Feras Abdulsamad
email: feras.abdulsamad@upmc.fr

Nicolas Florsch
email: nicolas.florsch@upmc.fr

Myriam Schmutz
email: myriam.schmutz@ensegid.fr

Christian Camerlynck
email: christian.camerlynck@upmc.fr

Assessing the high frequency behavior of nonpolarizable electrodes for spectral induced polarization measurements

Keywords: spectral induced polarization, phase shift, high frequency effects, nonpolarizable electrodes, measuring electrodes

Assessing the high frequency behavior of non-polarizable electrodes for spectral induced polarization measurements

Feras Abdulsamad, Nicolas Florsch, Myriam Schmutz, Christian Camerlynck

sometimes attributed to the so-called Maxwell-Wagner polarization which takes place when charges cross an interface. However, SIP measurements of tap water show a phase shift at frequencies higher than 1 kHz, where no Maxwell-Wagner polarization may occur. In this paper, we enlighten the possible origin of this phase shift and deduce its likely relationship with the types of the measuring electrodes. SIP Laboratory measurements of tap water using different types of measuring electrodes (polarizable and non-polarizable electrodes) are carried out to detect the origin of the phase shift at high frequencies and the influence of the measuring electrodes types on the observed complex resistivity. Sodium chloride is used to change the conductivity of the medium in order to quantify the solution conductivity role. The results of these measurements are clearly showing the impact of the measuring electrodes type on the measured phase spectrum while the influence on the amplitude spectrum is negligible. The phenomenon appearing on the phase spectrum at high frequency (>1 kHz) whatever the electrode type is, the phase shows an increase compared to the theoretical response, and the discrepancy (at least in absolute value) increases with frequency, but it is less severe when medium conductivity is larger. Additionally, the frequency corner is shifted upward in frequency. The dependence of this phenomenon on the conductivity and the measuring electrodes type (electrode-electrolyte interface) seems to be due to some dielectric effects (as an electrical double layer of small relaxation time formed at the

Introduction

While the induced polarization method is still widely used in mineral exploration (e.g. [START_REF] Pelton | Mineral Discrimination and Removal of Inductive Coupling with Multifrequency Ip[END_REF][START_REF] Luo | Theory and application of spectral induced polarization[END_REF], it is today currently used for engineering and environmental studies, mapping of polluted area and for detecting organic contaminated zones [START_REF] Vanhala | Detecting organic chemical contaminants[END_REF][START_REF] Slater | Electrical-hydraulic relationships observed for unconsolidated sediments[END_REF][START_REF] Gazoty | Mapping of landfills using time-domain spectral induced polarization data: the Eskelund case study[END_REF]. In addition, it is used in hydro-geophysical application to calculate the hydraulic conductivity of subsurface formation [START_REF] Binley | Relationship between spectral induced polarization and hydraulic properties of saturated and unsaturated Sandstone[END_REF][START_REF] Hördt | Hydraulic conductivity estimation from induced polarisation data at the field scalethe Krauthausen case history[END_REF][START_REF] Revil | Determination of permeability from spectral induced polarization in granular media[END_REF]. In parallel, the complex impedance is used in biomedical engineering in similar frequency ranges [START_REF] Schwan | Electrode polarization impedance and measurements in biological materials[END_REF][START_REF] Ragheb | The Polarization Impedance of Common Electrode Metals Operated at Low Current Density[END_REF].

So far, the physical mechanisms responsible for the induced polarization over the frequency range are not completely identified. Numerous mechanisms are able to generate the observed effects. The most involved mechanisms are (i) electrochemical polarization that corresponds to reversible electrochemical and diffusion processes (Zonge, 1972 ;[START_REF] Luo | Theory and application of spectral induced polarization[END_REF] which take place at low frequency, and (ii) interfacial polarization mechanisms, which is also called Maxwell-Wagner polarization (Vinegar and Waxman, 1984;[START_REF] Lesmes | Dielectric Spectroscopy of Sedimentary Rocks[END_REF][START_REF] Tabbagh | Modelling of Maxwell-Wagner induced polarisation amplitude for clayey materials[END_REF]) take place at higher frequencies (generally over 1 kHz).

Among the models used to study these mechanisms, electrical circuit analog which consists of resistors and capacitances are found useful. In this frame, each element of the circuit represents a physical phenomenon. The electric double layer may be simulated by the association of one capacitance Cdl with a resistance rct (charge transfer resistance across the electric double layer), whereas the diffusion effects at low frequency caused by the electrochemical polarization mechanism in the presence of metal-electrolyte interface could be presented by a Warburg impedance Zw [START_REF] Merriam | Induced polarization and surface electrochemistry[END_REF].

