Ra Úl

A Meléndez

Ioan-Dore D Landau

Luc Dugard

Gabriel Buche
email: gabriel.buche@gipsa-lab.grenoble-inp.fr

Raul Melendez

Data driven design of tonal noise feedback cancellers

Keywords: Active noise control, System Identification, Internal model principle, Band stop filters, Sensitivity functions

This paper emphasizes the design methodology for active tonal noise feedback cancellers starting from data collected on the system. To design such control systems, an accurate dynamic model of the system is necessary. Physical modeling can provide qualitative results but fails to yield enough accurate models for control design. The main point in the methodology is identification of primary path (noise propagation) and secondary path (compensation) models from data. The procedure is investigated in details starting with transfer functions' order estimations, continuing with parameters estimation and model's validation. The second aspect is the design of a noise canceller using the Internal Model Principle and the sensitivity function shaping in order to reduce the "water-bed" effect. The estimated model's quality for control design is illustrated by the experimental performance of a tonal noise feedback canceller implemented on a test bench.

INTRODUCTION

Active noise control (ANC) has been under research for many years and applied in various kind of applications. In most cases feed-forward broadband noise compensation is currently used for ANC when a disturbance's image is available (correlated measurement with the disturbance). See [START_REF] Elliott | Active noise control[END_REF], [START_REF] Elliott | Performance of feedforward and feedback systems for active control[END_REF], [START_REF] Kuo | Active noise control: a tutorial review[END_REF], [START_REF] Zeng | Recursive filter estimation for feedforward noise cancellation with acoustic coupling[END_REF].

However, these solutions, inspired by Widrows technique for adaptive noise cancellation, see [START_REF] Widrow | Adaptive Signal Processing[END_REF], ignore the possibilities offered by feedback control systems and have a number of disadvantages: they require the use of an additional transducer, difficult choice for its location and presence, in most cases, of a "positive" coupling between the compensator system and the disturbance image's measurement, which can cause instabilities. To achieve the disturbance's rejection (asymptotically) without measuring it, a feedback solution can be considered.

Residual noise can be described as the result of acoustic waves which pass trough the system, and the noise cancellers' objective is to attenuate it. In many cases, these waves can be characterized in the frequency domain either as tonal disturbances or as narrow band perturbations. The common framework is the assumption that a narrow band disturbance is the result of a white noise or a Dirac impulse passed through the "disturbance's model." In the case of tonal (narrow band) noise disturbances, the basic idea is to use the "internal model principle" to get a strong attenuation, combined with output sensitivity function shaping, in order to avoid unwanted amplifications in the tonal disturbances' neighborhoods.

Financial support thanks to Consejo Nacional de Ciencia y Tecnología de México, CONACyT.

However, the real time performance of the noise cancellers strongly depends on the secondary path dynamic model's quality used for designing the feedback control law. Many studies have been carried out to develop dynamic models for control design, starting from the basic physical equations describing the system and trying to determine, from the systems' geometry, the values of some basic constants. See [START_REF] Nelson | Active Control of Sound[END_REF]. [START_REF] Zimmer | An improved acoustic model for active noise control in a duct[END_REF] give a very complete evaluation of the physical modeling in the context of active noise control in ducts. Unfortunately on one hand the resulting models are not very good, since it is hard for a given system to find the correct physical constants, and on the other hand it is a PDE model for which there are not simple control design methods available.

What is needed in practice is a finite dimension discretetime model which reproduces the system's dynamical behavior. Once such a model is available, one can use digital control design techniques readily implementable on a real time computer. These models can be obtained directly from data using system identification techniques, see [START_REF] Ljung | System Identification -Theory for the User[END_REF]; [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]; [START_REF] Carmona | Active noise control of a duct using robust control theory[END_REF]. However these discretetime models for active noise compensation present a number of peculiarities which require to develop a specific identification procedure. One of the major objectives of the paper is to clarify how system identification from data should be done in the context of active noise control. Previous identification results given in Ben Amara et al. (1999) and [START_REF] Zeng | Recursive filter estimation for feedforward noise cancellation with acoustic coupling[END_REF] have been also considered.

The final quality test for an identified model is to verify how close are the real-time experimental results obtained and the designed controller's performances in simulation. As shown later, the results are very close, which indicates that the proposed procedure is reliable. Two control problems have been considered: the rejection of two tonal disturbances, and strong attenuation of interferences, caused by tonal disturbances with very close frequencies. The Internal Model Principle (IMP) combined with the sensitivity functions' shaping will be used for control design.

EXPERIMENTAL SETUP

The test bench used for the experiments is shown in Fig. 1, and its detailed scheme is given in Fig. 2. The speaker used as the source of disturbances is labeled as 1, the control speaker is 2 and finally, at pipe's open end, the microphone that measures the system's output (residual noise) is denoted as 3. The transfer function between the disturbance's speaker and the microphone (1→3) is denominated Primary Path, while the transfer function between the control speaker and the microphone (2→3) is denominated Secondary Path. Both speakers are connected to a xPC Target computer with Simulink Real Time environment through a pair of high definition power amplifiers and a data acquisition card. The current signals u(t) and p(t) are amplified and reach the speakers' voice coils and displace them, generating movement in the diaphragms and thus, sound waves. In Fig. 2, y(t) is the system's output (residual noise measurement). Both primary and secondary paths have a double differentiator behavior, since as input we have the voice coil displacement, and as output the air acoustic pressure. A second computer is used for development, design and operation with Matlab . PVC pipes of 0.10 m diameter are used in this test bench, with couplings of 135 • for the control speaker. Distances between disturbance speaker and microphone are 1.65 m, and to control input 0.80 m. Speakers are isolated inside wood boxes filled with special foam in order to create anechoic chambers and reduce the radiation noise produced.

SYSTEM DESCRIPTION

The linear time invariant (LTI) discrete-time model of the secondary path, or plant, used for controller design is

G(z -1 ) = z -d B(z -1 ) A(z -1 ) = z -d B (z -1 ) A(z -1 ) D F (z -1 ), (1)
where D F (z -1 ) is a double differentiator filter and

A(z -1 ) = 1 + a 1 z -1 + • • • + a n A z -n A , (2)
B (z -1 ) = b 1 z -1 + • • • + b n B z -n B ,
(3) with d as the plant pure time delay in number of sampling periods 1 . The system's order is

n = max(n A , n B + d) (4)
Fig. 3: Feedback regulation scheme.

Figure 3 shows the closed loop feedback regulation scheme, where the controller K is described by

K(z -1 ) = R S = r 0 + r 1 z -1 + • • • + r n R z -n R 1 + s 1 z -1 + • • • + s n S z -n S . (5)
The plant's output y(t) and the input u(t) may be written as (see Fig. 3):

y(t) = q -d B(q -1 ) A(q -1 ) • u(t) + p(t), (6)
S(q -1 ) • u(t) = -R(q -1 ) • y(t). (7)
In ( 6), p(t) is the disturbances' effect on the measured output2 and R(z -1 ), S(z -1 ) are polynomials in z -1 having the following expressions:

R = H R • R = H R • (r 0 + r 1 z -1 + . . . + r n R z -n R ), (8)
S = H S • S = H S • (1 + s 1 z -1 + . . . + s n S z -n S ), (9)
where H S (z -1 ) and H R (z -1 ) represent prespecified parts of the controller (used for example to incorporate the internal model of a disturbance, or to open the loop at some frequencies) and S (z -1 ) and R (z -1 ) are solutions of the Bezout equation:

P = (A • H S ) • S + z -d B • H R • R . ( 10
)
In ( 10) P(z -1 ) represents the characteristic polynomial, which specifies the desired closed loop poles of the system.

The transfer functions between the disturbance p(t) and the system's output y(t) and the control input u(t), denoted respectively output sensitivity and input sensitivity functions, are given by

S yp (z -1 ) = A(z -1 )S(z -1 ) P(z -1 ) (11)
and

S up (z -1 ) = - A(z -1 )R(z -1 ) P(z -1 ) , (12)
4. DATA DRIVEN SYSTEM IDENTIFICATION Model identification from experimental data is a well established methodology (see [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]; [START_REF] Ljung | System Identification -Theory for the User[END_REF]). Identification of systems is an experimental approach for determining a system's dynamic model. It includes four steps:

1. Input-output data acquisition under an experimental protocol and data pre-processing. 2. Estimation of the model complexity. A complete identification operation must comprise the four stages indicated above. The typical input is a PRBS, which is a persistent excitation signal allowing unique parameter estimation even for high order system. Model validation is the final key point. It is important to emphasize that it does not exist one single algorithm that can provide in all the cases a good model (i.e. which passes the model validation tests). System identification should be viewed as an iterative process which has as objective to obtain a model which passes the model validation test and then can be used safely for control design. The procedure will be detailed for the secondary path's identification. The same methodology has been used for the primary path identification also (which is used only for simulation), and only the final results will be given.

