem for studies that focus on the perceptual side of a perceptual-motor task is that they allow for 'an analytical stance', which is unrepresentative of the functioning of perceptual-motor mechanisms under normal circumstances. Although the problem of cognitive (in)penetrability of perceptual-motor mechanisms falls outside the scope of this commentary (see for example, [START_REF] Pylyshyn | Is vision continuous with cognition ? The case for cognitive impenetrability of visual perception[END_REF][START_REF] Ripoll | The understanding-acting process in sport: the relationship between the semantic and the sensorimotor visual function[END_REF]Paillard, 1991;McPherson, 1991), we do want to mention that this 'analytical stance' is more likely to be found in the 'head of a coach' than in the perceptual-motor mechanisms underlying the control of a given action. Note in this regard that the performance of coaches in prediction tasks has sometimes been taken as a reference level, deemed superior to the performance of at least some players (e.g., [START_REF] Ripoll | Analysis of visual scanning patterns of volleyball players in a problem solving task[END_REF].

In short, also following van der Kamp et al. ( 2007), we believe that experiments have too often been designed in ways that enhance the role of ventral stream functioning as compared to dorsal stream functioning. This is unfortunately because such protocols are not representative of the 'problems' that players encounter during games; perceptual information that players rely on is often not predictive and perception and action are always intertwined.

The emphasis on ventral stream functioning might have been at least partially due to the general (implicit) adherence to [START_REF] Poulton | On prediction in skilled movements[END_REF] definition of anticipation. In contrast to van der Kamp et al., we therefore suggest to redefine visual anticipation as the ability to use information scaled in units of maximum action capabilities. In the next sections we address the notions of prospective and action-scaled information.

Prospective control and perception-action coupling

If perceptual information is not predictive, then how does it look like? There is theoretical and empirical evidence in favour of the use of prospective information (see [START_REF] Montagne | Prospective control in sport[END_REF], for a review). Prospective information is information about the 'current future', that is, it informs the perceiver/actor about the future given that the current state is maintained. As a consequence it informs him/her how to modify (or not) his/her movement.

In the case of catching fly balls, for example, a negative optical acceleration informs the catcher that given his/her current velocity, the ball will land in front of him/her; a negative acceleration thus informs the catcher how to change his/her velocity [START_REF] Michaels | The optics and actions of catching fly balls: zeroing out optical acceleration[END_REF][START_REF] Mcleod | The generalized optic acceleration cancellation theory of catching[END_REF]. Prospective strategies have been shown to operate also in other tasks, including one-handed [START_REF] Peper | Catching Balls -How to Get Hand to the Right Place at the Right Time[END_REF][START_REF] Montagne | Movement reversals in ballcatching[END_REF] and intercepting a moving target on foot [START_REF] Fajen | Visual guidance of intercepting a moving target on foot[END_REF][START_REF] Chardenon | The perceptual control of goal-directed locomotion : a common architecture for interception and navigation[END_REF][START_REF] Bastin | Muscular proprioception contributes to the control of interceptive actions[END_REF].

Prospective strategies take advantage of multiple redundant sources of information (including sources of information that are independent of optic flow; e.g., Bastin et al., 2006;[START_REF] Bastin | The perceptual support of goal-directed displacements is context dependent[END_REF]. This allows the participant to produce adaptive behaviour independently of particular task constrains (e.g., [START_REF] Chardenon | A robust solution for dealing with environmental changes in intercepting moving balls[END_REF][START_REF] Bastin | Prospective strategies underlie the control of interceptive action[END_REF]. In this context a high level of expertise does not depend on the ability to predict a forthcoming event, but on the ability to optimally exploit the redundant sources of available information, which is to say, on the ability to establish a coupling between the movement and the prospective sources of information.

Action modes and the perception of affordances

Above we have emphasized the importance of prospective information for the control of movements. One might wonder whether some kind of predictive control is still necessary before engaging in an action. This issue is closely related to the choice of an action mode, that is, to affordances [START_REF] Warren | Action mode and laws of control for the visual guidance of action[END_REF]. In their article, van der Kamp et al. ( 2007) establish (though implicitly) a link between affordances (i.e., the choice of an action mode) and the ventral stream on one hand and laws of control (i.e., the control of the action) and the dorsal stream on the other hand, to (try to) get a function/structure mapping. This mapping has been extensively discussed, most notably with regard to the contrasting evidence about the neural pathways underlying the perception of affordances (e.g., [START_REF] Michaels | Information, perception, and action: What should ecological psychologists learn from Milner and Goodale[END_REF][START_REF] Norman | Two visual systems and two theories of perception: An attempt to reconcile the constructivist and ecological approaches[END_REF][START_REF] Young | Are different affordances subserved by different neural pathways?[END_REF]Dassonville & Kaur Balla, 2006). Whether the dorsal or ventral stream is relied on while perceiving and using affordances appears to depend on the functional nature of the For example, the dorsal stream would mainly be involved if affordances concern the actualization of behaviour [START_REF] Young | Are different affordances subserved by different neural pathways?[END_REF]. In this commentary we argue that this last function of affordances is of paramount importance in visual anticipation.

Note that we do not deny that the ventral stream might be involved in the selection of action modes, as argued by van der [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF]. However, the separation of the selection of action modes and the actual control of actions might in some cases be more complicated than suggested by the examples of van der Kamp et al. A first issue is that numerous studies have shown that affordances are action-scaled; the perception of affordances (e.g., the catchableness of fly balls) is more accurate when the participant is moving (e.g., [START_REF] Oudejans | The relevance of action in perceiving affordances: perception of catchableness of fly balls[END_REF]. What is relevant to the present debate is the fact that the use of information scaled in action units allows the actor to solve two central problems at the same time, namely, determining the action mode (the 'what' problem) and regulating the action (the 'how' problem). For example, a pedestrian that approaches a street crossing might directly perceive that the current traffic situation affords "crossing without any change in walking speed" rather than merely perceiving that it affords "crossing" (cf. [START_REF] Oudejans | To cross or not to cross: the effect of locomotion on street-crossing behavior[END_REF]. This indicates that choosing action modes and the control of action might be intimately entangled processes, which may make it more problematic to advocate the separate involvement of the two visual systems.

Moreover, new affordance-based models of perceptually guided action have recently been proposed [START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF]. These models challenge the classical 'double process' view according to which the specification of an action mode precedes the control of the action. These affordance-based models have been illustrated in braking studies. The results show that the participant's braking behaviour depends on the participant's sensitivity to his or her action boundaries (Fajen, 2005a;[START_REF] Fajen | Calibration, information, and control strategies for braking to avoid a collision[END_REF]. Said differently, throughout the execution of the action, the actor remains sensitive to whether the current situation affords breaking, and the control of the action is modified if the situation approaches the limits of the participant's maximum action capabilities. These models thus claim that affordances are perceived not only before the action, in order to select the appropriate action, but also to monitor the control throughout the execution of the action.