Furthermore, the problem of using noble measuring electrodes, which do not affect the measured electrical potential, goes back to the early days of using the geoelectrical method in geophysical prospecting [START_REF] Schwan | Electrode polarization impedance and measurements in biological materials[END_REF]. The effects of the measuring electrodes type on the apparent resistivity and the apparent chargeability measured in time domain induced polarization measurements were studied and proved by [START_REF] Labrecque | Assessment of measurement errors for galvanic-resistivity electrodes of different composition[END_REF]. From their measurements using direct current on tap water only, and on sand saturated with tap water, they found that, the errors in chargeability and in resistivity depend on the electrode type, and the measurements discrepancies depend on the electrode chemical properties. The discrepancies were lower in tap water case than the one with saturated sand. Furthermore, the influence of the type of measuring electrodes on chargeability is higher than on resistivity, and that is expected due to the weaker measured signal in chargeability measurements when signal to noise ratio is low.

On the other hand, spectral induced polarization (SIP) was extensively developed in laboratory, thanks to the possibility of carrying out measurements in a wide-frequency range without signal pollution caused by electromagnetic coupling. In order to acquire high quality data, both measurement devices and sensors (electrodes) have to be well suited [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF]. Measurement devices, such as SIP Fuchs (Radic Research Ltd) or SIP Zel (Zimmermann et al., 2008) are adapted for both field and laboratory experiments with high accuracy, but the electrode issue is still important and needs more developments to find the optimal electrode which has a minimum impact on the measured potential.

The electrode issue concerns more environmental and hydro-geophysics purposes where the measured phase is small (≤ 10 mrad) (Zimmermann et al., 2008) as noticed in many studies (e.g., [START_REF] Slater | Electrical-hydraulic relationships observed for unconsolidated sediments[END_REF][START_REF] Schmutz | Influence of Oil Saturation upon Spectral Induced Polarization of Oil-Bearing Sands[END_REF].

The influence of both distance between the measuring electrodes and their inclination on the SIP measurements was studied by [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF]. They found that the small distance (smaller than 5 cm) between electrodes could produce noise in the phase spectrum, furthermore, the tilt effect of non-vertical measuring electrodes decreases with increasing distance between them. The influence of the active geometry (surface of electrodemedium interface) of the measuring electrodes on the complex resistivity measured was studied by [START_REF] Riaz | Study of influence of electrode geometry on impedance spectroscopy[END_REF]. They found that at high frequency the complex resistivity is not as dependent on active geometry as at low frequency.

In fact, our work here was focused on the dielectric effect that appears on the phase spectrum at high frequencies, and on its dependence with the type of measuring electrodes.

The phase shift observed at high frequency is usually attributed to polarization mechanism which is called Maxwell-Wagner polarization (occurring when charges cross an interface). In water, the phase shift observed at high frequency is generally higher than that arises from the dielectric permittivity of water. The absence of Maxwell-Wagner polarization and coupling effects, leads us to think that another dielectric phenomenon occurs at the electrodeelectrolyte interface (absorption, diffusion or/and electrical double layer). So in the first part we interpreted the high frequency effect as a dielectric phenomenon and in the second part we encapsulated this effect in an electronical equivalent circuit.

Therefore, series of SIP measurements using tap water were carried out by using different conventional types of measuring electrodes (polarizable and non-polarizable). Each electrode has its own advantage and disadvantage regarding the accuracy and the usability in the laboratory or in the field.

As many other studies which aimed to test electrodes stability [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF] or to test the accuracy of SIP instruments (Zimmermann et al., 2008), we used tap water as a medium in our study: the theoretical spectral induced polarization response of water could be calculated using the approximate value of the water relative dielectric permittivity of (εr = 80) and the water measured conductivity.

Experimental setup

Complex resistivity measurements were carried out over the frequency range from 0.183 Hz to 20000 Hz using the SIP Fuchs III equipment developed by Radic Research (see http://www.radic-research.homepage.t-online.de/).

The measurements have been performed by using tap water with different salinity concentrations. Sodium chloride was used to change the conductivity and consequently the ions concentration in the medium. A plastic box with dimensions of 50 x 33 x 23 cm (LxWxH) was used as a sample holder, filled with 22 cm of water. Wenner configuration was used with electrode spacing of 10 cm. The small distance between electrodes could affect the phase spectrum [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF]. The current electrodes are made of stainless steel (Ag 316L) electrodes fixed at the extremity of the device (30 cm apart). A low current was used to ensure linearity and to avoid too large current density in the medium. To check for repeatability, the SIP measurements were repeated five times over the mentioned frequency range; later the standard deviation was calculated at each frequency and presented as error on each frequency... Temperature, pH and conductivity of the medium were measured in parallel. The pH of the medium varies between 7.62 and 7.92 depending on the salinity level. The temperature of the medium varies between 16.2 °C and 16.6 °C during the experiment. As the variation of the temperature is very small, no thermal correction has been applied on the complex resistivity measurements.

The conductivity of the water was measured directly by using a conductivity-meter (a WTW LF 340) which allows to calculate the water theoretical response, and to set the geometric factor taking into account the position of the electrodes and the boundary conditions. In this way, the apparent real and imaginary measured amplitudes are correctly calibrated, while the apparent phase is independent of the geometric factor.