Data Acquisition under the experimental protocol

For design and application reasons (the objective is to reject tonal disturbances up to 400 Hz), the sampling frequency was selected as f s = 2500 Hz (T s = 0.0004 s) i.e. approximatively 6 times the maximum frequency to attenuate, in accordance with recommendation given in (see [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]). The theoretical band pass of the system is 1975 Hz, using formula given in [START_REF] Zimmer | An improved acoustic model for active noise control in a duct[END_REF].

The experimental protocol should assure persistent excitation for the number of parameters to be estimated, thus a PRBS has been used. This signal's magnitude is constant allowing an easy selection with respect to the magnitude constraint on the plant input. One of the key points is the design of a PRBS in order to satisfy a compromise between the frequencies range to be covered (particularly those in the low frequencies region), and the test duration. One should apply at least on complete PRBS sequence, and its characteristics, including duration, will depend on the number of cells in the registers length used for its generation.

For identification, the signals' characteristics used in both paths are: magnitude = 0.15 V, register length = 17, frequency divider of 1, sequence length of 2 17 -1 = 131, 071 samples, guaranteeing a uniform power spectrum from about 70 Hz to 1250 Hz. Since the transfer functions have a double differentiator behavior, this is considered as a system's known part and the objective will be to identify the unknown part only. To do this, the input sequence will be filtered by a double discrete-time differentiator

D F = (1 -q -1 ) 2 such that u (t) = D F • u(t).
The double differentiator will be concatenated with the identified model of the unknown part in the final models.

Complexity Estimation

The basic idea in complexity estimation is to have, on one hand an unbiased estimator of the system parameters, which allows to obtain an unbiased evolution of the prediction error quadratic criterion that tends toward zero when the correct order is reached, and on the other hand a penalty term for the model's complexity. In order to get an unbiased estimation of the error criterion, the instrumental variable approach is used, see [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]; [START_REF] Duong | An IV Based Criterion for Model Order Selection[END_REF]. As instrumental variables, delayed inputs are used,

Z( n) = [U(t -L -1),U(t -1),U(t -L -2),U(t -2)...]
, where L > n and Y T (t) = [y(t), y(t -1)...]; U T (t) = [u (t), u (t -1)...]. Therefore the criterion used for the order estimation is:

V IV ( n, N) = min θ 1 N ||Y (t) -Z( n) θ || 2 , ( 13
)
where N is the number of samples. Adding a term which penalizes the model's complexity leads to

J IV ( n, N) = V IV ( n, N) + 2 n log N N , (14)
with n = min n J IV .

When identifying finite dimensional discrete-time models, J IV will show a minimum, function of n, allowing to define the estimated order of the model. Once an estimated order n is selected, one can apply a similar procedure to estimate nA , nd, and nB + d, from which nA , nB and d are obtained.

Results for the secondary path order estimation (without the double differentiator) are shown in Fig. 4 where both nonpenalized and penalized criteria V IV , J IV are represented. As it can be seen, the minimum is very flat (which is understandable since we are trying to approximate an infinite-dimensional system). It is therefore necessary to explore the model's properties for n between 36 and 41, in order to decide what order to take. Two additional criteria will be used to decide upon the best order estimation: I) comparison of the Power Spectral Density (PSD) of the identified model output, and that of the real data (in order to see if the identified model captures all the vibrations modes in the frequency range of operation) and II) comparison of the validation tests for the various models. To do this it is necessary to estimate the values of n A , n B and d for each order n selected, and to proceed with parameter estimation. To illustrate the details of orders estimations, the model with n = 40 is considered. The procedure for other values of n is similar.

For the secondary path, Fig. 4b shows that the minimum for nd is 32. From Fig. 4c one can see that the minimum for n A is given by n A = 38. From

Parameters Estimation

The algorithms used for parameter estimation will depend on the assumptions made on the measurements' noise characteristics, which have to be confirmed by the model validation. Order (n) Order (n A ) Order(n B + d) Among the various identification methods used, it was found that Output Error with Extended Prediction Model (XOLOE) identification algorithm gives the best results in terms of validation for a given order model (see [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]).

J IV (n) V IV (n) (a) Instrumental Variable (n) order estimation.
Order (n -d) J IV (n) V IV (n) (b) Instrumental Variable (n -d) order estimation.
J IV (n) V IV (n) (c) n A order estimation.
J IV (n) V IV (n) (d) n B + d order estimation.
It was concluded that the ARMAX model best represents the plant + noise model.

Model Validation

The validation procedure associated with the identification of ARMAX models is based on a whiteness test.

Whiteness test: Let {ε(t)} be the centered (measured value minus average) sequence of the residual prediction errors. One computes estimations of the normalized autocorrelations as:

R(i) = 1 N N ∑ t=1 ε(t)ε(t -i) (16)
R(0) = 1 N N ∑ t=1 ε 2 (t) ; RN(i) = R(i) R(0) (17) i = 1, 2, 3, . . . , n A , . . . , n
One considers as a validation criterion (extensively tested on applications):

RN(0) = 1 ; |RN(i)| ≤ 2.17 √ N ; i ≥ 1. (18)
Fig. 5 shows the validation results (whiteness test) for the unknown part model with n = 40 (effective n e = 38) and n = 38 (n e = 38). The results are summarized in Table 1.

Model n = 40 leads to better results, which is confirmed in Fig. 6 where the PSD of real data's measures is compared with the two complete models outputs' PSD (including the double differentiator). Therefore the XOLOE model n = 40 is chosen. It has 18 oscillatory modes with damping comprised between 0.0097 and 0.3129; also 13 pairs of oscillatory zeros Primary path Secondary path Fig. 7: Frequency characteristics of the identified primary and secondary paths models with damping comprised between -0.0159 and 0.5438. The presence of these low damped zeros make the control system's design difficult. Fig. 7 gives the frequency characteristics of the identified complete models for the primary and secondary paths. The basic specifications are that the attenuation of two tonal disturbances located at 170 Hz and 285 Hz must be at least -40 dB, and the maximum amplification at other frequencies be less than 7 dB. In order to strongly attenuate the two tonal disturbances the IMP has been used. This requires that the controller's fixed part H s incorporates the disturbance's model. See [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]. The tonal disturbances can be modeled by:

p(t) = N p (q -1 ) D p (q -1 ) • δ (t), (19)
with δ (t) as a Dirac impulse. D p has roots on the unit circle. In practice, the contribution of N p is negligible for steady state analysis in comparison with D p . So the IMP requires that the controller contains a disturbance's model, i.e. H S (q -1 ) = D p (q -1 ). For this specific case considered, H S = H S 1 H S 2 where:

H S i (q -1 ) = 1 -2 cos 2π f i f s q -1 + q -2 , (20)
with f 1 = 170 Hz and f 2 = 285 Hz. Also, since the system has a zero gain at 0 Hz and a very low gain at 1250 Hz, the loop has been opened at these frequencies by choosing H R = (1 + q -1 )(1q -1 ). Furthermore, in order to improve robustness, the input sensitivity function should be below -20 dB at frequencies over 600 Hz (beyond the system's bandpass). The dominant closed loop poles have been chosen equal to those of the secondary path.

Fig. 8 shows the resulting output sensitivity function S yp (curve IM + H r ). The specifications for maximum gain are violated.

To overcome this, 30 auxiliary real poles with value p i = 0.25 have been added in the form P F (z -1 ) = (1p i z -1 ) n F , without augmenting the controller's order (curve IM + Hr + Pol). The resulting sensitivity function is improved but the limit is still violated. To further shape the sensitivity function, Band-Stop Filters (BSF) have been used [START_REF] Landau | Adaptive and Robust Active Vibration Control[END_REF]); 3 on S yp , and 3 on S up to obtain a correct behavior (see table 2). The resulting output sensitivity function is shown in Fig. 8. Also the resulting input sensitivity function is shown in Fig. 9.

EXPERIMENTAL RESULTS

The experimental results have been obtained by implementing the designed controller on the test-bench described in Section 2.

Fig. 1 :

 1 Fig. 1: Noise control test bench (Photo).

Fig. 2 :

 2 Fig. 2: Noise control test bench diagram.

  3. Estimation of the model parameters. 4. Validation of the identified model (complexity of the model and values of the parameters).

  Fig.4d one concludes that n B + d = 38. Taking in account the definition of order n, one concludes that n A = 38, n B = 30 and d = 8, therefore the effective estimated order of this model is n e = 38. Similarly for the model with n = 38, one gets n A = 37, n B = 30, d = 8 (which means an effective order n e = 38) 3 .

Fig. 4 :

 4 Fig. 4: Secondary path, Variable order estimation.

  Fig. 6: System output PSD.