Let us further illustrate the affordance-based models with a second example. In the case of catching fly balls, for instance, current models (e.g., [START_REF] Michaels | The optics and actions of catching fly balls: zeroing out optical acceleration[END_REF] state that velocity changes of catchers are aimed to cancel optical acceleration. One limitation of such models is that they do not take into account the catchers' maximal running speed, a property that is nevertheless important to discriminate catchable from uncatchable fly balls.

Action boundaries (e.g., the maximal running speed) delimit a safe region within which the optical acceleration should be maintained to ensure success. Affordance-based models therefore rely on the detection of these boundaries [START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF]. For example, if one waits too long before cancelling out optical acceleration, the required speed might become higher than the catcher's maximal speed, which would mean that the ball cannot be caught anymore.

If, in contrast, one knows his/her action boundaries and maintains optical acceleration within the safe region delimited by the lower and higher boundaries on the speed capabilities, then catching action would always be possible. Recent evidence suggests that this kind of indeed seems to be used during the unfolding of the action to guide the current behaviour [START_REF] Fajen | Calibration, information, and control strategies for braking to avoid a collision[END_REF][START_REF] Fajen | Perceiving possibilities for action: On the necessity of calibration and perceptual learning for the visual guidance of action[END_REF]Bastin, Fajen & Montagne, submitted). To come back to the central issue discussed by van der [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF], the fact that the perception of affordances and movement control could belong to one single perceptual-motor process reinforces our feeling that the dorsal stream is greatly involved in visual anticipation in ball sports.

A final line of research that might be relevant to the current debate is the mode transition from inferential to perceptual functioning proposed by [START_REF] Runeson | Visual perception of dynamic properties: Cue versus direct-perceptual competence[END_REF]. These authors argue that novice perceivers function in an inferential mode and that with experience they come to function in a qualitatively different direct perceptual mode. The direct perceptual mode typically goes together with superior performance and with less explicit awareness of how the task is performed. It might be interesting to speculate that the inferential functioning described by Runeson and colleagues is related to the 'analytical stance' addressed by [START_REF] Heft | A methodological on overestimates of reaching distance: distinguishing between perceptual and analytical judgments[END_REF]. Likewise, the transition from an inferential to a direct perceptual mode might be related to a shift from more ventral to more dorsal functioning (cf., [START_REF] Norman | Ecological psychology and the two visual systems: not to worry![END_REF]. This would be consistent with van der [START_REF] Van Der Kamp | Ventral and dorsal contributions in visual anticipation[END_REF] suggestion that the relative contribution of the ventral stream is perhaps more important for novice perceivers and actors than for experts.

Summary and conclusions

To summarize, in this commentary we have shown how recent studies lead us to question the traditional definition of visual anticipation. In our view, visual anticipation is not the ability to predict a forthcoming event from incomplete advance cues, but the ability to use prospective information scaled in units of maximum action capabilities. This alternative definition, together with recent affordance-based models of perceptually guided action, indicates the important role of the dorsal stream in visual anticipation. Note that we do not deny the role of the ventral stream, we just want to suggest that part of the 'work' generally attributed to the ventral stream is probably realised by the dorsal stream. To conclude, it should be clear that the perceptual-motor mechanisms addressed in this commentary are influenced also by a different kind of parameters (including strategy, knowledge of the game, etc.), and that the behaviour of players in situ cannot be understood without taking into account the interacting contributions of the two streams.


Design of an UWB antenna and impact of the resulting pulse shape on the communication capacity Dorin Panaitopol, Jocelyn Fiorina, Antoine Diet, Nicolas Ribiere-Tharaud

Abstract-This paper presents the impact of the antenna design over the capacity of a IR-UWB system in a Multi-User Interference environment. A new antenna design is proposed and it is showed using a new performance criterion that for this design, the communication capacity given by the Sholtz's pulse [START_REF] Win | Ultra-Wide Bandwidth Time-Hopping Spread-Spectrum Impulse Radio for Wireless Multiple-Access Communications[END_REF] might be reached using some other types of pulses.

Index Terms-IR-UWB, Kurtosis, Capacity, Antenna

I. INTRODUCTION

W IRELESS communications systems have, for some years now, focused on the concept of a spread spectrum technique known as Ultra Wide Band. From an antenna point of view, the Ultra Wide Band (UWB) concept covers today three major types of applications [START_REF] Ghavami | UWB signals and systems in communications engineering[END_REF]:

i) Applications such as Ground Penetrating Radars (GPR, 1MHz to 10 GHz), ii) Multi-narrowband applications such as signal intelligence and detection, iii) Modern UWB operating in a 3.1 to 10.6 GHz frequency band.

Impulse Radio UWB, where very low power pulses are transmitted, is meant to be used for ad-hoc sensor networks with high density of users where multi-user interferences may be heavy. Antennas dedicated to such applications are not supposed to be multi-harmonics but they are required to be really non-dispersive and wideband, as illustrated on Figure 1. Furthermore, phase transfer response must be linear with respect to frequency and ideally for any direction of emission.

A performance criterion we propose to investigate is also the impact of the impulse shape created by the antenna on the multi-user interferences. Depending on the pulse shape, the interference may not have a Gaussian distribution and the distribution form affect the capacity of the communication link.