Before starting the SIP measurements, a test of both the instrument accuracy and calibration was performed by using electric circuit of known response [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF][START_REF] Titov | Relationships between Induced Polarization Relaxation Time and Hydraulic Properties of Sandstone[END_REF]. The circuit components (resistors and capacitances) and their installation were chosen to give weak phase response (a few mrad) at high frequencies. The relative theoretical response is supposed to be close to that measured in tap water by different kinds of electrodes. Figure 1 shows the measured phase and amplitude, and the theoretical electric circuit spectral response. The result shows the high accuracy of the instrument over the entire frequency range with error less than 0.1 mrad at low frequency (< 10 Hz) and less than 0.5 mrad at high frequency. This high accuracy used in our experiments excludes any impact of the instrument on the measured response.

Electrodes preparation

To characterize the impact of the measuring electrodes on the high frequency phase shift, we tested various conventional electrodes (figure 2 Ag/AgCl and Cu/CuSO4 electrodes have the same conceptual schema which is presented in figure 3. They consists in a metal wire immerged in oversaturated electrolyte of the metal and a permeable ceramic between the medium and the electrode. One can notice that even with low permeable ceramics, a little electrolyte is released into the medium, which may affect the SIP measurements over time. We operate as quickly as possible to minimize this risk. The preparation of the Ag/AgCl starts by bleaching the silver wire during few hours and then the silver wire is connected to a 6 Volt battery and placed in KCl solution during ten minutes (electroplating gives the electrode more stability with time). After that, the wire is soaked in a saturated KCl solution. The use of agar gel to prepare non-polarizable electrodes is common to overcome the leakage problem during long duration experiments. Precipitation of Agar powder is forced by using a 1 mol/l KCl solution. The agar gel is a good conductor without any polarization effects, and its use has already been described for polarization studies [START_REF] Scott | The origin of the observed low-frequency electrical polarization in sandstones[END_REF][START_REF] Hördt | Studies with gel-filled sandstone samples with implications for the origin of induced polarization[END_REF]. Solidified Agar gel avoids the use of porous ceramic.

Fig. 3: outline of the non-polarizable electrode

Since the electrodes pairs are built up following the same process, the DC potential difference between them will be very close to zero. For this reason, we may consider that they are non-polarizable (or more objectively, a constant weak differential polarization within each pair). In such a configuration, the measured data should not be affected by the presence of measuring electrodes and then the response should not depend on the electrode.

After preparing the non-polarizable electrodes, all (metal and non-polarizable) were placed in a water tank to measure the self-potential of each electrode type during 2 hours, using a recording digital multimeter (Keithley 2701 Multimeter). Figure 4 (left) shows example of self-potential values measured in the electrodes used in this study. Later the potential difference between each pair of electrodes was calculated and presented in figure 4 (right). This electrode monitoring shows that non-polarizable electrodes have more stability with time and lower self-potential values than the stainless steel electrodes (usually used in filed measurements) in correlation with the results of [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF] and [START_REF] Dahlin | Measuring techniques in induced polarisation imaging[END_REF]. The Ag/AgCl electrodes show the lowest self-potential and the best stability over time, with potential difference between electrodes less than 1 mV. The Cu/CuSO4 electrodes have the same trend of stability over time and the potential difference is around 1 mV, but we notice also that there are some more or less 40 min transients, may be due to electrode leakage. The Ag/AgCl filled with KCl-agar gel electrodes show high variations over the time at the beginning (2 hours after preparation) before reaching electrical stability and a potential difference close to 1 mV at least 3 hours after their preparation. It is therefore recommended to use these electrodes only a few hours after preparation. The stainless steel electrodes have the highest charge-up and no stability over the time, where the potential difference for the electrodes pair increases with time up to 12 mV at the end of monitoring.

Results and discussion

The complex resistivity measurements of tap water show a phase shift at high frequency for all the measuring electrodes types. In order to assess the own phase shift we compare the results of our measurements with the theoretical water spectral induced polarization response.

The theoretical dielectric response of the medium could be calculated by using the following equations 1 and 2:

𝜎(𝜔) = σ 0 + j ω ε 0 ε r (1) 𝜌(𝜔) = 1/𝜎(𝜔) (2)
where 𝜎(𝜔) and 𝜌(𝜔) are respectively the complex conductivity and the complex resistivity, σ 0 is the DC conductivity, j is the imaginary unit, ε r is the relative dielectric permittivity, and ε 0 is the free space permittivity and its value is 8.854 10 -12 F/m. By using the water conductivity measured with the conductivity-meter and using the approximate value of the relative dielectric permittivity of fresh water ε r = 80, we get the theoretical response of water over the frequency range of our measurements [START_REF] Vanhala | Laboratory Technique for Measurement of Spectral Induced Polarization Response of Soil samples[END_REF]Zimmermann et al, 2008). The measured instrumental phase 𝜑(𝜔) is defined as the ratio between real and imaginary components of the complex resistivity, whereas the theoretical phase could be calculated from equation 3:

𝜑(𝜔) = -ω ε 0 ε r /σ 0 = -ω ρ 0 ε 0 ε r ( 3
)
where ρ 0 = 1/σ 0 is the DC resistivity.

Figure 5 shows the amplitude and phase spectra of the complex resistivity (corresponding to 18 Ωm water resistivity) obtained by using the four different pairs of electrodes mentioned above. All measured amplitudes are close to the expected response with a satisfying repeatability, whereas the phase measured showed more variation over the frequency range especially at high frequencies.