Fig. 9 :

 9 Fig. 8: Frequency characteristics for Output sensitivity, S yp .

Fig. 10 :

 10 Fig. 10: Simulation results.

Fig. 11 Fig. 11 :

 1111 Fig. 11 shows the result for a real time test. Two tonal sinusoidal signals at 170 Hz and 285 Hz were used as disturbances p(t) from 1 s to 11 s. Control starts by closing the loop at 6 s and ends at 11 s. Performances during the first second and the last one are used as a reference for the ambient noise (no control, no disturbance). A global attenuation of 76.88 dB was achieved, with disturbance attenuations of -94.5 dB at 170 Hz, and -94 dB at 285 Hz. These results are very close to those obtained in simulation. Fig. 12 displays the effective residual PSD estimation, calculated as a difference between the openloop PSD and the closed-loop PSD of the residual noise.

Fig. 13 Fig. 13 :

 1313 Fig. 13 displays the results for a second real-time test. Two pairs of sinusoidal interference signals (170 Hz+170.5 Hz and 285 Hz+285.5 Hz) with amplitude of 0.14 V were used as disturbances p(t) from 1 s to 20 s. Control starts by closing the loop at 10 s and ends at 20 s. Performances during first and last second are used as a reference for ambient noise again. A global attenuation of 59.55 dB was achieved.REFERENCESBen Amara, F.,Kabamba, P., and Ulsoy, A. (1999). Adaptive sinusoidal disturbance rejection in linear discrete-time sys-

Table 1 :

 1 Summary of Whiteness tests validations

	Method	Model	Error	Maximum	RN(i)
			energy	RN(i)	over limit
	XOLOE n = 40 1.3307e-06	0.0154	15
	XOLOE n = 38 1.3337e-06	0.0177	14
		5. CONTROLLER DESIGN

Table 2 :

 2 Band-Stop Filters for sensitivity functions. Fig.10displays the system's output for a simulation using the models estimated for the primary and secondary paths. A pair of sinusoidal signals at 170 Hz and 285 Hz were used as disturbances p(t) from 1 s to 11 s. Control starts at 6 s and ends at 11 s. A global attenuation of 86.4 dB was achieved, with attenuations of -88.6 dB at 170 Hz, and -94 dB at 285 Hz.

	Freq.[Hz] Ampli.[dB]	Freq.[Hz] Ampli.[dB]
		90	-6.00		600	-6.00
	S yp	231	-8.00	S up	800	-1.00
		370	-5.00		945	+5.00

The complex variable z -1 is used to characterize the system's behavior in the frequency domain and the delay operator q -1 for the time domain analysis.

The disturbance passes through the primary path, and p(t) is its output.

Complete model's n B = n B + 2, due to the double differentiator addition.


Gashin Shahsavari
email: gashin.shahsavari@univ-paris-diderot.fr

Gilles Arnaud-Fassetta
email: gilles.arnaud-fassetta@univ-paris-diderot.fr

Roberto Bertilotti
email: bertilotti@prolog-ingenierie.fr

Hossein Bonakdari
email: bonakdari@yahoo.com

Isa Ebtehaj
email: isa.ebtehaj@gmail.com

Hamid Moeeni
email: h.moeeni68@gmail.com

Carlo Modica
email: cmodica@dica.unict.it

Alberto Campisano
email: acampisa@dica.unict.it

Preliminary analysis of turbidity measurements during a flushing operation in combined sewer channel

Keywords: ARMA model, combined sewer, flush, turbidity monitoring

Flow turbidity at different elevations was continuously monitored in five cross-sections of a real combined-sewer channel stretch during a flush experiment. The preliminary analysis of the collected turbidity data in terms of quality and spatial variability of the time series is presented in this paper. Also, an Autoregressive Moving Average (ARMA) model was setup to evaluate potential prediction of turbidity values showing a good estimation performance with relatively small errors.

INTRODUCTION

Sewer flushing is recognized as an economically and operationally efficient technique to limit the sediment accumulation in combined sewers and then to restore the full flow capacity of channels [START_REF] Ashley | Solids in sewers -Characteristics, effects and control of sewer solids and associated pollutants, Scientific and technical report[END_REF]. Since more than two decades, the use of this technique has been studied to evaluate the sediment-removal efficiency associated to different types of flushing devices. However, sediment transport during the flush in sewer channels is not yet well understood due to the complexity of sewer conditions as well as the lack of observations. Researches mainly conducted under laboratory controlled conditions have allowed characterizing the flow and sediment parameters playing a major role during the flush [START_REF] Dettmar | Performance and Operation of Flushing Devices -Results of a Field and Laboratory Study[END_REF][START_REF] Campisano | Experimental and numerical analysis of the scouring effects of flushing waves on sediment deposits[END_REF][START_REF] Creaco | Numerical simulation of flushing effect on sewer sediments and comparison of four sediment transport formulas[END_REF]. Field experiments pointed out the occurrence of both bedload and suspended load transport depending on established flow conditions [START_REF] Shahsavari | Non-uniform mobile sediment bed evolution under flushing operation in a real compound sewer channel[END_REF]. Turbidity has been recognized to be an indicator of suspended (total) sediment transported by the flow in natural and artificial channels [START_REF] Christensen | Continuous turbidity monitoring and regression analysis to estimate total suspended solids and fecal coliform bacteria loads in real time[END_REF][START_REF] Hannouche | Relationship between tubidity abd total suspended solids concentration within a combined sewer system[END_REF]. Recently, the use of turbidity sensors is considered to be effective for the continuous monitoring of in-sewer turbidity dynamics during dry weather as well as wet weather conditions [START_REF] Bertrand-Krajewski | TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables[END_REF]Métadier and Bertrand-Krajewski, 2011). Many challenges are rising from this technology, basically concerning methods to convert turbidity readings into sediment transport measurements or relationships between turbidity and flow [START_REF] Bertrand-Krajewski | On-line monitoring of particulate pollutant loads in urban sewer systems: stakes, methods, example of application[END_REF][START_REF] Lepot | Accuracy of different sensors for the estimation of pollutant concentrations (total suspended solids, total and dissolved chemical oxygen demand) in wastewater and stormwater[END_REF]. The Paris Municipality (Ville de Paris) has elaborated a plan for investigating the scouring performance of flushing operation in a large-size channel of the combined sewer network of the city. In this regard, a field flush experiment in a pilot channel was carried out for a full monitoring flow and turbidity during the flush.

Session xxx SPN8 2 This paper presents the preliminary results of the analysis of the turbidity time series collected during the experiment. First the analysis addressed to the data consistency. Secondly, the potentiality of using an Autoregressive Moving Average (ARMA) model to predict turbidity was analysed for one of the available turbidity time series.

METHODOLOGY Field experimental setup and data storage

Field experiments were conducted in a large size trunk characterized by a compound cross-section with lateral banks in the sewer subnetwork of combined sewer network of Paris 11 th arrondissement. The pilot channel is 1.1 km long (signed hereafter by S -50 and S +1050 with reference to the gate location at S 0 ) and it is prone to sedimentation. The average slope of the trunk is 0.8‰ and some longitudinal irregularities are present along the channel. The channel cross-section and longitudinal profile are illustrated in Figure 1a. The channel was equipped with a flushing gate that allows in-line storage of dry-weather flow in the upstream portion. For the experiment, the flush is produced followed by a sudden opening of the gate and release of the stored water through the channel.

The channel stretch was equipped by flow devices and automated turbidity sensors in five crosssections (called hereafter, S -50 , S -5 , S +5 , S +50 and S +100 ; see Figure 1b). Figure 1c shows the position of these devices in the cross-section. At each measuring section a flow meter, an ultrasound sensor and three turbidity sensors were installed. Turbidity was measured in nephelometric turbidity unit (NTU) in 10s time intervals. Technical properties of the monitoring devices such as sampling range, accuracy, and measuring interval are outlined in table 1. During the flush test, real-time data acquisition was obtained using a portable PC. Flow data time series were provided by all the flow sensors with the exception of sections S +100 as well as of S +50 during the peak period. Turbidity time series were obtained from all the sensors except for two (T15-S +5 and T25-S +50 ).

Turbidity data analyzing

The dataset was firstly "cleaned" by eliminating records showing values out of the detection range of sensor. The noisiest time series correspond to the sensors close to the bed (T15) where the sediment fouling and bed load transport during the flush were mainly were more developed. The analysis of turbidity values from all sensors was carried out to assess the quality of the measurements. To this purpose, the distribution of data obtained from each sensor was separately checked using Normal, Lognormal, Exponential and Weibull functions to verify the best fitting distribution. Then, the best fitting distribution was identified to compare distribution parameters among all the turbidity sensors installed at the same elevations to evaluate the consistency of recorded data. Furthermore, in order to show the application of the validated turbidity values (in NTU) the analyses developed by plotting these turbidity values against the corresponding flow discharge (in m 3 /s) in 10s time scale. This method could help to describe the overall impact of the flush on the turbidity during the fast rising and falling limbs of the flow showing by hysteresis loop of the both time series (discharge vs turbidity) which could support the investigation of flow turbidity variations.