II. AN ANTENNA FOR IR-UWB

UWB antennas history begins with the work of Maxwell followed by Bose, Marconi, Amstrong and Carter with biconical 3D antennas. It goes on with horns and spherical fat structures with interesting wideband properties (Lindenbald, Schelkunoff, Friis, Kings and Kraus). In 1940, arguing that Dorin Panaitopol {dorin.panaitopol@supelec.fr} and Jocelyn Fiorina {jocelyn.fiorina@supelec.fr} are with TELECOM Department at SUPELEC; Antoine Diet {antoine.diet@lss.supelec.fr} and Nicolas Ribiere-Tharaud {nicolas.ribiere-tharaud@lss.supelec.fr} are with LSS Department at SUP-ELEC the stored reactive energy is reduced in bulbous antenna due to smaller current concentration in thick structures, Kings claims that "fatter is better" for the design of such antennas [START_REF] Shantz | A brief history of UWB antennas[END_REF]. Then, a lot of work has been done on volumetric curve based or tapered antenna (Schekulnoff, Friis, Marié and Stöhr). A great contribution, concerning the frequency independence of some structures (based only on angles), has been added by Rumsey. This was the introduction of spiral and log-periodic antennas, although the latter are dispersive due to phase centre translation in frequency. Nowadays, modern UWB antennas have to account for several considerations: matching properties (which leads to tapered profiles), minimum reactive power and hence resonance (which leads to thick or bulbous structures), low cost and small size (which leads to printed antennas). Several technologies, already proposed for lower frequency band applications can be used to realize wideband antennas. Interesting states of the art about the latter are given in [START_REF] Peyrot-Solis | State of the Art in UWB Antennas[END_REF], [START_REF] Cheng | Update various kinds of UltraWideBand antennas[END_REF], [START_REF] Ma | Planar miniature tapered slot fed annular slot antennas for UWB radios[END_REF]. The most important constraint is to build a non-resonant structure, which generally implies wide bent surfaces of conductor printed on substrates. Traveling wave structures are also preferred because they allow avoiding geometric resonances. Considering practical realization requirements (size and cost), the printed antenna is very popular for UWB. The geometry is first designed for a wide radiation pattern and, then, optimized for frequency matching. It seems that thick printed structures offer the wideband behavior necessary for UWB in a first step, bent shapes being chosen to radiate with large beam-width. In [START_REF] Schantz | The Art and Science of UltraWideBand Antennas[END_REF], the author suggests to take care about the free space attenuation applied in a wideband signal transmission arguing about the complementarity between constant aperture and constant gain antenna. Also the shape of the radiation pattern (DDR) has to be conserved in function of the frequency, and in function of the polarization at the same time, to avoid an amplitude compression effect, see Figure 1. The dispersion that can be observed on radiated pulses reveals the antenna time behavior. Integration and production of UWB mobile transceivers imply an additional task that consists in building a minimum-size low-cost antenna. These considerations lead to avoid volumetric structures (i.e., bi-conical, 3D monopoles or horns). However studying the latter as possible UWB antennas is essential to find design rules for other technologies. Micro-strip antenna shapes, for example, are often inspired by projection of 3D existing antennas [START_REF] Ghavami | UWB signals and systems in communications engineering[END_REF], [START_REF] Shantz | A brief history of UWB antennas[END_REF]. Applying the consideration developed above, we focused on a modified simple monopole in order to minimize the dispersion. The ground plane was opened to enable a bi-directional radiation pattern in a first step although for UWB monopole it would be interesting to design a ground plane around the area of the radiating part as in [START_REF] Chawanonphithak | A circular microstrip antenna with tuning stub above wide-slot ground plane for band-notched UWB using inverted U-slot[END_REF] and [START_REF] Chawanonphithak | 5.8 GHz notched UWB bidirectional elliptical ring antenna excited by circular monopole with curved slot[END_REF] but this has the penalty of narrowing the bandwidth. Herein, the return loss criterion was preferred and the design antenna present characteristics of Figure 2, where the radiation pattern is shown for 3 frequencies in the UWB band.

The main lob is slightly moving in function of the frequency. This will undesirably distort any pulse shape transmitted by the antenna. A radio link is simulated between two antennas at 1 meter, to compare with the antenna surface: 7x7 cm 2 . Results of the propagation link transfer function (or S21) are shown on Figure 3. The important variation of the magnitude is due to the free space path losses (FSPL) combined with main lobe frequency dependence discussed above and shown in Figure 2. The phase of S21 in function of the frequency was not reported on Figure 3 but was simulated to be linear, as it is often expected for small symmetrical UWB dipole (the phase centre trajectory is very small compared to the distance in farfield hypothesis) [START_REF] Ma | Planar miniature tapered slot fed annular slot antennas for UWB radios[END_REF]. Now this transfer function, S21, can be used to characterise the modification of UWB pulses in our communication system. Other types of UWB antenna with a best control of the DDR, keeping the 3-10 GHz bandwidth and phase linearity, will be studied and designed at Supelec for the characterisation antenna deformation on UWB pulses. We consider an Impulse Radio Time-Hopping Pulse Amplitude Modulation (binary IR-TH-PAM) UWB system [START_REF] Di Benedetto | Understanding Ultra Wide Band, Radio Fundamentals[END_REF]; the time is divided in frames, each frame is divided in N h slots with T c being the duration of a slot and an user send one impulse per frame in a slot position given by a pseudorandom time-hopping code. We note w(t) the received pulse after the receiver antenna. We are interested in the effect of Multi-User Interferences, so we consider that the receiver is synchronized with user 1 (transmitter 1) and there are N u -1 interfering users which are not synchronized with user 1. It is known [START_REF] Di Benedetto | Understanding Ultra Wide Band, Radio Fundamentals[END_REF], [START_REF] Fiorina | On the Asymptotic Distribution of the Correlation Receiver Output for Time-Hopped UWB Signals[END_REF], [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF] that the MUI in IR-TH are not Gaussian. After the correlation part of the receiver for each impulse of the received signal, it has be shown [START_REF] Fiorina | A Simple IR-UWB Receiver Adapted to Multi-User Interferences[END_REF] that the MUI may be well fitted by a Generalized Gaussian which has the following probability density function:

p(x) = c1(β) √ α 2 exp -c 2 (β) x √ σ 2 2 1+β
(1) with c 1 (β) and c 2 (β) functions of β.

When β = 0 the Generalized Gaussian is not a classical Gaussian. In a communication system where the interference has a Generalized Gaussian distribution, the capacity (maximum achievable rate) depends on the parameter β of the Generalized Gaussian [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF]. The parameter β is linked to the kurtosis k of the MUI as we can see in the Figure 4, where is graphically represented the dependence k(β). It is known that for a random variable x, the kurtosis is

k = E[x 4 ] E[x 2 ] 2 -3.
Based on this relationship, in [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF] is given the equation linking the parameters of the signal to the kurtosis of the MUI:

k = [(Nu-1)Nhm4(w)+6 (Nu -1)(Nu-2) 2 m 2 2 (w)] [(Nu-1)m2(w)] 2 -3 (2) with m 4 (w) = 1 Tc ∞ -∞ γ w (z) 4 dz (3) m 2 (w) = 1 Tc ∞ -∞ γ w (z) 2 dz (4) γ w (z) = ∞ -∞ w(t + z)w(t)dt (5)
One can see that the kurtosis of the MUI depends not only on N u , N h and T c but also on the shape w(t) of the received impulse. So, the coefficient β of the Generalized Gaussian probability density function depends on the shape w(t) and as a consequence the capacity of the communication system depends also on w(t). The detail of the computation of the capacity in function of the coefficient β is given in [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF]. By following this analysis we have compared the capacity of IR-TH-PAM UWB systems using different pulse shapes at the output of the receiving antenna. This gives a new performance criterion of the pulse created through the antenna. The first pulse used is the pulse corresponding to the dirac impulse response of our antennas. Such a pulse has at the output of the received antenna a -10dB bandwidth of 4 GHz. The second pulse used is the classical Scholtz pulses used in many UWB studies [START_REF] Win | Impulse Radio: How it works[END_REF], it is the second derivative of a Gaussian pulse. We have chosen the characteristics parameter of this Scholtz pulse to be such as having the same -10dB bandwidth as our first pulse (4 GHz). The third pulsed used is the response of our antennas to a rectangle impulse input (100 ps square) which is easy to create with an impulse generator. Due to the transfer function of our antennas the received impulse has a bandwidth of 3.7 GHz. Thus we have consider a four impulse: the Sholtz impulse having a -10dB bandwidth of 3.7 GHz. Table 1 shows the performance of the communication with perfect power control [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF] in term of achievable capacity (in bits per channel use which is bound by 1 bit/channel use for binary PAM) for some values of the parameters N u and N h with T c = 0.25ns. We can see that the performance of Scholtz's pulse is reached by the pulse created by our antennas. Moreover, as expected when the -10dB bandwidth is larger, the capacity is better because the pulse is narrower leading to a more impulsiveness interference so a less Gaussian interference (which is the worst interference case [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF]).

IV. RESULTS

As in [START_REF] Fiorina | Revisiting TH-IR-UWB performance limits dependency on essential system parameters using the Generalized Gaussian Approximation[END_REF], for the simulations we have considered two possible scenarios: perfect power control and lack of power control, with the same network parameters and user distribution. By comparison with the second table, we could observe that the capacity is decreasing for the lack of power control scenario and also the capacity gap between the considered cases modifies. In Figure 5 we have compared the dirac response of our antennas (a) and the response of the antennas to a square pulse (b) with the Scholtz's pulse. As one can see, each figure contains the Capacity as a function of SIR and β for each type of considered response. The evaluation has been done in 5 points -denoted on the graph with 1,2,3,4 an 5 respectively and corresponding to the next pairs (N h ,N u ): (N h =100,N u =100); (N h =250,N u =100); (N h =500,N u =100); (N h =500,N u =50); (N h =1000,N u =50). Last but not least, we have reached the conclusion that the response of the antennas to a square signal of 70ps is the perfect equivalent in terms of capacity to the Sholtz's pulse with the same bandwidth at -10dB (3.84GHz, corresponding to a pulse of 306ns).

V. CONCLUSIONS

Figure 1 .

 1 Figure 1. Definition of wideband antenna (a) and frequency variation of main lobe (b)

Figure 2 .

 2 Figure 2. The antenna designed (a), the radiation pattern (b),(c),(d) and the configuration of the transfer

Figure 3 .

 3 Figure 3. Return loss (a) and link transfer coefficient (b) at 1m

Figure 4 .

 4 Figure 4. Kurtosis(β)

Figure 5 .

 5 Figure 5. Capacity(SIR,β) with (a) Dirac response of antennas ' ' vs. Sholtz's pulse '∇' and (b) Response of antennas to a square pulse ' 'vs. Sholtz's pulse '∇'

Table II CAPACITY

 II IN TERM OF BITS/CHANNEL USE FOR VARIOUS RECEIVED PULSE SHAPES -LACK OF POWER CONTROL, NU=100

	Received pulse shape	Nh=100	Nh=250
	Dirac response of our antennas	0.239 b/ch.u. 0.509 b/ch.u.
	Scholtz pulse 4GHz -10dB bandwidth	0.207 b/ch.u. 0.465 b/ch.u.
	Resp. of antennas to a 100ps sq. pulse	0.177 b/ch.u. 0.421 b/ch.u.
	Scholtz pulse 3.7GHz -10dB bandwidth	0.190 b/ch.u	0.443 b/ch.u.

We have developed an Ultra Wide Band antenna and proposed a new performance criterion: the communication capacity (achievable bit rate) in presence of the MUI modeled by the pulse shape fashioned by the antenna. The development of this study should lead to the investigation of more UWB antennas and pulse shapes optimizations.


Benjamin Bradu
email: benjamin.bradu@cern.ch

Philippe Gayet
email: philippe.gayet@cern.ch

Silviu-Iulian Niculescu
email: niculescu@lss.supelec.fr

A process and control simulator for large scale cryogenic plants

Keywords: Cryogenics, Dynamic models, Simulation, Thermodynamics, operator training

This paper presents a process and control simulator for industrial helium cryogenic plants controlled by Programmable Logic Controllers (PLC). This simulator can be used for different purposes such as operator training, test of the PLC programs or the optimization of the plant. The different component models used in the simulator are detailed and explained. Various large scale cryogenic plants used for the particle accelerator LHC (Large Hadron Collider) at CERN have been modeled and simulated. The good agreement between the simulation results and the dynamic behaviour of real plants is demonstrated with experimental results. Various discussions complete the presentation.

Introduction

In 2008, the European Organization for Nuclear Research (CERN) started the most powerful particle accelerator of the world, the Large Hadron Collider (LHC). The LHC accelerates proton beams which are driven by superconducting magnets maintained at 1.9 K over a 27 km ring. To cool-down and maintain superconductivity, large helium refrigeration plants are used, see [START_REF] Lebrun | Cryogenics for the large hadron collider[END_REF] for more details.

Large scale cryogenic plants are continuous industrial processes, very similar to petroleum [START_REF] Olsen | A rigorous and efficient distillation column model for engineering and training simulators[END_REF], chemical [START_REF] Szafnicki | Towards an integrated tool for control, supervision and operator trainingapplication to industrial wastewater detoxication plants[END_REF] or sugar industries [START_REF] Alves | Opc based distributed real time simulation of complex continuous processes[END_REF]. They are composed with the same kind of components (heat exchangers, valves, turbines, compressors, phase separators, etc.) but with additional constraints due to very low temperatures.

Cryogenic plants and their control are highly complex due to the large number of correlated variables on wide operation ranges. Currently, the design and the control of cryogenic systems are based on CERN and suppliers' experience on the process and on appropriate "static" calculations. Due to the complexity of the systems (coupled partial differential equations, propagation and transport phenomena), dynamic simulations represent the only way to provide adequate data during transients.

A new dynamic simulator, PROCOS (PROcess and COntrol Simulator), has been developed to improve knowledge on complex cryogenic systems (Bradu, Niculescu, and Gayet, 2008b). The main objectives of the proposed simulator can be summarized as follows: the operator training, the test of control programs on "virtual" plants before their implementation (virtual commissioning) and the test of new control strategies to optimize the overall behavior of complex systems. This simulator is able to simulate large refrigeration plants using helium and connected to the actual control system of CERN. Furthermore, the existing control policy and supervision systems can be fully reused in simulation. Some advanced control developments as predictive control have already been studied for some LHC cryogenic systems [START_REF] Blanco | Nonlinear predictive control in the LHC accelerator[END_REF] but it has not been implemented yet and this dynamic simulator can be used to demonstrate efficiency of such controllers.

