A Sanitas

M Bedel

M El Mansori

Experimental and numerical study of section restriction effects on filling behavior in aluminum low-pressure casting

Keywords: low pressure casting, aluminum alloys, mold filling, fluid flow oscillations 𝑣 𝑐

The effect of pressurizing speed and mold geometry on mold filling in aluminum low-pressure casting was studied. The filling of cylindrical sand molds was both experimentally measured and simulated to characterize the free surface front velocity as linked to the mold geometry. Cylindrical molds with different diameters were filled with liquid AlSi 13 alloy. The mold filling velocity was experimentally measured using electrical contacts and was simulated using ANSYS Fluent® simulation software. The experimental results show that during mold filling without any section change, the filling velocity is regular and follows the Bernoulli's equations. However, adding a sudden section restriction in the mold leads to oscillating filling phenomena. Moreover, the oscillation intensity is observed to be proportional to the imposed filling pressure ramp. These oscillations lead to a temporary increased velocity of the metal front, which could induce metal oxidization and reduced mechanical properties for the cast part. The presented fluid dynamics simulation results show the importance of simulating the tube and not only the mold to catch the dynamic effects of the process on filling. Simulating the tube or the whole system, the observed filling oscillations are quantitatively predicted by the model. An analytical model is proposed to forecast the effect of the restriction function of geometry and pressure ramp. It permits to define new guidelines for low pressure casting part designs and the associated process adjustments.

Melt velocity in the tube 𝑚. 𝑠 -1

Introduction

In gravity casting, as in Low Pressure Casting (LPC), the first step in making a part free of defect is to fill the mold properly. The filling optimization objective is to reduce the metal velocity to a minimum value without inducing misrun [START_REF] Edler | Thin-walled Mg Structural Parts by a Low-pressure Sand Casting Process[END_REF]. Indeed, on one hand, a too high velocity would induce a turbulent filling flow, leading to the risk of generating oxides in the parts. On the other hand, a too low velocity would lead to early solidification and therefore an incomplete filled part.

To determine the upper limit velocity in the system, a common criterion was proposed by Campbell, independent of the local geometry [START_REF] Campbell | CASTINGS[END_REF]. By analyzing the relationship between filling velocity of the metal front, melt density and surface tension, this criterion gives an upper limit velocity of 0.5 m/s for aluminum alloys. Experimental aluminum gravity casting [START_REF] Runyoro | Critical gate velocities for film-forming casting alloys: a basis for process specification[END_REF] and LPC casting both for industrial part [START_REF] Luk | Low pressure sand casting of ultrasonically degassed Al7SiMg alloy: Modelling and experimental validation of mould filling[END_REF] and for a simple plate geometry [START_REF] Liu | Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy[END_REF] confirmed that above this critical value, the mechanical properties of the parts are reduced due to oxides entrapment.

To identify the lower limit of filling velocity, coupled thermal, hydraulic and solidification mechanisms responsible for misrun were studied, mainly in gravity casting. Different models were developed to link casted length and casting properties, for different alloys. The most simple model links proportionally an empirically determined freezing time to the metal filling velocity [START_REF] Campbell | CASTINGS[END_REF]. More developed energetic models [START_REF] Flemings | Low-pressure die casting of magnesium alloy AM50: Response to process parameters[END_REF] or hydraulic models [START_REF] Luk | Low pressure sand casting of ultrasonically degassed Al7SiMg alloy: Modelling and experimental validation of mould filling[END_REF]) also directly link casted length and metal velocity. The importance of filling velocity on misrun was experimentally confirmed in aluminum gravity castings [START_REF] Flemings | Fluidity of metals -techniques for producing ultra-thin section castings[END_REF] but also in aluminum composite LPC [START_REF] Sahoo | The Influence of Pressure Die Casting Parameters on the Castability of AlSi11-SiCp Composites[END_REF] and magnesium alloy LPC for plates [START_REF] Sadayappan | Casting Fluidity of Magnesium Alloy AZ91 in Gravity and Low Pressure Casting[END_REF], bars [START_REF] Laws | Effect of die-casting parameters on the production of high quality bulk metallic glass samples[END_REF] or tubes [START_REF] Sanitas | Investigating surface roughness of ZE41 magnesium alloy cast by low-pressure sand casting process[END_REF]. Moreover, the filling time in LPC has a great influence on grain size and final mechanical properties [START_REF] Flemings | Low-pressure die casting of magnesium alloy AM50: Response to process parameters[END_REF]. Therefore, the velocity mastering during filling is clearly a key factor influencing the final cast part quality.

In gravity casting, the liquid metal velocity is mainly dependent on the metallostatic pressure evolution during mold filling, which is related to the mold design [START_REF] Campbell | CASTINGS[END_REF]. On the opposite, in low pressure casting, the liquid metal velocity is induced not only by the mold design but also by the filling pressure ramp [START_REF] Edler | Thin-walled Mg Structural Parts by a Low-pressure Sand Casting Process[END_REF]. This additional processing parameter should allow the control of the fluid flow without modifying the mold geometry. Indeed, in low pressure casting, the pressure of the gas above the liquid metal is gradually increased in the furnace. The metallostatic head pressure of the metal is hence compensated and the metal is forced to rise through the rising tube towards the mold.

To choose the adapted pressure ramp, numerical simulation is a precious tool. In previous numerical works, different system geometries were considered; when the whole LPC furnace is taken into account in some models [START_REF] Luk | Low pressure sand casting of ultrasonically degassed Al7SiMg alloy: Modelling and experimental validation of mould filling[END_REF], only the rising tube and the mold [START_REF] Liu | Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy[END_REF] or even only the mold [START_REF] Kuo | Development of an interactive simulation system for the determination of the pressure-time relationship during the filling in a low pressure casting process[END_REF] are considered in other works. In the last case, the pressurizing gas and the liquid metal in the furnace are not considered but replaced by a boundary condition at the riser tube -mold interface, resulting in calculation time savings. The pressure at the riser tube -mold interface was analytically linked to the gas pressure in the furnace. Assuming that the dynamic pressure of the fluid is negligible, the flow is laminar, the fluid density is constant and the evolution of the metal level in the furnace during filling is negligible, the relationship between the gas pressure 𝑃 𝑓 , the metal pressure at the tube -mold interface 𝑃 𝑚 and the rising tube height ℎ 𝑡𝑢𝑏𝑒 can be expressed [START_REF] Duff | Fluid Flow Aspects of Solidification Modelling : Simulation of Low Pressure Die Casting[END_REF]:

𝑃 𝑓 -𝜌𝑔ℎ 𝑡𝑢𝑏𝑒 = 𝑃 𝑚 (Eq-1)
The hypothesis of a steady state with perfect linear increase of gas pressure in the furnace has implicitly been made. Other works directly used analytical equations to link rising metal velocity to gas pressure ramp using the same assumptions. Considering 𝑃 𝑓 ̇ as the derivative of the gas pressure in 𝑃𝑎. 𝑠 -1 , the Eq-2 gives the vertical filling velocity 𝑉 𝑧 [START_REF] Hogg | Low pressure sand casting of magnesium alloys[END_REF].