Autoregressive model

Turbidity time series were used to test application of Autoregressive Moving Average model (ARMA) in order to obtain the relationship between turbidity values at different times. The used ARMA model is based on following equation:

t q q 2 2 1 t p p 2 2 1 )ε B θ ... B θ B θ (1 )x B ... B B (1            
(1) where x t is the observed value of turbidity, ε t is the stochastic or noise term, B is a differential operator as B(x t )=x t-1 , p and q are order of autoregressive and moving average (respectively) and φ and θ are the parameters of autoregressive parameters and moving average orders (respectively). Among the 469 turbidity records, 329 values are used to model calibration and the rest of data (140 data) were considered for the validation. The number of autoregressive order was taken between zero and ten and allowed combining 121 different models. To evaluate the performance of the proposed model Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Scatter Index (SI) were used as indicated in Eq. 2 to 4.

            n 1 i 2 i i y x n 1 RMSE (NTU) (2)                   n 1 i i i i x y x n 100 MAPE (%) (3) Session xxx SPN8 4 x RMSE SI  (-) (4)
where y i and x i are the modelled and observed values, respectively, and x is the mean of observed values.

RESULTS

Turbidity data analyzing

Preliminary fitting distribution check was applied for all T15 sensors and revealed that time series were Weibull distributed (Eq. 5), which was then confirmed by an analytical Chi-square test (with significant level of ≤ 0.05). The two maximum likelihood parameters of fitted Weibull distribution (a and b as scale and shape, respectively) are presented in Table 2.

(5) The probability distribution of turbidity time series for all T15 is illustrated in Figure 2 for the sections S -50 , S -5 , S +50 , S +100 . The figures clearly show good fitness of turbidity time series distribution to records for all the T15 sensors, which is indicating the data consistency among the sensors positioned at the same elevation. In other words, the sensors from the same position behaved in the same way to observe the turbidity variation during the flush and this is indicating the consistency of the recorded data. 5

The spatial variation of the turbidity records during the flush for all sensors was evaluated. Table 3 presents the main statistics of turbidity data. On one hand, the vertical comparison of mean values confirms that the higher values of turbidity in the water column were observed close to the bed with values decreasing going from the bed to the water surface. Also, the more the sensor is far from the bed (T15), the more it provided dispersed values. On the other hand, a decrease of the average turbidity throughout the channel reveals the effect of the flush. Sensors located close to the gate exhibit high values of the turbidity with a general decreasing trend toward downstream. The same decreasing trend was observed for all three vertical positions except for T65. In fact, the T65 sensor in the last measuring cross-section has strangely recorded a very high peak after about 45 minutes after the flush releasing which was lasting for a few minutes. This was perhaps due to a temporary covering of the sensor by sanitary paper. In addition, as an example, the flow and turbidity data from S-5 were chosen to investigate the relationship of the two time series. Linear regression between discharge and NTUs showed a weak relationship with a coefficient of determination R2 of 0.01. The fluctuations of turbidity time series were reduced by applying the linear filtering over 1-min time scale. The figure 3 shows the NTU values and corresponded flow discharge in m3/s. Interestingly, the figure shows a hysteretic behaviour with the rising and falling limbs of the flush hydrograph for flow discharge-turbidity relations similarly to what has been observed in river streams [START_REF] Lawler | Turbidity dynamics during spring storm events in an urban headwater river system: The Upper Tame, West Midlands, UK[END_REF][START_REF] Lloyd | Technical Note: Testing an improved index for analysing storm nutrient hysteresis[END_REF][START_REF] Mukundan | Factors affecting storm event turbidity in a New York City water supply stream[END_REF]. This anticlockwise hysteresis indicates the last-flush process with time-lagged arrival of turbidity peak after the flow peak characterizing the chronological contribution of turbidity sources to the flow. Also, the form of the hysteresis is indicating the rate of the transport of turbidity sources. The result would be a material for future work to analyse the response of the turbidity to the flush flow by evaluating the pattern of the hysteresis for other sensors.

Model results

The results of ARMA model provided the following equation: 4a shows the results of the calibration of the model. Globally, the observed values show the peaks larger than the modelled values. The model validation graph (figure 4b) shows also the same trend reducing the amount of maximum and minimum values in fluctuations. Overall, the ARMA model shows good performance in estimating turbidity so that the difference between predicted and observe value are relatively small. The RMSE, MAPE and SI for proposed model are equal to 174.3 NTU, 8.33 % and 0.107 (-) for validation. Therefore, the use of ARMA model (Eq. 6) in prediction of turbidity leads to reliable results.

2 1 2635 . 0 5184 . 0      t t 1 - t t 0.9994x x   (6) Figure

Figure 1 .

 1 Figure 1. a) Longitudinal profile of the sewer channel; b) five monitoring cross-sections; c) scheme of measuring devices and their position in the transversal cross-section.

Table 1 .

 1 Summary of flow and turbidity instruments used for the flush experiment. Conference on Sewer Processes and Networks August 31 -September 2, 2016, Rotterdam, The Netherlands 3 Three elevations were chosen for turbidity sensors installation (15, 25 and 65 cm from the bed, called hereafter T15, T25 and T65) at each of the five monitoring sections (15 turbidity sensors in total) in order to observe the turbidity vertical pattern during the flush. Turbidity sensors were calibrated off-line before the flush experiment using Formazin solution between 0-2000 NTU.

Figure 2 .

 2 Figure 2. Cumulative probability plot and fitted Weibull distribution for the sections S -50 , S -5 , S +50 , S +100 .

Figure 3 .

 3 Figure 3. Example of temporal dynamic between flow discharge (m 3 /s) and associated turbidity (NTU) in S -5 .

Figure 4 .

 4 Figure 4. Performance of ARMA model in prediction of turbidity in NTU for T15 sensor in S -50 , a) for the calibration, and b) for the validation.

Table 2 .

 2 The Weibull distribution parameters obtained for all T15 time series.

	parameters	S -50	S -5	S +50	S +100
	a (scale)	1683.8	4247.1	4806.0	2959.0
	b (shape)	3.21	1.54	1.94	1.01

Table 3 .

 3 Statistical description of all turbidity time series in NTU included mean and standard deviation (Values exceeding the detection range are excluded).

	Sensors	Mean (NTU)	SD (NTU)	CV (-)
		S -50	35.4	26.9	0.8
		S -5	335.5	355.2	1.1
	T65	S +5	132.8	16.4	0.1
		S +50	40.1	66.8	1.7
		S +100	428.1	989.7	2.3
		S -50	219.6	75.1	0.3
		S -5	460.3	283.6	0.6
	T25	S +5	2666.6	1469.5	0.6
		S +50	---	---	---
		S +100	1819.4	1890.3	1.0
		S -50	1515.3	497.0	0.3
		S -5	3836.0	2447.0	0.6
	T15	S +5	---	---	---
		S +50	4301.4	2210.8	0.5
		S +100	3105.3	2775.4	0.9

th International Conference on Sewer Processes and Networks August 31 -September 2, 2016, Rotterdam, The Netherlands

ACKNOWLEDGEMENT

This project has been financially supported by the PROLOG Ingénierie consulting company as well as the Paris Municipality-major structure services of sewer network of Ville de Paris. A special thank is given to Jean-François Ferrandez and Fabien Riou.