The phase measured by non-polarizable electrodes shows very small variation over the low frequency range (0.183 Hz to 1 kHz), while the one that is measured by stainless steel electrodes over the same frequency range shows larger variation. However, this result was expected because of the instability behavior of these electrodes showed by the self-potential difference record. It is therefore recommended to use non-polarizable electrodes over the whole frequency range (figure 5).

Fig. 5: amplitude (left) and phase (right) of the water complex resistivity by using four types of electrodes.

However, the phases at high frequencies (> 1 kHz) are larger than expected for the four electrode types. For instance, the phase relative to the Ag/AgCl electrodes is found three times larger than the theoretical phase within the high frequency range (> 1 kHz). Hence, these measurements should be linked to a relative dielectric permittivity 3 times larger than the water relative dielectric permittivity.

By comparing the phase measured over the last frequency decade (1 kHz -20 kHz)

with metal and Cu/CuSO4 electrodes, we notice that the respective phases are very close despite the difference in electrode nature. Through the contact resistance of the stainless steel electrodes might be notably lower than the one of the porous ceramic Cu/CuSO4 electrodes; it is likely that the contact quality is not a major causing the high frequency trend.

Figure 6 illustrates the phase measured at three salt concentrations (corresponding to 18, 10 and 5 Ωm water resistivity) when using Ag/AgCl electrodes and Cu/CuSO4 respectively.

One observes a dependence of the high frequency response on water conductivity, and phase responses shift toward high frequency when water conductivity increases. When changing the medium conductivity by adding a little NaCl to remain within the given indicated range (water resistivity between 5 and 18 Ωm, meaning water electrical conductivity less than 200 mS/m; for instance, see fig. 1 from [START_REF] Bouksila | Soil water content and salinity determination using different dielectric methods in saline gypsiferous soil[END_REF]), the relative dielectric permittivity remains unchanged, close to 80. While the measured phase shift is 3 to 5 times higher than the expected theoretical phase shift, a change in water dielectric permittivity cannot be invoked as an explanation.

Fig. 6: apparent phase measurements for the 3 salinity levels: left) Ag/AgCl electrodes; right) Cu/CuSO4 electrodes.

Figure 7 shows the phase difference between the measured phase and the theoretical one calculated from equation ( 3) at the highest frequency (20 kHz) where the difference is higher.

In all cases the theoretical responses were calculated with the same relative dielectric permittivity (ε r = 80) and by changing the solution conductivity in the equation (3). [START_REF] Ghorbani | CR1Dinv: A Matlab program to invert 1D spectral induced polarization data for the Cole-Cole model including electromagnetic effects[END_REF] and after checking with an accuracy test (figure1), these observations from figures 6 and 7 could only be explained by the change of the electrochemical conditions at the electrode-electrolyte interface, where the ions concentration around the measuring electrodes had increased after adding NaCl in the medium. That means that the electrochemical changes in the electrode vicinity impact the measure. Adsorption or diffusion of ions near the porous ceramic and/or the electrical double layer existing at the electrode-electrolyte interface are responsible for the additional effects measured at high frequency, that we call "dielectric" but just because it looks like dielectrical. Finally, the possible influence of the current density on high frequency effect was also studied, by using double and half of the initial current density (360 mA) used in our measurements. There is no remarkable influence on the phase measured when the current density varies between 180 mA to 720 mA.

Empirical modelling

The response of a traditional medium, as a soil sample, can be represented by one of the many empirical dielectric models (e.g. Debye model, Cole-Cole model or Cole-Davidson model). They are traditionally depicted by an equivalent electronic circuit [START_REF] Marshall | Induced polarization, a study of its causes[END_REF][START_REF] Dias | Analytical model for a polarizable medium at radio and rower Frequencies[END_REF]2000). The simplest equivalent circuit contains two parallel electric paths: the first path consists of a simple resistor and represents the pure ohmic conduction associated to a free ionic path (current path at low frequency or direct current), whereas the second path contains several electronic components (resistors and capacitances) which model the polarization effects of the medium associated with the metal or clay grains in contact with the pore electrolyte. The latter part of the circuit models the response of the medium in the SIP frequencies. [START_REF] Dias | Developments in a model to describe low-frequency electrical polarization of rocks[END_REF] provides such a collection of equivalent circuits.

One classical microscopic polarizable model and its circuit is shown on figure 8 [START_REF] Dias | Analytical model for a polarizable medium at radio and rower Frequencies[END_REF]2000). Fig. 8: the electric paths in polarizable medium (left) and equivalent electrical circuit for a polarizable medium (right), (modified from [START_REF] Dias | Developments in a model to describe low-frequency electrical polarization of rocks[END_REF]). Here Cdl is the electric double layer capacitance, Z dl the complex impedance of the electric double layer, rct the charge transfer resistance, R s is the resistivity of the electrolyte filling the material and R dc is the sample resistivity in direct current.