The superconducting magnets of the LHC were successfully cooled at 1.9 K during 2008. The real operation of the cryogenic plants has started and the use of a dynamic simulator is now the only way to test new control strategies in order to enhance the cryogenic systems without disturbing the LHC operation.

First, various studies devoted to the simulation, analysis and design of cryogenic plants are presented. Next, the simulation approach is outlined in Section 2 and various comparisons with the existent results are proposed. A particular attention has been paid to the simulation architecture and on the explicit modelling approach of the components. Next, simulation results are detailed (CMS cryoplant at 4.5 K and 1.8 K unit for the LHC), compared with experimental data and discussed (simulation speed and some comparisons) in Section 4. Some concluding remarks and perspectives end the paper.

Process and Control simulation approach

Existing cryogenic modelling approaches

In various fields of industry, dynamic simulators have been developed to train operators or to design new control techniques, see [START_REF] Alves | Opc based distributed real time simulation of complex continuous processes[END_REF]; [START_REF] Olsen | A rigorous and efficient distillation column model for engineering and training simulators[END_REF]; [START_REF] Szafnicki | Towards an integrated tool for control, supervision and operator trainingapplication to industrial wastewater detoxication plants[END_REF]. To the best of the authors' knowledge, there exists only a few dynamic simulations devoted to helium cryogenic plants: [START_REF] Maekawa | Dynamic simulation of the helium refrigerator/liquefier for LHD[END_REF] have simulated a 10 kW @ 4.5 K refrigerator used in a fusion experiment;

• next, [START_REF] Butkevich | Cryogenic helium units simulation model[END_REF] developed an educational training tool on an helium liquefier;

• [START_REF] Kutzschbach | Dynamic simulation of a helium liquefier[END_REF] have simulated a commercial helium liquefier;

• finally, [START_REF] Deschildre | Dynamic simulation of an helium refrigerator[END_REF] made simulations on a 800 W @ 4.5 K refrigerator.

The proposed simulation approach presents some similarities with the above simulators but it shows also new features and concepts. All simulators perform dynamic simulations by using an object oriented modelling where each cryogenic component is individually modeled by physical differential and algebraic equations (DAEs), excepting the simulator of [START_REF] Butkevich | Cryogenic helium units simulation model[END_REF] which is based on mathematical and heuristic modelling.

To model CERN cryogenic systems, a commercial modelling and simulation software was used: EcosimPro c (EA Internacional, 2007). This software was chosen for its flexibility and its ability to export models in C++ classes in order to embed models in larger simulation environments.

All cryoplants that have been modeled are large-scale cryogenic systems. In the adopted object approach, the number of equations is proportional to the number of objects: the complexity of the model is directly linked to the number of the main components, namely the heat exchangers (HXs) and turbines. In terms of numerical computation, large-scale systems can be defined for cryogenic plants containing more than 5 HXs and more than 2 turbines. This configuration generates around 1500 DAEs and it corresponds to a helium refrigerator with a refrigeration power of 400 W at 4.5 K.

All existing simulators have been used only to model helium liquefiers or 4.5 K helium refrigerators. Here, in addition to 4.5 K refrigerators, the modelling was extended to a 1.8 K refrigeration unit using coldcompressors.

Simulations are generally including a simplified control in the model. Therefore, most of existing simulators are not taking into account the real process control and cannot be used as a real-time training simulator, except the simulator of [START_REF] Maekawa | Dynamic simulation of the helium refrigerator/liquefier for LHD[END_REF]. The originality of PROCOS resides in the fact that it is based on the real process control architecture including the supervision system. The process and the control duties are simulated separately and synchronized together. Hence, it allows the simulation of large-scale systems on several computers by decoupling main parts of a plant.

Simulation architecture

All CERN cryogenic systems are controlled by industrial Programmable Logic Controllers (PLCs). The control architecture and the control policy are based on an object-oriented hierarchical multilevel and multilayer control framework developed at CERN and called UNICOS. This control framework is based on the IEC 61512 control standard (IEC, 1997), for a complete description of the control system, see [START_REF] Gayet | UNICOS : a framework to built industry-like control systems, principles and methodology[END_REF].

PROCOS reproduces the UNICOS architecture in simulation even for the three hardware layers as it is shown in the Fig. 1. The supervision layer remains the same with operator consoles, PLCs which perform the control are replaced by PLC simulators (softwares) provided by PLC manufacturers and the cryogenic plant is replaced by a cryogenic process simulator integrating physical equations of the system.

The same PLC I/O names are used in the process model (inputs are sensors and outputs are actuators). Hence, an OPC server can be configured automatically to establish the link between the PLC and the model. A lot of time can be saved for large applications, see Fig. The simulation speed is not constant, therefore a synchronization between the PLC and the simulation is necessary: PLC ramps and timers are now based on the simulation time. Moreover, PID controllers are not executed in the PLC anymore but directly in the model and main parameters are sent back to the PLC to ensure the consistency in the PLC program and in the supervision, see Fig. 3. This simulation environment is especially adapted for operator training since the supervision interface remains the same as for the real plant. All LHC cryoplants represent more than 42000 inputs/outputs and around 5000

2

Cryogenic modelling

EcosimPro c allows to model cryogenic systems by using an object-oriented approach where each cryogenic component is described by a set of differential and algebraic equations (DAEs). Helium and material properties are obtained by linear interpolations from large properties tables. Material properties are calculated as function of temperatures using some empirical formulations given by [START_REF] Marquardt | Cryogenic material properties database[END_REF]. Helium properties are obtained with the specialized helium library HEPAK c off-line and then integrated in the simulator to increase the simulation speed : interpolations performed in tables are 5 times faster than using 3 directly HEPAK c online. Properties tables are available for a temperature range between 1 K and 400 K and for a pressure range between 1 kPa and 2.5 MPa including the two-phase region.

Model Building

A cryogenic library was developed to build easily complex cryogenic systems by drag and drop in the Ecosimpro c graphical user interface. Component models are divided in two main categories:

(a) Pressure components imposing a pressure;

(b) Flow components imposing a mass flow.