PhD Gashin Shahsavari
email: gashin.shahsavari@gmail.com

Gilles Arnaud-Fassetta
email: gilles.arnaud-fassetta@univ-paris-diderot.fr

Alberto Campisano
email: acampisa@dica.unict.it

Roberto Bertilotti

Co

Fabien Riou

Keywords: Bed-material load evolution, combined sewer systems, flushing efficiency, sediment transport

Sewer deposits have been identified as a major cause of dysfunctions in combined sewer systems regarding sewer management, which induces different negative consequents resulting in poor hydraulic conveyance, environmental damages as well as worker's health. In order to overcome the problematics of sedimentation, flushing has been considered as the most operative and cost-effective way to minimize the sediments impacts and prevent such challenges. Flushing, by prompting turbulent wave effects, can modify the bed form depending on the hydraulic properties and geometrical characteristics of the conduit. So far, the dynamics of the bed-load during high-flow events in combined sewer systems as a complex environment is not well understood, mostly due to lack of measuring devices capable to work in the "hostile" in combined sewer system correctly. In this regards, a one-episode flushing issue from an opening gate valve with weir function was carried out in a trunk sewer in Paris to understanding its cleansing efficiency on the sediments (thickness: 0-30 cm). During more than 1h of flushing within 5 m distance in downstream of this flushing device, a maximum flowrate and a maximum level of water have been recorded at 5 m in downstream of the gate as 4.1 m3/s and 2.1 m respectively. This paper is aimed to evaluate the efficiency of this type of gate for around 1.1 km (from the point -50 m to +1050 m in downstream from the gate) by (i) determining bed grain-size distribution and sediments evolution through the sewer channel, as well as their organic matter content, and (ii) identifying sections that exhibit more changes in their texture after the flush. For the first one, two series of sampling were taken from the sewer length and then analyzed in laboratory, one before flushing and second after, at same points among the sewer channel. Hence, a non-intrusive sampling instrument has undertaken to extract the sediments smaller than the fine gravels. The comparison between sediments texture after the flush operation and the initial state, revealed the most modified zones by the flush effect, regarding the sewer invert slope and hydraulic parameters in the zone up to 400 m from the gate. At this distance, despite the increase of sediment grain-size rages, D 50 (median grainsize) varies between 0.6 mm and 1.1 mm compared to 0.8 mm and 10 mm before and after flushing, respectively. Overall, regarding the sewer channel invert slope, results indicate that grains smaller than sands (< 2 mm) are more transported to downstream along about 400 m from the gate: in average 69% before against 38% after the flush with more dispersion of grain-sizes distributions. Furthermore, high effect of the channel bed irregularities on the bed material evolution has been observed after the flush.

I. INTRODUCTION

ANY urban drainage networks in Europe are combined which can convey domestic wastewater, rainfall and also water used for cleaning streets and gutters. Due to complex geometric characteristics of sewer conduits, self-cleaning of combined sewers is not always guaranteed within hydraulic conditions occurring during both dry and wet weather [START_REF] Chebbo | Technical solutions envisaged in managing solids in combined sewer networks[END_REF]. In order to overcome the significant problems associated with accumulated in-sewer deposits (e.g. the degradation of the hydraulic capacity and the increase of surcharge risks during storm flows, sediment removal from networks becomes essential [START_REF] Ashley | Solids in sewers -Characteristics, effects and control of sewer solids and associated pollutants[END_REF]. This can be hydraulically pursued by producing artificial high flows able to flush sediments from the invert of sewer channel. In the point of view of network managers, automated flushing techniques are considered as a cost-effective and practicable way to reduce the accumulation of sediments as they are a healthier way to proceed for sewer workers [START_REF] Pisano | Dry-weather deposition and flushing for combined sewer[END_REF], [START_REF] Bertrand-Krajewski | Experimental analysis of the hydrass flushing gate and field validation of flush propagation modelling[END_REF]. Flushing, by prompting turbulent wave effects, produced high bed shear stresses that modify the sediment bed profile and properties. Description of sediment movement and knowledge of mechanisms through a flush with in real sewer studies with high resolution in spatial and temporal observations is not completely known, basically because of the complexity of the processes and conditions that may vary among different sewers [START_REF] Ristenpart | Solids transport by flushing of combined sewers[END_REF], [START_REF] Ristenpart | Sediment properties and their changes in a sewer[END_REF] . The need for high values of critical shear stress in order to start erosion of in-sewer deposits in combined systems can be explained by bed formation and consolidation also due to the potential presence of significant amounts of organic solids that increase resistance to erodiblity [START_REF] Lau | Influence of antecedent conditions on critical shear stress of bed sediments[END_REF].

For more than two decades, many types of flush devices have been investigated in terms of both their hydraulic performance and their capability of sediment removal [START_REF] Bertrand-Krajewski | Experimental analysis of the hydrass flushing gate and field validation of flush propagation modelling[END_REF], [START_REF] Shirazi | Preliminary results of investigating proper location of flushing tanks in combined sewer networks for optimum effect[END_REF]- [START_REF] Bong | Hydraulics characteristics of tipping sediment flushing gate[END_REF]. Experimental and numerical investigations have been carried out under laboratory controlled conditions [START_REF] Campisano | Dimensionless approach for the design of flushing gates in sewer channels[END_REF], [START_REF] Campisano | Experimental analysis of the Hydrass flushing gate and laboratory validation of flush propagation modelling[END_REF]- [START_REF] Dettmar | Behavior of the activated storage-volume of flushing waves on cleaning performance[END_REF] and in real sites [START_REF] Bertrand-Krajewski | Experimental analysis of the hydrass flushing gate and field validation of flush propagation modelling[END_REF], [START_REF] Balayn | Modélisation du transfert de sédiments lors d'un lâcher d'eau en réseau d'assainissement -approche numérique[END_REF], [START_REF] Sakakibara | Sediments flushing experiment in a trunk sewer[END_REF] in order to understand the cleansing effectiveness of various flushing devices and to obtain data for the development and calibration of predictive numerical models [START_REF] Bertrand-Krajewski | Experimental analysis of the hydrass flushing gate and field validation of flush propagation modelling[END_REF], [START_REF] Campisano | Experimental analysis of the Hydrass flushing gate and laboratory validation of flush propagation modelling[END_REF], [START_REF] Dettmar | Modelling of Flushing Waves for Optimising Cleaning Operations[END_REF], [START_REF] Kadi | Numerical modeling of flushing waves in sewer channels[END_REF]. These studies point out a number of key parameters which play an important role to ensure a satisfactory performance of flushing operation. Based on in-situ observations, it was estimated that a flush event may decrease by 1-30% the total deposits with high impact on resuspension of sediments accumulated over the pipe invert [START_REF] Sakakibara | Sediments flushing experiment in a trunk sewer[END_REF]. Research conducted by other authors [START_REF] Ristenpart | Solids transport by flushing of combined sewers[END_REF], pointed out that even though flushing practice is an economic way to control sedimentation, total cleansing of long sewer reaches may not Gashin Shahsavari, Gilles Arnaud-Fassetta, Roberto Bertilotti, Alberto Campisano, Fabien Riou Bed Evolution under One-Episode Flushing in a Trunk Sewer in Paris, France M be feasible in combined sewers. Specifically, many researchers assume that the flush removal efficiency is normally limited to a distance close to the gate [START_REF] Ristenpart | Solids transport by flushing of combined sewers[END_REF], [START_REF] Guo | Gate and Vacuum Flushing of Sewer Sediment: Laboratory Testing[END_REF].

Recently, special attention is paid to the flushing performance of sewer gates, as an additional objective with respect to their function to control flows and increase the sewer online retention. Such gates are commonly activated by wastewater storage so that to use them for flushing purposes is an ecological and economic way for scouring conduits without using external water supply by off-line tanks [START_REF] Dettmar | Behavior of the activated storage-volume of flushing waves on cleaning performance[END_REF], [START_REF] Williams | In-sewer sedimentation associated with active flow control[END_REF], [START_REF] Staufer | In situ measurements of shear stresses of a flushing wave in a circular sewer using ultrasound[END_REF]. The gate stores wastewater within the sewer during dry-weather conditions and then, its quick automatic opening induces a rapid discharge of the stored flow in the downstream reach with the generation of the flush. The main objectives of the previously developed experimental researches were to examine the flushing efficiency by investigating the shear stress generated over the downstream mobile sediment bed. The results from experiments show that scouring effects of the flushing wave may be particularly effective in large sewers due to the high volume of water stored behind the gate previous to the flush event. However, the flush was not always able to wash off particles with diameters higher that fine gravel in combined sewers [START_REF] Dettmar | Behavior of the activated storage-volume of flushing waves on cleaning performance[END_REF]. Besides, the presence of organic matters and the modification of their rheological properties through the time, may significantly modify the processes of sediment transport in the sewer due to the cohesion bonding effect that is developed between particles [START_REF] Shirazi | Preliminary results of investigating proper location of flushing tanks in combined sewer networks for optimum effect[END_REF], [START_REF] Campisano | Flow velocities and shear stresses during flushing operations in sewer collectors[END_REF]; this effect also varies with sediment bed depth. Moreover erosion resistance change also during time due to physical and biological activities [START_REF] Campisano | Dimensionless approach for the design of flushing gates in sewer channels[END_REF], [START_REF] Campisano | Laboratory investigation on the effects of flushes on cohesive sediment beds[END_REF].

Much of the existing studies basically focused on the bed height evolution as the consequence of flushing in combined sewer systems [START_REF] Chebbo | Technical solutions envisaged in managing solids in combined sewer networks[END_REF], [START_REF] Ristenpart | Solids transport by flushing of combined sewers[END_REF], [START_REF] Lorenzen | Flush cleaning of sewers[END_REF]. Oppositely, little research has been carried out about the modifications of the grain-size distribution and organic content of the deposited sediments due to the sewer flush.