From the measurements of this study and other data sets (e.g. [START_REF] Joseph | Spectral induced polarization measurements on New Zealand sands -Dependence on fluid conductivity[END_REF], we notice the correlation between the effects at high frequency and medium resistivity. As long as the medium resistivity increases, the high frequency effect appears earlier on the frequency range (shifted to low frequency) and its amplitude becomes significant. Therefore, a model including the high frequency dielectric phenomena formed at the electrode-electrolyte interface (clearly linked to the measuring electrodes type) in the equivalent electric circuit should be sensitive to the total electric response of the medium, since the electric response is related to the electrical and electrochemical properties of the medium. Figure 9 presents the equivalent circuit we propose to accommodate the "high frequency dielectric phenomenon", where it is modeled by a capacitance C hf connected in parallel with the equivalent circuit of the medium response. The magnitude of this capacitance is small in order of nanoFarads.

This model is a general model to fit SIP measurements from medium contains different phases (solid and liquid).

Fig. 9: the equivalent electric circuit models the high frequency dielectric phenomenon where C hf is a low value capacitance. Finally, in order to fit our data measured which are a special case of SIP measurements (the medium is a one phase medium), a simple circuit consists of resistance (Rdc) connected on parallel with a capacitance C ε ((C ε = εr * ε0) which represents the dielectric response of the medium), could present the medium theoretical response. Figure 10 shows the amplitude and phase fitting the Ag/AgCl electrodes in water with resistivity 18.2 Ωm. At frequencies greater than 1 kHz, measured and modeled (from electric analog) phase curves are superimposed. At lower frequencies, the fit is not so accurate with a small discrepancy of 0.3 mrad, which may be linked to the filter 60 Hz (does not work well), but this frequency range is not targeted in study.

Discussion and conclusion

Once the instrument accuracy is checked and the stability with time of a set of 4 various built up electrodes verified, we observe a dependence of the response of the whole on the measuring electrode nature. The differences emerge in the high frequency part of the spectrum, saying above 1 kHz. It is the range where the IP response origin is neither consensual nor really understood yet. Actually, the "normal" permittivity of water, playing the role of the medium, impact this high frequency (> 1 kHz) domain as well, but is typically 3 or 4 times smaller than the actual observed response. We get satisfactory data fits by assuming a superimposition of the response relative to the electric permittivity of the medium plus a contribution of the electrode which depends on the electrode nature. We can presume that the electrode/electrolyte double layer is responsible for that contribution, for which the theory must still be done. As a matter of fact, a capacitance just set in parallel with the measuring electrodes permits to fit the observation in a very satisfactory manner. It is equivalent to introduce the same electronic (fitting device) in parallel with the medium, but this approach is only opportunistic since we are supposed to measure only the medium response and not the electrodes which have only the role of sampling the potential within the medium.

As far as the SIP or TDIP are concerned, it is useful to split the high frequency response into these two contributions: one representative of the medium (and it generally assumes a relative permittivity of reasonable value), and the other relative to the electrode pair. This double contribution permits finally to take into account, -to accommodate for-the measuring electrode specific response when processing IP data, especially when processing high quality data at the lab scale as well as on the field, to finally correctly assess the chargeability, time constant of any and other IP parameters describing the investigated medium.

Fig. 1 :

 1 Fig. 1: instrument accuracy test with the electric circuit used.

  ) usually used in SIP. Polarizable 316L stainless steel metal electrodes and three non-polarizable electrodes (Ag/AgCl filled with saturated KCl solution; Ag/AgCl filled with a mixed of KCl-agar gel; Cu/CuSO4 electrodes) are used in this study. The non-polarizable electrodes are hand-made and prepared in our laboratory.

Fig. 2 :

 2 Fig. 2: the electrodes used in our study, a) Cu/CuSO4 electrodes; b) Ag/AgCl-KCl electrodes; c) Ag/AgCl-Agar-gel electrodes; d) 316L Stainless steel electrodes (with yellow plastic coating).

Fig. 4 :

 4 Fig. 4: left) electrode self-potential. Reference electrode is a NaCl-type Pb-PbCl2 electrode (Petiau, 2000); right) potential difference for each electrode pair type. Origin time corresponds to 1 hour after electrode preparation.

Fig 7 :

 7 Fig 7: phase difference between measured and expected data as function of the solution resistivity at frequency 20 kHz.

Fig. 10 :

 10 Fig. 10: modelling the measurements with Ag/AgCl electrodes. Water DC Resistivity is 18.2 Ω m. Left) the equivalent electric circuit with adjusted values is shown and amplitude spectrum; right) phase spectrum.

Acknowledgement:

We wish to thank particularly Dr. A. Maineult for his very helpful availability, especially during the experimental phase of this study. We also thank anonymous reviewers for their helpful suggestions. by spectral-induced polarization method in glacial till environment . Geophysics, 57 (8): 1014-17. doi:10.1190/1.1443312. Vinegar, H., and M. Waxman. 1984. Induced polarization of shaly sands. Geophysics, 49 (8): 1267-87. doi:10.1190/1.1441755. Zimmermann E., Kemna A., Berwix J., Glaas W., Münch H. M and Huisman J. A., 2008. A High-Accuracy Impedance Spectrometer for Measuring Sediments with Low Polarizability. Measurement Science and Technology, 19(10):105603. doi:10.1088/0957-0233/19/10/105603. Zonge, K.L., 1972. Electrical properties of rocks as applied to geophysical prospecting. http://arizona.openrepository.com/arizona/handle/10150/287835.