𝑉 𝑧 = 𝑃 𝑓 ρ𝑔 (Eq-2)
However, in this very simple model, furnace and mold geometries are neglected. Liu et al. added in this analytical equation the influence of horizontal furnace and mold cavity cross sections [START_REF] Liu | Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy[END_REF]. Defining 𝑆 𝑐 , 𝑆 𝑡 and 𝑆 𝑓 respectively as filled mold cavity, rising tube and furnace horizontal cross sections, Eq-3 links 𝑃 𝑓 ̇ the pressure ramp in 𝑃𝑎. 𝑠 -1 ; 𝜌 the alloy mean density in 𝑘𝑔. 𝑚 -3 ; 𝑔 = 9.81𝑚. 𝑠 -2 and 𝑣 𝑡 the mean velocity in the tube in 𝑚. 𝑠 -1 :

𝑃 𝑓 ̇= 𝜌𝑔 (1 + 𝑆𝑐 𝑆𝑓 ) 𝑆𝑡 𝑆𝑐 𝑣 𝑡 (Eq-3)
Those analytical equations can then be compared to experimental studies. Liu et al. [START_REF] Liu | Characteristics of mold filling and entrainment of oxide film in low pressure casting of A356 alloy[END_REF] studied the effect of different pressure ramps on the metal front filling behavior for a sudden section increase between tube and part (by a factor 20). Simulating the tube and mold geometries, they revealed that a section change induces oscillations that increase in amplitude when increasing the pressure ramp. Fan et al. [START_REF] Fan | Low pressure lost foam process for casting magnesium alloys[END_REF] used similar hydrodynamics hypothesis to estimate the filling velocity in low pressure casting of a lost foam mold with sudden section reduction by a factor 0.76. The comparison showed a good correlation between experiment and analytical model without showing clear oscillations. [START_REF] Luk | Low pressure sand casting of ultrasonically degassed Al7SiMg alloy: Modelling and experimental validation of mould filling[END_REF] studied two different pressure ramps applied on an industrial complex geometry. This works shows qualitatively that the pressure ramp increase leads to higher fluid velocity oscillations and a reduced part quality. Zeng et al. [START_REF] Zeng | Simulation of mold filling under counter gravity for A356 alloy and A356/SiCp composite[END_REF] studied the filling of a plate in counter gravity casting for aluminum and for aluminum composite alloys. They also encountered velocity oscillations which are lower when the viscosity of the melt increases. It was shown in the example of an automotive industrial part that the measured filling velocity was lower than the estimation given in Eq-2 due to pressure losses [START_REF] Hogg | Low pressure sand casting of magnesium alloys[END_REF]. Therefore, previous works showed clearly that mold geometries and pressure ramp can significantly impact the fluid flow. However, the relationship between pressure ramp, mold geometry, melt properties and resulting filling velocity is still unclear.

The present work aims to study the relationship between the imposed gas pressure evolution in the furnace and the induced filling behavior of molds by liquid metal in low pressure sand casting (LPC), both experimentally and numerically. It evaluates in what extent simple hydraulic models can be representative of the filling of a cavity when using LPC, by using adapted simulation hypotheses. This way, numerical experiments can be performed to define filling rules according to mold geometries in LPC.

Filling characterization methods

Filling experiment

Around 170𝑘𝑔 of commercial aluminum 13% silicon alloy was melted in electrical low-pressure Kurtz furnace and maintained at 750°C  5°C. A cross-section view of the whole LPC system is shown in Figure 1. The crucible diameter 𝜙 𝑓 is 545 𝑚𝑚 , the tube length ℎ 𝑡𝑢𝑏𝑒 is 953 𝑚𝑚, the tube internal diameter 𝜙𝑖 𝑡𝑢𝑏𝑒 is 70 𝑚𝑚 and the tube external diameter 𝜙𝑒 𝑡𝑢𝑏𝑒 is 100 𝑚𝑚. The furan resin bonded molds placed at the top of the rising tube were designed and printed using an Exone S-print 3D printer. They were 400 mm in height and cylindrical in shape with a sudden section change of a defined ratio R. During the filling, the pressure ramp was mastered in the low-pressure machine by a pressure regulation system and the air pressure in the furnace was monitored using a pressure sensor. The pressure ramps range admissible by the system is 1 to 30 mbar/s. The cross sections are defined as: A typical graph of the pressurizing ramp during filling in LPC process is presented in Figure 2. The set pressure evolution has to be distinguished from the actual measured pressure evolution as the pressure regulating system induces some discrepancies. The set pressure evolution corresponds to the set data of the LPC machine when the actual pressure evolution is recorded during each experiment with a pressure uncertainty of 1 mbar, comparable to the 3 mbar precision encountered by [START_REF] Li | Development of pressure control system in counter gravity casting for large thin-walled A357 aluminum alloy components[END_REF]. The set pressure evolution can be divided into five typical stages. The first stage is the filling of the bottom part of the rising tube with a ramp equal to 5 𝑚𝑏𝑎𝑟. 𝑠 -1 , until a fixed pre-pressure value. This first stage is identical in all the experiments. Stage II is the stabilization at the pre-pressure value for one second. Then during stage III, the top of the tube and the mold are filled at the filling pressure ramp called here "set pressure ramp" P f ̇ . To avoid any mold overflow, the pressure is then stabilized for 4 seconds in stage IV before pressure release in stage V. The experimental pressure evolution can be compared with the set pressure evolution by analyzing Figure 2. The actual pressure evolution is globally delayed and the pressure ramp is progressively slowed down by servo regulator until reaching the final set value.

𝑆 𝑓 = Π 4 (𝜙 𝑓 2 -𝜙𝑒 𝑡𝑢𝑏𝑒 2 ), 𝑆 𝑡 = Π 4 𝜙𝑖 𝑡𝑢𝑏𝑒 2 and 𝑆 𝑐 = Π 4 𝜙 𝑐 2 .
Two types of experiments were realized. In the first type of experiment, the sand molds are vertical tubes with a constant inner diameter which is equal to the rising tube diameter. Therefore, no section restriction is made and the restriction factor

𝑅 = 𝑆𝑐 𝑆𝑡 = ( 𝜙 𝑐 𝜙𝑖 𝑡𝑢𝑏𝑒 ) 2
is equal to 1. In the second group of experiments, the cylindrical molds are designed with a sudden section restriction at ℎ 𝑚 = 100 𝑚𝑚 from the bottom of the mold. The four geometries studied are presented in Table .   Group 1 Group 2 Section restriction factor 𝑅 (-) 1 0.5 0.24 0.125 Shape For each experiment performed in this study, the rising of the horizontal free surface in the mold was tracked using electrical contacts. It consisted on 1 mm steel rod, inserted until the center of the cylinders at every 10 mm in height. The contact position precision was  2 mm. Each contact was connected to the electrical circuit. During filling, an initial output tension of 11 V was gradually reduced by 0.05 V steps by the electrical circuit when the metal reached a contact. The measurement of the output tension versus time was performed by National Instrument data acquisition at a frequency of 100 Hz.

Filling simulation

Computational modeling of the filling processes was performed using the commercial software ANSYS Fluent®. The finite volume model associated with free surface volume of fluid modelling (VOF) are described in detail in Fluent theory guide (ANSYS Inc., 2013). The metal was considered as a Newtonian fluid with a constant cinematic viscosity of 0.00113 𝑘𝑔. 𝑚 -1 . 𝑠 -1 and a density of 2495 𝑘𝑔. 𝑚 -3 [START_REF] Kund | Numerical study of influence of oblique plate length and cooling rate on solidification and macrosegregation of A356 aluminum alloy melt with experimental comparison[END_REF]. The same assumption was made for the air above the metal in the furnace with a viscosity of 0.0001 𝑘𝑔. 𝑚 -1 . 𝑠 -1 and a density of 1.442 𝑘𝑔. 𝑚 -3 (from Ansys Fluent 16.1 database). To reduce simulation times, the study was performed using a 2D axisymmetric model. To validate the convergence of the computations on the tube case, three different meshes with a mean size of 2 mm, 1 mm and 0.5 mm squared elements were considered. The 1 mm mesh was used in the presented results. The studied geometry being axisymmetric, only one half of the 2D system was computed.

To rate the effect of each LPC system element (i.e. mold, rising tube and furnace) on the observed filling, different geometries were numerically studied from the most simplified case to the most complete one. Figure 3 shows the different studied geometries. In "case A", only the mold is taken into account (Figure3-(a)). In "Case B" the tube is additionally considered (Figure 3-(b)). Finally in "Case C", the mold, the rising tube and the metal in the crucible are taken into account (Figure 3-(c)).