From many years, The Paris Municipality (Ville de Paris) tries to improve the sewer scour processes of cleaning in the combined sewer network by testing the hydrodynamic effects of the mechanical equipment of flushing. In this perspective, the Sewer Network section of the Paris city, in 2014, realized a set of complete experimentation in order to evaluate the scouring effect for a sector gate throughout a man-entry trunk sewer (collector). The collector of Des Coteaux, with a largest diameter of 2.40 m and 3.20 m of height, consists of a cunetteshaped channel to discharge the dry-weather water. The observations were effectuated over about 1km by the mean of five measurement sections along the flushing line on the sediments which are naturally accumulated on the channel invert potentially 0-30 cm per year.

In this study, bed modifications after a single flush event are investigated by a set of experiments carried out in a combined trunk sewer of Paris, France. The flush was generated by an existing gate which is placed upstream of a high deposition reach of the sewer. The reach was equipped with a high-resolution monitoring system which was customized in order to measure water levels and flow rates in different sections of the sewer channel, together with a video camera system for direct observation of flushing effects over the sediment bed. This paper aims at (i) presenting the preliminary results of the evaluation of the spatial and temporal evolution of the sediment bed within the complex sewer channel after the flush has occurred, and (ii) exploring the areas where the flow provided a higher interaction with the bed in terms of scouring effects. Emphasis is put on comparing the sediment bed changes in terms of spatial grainsize distribution and organic fraction with reference to the initial bed conditions.

II. FLUSHING PROCEDURE

A. Site Description

The experimental field is an 1100 m long combined sewer reach of a man-entry size trunk sewer called Des Coteaux in the center of Paris (11 th ). The mean bed slope of longitudinal channel axis is about 0.8 ‰. The compound section is cunetteshaped section with two lateral banks in both sides of the channel for worker's mobility (Fig. 1). This trunk sewer is dealt with an estimated yearly sediment accretion maximum height of about 20-30 cm. The specific topography of the study sewer channel is illustrated in Fig. 2. The sector gate valve has been designed and implemented to control the overflow in flood cases. It is driven by hydraulic jacks to adjust the weir head, which can reach up to 2.37 m when it is fully closed.

A single flushing experiment was conducted in the combined sewer which took place in more than 1h after the gate-opening up to the time that flow rate began to be stable at about 1 m.s -1 .

B. Data Collection

Bed height and profile measurement in sewers is a difficult task; methods to develop more applicable and more resistant and accurate measuring devices in sewers have been attempted [START_REF] Staufer | In situ measurements of shear stresses of a flushing wave in a circular sewer using ultrasound[END_REF]. Despite such difficulties, a complex experimental setup was implemented in the field reach to allow accurate data collection regarding the sediment entrainment due to the flush. Data acquisition was carried out in three episodes of times: before, during, and after that the flushing event occurred.

C. Experiments during the Flush Event

A set of measurement devices has been installed in the trunk channel in order to remotely monitor the flow and the sediment parameters continuously during the flush. Fig. 1 and Fig. 2 illustrate the general arrangement of the in-site apparatus in five observation sections along the sewer channel reach. The measuring devices were installed by trying not to modify the initial bed and not to disturbing the sediment transport during the flush. Hydraulic parameters such as flow velocity and flowrate were sampled by a beam pulsed Doppler, SonTek IQ Plus TM , to obtain continuous and high-accuracy data with 10s of interval. The advantage of this device is to obtain 3D velocity components with its four velocity beams. Water level measurements were taken on every 15s using an Ultrasonic sensor installed on the celling of the channel. Moreover, high precision and chemical resistant apparatuses were used for turbidity and velocity measurements. In particular, turbidity was sampled on every 10s during the flow by using optical turbidimeters with measuring range up to 10,000 NTU. Three turbidity sensors were installed at three various levels within the channel: 15, 25, and 65 cm above the invert.

In order to observe directly how the bed material dynamics and the sediment behavior are affected by the flush, two boxes were equipped with video cameras. These cameras were installed in the channel bank at sections S +50 and S +100 . Two other video cameras were installed at S +5 and S +50 on the celling of the channel to visualize surface flow and opening displacements of the gate.

D. Experiments Before and After the Flush

To assess the flush consequences on the deposits, the sediment grain-size distribution and the sediment organic matter content were considered and compared in space and time (before and after the flush). In fact, most of flow behavior aspects (hydraulic, geomorphologic and ecological) are closely linked to the bed-material characteristics such as grainsize distribution [START_REF] García | Sedimentation engineering[END_REF]. Two series of tests were conducted to evaluate and determine these sediment characteristics: a) Before the flush in 8 sampling sections of the first 180 m of the sewer reach, for two mineral and organic bed layers of deposits. The 8 sections were selected according to the presence of sediments on the channel invert before the flush event. Because of the delicate nature of organic matter's layer, particular attention was paid during sampling to extract with minimum disturbance fine materials deposits containing the organic portion at the bed surface. Thus, a manual device was applied for sampling sediment bed at various depths, basically consisting of vertically taking out a carrot of deposits.

b) Before and after the flush, along the sewer channel axis (by considering total depth of the sediment as a sample for that section). Fig. 3 shows sampling sections of this second test set. The planned activity was to take samples with interval of 20 m; however, after the flush, areas of deposition before the flush were washed off while other previously clean areas were covered by sediments after the flush event. Then, totally 20 points and 17 points were sampled through the sewer channel between S -50 m and S +1010 m before and after the flush, respectively. Moreover only 9 samples were taken from common sections for proper comparison before and after the flushing event. In addition two more samples at S +350 m (S +385 m after the flush) and S +710 m (S +750 m after the flush), were considered for the comparative analysis. The sampler device used for the first test, where sediment diameters were enough small, is used also in this test for the distance before S +400 . Because the device function is limited to extract the sediments with a diameter that not exceed more than 35 mm. Deposits with coarser fractions were collected with a manual sampler. The sediment sample quantities were different because of the availability of the deposits which was variable along the bed profile. To determine granulometric curves and organic fraction of in-sewer deposits, all sediment samples were collected, secured and immediately transported to the PRODIG laboratory in hygienic kits. Samples were treated in less than 24h after the collection by drying them in the oven at temperature of 105°C during 24h.

Dried samples were split into two parts: the first for the grain-size distribution analysis and the second for the evaluation of the organic matter content. For the grain-size distribution analysis, samples were separated: grains with diameters more than 1 mm were weighted and sieved with French AFNOR standard sizes. Fine grain fractions (<1 mm) were treated by laser diffraction technique (Coulter LS300). Coarse grains with irregular shape were assumed to be spherical in their smallest diameter. Results from both techniques contributed to build the curve of the cumulative weight of the mass whose size is smaller than the mesh size. Then, granulometric curves were used to evaluate sedimentary indexes such as D 50 (median) and sorting index.

As the analysis of the organic matter fraction is concerned, oxidation method by using hydrogen peroxide (H 2 O 2 ) digestion was applied following ORSTOM protocol [START_REF] Orstom | Méthodes d'analyses utilisées au laboratoire de physique des sols[END_REF]. For this purpose, a defined volume of hydrogen peroxide was added to 20 g of grains finer than 2 mm. After the complete reaction, the sample was dried in 105°C and re-weighted. The difference in the weight was calculated in percentage.

III. RESULTS AND DISCUSSION

A. Hydraulic and Turbidity Variation during the Flush Flush variables are measured in the five observation sections that are illustrated in Fig. 4 (a -e). Some data are missing due to malfunctioning of the measurement device (see for example the flow rate at S +100 and S +50 ). Higher fluctuations of monitored variables during the initial phase of the flush release were observed due to the surge front developing and advancing along the sewer channel. The figure firstly shows flow rate to decrease down rapidly within a short time. For example in S -5 , the flow rate drops down to its half in about 9 min. As the data shows, the maximum flowrate (4.1 m3.s -1 ) is recorded just upstream the gate S -5 (section II) after 40s after the gate started to open. The maximum velocity observed in this section was 1.9 m.s -1 . This agrees with one of the conclusions outlined by reference [START_REF] Campisano | Flow velocities and shear stresses during flushing operations in sewer collectors[END_REF]. The mean local average shear stress at S -5 was then estimated to about 4.5 N.m -2 based on the classic relation for uniform flow. This value is in the range of the critical shear stress for the beginning of the erosion to expect a cleaning performance [START_REF] Dettmar | Behavior of the activated storage-volume of flushing waves on cleaning performance[END_REF]. With regard to the turbidity raw data averaged on every 1 min in this section, in the first half an hour, a huge quantity of sediments was observed to be re-suspended and to be transported along the sewer. According to the turbidity measurement at 15 cm from the bottom we can observe an average of about 3700 NTU (Nephelometric Turbidity Unit) and a peak of about 6600 NTU. As in this section the initial height of deposits was close to zero before flushing, these high turbidity values can be explained by the transport of sediments deposited in the closer upstream sections after erosion and resuspension due to the shear stress produced by the flush in the vicinity of the gate [START_REF] Staufer | Upstream processes within a flushing wave[END_REF].