Shuqing Cui
email: shuqing.cui@mines-paristech.fr

Riccardo Issoglio

Juslin Koffi

Mohamed El Mankibi

Pascal Stabat

Dominique Marchio

Topic B4: Ventilation PERFORMANCE EVALUATION OF NATURAL VENTILATION THROUGH WINDOWS WITH HORIZONTAL BLADE SHUTTERS

Keywords: Natural ventilation, Gas tracer, Shutter

The use of shutters can reduce the drawbacks of natural ventilation on intrusion risks and acoustic problems. However, when the shutters are used with single-sided ventilation, the air change rate is too deeply reduced. This study shows that the air change rate is maintained to acceptable values in cross-ventilation contrary to single-sided natural ventilation for horizontal blade shutters. An experimental investigation is carried out on a hotel room with or without shutter in Corsica, France. The air change rates are given out by the tracer gas technique using carbon dioxide. The concentration decay method is applied and the decrease of tracer gas concentration in the room is measured by 9 CO 2 sensors in various positions. Air change rates are given for different wind orientations and speeds. Even if the shutters reduce by 45% the window opening surface on one side, the air change rate can be kept up to 80% of that with a plain opening without shutter since it is limited by the other side window which has smaller opening surface.

INTRODUCTION

Natural ventilation is an attractive solution for cooling buildings, but is often rejected because of intrusion risks or acoustic problems. [START_REF] Cui | How far can 'baffle shutters' attenuate outdoor noise while maintaining acceptable natural ventilation rates[END_REF] studied different designs of acoustic shutters seeking a trade-off between ventilation and noise attenuation. The air change rate (ACR) was evaluated by computational fluid dynamics (CFD) using Reynolds Average Navier-Stokes equations. The results showed that shutters could reduce from 60 to 80% the ACR while attenuating from 3 to 12 dB external noise compared to a fully opened window in the single-sided ventilation mode. Such reduction on the ventilation performance is hardly acceptable, leading to assessing if the cross-ventilation mode could broaden the frontier of using acoustic shutters.

As reported by many papers, the validation of numerical calculation is realized by scaled models and in-situ measurements [START_REF] Chao | Ventilation performance measurement using constant concentration dosing strategy[END_REF][START_REF] Stavrakakis | Natural crossventilation in buildings: Building-scale experiments, numerical simulation and thermal comfort evaluation[END_REF][START_REF] Hooff | PIV measurements and analysis of transitional flow in a reduced-scale model: Ventilation by a free plane jet with Coanda effect[END_REF]. The scaled models seem to be a good solution due to their low cost, easily controlled boundary conditions and strong repeatability. However, the in-situ measurements are indispensable as the tests are conducted in real conditions where wind turbulence and buoyancy effects are taken into account. A hotel room located in a gulf in Corsica, France has been chosen to be the test space, as shown in Figure 1.

In this study, it is decided to investigate experimentally how the ACR can be reduced in the case of cross-ventilation and single-sided ventilation when using of horizontal blade shutters.

METHODOLOGY Air change rate measures

The ACR is a key indicator to understand ventilation performance. In this study, the ACR is assessed by the tracer gas technique using carbon dioxide and the concentration decay method. The tracer gas CO 2 is injected into the room via two flow regulators, by several injection sets. The CO 2 flux and amount is instantaneously supervised and controlled by a manual valve. The injection of tracer gas is stopped when its concentration in the room reaches 3000 ppm while the central ceiling fan stays in operation several minutes. Once the gas is mixed in the room, both the loggia window and the hopper window above the door are opened. The concentration of tracer gas falls exponentially with the ACR, defined as the volume air inflow rate divided by the space volume, N=q/V (vol/h) (ASHRAE, 2001).

The CO 2 concentration in inlet air flow from the loggia window is equal to the outdoor concentration C ext which is roughly constant on the site while the average concentration of CO 2 in the room is marked as (ppm):

0 (1)
When the mixture of CO 2 in the room is ensured, the initial value is calculated as average of the sensors 0 . The equation can be integrated as:

• • (2)
In a logarithmic coordinate chart for the average concentration decreases linearly. The ACR is then obtained using a simple linear regression analysis on the logarithmic curve. Because this method is sensitive to the zero drift and tends to overweight the contribution of lowest concentration values, the measure interval is chosen to be between 3000 and 600 ppm in the analysis to avoid the concentration to fall into the range where the relative error of sensors is too significant. The fresh air inflow q (m 3 /s) drives the decreasing of CO 2 concentration. If there is a high air change, the concentration falls sharply. However, short In [START_REF] Sherman | Tracer-gas techniques for measuring ventilation in a single zone[END_REF], the uncertainty related to air change, , in regression method can be estimated according to the average decay method.

1 ∆ 2 (3)
Assuming all the concentrations are measured equally and correctly, the error on sampling is then derived from the measure errors 3.3 .