In "Case A", the considered geometry was only the mold, as shown in Figure 3-a. The system was initially fully filled with air and a metal pressure boundary condition was applied on the bottom surface, inducing the rising of the metal in the mold. The pressure boundary condition was a pressure ramp as set in the experiment. The imposed atmospheric pressure on the top surface of the mold permitted the air to escape during metal rising. In the other surfaces of the system, no mass exchange was allowed and a zero-fluid velocity was applied as a boundary condition.

In "Case B", illustrated in Figure 3-b, the system geometry was modified to consider both the mold and the total length of the rising tube. The initial and boundary conditions remain unchanged, only the metal volume at the time of section restriction will be higher.

In "Case C", the whole LPC system including furnace, metal and the air above it in the crucible were additionally simulated as can be seen in Figure 3-c. The pressure inlet boundary condition was then applied to the air. This case was therefore the most complete studied model of the actual LPC system. The alloy temperature measurements obtained by thermocouples embedded in the molds showed for all the experiments a temperature between 750°C (at the bottom of the mold with the highest filling speed) and 650°C (at the top of the mold with the slowest filling speed). This temperature range -far from the liquidus-allows the assumption of a low variation of density.

The experimental setup permitted to extract the evolution with time of the metal front position in the mold cavity with the same diameter as the rising tube internal diameter. Those positions are then compared with the calculated positions using the measured air pressure in the furnace and the Pascal principle (Eq-1). This comparison, given for R=1 and P f ̇= 25 mbar/s in Figure 5, shows a good correlation with the metal height position. For each sample, a linear regression was made on the steady state pressure evolution during stage III of the filling for each of the 11 experiments cast at 5, 15 and 25 mbar/s set pressure ramps. Similarly, a linear regression was made on the steady state part of the height versus time curve giving the average velocity of the metal front. Then, the average velocity was plotted versus the average pressure ramp as shown in Figure 6. This graph shows a linear relationship between filling velocity and the pressure ramp. The highest ramps lead to a higher dispersion. It might be due to the lower accuracy of pressure regulator system at higher pressure ramps. Using equation 3 with a restriction ratio of 1, the pressure ramp and the rising velocity can be linearly linked to the alloy density and furnace geometry:

𝑣 𝑡 = 𝟏 𝝆𝑔 (1 + 𝑆 𝑡 𝑆 𝑓 ) 𝑷 𝒇 ̇ (Eq-4)
If the furnace geometry is easily characterized (𝛼 = 𝑆𝑡 𝑆𝑓 = 0.017), the alloy density is not precisely known as its temperature evolves in the system during filling. However, thanks to the measures presented in Figure 6, the ratio 𝑣 𝑡 𝑷 𝒇 ̇ can be approximated as a constant:

𝑣 𝑡 𝑷 𝒇 ̇= 4.1 𝑚𝑚. 𝑚𝑏𝑎𝑟 -1 .
Therefore, a density of 2445 𝑘𝑔. 𝑚 -3 is obtained. This value is in the same order of magnitude than the value of 2495 𝑘𝑔. 𝑚 -3 proposed before [START_REF] Kund | Numerical study of influence of oblique plate length and cooling rate on solidification and macrosegregation of A356 aluminum alloy melt with experimental comparison[END_REF]. It confirmed that a mean value of density can be used to estimate a mean velocity with equation 3 during filling without section change. Thus, hypothesis of non-viscous fluid and steady state flow can be applied to link height, velocity and pressure ramp when no restriction is present during mold filling. Therefore, once the velocity range permitting to avoid both misrun and oxidization is chosen, the associated pressure ramp range can be calculated using Eq-4. Considering the typical value of 0.5 m/s as an upper velocity limit, the associated upper limit pressure ramp is 122 mbar/s. This value, well above the machine limit (30 mbar/s), indicates that the filling should not induce oxide film defects in parts when no restriction is present.

3.1.2.

Mold filling behavior with section restriction (R<1)

The effect of a section restriction on filling behavior was studied. The three section restriction factors (0.5, 0.24 and 0.125) were tested at five set pressure ramps (5,10,15,20,25). For each experiment, the actual furnace pressure was measured. It was observed to be similar to the measured pressure when no section restriction was present (Figure 2). It indicates that restriction does not change significantly the gas pressure into the furnace. Then, the metal height versus time curves were analyzed. Figure 7 presents the metal height evolution for the R=0.125 molds. The first stage below the section change shows a steady state filling as for R=1 molds. Above the restriction (at ℎ 𝑚 = 100 𝑚𝑚), the metal does not rise linearly anymore. The velocity increases and an oscillating phenomenon starts. Therefore, an over-height of the metal front can be observed comparatively to molds without section restriction. To quantify this over-height, the curves were split into two parts: before restriction and after restriction. First part of the curve (i.e. from 20 mm to 100 mm) was fitted with a linear function. This fitted function was then extrapolated to the second part of the curve (i.e. from 100 mm to the top of the mold). This extrapolated function was subtracted to the experimental curve to obtain the effect of section change. The maximum value of this function was extracted and chosen to represent the maximum over-height. For each restriction ratio, this over-height was plotted function of the measured pressure ramp in Figure 8 (a,b,c). The lower error bar represents the contact imprecision (2 mm). The upper error bar represents the spacing between two contacts (10 mm) plus the uncertainty of position (2 mm). Indeed, the metal over-height was between the last-reached contact and below the next one (which was not touched yet). As expected, the over-height increases with the increasing of the pressure ramp. A linear regression for each graph shows that the over-height is proportional to pressure ramp. The regression slope for each restriction factor was then plotted function of restriction ratio R in Figure 8-(d). Without restriction, there is no over-height; therefore the impact is set to zero. Thus, the evolution of the over-height with restriction ratio R is not linear. It increases slightly when R decreases from 1 to 0.5. Then the over-height remains relatively stable from 0.5 to 0.24 and it rises for R value below 0.24.

Figure 8-(c) shows a maximum over-height close to 100 mm for R0.125 and 25 mbar/s. Assuming a typical industrial mold height of 400 mm, this over-height corresponds to 25% of the total part height. In practice, this over-height can even lead to overflowing if the mold design is not well adapted to this effect.

Experimental curves were also treated to extract maximum velocities reached during the first wave after section restriction. This velocity was extracted by taking the mean value between two successive experimental contacts. Figure 9 presents the ratio of this measured velocity and the theoretical velocity from equation 3 without restriction for the different experimental cases. For R=0.24 and R=0.5 restrictions, the maximum velocity ratio increase slightly with the pressure ramp. However, this ratio is significant and remains at a factor close to 3. When the restriction ratio is 0.125, the velocity ratio increasing is clearly linked to the pressure ramp. Velocity can be multiplied by more than a factor ten due to restriction. Actually, in the most critical cases (R=0.125 and 25 mbar/s), acceleration was so high that it could not be measured. Indeed, as the contact spacing is 10 mm and acquisition frequency is 100 Hz, the maximum measurable velocity is 1m/s. Thus, for R=0.125 ratio and 25 mbar/s, the maximum velocity exceeds this value. For the cases with a ratio R<0.125 and 𝑃 𝑓 ̇>15 mbar/s, the maximum measured velocity exceeds 0.5 m/s. Therefore, the filling might create oxide film defects in parts even if mean fluid velocity calculated with equations-3 is well below this critical velocity limit.