It is clear that the amount and rate of sediment that can be carried out by the flush are observed by the turbidity data in three levels. According to the turbidity data, captors recorded sharp gradient of turbidity in the water column. As expected, higher values of the turbidity were observed by the captor situated close to the channel invert for all sections during the flush. The high difference of this parameter at the position of 15 cm and both other positions in the sections could prove the large bed load transport during the flush.

Overall, differences between turbidity variations in S -50 in upstream of the gate shows that flush influenced this section but not as high as the sections downstream the gate. The high concentration in upstream section is then produced near the gate location, which shows the influence of the flow propagation toward upstream due to the weir function by stirring the bed materials. Unfortunately, in S +5 , the turbidity sensor in the lowest level was not working perhaps due to conveyance of the huge amount of stocked-sediment behind the gate by the water stocking procedure. But from the two sensors in upper positions, high values of the concentration at the very near bed level could be figured out.

In S +50 , the both upper sensors registered very low data in contrast with that in the lower level which shows the high transport of bed-material load near the bed. Comparing S +50 with the section in 50 m further in downstream, noticeable difference in turbidity values signifying sediment entrainment is found going up again despite the flow decreasing further downstream. This may be derived from the presence of fine particles between after S +50 , which induce the increasing of the turbidity in particular toward the inner region of the flow. Fig. 5 shows the difference between the fluid turbidity in both last sections (S +50 and S +100 ) taken from the cameras installed in the lateral bank. The snapshots present the instance of the flush wave entrance which mobilizes the bed material load. In addition, the form of the longitudinal bed slope, available energy, grain interactions could be the factors which induce resuspension of the bed by increasing the driving force [START_REF] Chamley | Sedimentology[END_REF]. According to the videos recorded from both bank cameras implemented in S +50 and S +100 , the turbulent front wave entrance to the observation section is visible. A snapshot taken at the first instance of the entrance of these two the sections is shown in Fig. 5. At S +50 , according to the video, particles are coarser which passed through the observation section whereas at the second one an abrupt increase of the concentration wastewater as the high-density 'cloud'. This is related to the "first foul flush" phenomenon as happens at the first stage of the storm event. The flush's boundary shear and wave propagation is impacted by the suspended solids transported by the flow and also the bed load transport at the bottom and wall friction of the bed materials. Furthermore, the height of the bed and the water depth alter the effective shear stress and the turbulent velocities. This is why the flow misses gradually its energy and the transport capacity [START_REF] Ashley | Solids in sewers -Characteristics, effects and control of sewer solids and associated pollutants[END_REF].

B. Bed-Materials Modification in Space and Time

1. Variation of Organic Matter Content Laboratory analysis of the first test provided quantification of organic content in both the upper and the lower layers of the deposits, which is presented in Fig. 6. The results are in agreement with previous studies, i.e. the higher percentage of organic matters was generally found at the top of the bed that overlay the mineral sediments on the bottom [START_REF] Crabtree | Sediments in Sewers[END_REF]- [START_REF] Oms | In situ observation of the water-sediment interface in combined sewers, using endoscopy[END_REF]. For example, compared to other experimental studies [START_REF] Crabtree | Sediments in Sewers[END_REF], the socalled "A" and "C" layers correspond to the coarse mineral and fine organic layers, respectively. The results of the present study are generally in agreement with the vertical quantification of organic matter in deposit profile regarding the organic fraction percentage. However, the organic fraction in upper layer of deposit's profile was not as high as found by reference [START_REF] Crabtree | Sediments in Sewers[END_REF] showing the sediment material transported by the channel in the previous study to be prevalently not cohesive.

Fig. 6 Organic proportion vertical layers of settled bed

Furthermore, as the second part of the experiments, the amount of organic fraction obtained from the samples between S -50 and S +400 before and after the flush, was quantified and compared. Results are summarized in Fig. 7. The figure also shows the spatial and temporal evolution of the median diameter (D 50 ) associated to each sample. The content of the organic fraction is shown to decrease after the flush over almost all the stretch downstream the gate position, with the exception of section S +30 where an increase was observed. This opposite behavior is attributed to the reduced shear stress due to the invert counter-slope and to the existing sewer inverts irregularities and will require future specific analysis.

Detailed results of organic content at the various sections before and after the flush are presented in TABLE II. The table shows a reduced quantity which is been impacted by the flush over the entire sewer reach, although surface and depth sediments are poor on organic matter compared to the results of reference [START_REF] Crabtree | Sediments in Sewers[END_REF]. In fact, the amount of accumulated organic matter highly depends on many factors as sewer conduit size and roughness, flow condition as well as range of the sediment characteristics [START_REF] Pisano | Sewer and Tank Sediment Flushing, Case Studies[END_REF]. Fig. 7 Organic fraction and associated median of samples before and after the flush event 2. Grain-Size Distribution Two series of in-sewer samples were analyzed in the laboratory to consider their grain-size distribution before and after the flush. Judging from the results and observations, a large sediment heterogeneity and diversity was observed during the analysis at the laboratory, in terms of diameter and shapes as well as the nature. Results presented in TABLE II confirm that the sediment all along the sewer is basically sand of various sizes with a portion of cobbles with higher size.

More detailed analysis from all samples taken before the flush have shown two distinct grain-size distribution patterns corresponding to both areas between S -50 and S +400 and between S +400 and the channel end. The first area is dominated by more fine grains with averaged values of d 50 = 1.0 mm; beyond S +400 the deposits are coarser with an averaged d 50 = 23.6 mm.

Results of bed sample are analyzed and illustrated in Fig. 8, which expresses the percentage by weight falling within each size class computed for all samples from bed materials over the first 450 m of the sewer section. Data shows that, compared to sandy portion, the percentage of cobbles-diameters increases in the upstream section and decreases in the downstream sections due to the flush sorting effects. Based on curves in the figure, grains with diameter smaller than 0.9 mm show a good sorting particularly at upper reach of the sewer channel both before and after the flush event, but in overall, sample ranges spin up to large diameters of the solids. Before the flush, whole samples present poor to very poor sorting. Sample sorting indexes indicates the fluctuations in kinetic energy or flow velocity of the flow in which particles are deposed. The sediments after the flush contained more chaotic mixture of different gains which is expressed in the sorting index of the samples.

Compared to the results of other studies, this study shows an overall larger range of diameters confirming how the variability of the sewer solids may vary locally due to several factors such as the sewer network geometry and catchment size, the type of materials entering the sewer, the mesh size of the sewer entrance, etc. [START_REF] Pisano | Sewer and Tank Sediment Flushing, Case Studies[END_REF].

By classifying sediment diameters of the samples in AFNOR standard (mm), details of four fraction ranges, show that the bed material composition ranges from silts to cobbles (i.e. mainly gravels). Fine particles with diameters smaller than sand (2 mm) are mainly flushed away after the flush event.

Based on the data, a general textural analysis of the collected samples from upper reach (< S +400 ) represents "sandy very fine gravel" or "very fine gravelly coarse sand" and beyond this distance sediments are in the coarse gravel category. After the flush, the texture in upper and lower reaches mainly modified to sandy coarse/very fine gravel and very coarse gravel, respectively. Besides, by further analyzing the data, it is appeared that the mobilization of the grains under this flush is mainly limited to the grains smaller than 1.25 mm (smaller than coarse sands), which explains the selectivity of the flow due to its available energy. Sands (63 µm-2 mm particle size) appeared to be the main fraction of the present in the deposits composition in particular over the distance before +400 m with an average of 74%, whereas after the flush this value arrived to 54%.

Comparing two time-episodes, quantities of grains with ranges > 2 mm, are increased after the flush over the sewer reach. Other compositions are relatively reduced along the channel to half of their amount which means that the flush had a scouring effect on the grains sized almost less than 2 mm. Over the first 450 m of the sewer reach, the average percentage value of these bed-materials size is 69.4% before the flush, comparing to 38% after the flush. In contrast, the quantity of particles greater than 2 mm in size is increased about 20% after the flush.

Sections with complete removal of sediments and areas with sedimentation magnitude after the flush could be deduced from the table. Based on the preliminary analyzed data, most areas where sedimentation is occurred after the flush have negative slope. This is confirmed grace to the video cameras installed at the banks. As the snapshot of the camera in Fig. 9 shows, an immediately sediment build is observed just after 1 minute after the first wave passing from the observation section situated in S +50 where the bottom invert is negative whilst the other camera located on a positive slope at S +100 , did not show any sediment build-up along the whole flow. Fig. 9 Snapshot from video camera situated on a negative slope invert at 50 m from the gate after one minute since the flush beginning Despite those locations near to the gate, various locations with the presence of irregularities are prone to build-up a deposits. Moreover, regarding the grain size distribution, the sediments deposited are mostly sand or fine gravels, which are left by the flow.