Another important error source is the mixing imperfection, . As the volume weighting coefficient of each sensor is not easy to determine, the error is found by the deviation from average values of all sensors in one test. Assuming all these uncertainties are independent, the standard deviation is given out by:

(4)

Location of CO 2 sensors

The location of CO 2 sensors is shown in Figure 3. Two types of sensors are used: three Telaire Ventostat 800 CO2 sensors of 0-4000 ppm, accuracy 5% of span. six Vaisala G70 CO2 sensors of 0-4000 ppm, accuracy 1.5% of span.

Figure 3. Location of CO 2 sensors ( top and 3D view)

Thermometers are placed on each wall and in several positions evenly distributed in the room. The average temperature in the room is obtained as the arithmetic mean value of the measured temperature. Three ultrasonic 3D anemometers are set in the loggia, in the center of the room and on the hopper window. Two differential pressure sensors are set on the two facades of the building near the windows. The environmental data are recorded by a meteorological station on the roof.

Figure 4. Setting of the sensors

Ventilation space configuration

The hotel room is on the second floor, 18 m 2 large, 2.68 m high, making a total ventilated volume of 48 m 3 . A loggia outside is closed by 4 faces. The sealed washroom is not ventilated. The window on the loggia side is 1.08 m 2 large and at the opposite of the room, above the entrance door, another window is 0.35 m 2 large.

On the loggia side window, a movable blade shutter composed of 12 blades with 60° inclination is installed. The effective surface, S, through which the air flows, is defined as the sum of all the narrow passages between the blades (Figure 5). S is equal to 0.58 m 2 , representing 58.5% of the total opening surface.

RESULTS AND ANALYSIS

Table 1 sums up the test results, using two methods to calculate the ACR. Method 1 averages the measured concentration by each sensor that is rather stable according to the correlation coefficient of regression curves. On the contrary, method 2 gives out the algebraic average of ACR obtained by the sensors.

In the cases of cross-ventilation, the wind is too variable during the test period to find two cases identical in the point of view of environmental conditions. The building is located on the seaside and the wind alternates between two wind regimes, typically, coming from sea to mountain in the day and reverse at night. However, in both wind regimes, the ACR is maintained on an acceptable level and is not reduced significantly compared to the cases without shutter at the same experiment time, noted that in this room, the infiltration rate is 0.5±0.1 vol/h and the minimum ACR to keep comfortable indoor air quality is 2 vol/h [START_REF] Cen | Ventilation for buildings-Calculation methods for the determination of air flow rates in buildings including infiltration[END_REF].

Compared to the cross-ventilation cases, the two single-sided ventilation cases picked up are quite similar on environmental conditions. The ACR with shutter (SWS1), N =1.1 vol/h, equals to 60% of the value without shutter (SNS1), N =1.8 vol/h. ΔT: difference between indoor and outdoor temperature.

If the wind comes from the normal direction to the loggia opening, the incident angle is defined to be 0°, the clockwise is positive and anti-clockwise is negative.

The sampling error is calculated using only the most precise sensor (Vaisala).

Cross-ventilation correlations

In cross-ventilation, wind is the major driving force of air flow. Nearly all correlations are established on the relation between ACR and dynamic pressure. (Etheridge and Sandberg, 1996) put forth that this ratio can be correlated with differential pressure ∆p between two facades, the wind speed U with a discharge coefficient ΔC p .

∆ 2∆ / (5)

The calculation formula of crossing air flow is generalized by (Seifert et al., 2005):

∆ ∑ 1/ (6)
The calculation of ΔC p is performed by empirical diagram based on environment obstacle and incident angle. According to experimental results of [START_REF] Rousseau | A new integrated design tool for naturally ventilated buildings[END_REF], another approach to calculate ΔC p is also proposed in function of wind incidence. Both approaches are compared.

Comparison and discussion

The experiments are often disturbed by various noises which question the validity to apply the correlations. The value of wind speed in Table 1 is the algebraic mean value of velocity of all registered points in the experiment. As the wind speed is rather instable, it has been found that the use of algebraic mean value of the wind direction for the correlation produce poor results (ACR overestimated by 50% for the cases with shutters and 60% without shutters). Therefore, a new mean value of wind is derived by projecting each wind vector of every time interval in the direction perpendicular and parallel to the opening, then by averaging, and finally by recomposing a new vector. The results are shown in Figure 6. The Rousseau and Mathews correlation, marked RM, is closer to the experimental results than the one of Etheridge and Sandberg, marked ES. However, both overestimate the air flow rates. The ACR is overestimated by 40.3% for CWS, 64.2% for CNS with ES correlation, by 38.8% for CWS, 43.5% for CNS with RM correlation, respectively. The single-sided ventilation correlation developed in [START_REF] Caciolo | Development of a new correlation for single-sided natural ventilation adapted to leeward conditions[END_REF] gives also poor results: 0.81 vol/h (-26%) in SWS1 and 3.3 vol/h (83%) in SNS1. Figure 7 is a projection into an azimuth map of each test (Table 1), represented on a line of same radial direction, with 3 points corresponding to measured and calculated ACR. It is clear that most cases in the daytime with the wind from sea, coming into the large opening, tend to overestimate the ACR. In the evening with the wind from mountain, the practical value is not always overestimated and the difference to theoretical values is lower.