Simulation results

One experiment was used as a reference to compare the measured and the numerically predicted fluid flow during LPC filling. In the considered experiment, the restriction factor was 𝑅 = 0.125 and the set pressure ramp was P f ̇= 15 𝑚𝑏𝑎𝑟/𝑠. To understand the origin of the oscillations, the experimental and numerical metal height evolutions can be compared. The evolution of the metal heights with time is given in Figure 10. The experimental points are presented with ± 2𝑚𝑚 error bars corresponding to the position uncertainty of the contacts. They show the linear rise before restriction. Then, an acceleration of flow is visible at the early instant until a maximum over-height. After that, metal starts to oscillate in height until the end of the filling. All the simulated cases show a good correlation with experiment before the restriction. After the restriction, their behaviors are different. For the simulation of the mold only ("Case A") a minor over-height is observed. Then, oscillating phenomenon is visible but its amplitude is much lower than measured experimentally. For the simulation of the mold and the tube ("Case B") the over-height observed is slightly higher than in the experiment. Then the oscillations fit globally the experimental ones. It reveals that section restriction induces a back flow after restriction. Thus, metal drops below 100mm and pass through the section restriction again. The simulated curve of the whole furnace simulation ("Case C") shows the same tendency as "Case B". However, some little variation can be observed at the top of the over-height.

Simulation showed a complex phenomenon of air bubbles appearing at the restriction, as illustrated in Figure 11. As the model does not simulate the permeability of the sand mold, air bubbles are confined in the liquid. Therefore, it might be responsible of the small measured variations in height in the simulation "Case C". Those bubbles create disturbance of the metal front and might lead in real cases to porosities in the cast part. It suggests to design mold geometries to avoid sharp horizontal section restriction, but to prefer continuously varying sections instead. It might enable the air to escape before the metal front arrival. Considering the numerical and expefigrimental mold filling, the results of "Case A", where only the mold is considered for the simulation, is not representative. The representability of the two other cases shows that metal volume of the furnace and tube are inducing the oscillating phenomenon due to their dynamic effect. The comparison between "Case B" and "Case C" indicates that the major part of this effect comes from the tube.

Discussion

By comparing experimental and analytical filling flow behavior in LPC, it has been shown that without any section change, an average velocity function of pressure ramp and furnace dimensions (given in Eq-3) is representative of the actual metal flow. In this case, the hypotheses of a steady state without pressure losses and an incompressible, irrotational and non-viscous fluid are a good approximation. Then, when a section restriction is considered, oscillations are induced. The intensity of these oscillations was principally characterized by the generated maximum over-height. It was shown experimentally that this over-height is proportional to the pressure ramp (Figure b,c)) and exhibits a nonlinear variation with section restriction factor (Figure 8 -d). As the pressure evolution in the furnace remains linear, the oscillations should originate in the dynamic effects of the metal. More precisely, the presented simulations suggest that the main origin of the oscillations is the inertia of the metal in the rising tube. In order to validate this hypothesis, we propose in the following an analytical model to forecast the oscillations intensity as a function of process parameters and geometry.

First of all, we can determine the metal velocity in the different parts of the system. Only the mold and the tube are considered in the following according to simulation results. Assuming that the fluid is non-viscous and incompressible, its velocity in the mold cavity 𝑣 𝑐 is linked to its velocity in the tube 𝑣 𝑡 by the mass conservation:

𝑣 𝑐 = 𝑆𝑡 𝑆𝑐 𝑣 𝑡 (Eq-5)
Moreover, rewriting Eq-3 by introducing the surfaces ratio 𝑅 = 𝑠 𝑐 𝑠 𝑡 and 𝛼 = 𝑆 𝑡 𝑆 𝑓

, Eq-6 gives the fluid velocity in the tube in steady state:

𝑣 𝑡 = ( 𝑃 𝑓 𝜌𝑔 ) ̇( 𝑅 1+𝑅.𝛼 ) (Eq-6)
Considering a ramp of 15 𝑚𝑏𝑎𝑟. 𝑠 -1 , the velocities of the metal in the rising tube and in the mold cavity are given depending on the section restriction factor R in Figure 12. If we consider a transition from a steady state with R=1 (i.e. no section change) to a steady state with R=0.125, we see a relative stability of the theoretical metal front velocity 𝑣 𝑐 (respectively 60.2 𝑚𝑚. 𝑠 -1 and 61.3 𝑚𝑚. 𝑠 -1 ). However, due to mass conservation, the velocity in the tube is divided by a factor close to 1 𝑅 = 8; it decreases from 60.2 𝑚𝑚. 𝑠 -1 before the section restriction to 7.6 𝑚𝑚. 𝑠 -1 after the section restriction. It clearly appears that the metal velocity reduction in the tube cannot be instantaneous when the metal reaches the section restriction height and the metal kinetic energy has to be transformed. Considering that the system is the metal in the tube when reaching the section restriction height, the kinetic energy of the metal is given by:

𝐸 𝑐 = 1 2 . 𝜌. 𝑆 𝑡 . ℎ 𝑡 . 𝑣 𝑡 2 (Eq-7)
Where ℎ 𝑡 =1.053m is the height of the tube (ℎ 𝑡𝑢𝑏𝑒 = 0.953 𝑚) plus the height in the mold below the section change (ℎ 𝑚 = 0.1 𝑚). At the exact instant of the restriction, a velocity discontinuity appears, giving two values of 𝑣 𝑡 . 𝑣 𝑡-is the tube velocity just before the restriction and to R=1. 𝑣 𝑡+ is the theoretical steady state tube velocity just after the restriction corresponding to a value of R<1. Between these two theoretical states, the kinetic energy difference is:

𝐸 𝑐--𝐸 𝑐+ = 𝑆 𝑡 .ℎ 𝑡 .𝑃 𝑓 2 2𝜌𝑔 2 [( 1 1+𝛼 ) 2 -( 𝑅 1+𝑅𝛼 ) 2 ] (Eq-8)
This excess of energy could be compensated by pressure losses in the restriction or transformed into potential energy through an over-height. Assuming that the metal is a perfect fluid, the excess energy would be counterbalanced by the potential energy of an over-height ℎ in the restriction:

𝐸 𝑝 = 1 2 𝜌𝑔𝑅. 𝑆 𝑡 ℎ 2 (Eq-9)
Then, by combining Eq-8 and Eq-9: In Figure 13-(a) corresponding to a section restriction factor R=0.5, the model fits well the experimental data; the over-height increases linearly with the pressure ramp. For R=0.24 (Figure 13-(b)), the experimental points tend to be slightly below the model but with the same tendency. In the case R=0.125 (Figure 13-(c)) with a ramp beneath 20 mbar/s, experimental points are relatively below the model. Above 20mbar/s, the over-height is well predicted. The simulation result for "Case B" shown in figure 13 -(c) is also slightly below the analytical model. We can see in Figure 13-(d) that between R=1 and approximatively R=0.3, the model predicts a relatively linear effect of the restriction factor on the maximal over-height. When R<0.3, the effect increases very quickly. Therefore, the non-linear effect of R on the experimentally observed oscillations is qualitatively explained by the 𝛾 function non-linearity. As we can see, the best fit between the experiments and the analytical model is obtained for the R=0.5 case. Indeed, the hypotheses of no pressure losses and constant density become less and less applicable when R decreases.

Even though the fluid hypotheses used to develop our analytical model are too basic to precisely quantify the oscillation phenomena, it permits to highlight the influential parameters. Therefore, for a given LPC furnace, two main factors can be extracted: the pressure ramp and the restriction ratio.

When the pressure ramp has a linear effect on the maximal over-height, the restriction factor impact, expressed through the 𝛾 function, is more ambiguous and should be studied in more detail.

However, this analytical model already suggests different ways to optimize the filling. A first guideline is to limit the horizontal section changes in the parts for low-pressure casting. Indeed, the R factor has a huge impact on oscillations generation. This impact is critical for value below 0.125. However, modifying the casted part geometry is not always possible. A second solution to reduce unsteady flow could be the use of a variable pressure ramp. By applying the same methodology as Duan et al. [START_REF] Duan | Development of a 3D Filling Model of Low-Pressure Die-Cast Aluminum Alloy Wheels[END_REF], the pressure ramp can be reduced just before the section changes and increased again after. However, as the pressure needs to be high enough to prevent misrun, it could be insufficient to prevent oscillations. Another way to limit flow disturbance is to modify the machine dimensions, through the section and height of the rising tube. In the case of an approximately constant part horizontal section, the riser tube section could be adapted to the section of the casted part, keeping R closed to one. Moreover, as the maximal over-height is proportional to the square root of the tube height ℎ 𝑡 (Eq-11), it should be kept as small as possible.