Although flushing efficiency is the result of the main parameters such as dam-height, storage length, storage volume and bottom slope in the downstream the gate as well as in the storage section [START_REF] Campisano | Dimensionless approach for the design of flushing gates in sewer channels[END_REF], [START_REF] Dettmar | Behavior of the activated storage-volume of flushing waves on cleaning performance[END_REF], [START_REF] Guo | Gate and Vacuum Flushing of Sewer Sediment: Laboratory Testing[END_REF], [START_REF] Bertrand-Krajewski | Hydraulics of a sewer flushing gate[END_REF]. Hence, bottom slope variability has shown a considerable effect on the grain-size distribution and organic fraction especially before flushing where they accumulated under dry weather conditions. Reciprocally, the bed-material characteristics impact also the flushing efficiency, depending on their dry-weather history of being scoured and level of their consolidation.

In-site observations of the gross sediments at the site found mostly at the distance more than +400 m from the gate show that the sediments usually the solids that are consist of constructions or street asphalt debris. This is perhaps due to the altered gullies who plays an important role on the solid particle size distribution [START_REF] Ashley | Solids in sewers -Characteristics, effects and control of sewer solids and associated pollutants[END_REF]. In the last 200 m, gross solids contain those from the pipe repairing such as large metal nuts and bolts. In addition to their large distance from the gate, due to the high density of these solids, their transport is less probable [START_REF] Walski | Transport of large solids in unsteady flow in sewers[END_REF]. Moreover, deposit structures were observed to be different in different areas along the sewer reach. For example as deposits were found to be the form of paved. These sediments showed a tendency to be 'armoured' bed structure which takes place in gravel-bed. Therefore in addition to the hydraulic and complex geometrical properties and characteristics of the particle such as density, the structure of the bed is decisive for the removal effectiveness of the flush.

The high difference of concentration between the level above the bed invert at 15cm and two other levels at 25 and 65 cm from the bottom is evident. This means that during the flush a great part of the bed materials is eroded and transported just near the bed. This is why flush could be a mean to improve the water quality in sewer network [START_REF] Staufer | Improvement of water quality by sewer network flushing[END_REF].

It has to be stressed that sediment consideration from 50 m at the upstream of the gate while the upstream end extends to a long distance. Due to the large distance influence of the in-line storage during the experiment, it is probable that sediments are remobilized beyond this distance. Besides, the occurrence of a rain event during the night after the flush should be taken in account. According to recorded data from Mont-Souris weather station, the accumulated amount of precipitation of 40.8 mm in 48h with maximum intensity of 12.4 mm.h -1 [36]. Unfortunately, it is possible that the adverse has brought grits and debris into the sewer section and left its effects on the flush efficiency.

In summary, erosive effects of flushing depend significantly on geometrical properties of the sewer conduit (e.g. bottom slope), as well as on the hydraulic performance of the flush. In fact, the response of the sediments to flush characteristics varies in different sewers according to their geometrical properties. Moreover, sediment characteristics, in particular their size, shape and density, and their position along the sewer is important; due to their characteristics, they show different mode of transport and response to the flow energy during the flush. In-sewer sediments are predominantly containing the sand ranges and, based on their characteristics, this particle fraction is subjected to the main erosion under the flush.

Many authors outlined that a single intensive flush is less effective than repetitive and less intensive flushes. However, a single flush could considerably modify sediments composition and structures depending on the characteristics of the flow as well as the sewer conduit's geometry. Moreover, large range of bed materials with high heterogeneity is varied in different sections of the studied reach.

IV. CONCLUSION AND PERSPECTIVE

This article deals with preliminary results of the influence of an individual flush in a real large size trunk sewer on effectiveness of in-sewer sediment removal. Considering grain-size distribution and organic fraction along the sewer reach in space and in time, used gate's flush may impacts the bed over a significant distance. But regarding the geometry of the trunk sewer, it cannot ensure the high performance of sediment removal. This study has shown that single flush by such gate could degrade mainly the particles smaller than sand diameters (d < 2 mm). The parameter that may considerably impact the sediment transport mechanism during the flush realized in such a big combined sewer with high heterogeneity in bed materials is the bottom slope. Higher slopes entrain more bed materials during the flush despite of the distance from the gate.

Dam-beak wave effects of the flush have induced the quantitative and qualitative modifications of the bed height and composition throughout the channel sewer. During the flush, solids in suspension and as the bed material degrade reciprocally to the wave propagations of the flow. Maximum bed-erosion occurred in the sections with the higher bed slope.

Decreasing of the organic fraction which contains a high quantity of pollution, and optimizing such gates could be effective in the managers' point of view to minimize the sedimentation problems.

Even though this type of gate, by just one flush did not manifest a high efficiency distance for flushing away of deposits, but it may reduce the amount of such pollution over a long distance by using repetitive flushes before the storm event as concluded also by [START_REF] Laplace | Removal of the organic surface layer in combined sewer sediment using a flushing gate[END_REF]. This could help the wastewater treatment plant's function during storm events and surcharges to avoid surges of pollution.

These results and the additional experiments data which are expected to be used for next step of the study to develop a model to simulate the flush in order to better understand the transport mechanism of the sediments during this event. The simulation work is in progress. The aim is to focus on the prediction of particle behavior bed evolution in such unsteady turbulent flow by taking account of geometrical parameters and then understanding the efficiency of flushing by considering two key mechanisms for in-sewer sediments: bottom shear stress variations along the bed as well as organic substances as a responsible for shear resistance.

Fig. 1

 1 Fig. 1 Sewer Cross-section Details and Measurement Equipment Position

Fig. 2

 2 Fig. 2 Profile and plan view of sewer channel axis and implemented measurement tools During the flush, the speed rate for the gate opening was recorded at about 4.5 cm.s -1 . The flushing procedure involves a dry-weather storage phase and a successive flush releasing phase. During the flush, flow measurement (flowrate and water height) and sediment transport measurements (turbidity) took place.

Fig. 3

 3 Fig. 3 Sampled points before and after the flush event along the sewer invert

Fig. 4

 4 Fig. 4 Sampled variables during the flush in five observation sections

Fig. 8

 8 Fig. 8 Grain-size distribution of samples between -50 and +400 m taken before and after flushing As expected, after the flush, diameter distributions are more dispersed with average d 50 equal to 2.1 and 18.0 mm for the two previous areas, respectively.

TABLE I DURATION

 I

		OF THE MEASUREMENT CAMPAIGN
	Date [d/m/y]	experimentations
	07/07/2014	Sample collection before the flush (part I)
	08/07/2014	Sample collection before the flush (part II)
	09/07/2014	Measuring setup installation
	10/07/2014	Flush procedure and data collection
	11/07/2014	Sample collection after the flush

TABLE II SUMMARIZED

 II CHARACTERISTICS OF THE SAMPLES COLLECTED BEFORE AND AFTER FLUSHING ALONG THE SEWER CHANNEL

	Distances (m)	d 50 (mm) Before After	Organic fraction (%) Before After
	-50	1.1	1.0	1.0	3.0
	-30	-	1.1	-	0.5
	-15	0.7	4.2	2.5	3.0
	-5	1.0	1.2	1.0	2.0
	+10	0.7	1.2	2.0	1.5
	+30	0.6	1.1	4.0	3.0
	+50	1.1	0.8	1.0	2.0
	+70	0.8	1.1	6.9	2.0
	+90	1.0	-	4.5	-
	+130	0.9	1.4	1.5	1.0
	+150	2.1	-	5.0	-
	+180	1.7	-	2.1	-
	+230	0.7	9.6	2.5	1.0
	+330	1.6	-	1.5	-
	+350	0.7	-	1.5	-
	+385	-	0.9	-	1.0
	+400	-	10.2	-	0.0
	+420	-	10.1	-	1.0
	+510	23.3	-	1.0	-
	+540	24.3	-	3.0	-
	+680	-	28.1	-	1.5
	+710	-	26.6	-	0.5
	+750	23.7	-	1.5	-
	+790	21.8	-	2.0	-
	+830	21.7	-	1.5	-
	+870	26.5	-	0.3	-
	+950	-	5.1	-	0.5
	+1010	-	28.2	-	0.3

ACKNOWLEDGMENTS

The authors express their gratitude to Paris Municipality "Ville de Paris" for data funding and provision as well as to Mr. Jean-François Ferrandez for his support during the project. Authors are also grateful to the Campus France and the Kurdish Institute of Paris for their funding partnership. Special appreciation is given to the PRODIG geographyphysic laboratory.