First, the cryogenic system must be built in a such way that pressure components are connected to flow components and vice-versa to ensure the overall consistency of the system, i.e. to have as many equations as unknown variables. Figure 4 illustrates the interconnection between a flow component and two pressure components. Then, each component must be parameterized with its own characteristics (e.g., material, design temperature, mass, etc.). Finally, some boundary conditions have to be chosen if the system is not in closed-loop. Figure 5 represents a model overview of a small cryogenic system under EcosimPro using a compressor, two heat exchangers, one turbine, five valves, six pipes and one phase separator at the end. Pressure components are colored in yellow and flow components in blue whereas all orange objects are inputs/outputs with controllers that are included in the model and linked to the PLC.

m 0 .h 0 m 1 .h 1 S 2 S 2 T 1 T 2 Component 1 (pressure component) Component 2 (flow component) Component 3 (pressure component) m 1 =m 2 m 2 .h 2 P 1 M 1 Q conv1 Q rad1 Q ext1 T 1 P 3 M 3 Q conv3 Q rad3 Q ext3 T 3 m 3 .h 3

Pressure components

Pressure components perform isochore transformations and they have uniform pressure and temperature. They represent cryogenic equipments of non-negligible volume like pipes or phase separators.

J.kg -1 .K -1 E Energy J h Specific enthalpy J.kg -1 k Conductivity W.m -1 .K -1 Lv Latent heat of vaporization J.kg -1 Pr Prandlt number - P Pressure Pa s Specific entropy J.kg -1 .K -1 T Temperature K u Internal energy J.kg -1 x Mass vapor fraction % γ Specific heat ratio ( C p Cv ) - µ Viscosity Pa.s ρ Density kg.m -3 Ḟ Volumetric flow m 3 .s -1 hc Heat transfer coefficient W.m -2 .K -1 ṁ Mass flow kg.s -1 Q Heat flow W Re Reynolds number - η Efficiency - D Diameter m L Length m M Mass kg S Surface; Cross section m 2 V Volume m 3

Pipes

The pipes are characterized by their material, geometry (length and diameter), heat transfer coefficient and eventually their pressure drop coefficient. The following mass and energy balances are performed to compute the internal pressure and temperature:

dM dt = ṁin -ṁout (1) d dt (M • u) = ṁin • h in -ṁout • h out + i Qi (2)
where Qi are the different heat flows applied to the fluid by convection or radiation (conduction is neglected). Convection and radiation heat flows are computed according to:

Qconv = h c • S W • (T -T W ) = M W • C p W • dT W dt Qrad = C • (T 4 ext -T 4 ) (3)
where Qconv is the convection heat between the fluid and its enclosure and Qrad is radiative heat loss. Subscripts If the pipes are long enough or have pressure drop singularities, the pressure drop can be not negligible and in this case this pressure drop is computed by:

∆P = K • ṁ2 2ρ , ( 4
)
where K is the singular pressure drop coefficient depending of the roughness and the geometry of the pipe (length, diameter, elbows...).

Phase separator

The phase separator is filled with a liquid/gas mixture and the liquid falls down due to the gravity. The mixture is considered at the equilibrium point with homogeneous pressure and temperature. There are a diphasic input and a gaseous output at the top, and a liquid output at the bottom. The phase separator is also equipped with an electrical heater at the bottom and all the heat sources (convection, radiation, heater) are applied in the liquid phase only. Hence, the evaporated mass per second can be computed as:

ṁevap = Qconv + Qrad + Qeh Lv ( 5
)
where Qeh is the power delivered by the electrical heater.

The gas and liquid mass balances are defined as:

dM g dt = ṁin • x in -ṁout,g + ṁevap , (6)
dM l dt = ṁin • (1 -x in ) -ṁout,l -ṁevap , (7)
where subscripts g and l are referring to the saturated gas and saturated liquid. The gas and liquid energy balances become:

d dt M g • u g = ṁin • h g • x in -ṁout,g • h out,g + m evap • h g , (8) d dt (M l • u l ) = ṁin • h l • (1 -x in ) -ṁout,l • h out,l -m evap • h l . (9)
Mass and volume repartitions between gas and liquid are deduced by:

V l = M l ρ l V g = V -V l , (10)
and the liquid level z is then computed as:

z = V l V • L, ( 11
)
where L is the height of the tank. Then, the average quality x, density ρ and internal energy ū are computed to deduce all other thermodynamic properties in the phase separator (pressure, temperature...):

           x = M g M ρ = M V ū = x • u g + (1 -x) • u l (12)

Flow components

The flow components impose a mass flow according to a pressure drop at their boundaries. Only algebraic equations are used to compute mass flows because flow dynamics are negligible in comparison to thermal dynamics. Thus, there is not any mass accumulation in components and the mass balance is hence defined by:

dM dt = 0 ⇔ ṁin = ṁout . ( 13
)

Valves

Thermodynamic transformation through valves is considered isenthalpic (h in = h out ) and the mass flow is computed according to a classical CV (valve coefficient) formulation. The input parameters of valve models are their maximum CV, their opening and closing speed and their rangeability (characterize the non-linearity of the opening). Cryogenic valves have generally an equal percentage opening, thus the CV coefficient is computed by:

C V = CV max R • e u 100 •log(R) - CV max R • (100 -u), (14)
where CV max is the CV of the valve for a complete opening, R is the rangeability of the valve (generally R = 50) and u is the position of the valve between 0% and 100%. Then, mass flow is given by:

ṁ = 2.4 • 10 -5 • C V • Y • ρ∆P, ( 15
)
where Y is either a function of the pressures, either a constant according to the flow regime:

Y = 1 -∆P/P in (3• γ 1.4 •0.65)
if ∆P/P in < 0.65γ 1.4 (subsonic flow), else Y = 2/3 (sonic flow).

Turbines

Cryogenic turbines are gas turbo-expanders allowing to extract heat into work. They are defined by their design operating point (pressure, temperature, rotational speed, mass flow and efficiency), by their blade diameter D and the inertia momentum of the shaft.

If helium is considered as a polytropic gas, the flow becomes sonic if the pressure ratio pr = P out P in is above a critical pressure ratio defined by:

pr crit = 2 γ + 1 γ/(γ-1) . ( 16
)
An isentropic flow for ideal gas through a nozzle is considered to calculate the turbine mass flow. It is computed from:

ṁ = m d • G G d , (17)
G sonic = P in • ρ in • γ • 2 γ + 1 (γ+1.)/(γ-1) , (18) G sub = 2 • ρ in • P in • γ γ -1 • pr 2/γ -pr (γ+1)/γ , ( 19
)
where G is calculated according to the flow regime: ( 18) is used for a sonic flow and ( 19) for a subsonic flow. These calculus are ratios between design values represented by subscripts " d " and current values to be sure to obtain good results at the design operating point and remove all constants of the system generally not well known and inducing errors.