The final part quality cannot be directly linked to the maximal over-height. A better indicator could be the maximum velocity. Indeed, these fast accelerations above 0.5 m/s, can be responsible for oxides inclusions. Unfortunately, a simple model linking process parameters and maximal velocity could not be found. However, the simulation model "Case B" with simple hypothesis and relatively low calculation time permits to forecast with good accuracy this phenomenon. Further studies would focus on the relationship between velocity oscillations and defects creation. It would allow to give more precise rules to design parts in low pressure casting by reducing defects creation during filling.

Conclusions

The effect of pressure ramp and section restriction factor on mold filling behavior in low pressure casting has been investigated:

 Steady state filling and non-viscous fluid are good first approximations to estimate mean fluid flow velocity in low pressure casting.

 It has been shown that the dynamic effects of the fluid cannot be neglected when the mold section is not constant anymore. The higher the pressure ramp and the section restriction factor are, the more significant the unsteady flow will be. With our LPC machine, it can lead to velocities higher than 0.5 m/s when R<0.125 and 𝑃 𝑓 ̇>15 mbar/s.

 In order to minimize the oscillations in LPC, the casted part horizontal section should be as constant as possible.

 Fluid flow simulation software enables to quantitatively predict those dynamic effects considering a viscous newtonian fluid by taking into account both the mold and the rising tube geometries.

 An analytical model was proposed to link the process parameters, the part geometry and the resulting flow disturbances.

 To limit flow disturbances, we also advise to adapt the low-pressure system to the casted part through the tube height and section.

Figure 1 :

 1 Figure 1 : Vertical cross section view of the Low-pressure casting system and instrumented sand mold

Figure 2 :

 2 Figure 2 : Typical set and measured pressure time evolutions, for R=1 and 𝑃 𝑓 ̇= 25 𝑚𝑏𝑎𝑟/𝑠

  Figure 3 : Boundary and initial conditions for the three studied 2D axi-symmetric model geometries (a) mold, (b) mold and rising tube and (c) mold, rising tube, furnace metal and air

Figure 4 :

 4 Figure 4 : Definition of simulated metal height at a given time step: (a) 2D visualization of the simulated metal front in the mold cavity and (b) vertical profile of metal volume fraction along the symmetry axis

Figure 5 :

 5 Figure 5 : Calculated and measured position of metal front for R=1 and 𝑃 𝑓 ̇=25 mbar/s, obtained experimentally and by calculation using Pascal principle

Figure 6 :

 6 Figure 6 : Experimental average metal front velocity versus average measured pressure ramp in R=1 molds, obtained for 11 filling experiments

Figure 7 :

 7 Figure 7 : Measured position of metal height in R=0.125 molds with various measured pressure ramps

  Effect of section restriction coefficient R on the regression coeficient of the overheight

Figure 8 :

 8 Figure 8 : Maximal measured over height versus measured pressure ramp for the three studied restrictions factorsexperimental measurements in dots (•) and interpolated tendency in dashed line (--)

Figure 9 :

 9 Figure 9 : Ratio of maximum measured metal front velocity and theoretical steady state velocity versus measured pressure ramp

Figure 10 :

 10 Figure 10 : Comparison of experimental and simulated metal height in the mold versus time

Figure 11 :

 11 Figure 11 : Observation of simulated air pockets entrapped in the metal at two instants after the section restriction in "Case C"

Figure 12 :

 12 Figure 12 : Theoretical steady state velocities in the rising tube and in the mold cavity for 𝑃 ̇𝑓 = 15 mbar/s as a function of the restriction factor R

  terms, the value of the over-height ℎ can be expressed as a function of the pressure ramp 𝑃 𝑓 ̇, a constant 𝐶 𝑓 .̇𝐶. 𝛾(𝑅) (Eq-11)Using this equation, the calculated over-height is plotted as a function of pressure ramp and compared to experimental results for three restriction factor values in Figure13(a,b,c). Moreover, in order to illustrate the effect of the R ratio in the over-height, C.γ(R) is plotted in Figure13-(d):

Figure 13 :

 13 Figure 13 : Calculated and experimental over-height evolution with pressure ramp for (a) R=0.5, (b) R=0.24 and (c) R=0.125, and (d) the proportional coefficient between pressure ramp and over-height C.𝛾(𝑅) as a function of the section restriction factor R

Figure 1 :Figure 7 :

 17 Figure 1 : Vertical cross section view of the Low-pressure casting system and instrumented sand mold ................................................................................................................................................................. 5 Figure 2 : Typical set and measured pressure time evolutions, for R=1 and 𝑃𝑓 = 25 𝑚𝑏𝑎𝑟/𝑠 ............ 6 Figure 3 : Boundary and initial conditions for the three studied 2D axi-symmetric model geometries (a) mold, (b) mold and rising tube and (c) mold, rising tube, furnace metal and air .............................. 9 Figure 4 : Definition of simulated metal height at a given time step: (a) 2D visualization of the simulated metal front in the mold cavity and (b) vertical profile of metal volume fraction along the symmetry axis ........................................................................................................................................ 10 Figure 5 : Calculated and measured position of metal front for R=1 and 𝑃𝑓 =25 mbar/s, obtained experimentally and by calculation using Pascal principle ..................................................................... 10 Figure 6 : Experimental average metal front velocity versus average measured pressure ramp in R=1 molds, obtained for 11 filling experiments ........................................................................................... 11 Figure 7 : Measured position of metal height in R=0.125 molds with various measured pressure ramps ..................................................................................................................................................... 12 Figure 8 : Maximal measured over height versus measured pressure ramp for the three studied restrictions factors -experimental measurements in dots (•) and interpolated tendency in dashed line (--) .................................................................................................................................................. 13 Figure 9 : Ratio of maximum measured metal front velocity and theoretical steady state velocity versus measured pressure ramp ........................................................................................................... 14 Figure 10 : Comparison of experimental and simulated metal height in the mold versus time........... 15 Figure 11 : Observation of simulated air pockets entrapped in the metal at two instants after the section restriction in "Case C" ............................................................................................................... 16 Figure 12 : Theoretical steady state velocities in the rising tube and in the mold cavity for 𝑃𝑓 = 15 mbar/s as a function of the restriction factor R .................................................................................... 18 Figure 13 : Calculated and experimental over-height evolution with pressure ramp for (a) R=0.5, (b) R=0.24 and (c) R=0.125, and (d) the proportional coefficient between pressure ramp and over-height C.𝛾(𝑅) as a function of the section restriction factor R ........................................................................ 20

Table 1 :

 1 Samples geometries of the sand molds used in LPC filling experiments at different pressure ramps

Acknowledgements

The authors would like to acknowledge the contribution of colleagues. Thanks are due to J. BOURGEOIS, J. NEGRE and S. BUSSETTA of the Arts et Métiers ParisTech for their precious technical support. S.KHELLADI is thanked for its precious help on fluid flow simulation. The authors also gratefully acknowledge INOVSYS platform for providing the equipment used for this work.