The difference between the experimental results and the correlations could be especially attributed to the configuration of the openings. On one hand, the room has a loggia on the sea side. On the other hand, the effective opening surface is different between the facade and the hopper windows.

Figure 7. Measured and calculated ACR N (vol/h) in azimuth map

To check the influence of the loggia, a CFD simulation has been carried out reproducing the experiment environment conditions. The CFD model specification could be found in (Cui et al., 2013). The CFD demonstrates that the ACR of the room with or without loggia are different. In all windward cases, the loggia reduces by 22% the ACR in average. The effect of loggia can be observed by the top view in the example presented in Figure 8. When the incident angle increases regarding to the normal direction of opening, the turbulence becomes less negligible. Using the profile of test CNS4, where the incident angle is taken as 29°, the air flow is not stopped by the side of the loggia and normally enters directly the room. But a small turbulence is formed between the window and the loggia edge, changing the direction and reducing the speed of the air flow compared to the case without loggia.

Another reason of the difference between the correlations and the experiments is the large difference of the size between the two openings. In fact, when the shutter is closed, the theoretical results are closer to experiment results because these two open areas are closer in size. In addition, the correlations for air change rate (ES and RM) do not distinguish if the air enters the room by one opening or by another one on the opposite side of building. These results demonstrate the limits of the correlations which require symmetrical building configuration.

CONCLUSIONS

This work uses tracer gas technique to determine the ventilation rate in a hotel room in Corsica. The experimental data demonstrates that under cross-ventilation situation, the air change rate remains to acceptable value even when a blade shutter is used contrary to singlesided cases. The gas tracer method is a good technique to determine indoor ventilation rate.

The measurements errors on gas concentration appear small between different positions and types of sensors in this study.

Though the correlations are simple to apply in practice, there are many limitations in use:

In case of a room with a loggia, the correlations are not suitable anymore. A CFD study shows that a 4-faces-closed loggia can decrease by 22% the ACR in comparison with a room without loggia in case of a windward condition. The two correlations do not well take into account the obstacles in building environment. They don't distinguish the conditions that whether the wind comes from the front side or back side, from the large opening or the small opening. However, the ventilation performances obtained in experiments are not the same.

Figure 1 .

 1 Figure 1. Hotel top view (upper). Loggia (bottom left). Entrance door (bottom right).

  time of experiment contributes to the instability on measuring the ACR. A test example of cross-ventilation with shutter is presented on Figure2.

Figure 2 .

 2 Figure 2. CO 2 concentration decay measure example, test CWS2, sensors Vaisala

Figure 5 .

 5 Figure 5. Shutter configuration and air flow section

Figure 6 .

 6 Figure 6. Comparison between ACR obtained in the tests and from correlations (sea : wind from the sea; mt : wind from the mountain)

Figure 8 .

 8 Figure 8. Wind velocity vector field in CFD simulation (top view)

Table 1 . Measured air change rates

 1

			Environmental conditions	Method 1	Method 2
	Test	Time	Wind speed (m/s)	Wind direction (°)	ΔT (℃) (vol/h)	R²	N (vol/h)
	CWS1 11:40 3.43	22.3	2.0	27.9 0.996 27.9±3.2	11.4%	12.5%
	CWS2 12:20 2.97	40.8	1.8	27.1 0.992 27.2±0.9	3.4%	6.2%
	CWS3 18:55 1.51	74.3	2.1	13.6 0.995 13.9±0.6	4.1%	6.6%
	CWS4 22:15 0.98	178.5	3.6	6.7	0.986 6.6±0.3	3.8%	6.4%
	CWS5 11:20 1.93	29.9	2.1	9.6	0.977 8.9±0.6	6.7%	8.5%
	CWS6 15:15 2.29	55.2	1.7	14.1 0.993 14.4±1.9	13.2%	14.2%
	CWS7 21:21 0.74	-174.1	3.4	8.4	0.988 8.1±0.5	6.4%	8.2%
	CNS1 23:00 0.63	-158.2	3.7	8.3	0.985 8.2±0.6	7.0%	8.7%
	CNS2 08:08 1.98	43.9	1.6	11	0.989 10.4±1.5	14.0%	14.9%
	CNS3 11:50 2.32	22.7	1.9	18.8 0.984 19.0±1.7	9.2%	10.6%
	CNS4 13:45 2.90	29.3	1.8	28.5 0.981 28.7±2.8	9.7%	11.0%
	CNS5 14:35 2.12	-52.5	1.7	21.2 0.988 19.3±2.3	12.0%	13.1%
	SWS1 16:30 1.64	-22.3	1.9	1.1	0.976 1.0±0.1	11.7%	12.8%
	SNS1 18:00 1.40	-40.8	2.0	1.8	0.994 1.6±0.1	7.6%	9.2%

ACKNOWLEDGEMENTS

This work is financially supported by French National Research Agency. The experiments were carried out in Institute d'Etudes Scientifique de Cargèse, Corsica, France.