If an isentropic expansion is considered (s in = s out ), the corresponding output enthalpy hout s can be directly deduced from the output pressure and the entropy. The real output enthalpy hout is computed using an isentropic efficiency:

η = η d •        2 • v v d - v v d 2        , (20)
v = c b c j = π • D • N √ 2 • (hin -hout s ) , ( 21
)
where v is the ratio between the blade velocity (c b ) and the jet velocity (c j ) of the turbine, N is the shaft speed of the turbine and D the diameter of the blade. Considering the isentropic efficiency in the energy balance, the real output enthalpy h out can be computed from:

E = ṁ • (hin -hout) = η • ṁ • (hin -hout s ). (22)
The shaft speed N is computed by the differential equation:

I • N • dN dt = E -E d • N N d 4 , ( 23
)
where I is the inertia momentum of the shaft.

Warm compressors

Warm compressors compress gaseous helium at ambient temperature, an isothermal compression is considered here (T in = T out ). They are volumetric machine, thus the mass flow simply depends on the density of input helium and of the volumetric flow Ḟ of the compressor which is constant:

ṁ = Ḟ • ρ in . ( 24
)
Cold compressors They are non volumetric machines and the model is based on their internal pressure field. This pressure field defines the compressor operating points according to three correlated variables: the pressure ratio, the reduced mass flow and the reduced speed. Several operating points of this pressure field are entered as parameters and the mass flow is computed by interpolations between operating points. The pressure fields are adjusted by a parameter identification made on each compressor to fit experimental operating points. Isentropic efficiencies are also defined from this pressure field and the same method of interpolation is used to calculate the output temperatures, see, for instance, Bradu, Niculescu, and Gayet (2008a) for more details on the cold-compressor model.

Heat exchangers

Cryogenic heat exchangers (HXs) are generally platefin counter-current HXs in brazed aluminum. HXs are composed of different streams which are characterized by their design operating point (temperature, pressure, mass flow, pressure drop, global heat transfer coefficient). The material, the volume and the mass of each stream are also taken into account to have good dynamics. To cope with the complexity of the HX and to take into account the dynamics along the streams, a space discretization is performed on each stream which is divided in N nodes, see Fig. 6. Equations ( 1), ( 2) and ( 3) previously defined for pressure components are used to estimate the temperature of each nodes and a term q i is added in (2) to compute the convective heat fluxes between hot and cold streams. This term is calculated using a logarithmic mean temperature difference method (LMTD):

q i = hc i • ∆T i+1 -∆T i log ∆T i+1 ∆T i ( 25
)
with ∆T i = T hot i -T cold N-i+1 and the global heat transfer coefficient hc i in a node is computed on the basis of the empirical formulation of Colburn, especially adapted for turbulent flow in HXs [START_REF] Colburn | A method of correlating forced convection heat transfer data and a comparison with fluid friction[END_REF]:

hc i = k i D • 0.023 • Pr 1/3 i • ṁi • D µ i 0.8 . ( 26
)
Many phenomena occur in HXs and the empirical formulations used do not take into account complex geometries. For those, other methods exist to compute global heat transfer coefficients according to the HX type. These methods imply a perfect knowledge of the equipments and the formulas are specific to each configuration. To cancel the geometrical constant D generally not known and to avoid static error at the design operating point, the ratio between the (26) and the design global heat transfer coefficient hc d , known and guaranteed by suppliers is performed:

hc i = hc d N • k i k d • ṁi • µ d ṁd • µ i 0.8 • Pr i Pr d 1/3 (27)
to compute the global heat transfer coefficient as function of design values identified by the subscripts 'd'.

In other simulation approaches, [START_REF] Kutzschbach | Dynamic simulation of a helium liquefier[END_REF]; [START_REF] Deschildre | Dynamic simulation of an helium refrigerator[END_REF] use an equation based on the Colburn formulation by adjusting parameters related the type of HXs. Next, [START_REF] Maekawa | Dynamic simulation of the helium refrigerator/liquefier for LHD[END_REF] makes also a ratio with the design heat transfer coefficient and it considers a constant conductivity, viscosity and Prandl number.

The mass flows between each node are deduced from the friction equation given by:

∆P = f r • ṁ2 • L S 2 • 2 • D • ρ , (28)
where f r = 0.184 • Re -0.2 is the Darcy friction factor. The same method is used to remove all geometrical parameters and empirical constants. Finally the following equation using the known design values is obtained:

∆P = ∆P d • ṁ ṁd 1.8 • µ d µ -0.2 ρ d ρ .
(29)

Numerical solvers

The numerical solvers embedded in the software EcosimPro are used to solve the differential and algebraic equations (DAEs) of the form:

F(t, y(t), ẏ(t)) = 0 (30)
The following steps are performed to solve the entire system at each integration step:

• Symbolic solutions of independent linear equations are found;

• Subsets of linear systems are detected and solved by a linear equations system solver;

• Subsets of non-linear systems are detected and solved by a tearing technique which performs iterations on some selected tearing variables to calculate unknowns as function of theses variables;

• Then, the DAE system is solved by a DASSL algorithm [START_REF] Petzold | A description of DASSL: a differential-algebraic system solver[END_REF].

Simulation results

The different cryogenic components described previously were used to model different CERN cryogenic systems. All simulations are performed in closed-loop as on real plants, the PLC give the different orders to actuators and controllers, there is no experimental data included in simulations. Nevertheless, some manual operations can be reproduced in simulations (set-point changed manually, valve forced by operators, etc.).

CMS cryoplant

The first cryogenic system modeled and simulated is the cryoplant of the particle detector CMS where a large superconducting magnet of 225 tons has to be cooled at 4.5 K with liquid helium. The CMS cryoplant is composed of a compression station, a helium cold-box (refrigerator) and a coil cryogenic system as it is shown in Fig. 7. The model of the entire plant under EcosimPro c is made of 3080 DAEs containing 219 derivatives. Around 30 control loops and more than 300 inputs/outputs are simulated in the PLC.

First simulations have been performed with the coldbox alone. The dynamic behavior simulated agrees to the one observed on the real plant as it is shown on the first graph of the Fig. 8 where the simulated temperature at the beginning and at the cold-end of the cold-box is plotted and compared with a real cool-down achieved in January 2008. The maximum relative error observed in simulation on temperatures, pressures and mass flows is about 5% around turbines.

The second graph of the Fig. 8 shows the temperature after the first HX during a manual action on the turbine inlet valve performed in February 2006 (the first cooldown of the magnet) and reproduced in simulation. The good agreement between the real and the simulated behavior shows that the global dynamic response of the plant is correct. Note that the delay between the precooler stop on the real plant and in the simulation comes from the difference of the cool-down speed due to the under-estimation of the cold-mass.

1.8 K refrigeration unit for the LHC

A 1.8 K refrigeration unit for the LHC was also simulated. This unit is used to cool-down the LHC magnet helium baths from 4.5 K down to 1.8 K following the saturation line of helium. These units pump gaseous helium over 3.3 km sector from atmospheric pressure until 1.6 kPa using cold-compressors, see Fig. 9. The entire model contains 3056 DAEs with 249 derivatives and 150 inputs/outputs are simulated with 14 control loops.