M Suisse

Bouvet

M Mateos

R Meunier Prest

J M Suisse

M Bouvet

Electrochemical deposition of aniline derivatives for conductometric gas sensors

Keywords: ammonia, heterojunction, conductometric sensors, polyaniline

Electrochemical deposition of aniline derivatives for conductometric gas sensors

Introduction

Widely used in the field of organic electronics and sensors, conducting polymers are the object of intensive research due to their great chemical variety and their processability by electropolymerization [1]. The electronic properties of polyaniline derivatives are highly dependent on the nature of the substituents on the aniline ring. Numerous studies have focused on the properties of monofluorinated derivatives [2,3], but only a few on the fully fluorinated polyaniline (PolyTetraFluoroAniline, PTFA) [4]. In this study, we present electrochemical and morphological properties of PTFA polymer electroplated on conductive substrates, in comparison with polyaniline already used as sensing material in NH3 sensors [5,6]. Finally, this polymer was incorporated into a patented device called Molecular Semiconductor -Doped Insulator (MSDI) heterojunction [7] that combined a poor conductive material such as monophtalocyanines or perylene and an intrinsic molecular semiconductor, the lutetium bisphtalocyanine (LuPc2).

Experimental

2,3,5,6-terafluoroaniline (TFA, Aldrich) was used as received. Ammonia gas, 1000 ppm in synthetic air, and synthetic air were used from standard cylinders, purchased from Air Liquide, France. Lutetium bisphtalocyanine (LuPc2) were synthesized according to previously described methods.

All electrochemical experiments were performed with a PGSTAT302 N (Metrohm) potentiostat connected to a PC and the data collected were analyzed using Nova® 2.1 Software. Cyclic Voltammetry (CV) and Chronoamperometry (ChA) was carried out by means of a three-electrode configuration consisting of a carbon disk (3 mm diameter, Bioanalytical Systems) or Indium Tin Oxide (ITO) plate (9 mm diameter of active surface, 8-12 Ω of square resistance, Solems, Palaiseau, France) or ITO interdigitated electrodes (IDE, deposited onto a 1 x 1 cm² glass substrate and separated by 75 m with 50 nm thickness) as working electrode, a platinum wire as counter electrode and a saturated calomel electrode (SCE) as reference electrode. Potentials were reported versus SCE.

SEM imaging was performed with scanning electronic microscope JEOL JSM6400F with an acceleration voltage of 2 kV. XPS analysis of the PTFA powder and polymer films deposited on ITO was performed on a SIA100 spectrometer (Cameca Riber apparatus) using non-monochromated Al Kα X-ray source (1486.6 eV photons). PANI films were deposed on ITO electrodes by chronoamperometry at 1 V in a solution of aniline 0.15 M in HClO4 2 M with a surface charge of 265 mC.cm -2 , then rinced with HClO4 0.1 M, absolute EtOH and dried under vacuum at room temperature. PTFA films were prepared by chronoamperometry at 1.4V in a solution of TFA 50 mM in HClO4 2 M The electrolysis was stopped after consumption of 250 mC.cm -2 . The modified electrode was rinsed in HClO4 0.1 M and dried under vacuum at room temperature.

Electrical measurements were carried out with IDE. First, a PTFA bottom layer (thickness 1µm) was electrodeposited on both IDE by chronoamperometry at 1.4V (surface charge of 250 mC.cm -2 ) in a solution of 50 mM TFA in HClO4 2M. Then, the top layer was coated by sublimation of the bisphtalocyanine of lutetium (LuPc2) in an UNIVEX 250 thermal evaporator (Oerlikon, Germany), under secondary vacuum (ca. 10 -6 mbar), at a rate of 1 Å.s -1 , by heating in a temperature range of 400-500 °C. The workbench used for NH3 exposure, at different relative humidity (rh) values, was described previously [8]. The total flow was in the range 0.5-0.55 mL.min -1 depending on ammonia concentration and the volume of the test chamber was 8 cm 3 . Gas sensing experiments were carried out in a dynamic way, with 4 min-long rest periods alternating with 1 min-long exposure periods.

Result and discussion

Electrochemical characterization of the PTFA polymer

Firstly, a CV of the monomer (TFA) in an acid medium was carried out on a carbon electrode (see Fig. 1a). The voltamperogram shows an important peak of oxidation at 1.2V which is weakened over the cycles, attributed to the oxidation of the monomer during the polymerization mechanism. The redox system centered at 0.6V and becoming more and more irreversible over the cycles, corresponds to the polymer system (passage from the amine form to the imine). The increase of the irreversibility is due to the passivation of the active surface of the electrode by the polymer.

The Fig. 1b shows the electrochemical response of a 1 μm film of PANI (green) and PTFA (red) electrodeposited on an ITO substrate respectively at 1V (charge surface of 265 mC.cm -2 ) and 1.4 V 250 mC.cm -2 ). As expected for the PANI film, we notice two redox systems with a high current range (1 mA). The first one slightly irreversible (Ered = 0.05V and Eox = 0.34V) corresponds to the passage from the leucoemeraldine to the emeraldine state and the second reversible system (Ered = 0.52V and Eox = 0.62V) corresponds to the transition from the emeraldine to the pernigraniline state. While, we only observe an strongly irreversible system for PTFA (Ered = 0.02V and Eox = 1.38V) with a lower current range (30 µA) with a large shift of the anodic peak due to the fluorination of the aniline ring which make the oxidation more difficult.

SEM images and XPS analysis of coated PTFA and PANI

SEM imaging revealed a profound difference in morphology between PTFA and PANI layers. The growth of PTFA on ITO is slow (strong decrease of the current during the ChA) because this fluorinated polymer has a little affinity for the surface and its low conductivity limits its growth. Then, a very compact layer is obtained with the presence of amorphous grains (see Fig. 2b). While, the PANI layer has a rapid growth (increase of the current during the ChA after passing through a minimum) in the form of fibers because the high conductivity of the polymer in its doped form allows it to rapidly grow on itself (see Fig. 2c). The atomic ratio (nitrogen used as reference) of C, N, O, F and Cl has been calculated using peaks area of the different elements for each polymer films. These results are in a good agreement for PANI, 5.7 C for 1 N with a high chlorine level (0.8) that revealed an important acid-base doping (protonation of imine) due to the presence of ClO4 -. However, for PTFA the results indicated a carbon pollution (7.7) and a lack of fluorine (2.7). Moreover, we notice a low level of chlorine (0.2) that indicated the absence of doping. The analysis of N1s spectra of PANI (see Fig. 2a) revealed three different form: the imine at 400.1 eV, the amine at 402.2 eV and the protonated imine at 404.3 eV that confirmed the acid-base doping. We also found the imine and amine form for PTFA at 400.4 and 402.5 eV but the protonated imine form at 405.2 eV was very weak that confirmed the absence of doping.

I(V) Characteristics and electrical response under ammonia and humidity

Electrical measurements in ambient atmosphere of LuPc2 resistor (50 nm) coated by sublimation on IDE (see Fig. 3a in blue) showed a linear I(V) characteristics in accordance with an ohmic behavior. However the characteristic of the MSDI heterojunction made with film (1 µm) as bottom layer and LuPc2 (50 nm) as top layer revealed a non-linearity at low bias. This typical behavior can be explained by the formation of an energy barrier caused by the difficulty for the electrons to cross both the bottom layer and the heterojunction between the two layers.

The MSDI device was then used as an ammonia sensor (see Fig. 3a) in a humid atmosphere (from 70% to 10% of relative humidity). We noticed a decrease of the current during the NH3 exposition phase (from 90 ppm to 30 ppm) at any relative humidity that demonstrate the ability of NH3 detection in humid atmosphere. However, the relative change of the current remained weak in the NH3 range used that indicated the possible overload of the sensor. We can also conclude the p type character of the PTFA film because we observed that a reductive gas such as NH3 involved a drop of the conductivity of the device.

Conclusion and perspectives

We demonstrated that a fully fluorinated polyaniline has different properties than its unsubstituted analogue. It is an undoped dense polymer with a non-measurable conductivity. Its use in an MSDI heterojunction device confirmed its p type character despite the full fluorination, without demonstrating a higher relevance compared to copper perfluorophthalocyanine (CuF16Pc) usually used in n-MSDI heterojunction [7].

Fig. 1 :

 1 Fig. 1: (a) Cyclic voltammetry on glassy carbon of TFA 30 mM in HClO4 0.66 M, v = 0.1 V.s -1 ; (b) Cyclic voltammetry PTFA and PANI polymer, coated on ITO, in HClO4 0.5 M, v = 0.04 V.s -1

Fig. 2 :

 2 Fig. 2: (a) N1s spectra of PTFA and PANI layers coated on ITO substrate by chronoamperometry ; (b) SEM images of PANI and (c) PTFA layers coated ITO substrate.

Fig. 3 :

 3 Fig. 3: (a) Current-voltage characteristics of a LuPc2 resistor (50 nm, blue) and a PTFA (~1 µm) / LuPc2 (50 nm) MSDI (red); (b) Response of a PTFA / LuPc2 MSDI polarized at 1.5 V depending on NH3(g) (ppm) and relative humidity (%HR)

Acknowledgements

The authors acknowledge the ANR for funding through the projects CAP-BTX 2010 and OUTSMART 2015, the Conseil Régional de Bourgogne (projects FABER, PARI SMT08 and CDEA), the European Union (FEDER and Cost action TD1105 EuNetAir) and finally the MENESR for funding the PhD grant (M. M.).


Qasim Khalid
email: qasim.khalid@urjc.es

Alberto Fernández
email: alberto.fernandez@urjc.es

Marin Lujak
email: marin.lujak@imt-lille-douai.fr

Arnaud Doniec
email: arnaud.doniec@imt-lille-douai.fr

A Group Evacuation Method for Smart Buildings

Keywords: Multi-agent system, evacuation, semantic technology, situation awareness, smart buildings

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

Introduction

We would like to draw your attention to the fact that it is not possible to modify a paper in any way, once it has been published. This applies to both the printed book and the online version of the publication. Every detail, including the order of the names of the authors, should be checked before the paper is sent to the Volume Editors.

Emergency evacuation is performed in case of fire, terrorist threat, earthquake or any other hazardous situation in buildings. The ultimate goal of evacuation for all the occupants is to move out of a specific area or a building safely and as quick as possible. In general, a static route provided in an evacuation plan is followed, which is developed during the erection stage of a building. However, static evacuation plans lack coordination between evacuees and administration as it does not consider contingencies that may occur during evacuation. Due to the absence of any coordination during the whole evacuation process, several problems may occur, e.g., congestion along evacuation routes or at exit points or locations that are frequently used, and as a result, casualties may occur. Among important factors associated with an emergency evacuation are evacuees' behavior, their mobility characteristics, and their coordination in following the evacuation route related to a type of emergency and relevant evacuation strategy. Vermuyten et al. have conducted a literature survey in which they discussed various evacuation strategies and crowd behavior, crowd flow, optimized paths, bottlenecks, etc. (see, e.g., [START_REF] Vermuyten | A review of optimisation models for pedestrian evacuation and design problems[END_REF]). These strategies have somehow improved the evacuation process, but most of the studies are static. Due to this reason, real-time status of buildings and evacuees is not known in case of an emergency and during the evacuation process. Therefore, there is still a need for an intelligent system that could detect hazardous situations and handle evacuation problem on a real-time basis.

To this end, we propose a real-time multi-agent architecture for mass evacuation in smart buildings. The novel concept for this study is that the information of events and evacuees is processed on a microscopic level and the evacuation is done in the form of groups on a macroscopic level. These groups are created on the basis of momentary location, possible family ties with other evacuees, and mobility characteristics of each evacuee. Later, groups evacuate the building under the supervision of their assigned group leaders. These group leaders use hand-held devices that provide all the information needed for the evacuation process. At this point, our system is designed for the office spaces and the places where people are slightly aware of topology of the buildings. Due to the scope of this paper, we briefly discuss our proposed architecture and its functioning. Also, we specifically focus on the group formation module of the proposed architecture.

The rest of the paper is organized as follows. Section 2 states the contribution of this research to service systems. Related works in the area of crowd evacuation and smart buildings with their limitations are discussed in Section 3. The structure of the proposed architecture is explained in Section 4. The group formation methodology is discussed in Section 5 and we give a functioning example in Section 6. In the end, we conclude the paper with further research directions in Section 7.

Relationship to Industrial and Service Systems

In a service system, both technology and manpower work together to fulfill the requirements of its user(s). As service systems maximize the output as well as ensure the safety and reliability of the system, therefore its applications are found everywhere such as in the sectors of engineering, banking, supermarkets, and even on internet.

The main objective of service systems is to serve its customers and provide them what they want in minimum time. Customer satisfaction is also an important factor in this type of systems. A service system must be safe and reliable enough so that its users can use it without any risk or danger. When it comes to the cyber-physical world where machines perform most of the work, while humans are involved mostly as users, then the question of user satisfaction and safety is of special importance.

In this study, we propose an intelligent architecture for evacuation of buildings. The purpose of this system is to minimize the involvement of a specialized personnel for evacuation management during any emergency situation, while providing decision support for evacuation of a multitude of people according to their evacuation requirements and preferences, such as mobility characteristics and mutual family ties.

Since the lives of people are dependent on it, the reliability of such a system is of utmost priority. In this respect, the proposed decision support architecture for emergency evacuation may be seen as a service system whose aim is to minimize the evacuation related risks considering the mobility requirements of its users.

Related Work

Multiple approaches have been proposed for the emergency evacuation of buildings (see, e.g., [START_REF] Lujak | A distributed architecture for real-time evacuation guidance in large smart buildings[END_REF][START_REF] Lujak | Intelligent people flow coordination in smart spaces[END_REF][START_REF] Wagner | An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster[END_REF][START_REF] Zheng | Modeling crowd evacuation of a building based on seven methodologic al approaches[END_REF]). Vermuyten et al. performed a literature survey in which they have classified the research topics according to the phases and requirements of the whole evacuation system, e.g., pedestrian dynamics, evacuation design, optimization of evacuation routes, crowd management, model movement, and crowd flow (see, e.g., [START_REF] Vermuyten | A review of optimisation models for pedestrian evacuation and design problems[END_REF]).

There are generally two kinds of crowd evacuation models: macroscopic and microscopic one. Huges explained the significance and the applicability of both approaches for handling the crowd in [START_REF] Hughes | A continuum theory for the flow of pedestrians[END_REF]. The macroscopic model is used at a crowd level to implement the rules over a whole crowd whereas the microscopic model is used to model every individual participating in the evacuation process. Here, some authors have proposed a hybrid approach to use both strategies as they complement each other (see, e.g., [START_REF] Xiong | A hybrid model for simulating crowd evacuation[END_REF]).

On the other hand, Liu et al. explained that the evacuation process is not only individual action, but it is a group activity where the efficiency of the whole process is dependent on everyone involved. Hence, coordination and cooperation of evacuees is needed to make that evacuation process efficient (see, e.g., [START_REF] Liu | Context-aware agents in cooperative design environment[END_REF][START_REF] Liu | Modeling and path generation approaches for crowd simulation based on computational intelligence[END_REF]). In this manner, researchers have proposed some interesting strategies to observe the behavior of groups during an evacuation. For example, [START_REF] Abdelghany | Modeling framework for optimal evacuation of large-scale crowded pedestrian facilities[END_REF] developed a genetic algorithm with a combination of a cellular automata model to evacuate groups of people. In this model, each group is assigned to a specific exit which evacuates within a specific time because the next group has to evacuate afterwards. The best evacuation plan is found after finding the route having minimum evacuation time.

On the other hand, when the crowd evacuates in the form of groups, synchronization and coordination issues arise. In case of any wrong decision by the group leader, even worse disaster could happen as the time is a most critical thing during that process (see, e.g., [START_REF] Oxendine | A multi-objective, multi-criteria approach to improve situational awareness in emergency evacuation routing using mobile phone data[END_REF]). However, it is also found that the evacuation time increases because of the presence of groups in the crowd (see, e.g., [START_REF] Li | A grouping method based on grid density and relationship for crowd evacuation simulation[END_REF]).