Similarly to the previous case study, the simulation agrees with the real data acquired during the final cooldown of the sector 5-6 of the LHC achieved in April 2008, see Fig. 10. Transients are well simulated and the control of cold compressors is well reproduced. The output temperatures of cold compressors are lower in simulation than on the real plant due to isentropic efficiencies which are over-estimated in the model.

The simulator was also used to test a new set-point management for cold compressors avoiding steps on compressor speeds. As simulation shows a real improvement, with a smoother control and lower constraints on machines, see Fig. 11, this new set-point management was successfully implemented on the real plant. [START_REF] Bradu | Control optimization of a LHC 18 kW cryoplant using dynamic simulations[END_REF]).

An Internal Model Control (IMC) with 2 degrees of freedom was developed: one degree to follow the setpoint and one degree to compensate unmeasured disturbances. An anti-windup system to take into account saturations of actuators (valves) was also included, see [START_REF] Zheng | Anti-windup design for internal model control[END_REF] for details. Moreover, a feed-forward action was added to take into account a measurable disturbance. Note that the internal model of the controller was obtained by a parameter identification made on the simulator because it was not possible to perform an identification procedure on the real plant for availability and safety reasons.

Two simulations were performed to compare the former PI controller and the IMC controller during a turbine stop that constitute an important disturbance on the high pressure, see Figure 12. Oscillations in steadystate were removed and the disturbance rejection is efficient as the overshoot, initially of 8%, was reduced to less than 2% and the recovery time passed from 40 minutes with the PI controller to 10 minutes with the IMC controller. The IMC has not been tested yet on the real plant because the system is currently in operation but some experimental tests should be done in future.

Simulation speed

Simulations are performed on a classical computer, a Pentium c D 3.4 GHz with 1GB of RAM. The simulation speed is highly dependent on the communication interval of the integration (the frequency with which data are available for the PLC and for the plots), on the number of DAEs, derivative variables and coupled linear subsystems which are generating a lot of computation. A HX stream divided in N nodes generates N + 1 derivative variables and one coupled linear subsystem of N equations. Hence, the simulation speed is directly linked to the total number of HX nodes.

After various simulations and comparisons, it was ob- served that a communication interval of 2 seconds is sufficient to simulate the fastest dynamics between the model and the PLC and to have a sufficient number of points in plots. Moreover, it was noticed that for classical plate-fin HXs (between 300 K and 6 K), streams have to be divided in 5 nodes minimum to appreciate the spatial dynamic. On the basis of the above considerations, the trade-off between the computational time and the precision is in agreement with the requirements : simulations are much faster than real-time. The Table 2 compares simulation speeds during the cool-down and a 1.8 K refrigeration unit for the LHC (QURC). Each system is simulated in different configurations : alone, with its warm compression station (WCS) or with its thermal load. Note that for operator training, it is possible to activate a real-time option.

Concluding remarks and perspectives

The tools and methods used for these simulations are close to those used in continuous processes like in petroleum or sugar industries (see for instance [START_REF] Alves | Opc based distributed real time simulation of complex continuous processes[END_REF]), and the different constraints related to cryogenic specificities was introduced. Cryogenic components have been designed and tested successfully with different CERN cryogenic systems. The simulation environment PROCOS proved its ability to conduct pertinent dynamic simulations for large-scale cryogenic systems during a complete cool-down phase.

Errors mainly come from bad component parameters (e.g., thermal efficiency, volume, etc.), model approximations (e.g., conduction neglected) and unknown pressure drops due to complex geometries (e.g., pipe network). Some component parameters not well known such as design pressure drops have been adapted to cope with real data. Moreover, some empirical characteristics as pressure fields of compressors can be corrected using parameter identification experimentally. Uncertainties on helium and material properties are minimal and do not imply significative errors, excepting in the helium supercritical region. It was also noticed that the modelling of large volumes and metal mass is very important to obtain good dynamics illustrated by some delay and buffer effects even if volumes and masses are generally not well known in such large-scale cryogenic systems.

The simulator was also used for different purposes and showed its flexibility. For instance, it was used to perform a virtual PLC commissioning on the CERN central helium liquefier, allowing developers to debug the control program before its implementation on the real plant. This method proved its interest by the gain obtained in terms of running time.

Then, a new set-point management for coldcompressors was tested in simulation and successfully implemented on the LHC cryogenic system. In addition, the simulator was used as a test bench to test a new Internal Model Control on a warm compressor station and the internal model of the controller was identified parametrically using the simulator.

Finally, a special room will be available soon at CERN to train people on the cryogenic operation on the CMS cryoplant and on the 1.8 K refrigeration unit for the LHC.

In the future, the remaining components of the LHC cryogenic systems will be modeled, especially the 18 kW @ 4.5 K refrigerators and the thermal load related to the LHC magnets.

Figure 1 :

 1 Figure 1: The real and the simulated control architecture for CERN cryogenic systems

Figure 2 :

 2 Figure 2: Links between the PLC and the process model

Figure 3 :

 3 Figure 3: PID controller management in simulator

Figure 4 :

 4 Figure 4: Connections between flow and pressure components

Figure 5 :

 5 Figure 5: Small cryogenic model overview in EcosimPro

  Figure 7: The CMS cryoplant

4. 3 .Figure 8 :Figure 10 :

 3810 Figure 8: Comparison between experimental and simulated temperatures during a cool-down of the cold box alone and with the CMS magnet

Figure 11 :

 11 Figure 9: A 1.8 K refrigeration unit for the LHC (Air Liquide)

Figure 12 :

 12 Figure 12: Comparison in simulation between PID and IMC control

Table 1 :

 1 Main thermodynamic and geometric variables

	Symbol Description	Unit
	C p	Specific heat

Table 2 :

 2 Simulation speed average during cool-down phase

	System	DAE a Der b Sys c Spd d
	TCF50	1786	146	10	x18
	TCF50+WCS	2050	175	11	x15
	CCB	2581	205	14	x14
	CCB+WCS+load	3334	296	16	x8
	QURC+WCS	1921	147	6	x130
	QURC+WCS+load 3056	249	6	x83
	a Number of differential-algebraic equations
	b Number of derivative variables
	c Number of coupled linear sub-systems
	d Simulation speed (ratio to real time)

W refers to the metal enclosure (wall), C is a coefficient

Acknowledgments

The authors would like thank the CERN cryogenic group in Geneva who supported this work as well as the anonymous reviewers of Control Engineering Practice whose comments and pertinent remarks helped us to improve the overall contribution of the paper.