A hybrid evacuation strategy that could assist the evacuees in the formation of groups as well as in choosing an optimal route for them is still an open challenge that we treat in this paper.

Evacuation Architecture

In this section, we explain the architecture of our proposed system for evacuation. The system consists of three agents namely, Situation Awareness Agent, Route Optimization Agent, and Group Management Agent. Situation Awareness Agent handles and processes the information of events coming from sensors on a real-time basis. Various sensors are installed that detect fire, smoke, terrorist attack or threat, quarrel or any other risk that could lead to an emergency evacuation (see, e.g., [13][START_REF]Temperature sensors, Texas Instruments incorporated[END_REF][START_REF]Intelligent smoke detectors[END_REF]). This agent also communicates with other agents to assign an alternate safe route to evacuees in case of any blockage or congestion in the existing provided path. Route Optimization Agent chooses the optimal exit path for each group. For this purpose, the building is represented as a graph and divided into different sections called nodes, and the paths that connect these sections called edges or links. Later, the safest and fastest route is calculated with the help of the live information received from the sensors. Group Management Agent creates groups which are supposed to evacuate together under the supervision of (human) group leaders. Groups are created using evacuees' live locations, family ties and physical characteristics whereas group leaders are trained occupants of a building chosen beforehand on a voluntary basis. This agent provides the formation of groups with their relevant collection points and the allocation of groups' leaders. The block diagram of the architecture is shown in Fig. 1.

Fig. 1. Evacuation Architecture.

Personal information of evacuees related to evacuation is crucial for wellfunctioning of the proposed system and should be obtained beforehand. As this information is static, it is stored in the database with the help of an ontology. Upon entering the details into the system, a special RFID or a class of Bluetooth low energy (BLE) device (e.g., iBeacon) tag is provided to each person which they have to wear all the time during the evacuation so that all the movements and locations of people are monitored.

As it is discussed in [START_REF] Talebi | Stochastic network evacuation models[END_REF], the evacuation process is a multi-phase procedure. Therefore, we divide the whole evacuation process into two phases: notifying phase and evacuation phase. Firstly, during the notification phase, we use an audio or a text message or an email to inform every evacuee about his/her assigned group collection point. Also, this information (based on the momentary location) is shown on public screen displays. During the evacuating phase, real-time location of every occupant is processed individually, and groups are updated on the basis of this information. Later, the groups gather at their relevant collection points, and then group leaders start the evacuation process under their supervision. Note that the group leaders use smart devices with an app where all information of group members are shown along with their calculated evacuation route plans.

Group Formation

In this section, we focus on the group formation module of group management agent of the proposed architecture.

In general, groups are formed by combining the static and dynamic information of evacuees. Static information includes information of mobility characteristics and family ties with other evacuees. Dynamic information consists of live locations of the people which are continuously detected by sensors. Every person in the building has a RFID or iBeacon tag which is provided before entering into the building or during the registration process. These tags have a unique ID by which the data of every person can be accessed.

If any hazard or risk is detected and evacuation is necessary, evacuees are provided by a location (known as collection points) where they have to gather and wait for next instructions from their group leaders. These collection points are assigned individually in line with the mobility characteristics of each evacuee. In this regard, a procedure for group creation is given in the following. This procedure also provides collection points to evacuees individually.

The procedure requires the following information: i) an updated momentary location and mobility characteristics and family ties for each evacuee, ii) location and capacity of each collection point, iii) available evacuation routes with their free-flow capacities from each one of the collection points, iv) maximum evacuation time given by the evacuation authority.

The procedure runs in the following steps that assign each evacuee to its collection point considering the capacity of the collection points and their evacuation routes.

Step 1: Calculate the shortest evacuation time of each person from his/her momentary location to each collection point; Step 2: Assign each evacuee whose evacuation time is larger than the maximum evacuation time to the closest collection point with the shortest evacuation time; Step 3: Assign each mobility impaired person to his/her evacuation leader and a collection point with sufficient capacity and the shortest evacuation time; Step 4: Assign evacuees with family ties (members of the same family) to the same collection point. In case they are not located in the same area, choose a collection point that is the closest to the same distance from all of them; Step 5: Assign the rest of the evacuees to their nearest collection points based on the available capacity.

Case Study

In this section, we use an example as a test case to check the functionality of our proposed algorithm for group formation of people and their allocation to their feasible collection points. For this purpose, the output of the algorithm is shown in Fig 2. Given is a layout of a floor in which people are located in possibly different sections. These sections can be rooms and/or corridors, and all sections are connected to every collection point. For simplicity, we assume that the geo-positioning of evacuees is flawless.

In our example in Fig. 2, there are three collection points and 50 persons of which two are mobility impaired (P8, P43) and other two have family ties (P5, P45). We assume that all people are located in the areas that are within the maximum evacuation time from safe exits. Therefore, the priority will be given to mobility impaired people and people with family ties. As a result, P8 and P43, who are mobility impaired are assigned to their nearest collection points, CP2 and CP3 respectively. Also, persons with family ties, P5 and P45, are assigned to the same group/collection point, CP2, because collection point CP2 is the nearest one to both of them. The rest of the persons are allocated to their nearest collection point based on the available capacity. Once the capacity of a collection point is reached, remaining persons are allocated to the nearest collection point with available capacity.

In Fig. 2, collection points are colored in green, red, and blue. People are grouped in groups based on their assigned collection points (red, green, and blue) so that they have to gather at their allocated collection point. People who are mobility impaired have a bigger ring around them to represent their priority. Similarly, people who have family ties also have a bigger ring around them as well as a star over them to distinguish them from able-bodied and mobility impaired people.

The output of the proposed procedure will be in the form of a couple person's ID, collection point of that person (assigned group).

Following are the sets of persons who are allocated to a specific collection point on the basis of knowledge acquired by them. Red, green, and blue sets are formed according to the capacity of collection point 1, collection point 2 and collection point 3 respectively.

Red set = {P1, P2, P3, P16, P17, P18, P19, P20, P21, P22, P23, P24, P25, P26, P27, P28, P29, P30, P31, P32}

Green set = {P5, P45, P8, P4, P6, P7, P9, P10, P11, P12, P13, P14, P15, P33, P34, P35, P36, P40, P41, P42}

Blue set = {P45, P37, P38, P39, P44, P46, P47, P48, P49, P50}

Conclusion

In this study, we proposed an evacuation architecture that coordinates the evacuation process from the detection of the hazard or emergency to the evacuation of people in buildings. Our system is based on three different agents that perform their evacuationrelated tasks. In our proposed strategy, evacuation is done in the form of small groups, each one led by a group leader. Each group leader gets live information from an app installed on a smart device specifically designed as an interface to our proposed architecture. In this paper, we discussed the module of group management agent, a module of our proposed architecture together with group formation and allocation of collection points. Also, due to the hierarchy of this solution proposed for buildings, people may have some training for specific scenarios. As a future work, we plan to develop other agents of our proposed architecture. Also, we will integrate the group formation module, developed in this study, with other parts of the architecture. We also plan to compare the performance of the proposed system with other state-of-the-art evacuation coordination systems.

Fig. 2 .

 2 Fig. 2. Final grouping with respect to their feasible collection points.

Acknowledgments. Work partially supported by the Autonomous Region of Madrid (grant "MOSI-AGIL-CM" (S2013/ICE-3019) co-funded by EU Structural Funds FSE and FEDER), project "SURF" (TIN2015-65515-C4-4-R (MINECO/FEDER)) funded by the Spanish Ministry of Economy and Competitiveness, and through the Excellence Research Group GES2ME (Ref. 30VCPIGI05) co-funded by URJC-Santander Bank.