EDF R&D Jérôme Ferrari

Zachary Moret-Sur-Loing

Kalsi Leutwyler

Engineering

Measurement Of The Fluid Flow Load On A Globe Valve Stem Under Various Cavitation Conditions

Keywords: globe valve, cavitation, fluid flow load, CFD

The evaluation of fluid forces on the stem is important for wear prediction of valve and actuator guidance parts. While estimating the axial load is straight forward, estimating and/or measuring the side load is more difficult, especially for globe valves. Therefore, measurements are carried out on an ad hoc, scale 1, model of a 2" globe valve. The body is replicated in Plexiglas to enable flow visualization and the original stem is heavily instrumented to allow force measurements in every direction. The flow loop facility used for this experiment is designed to allow fine-tuning of the cavitation intensity.

The experiment is run for a set of cavitation conditions, flow rates and disc positions. The results show that the transverse force (perpendicular to the stem) can reach the order of the axial force; thus it should not be ignored. We also observed that it depends very weakly on the cavitation levels. Videos made with a high-speed camera allow an interesting understanding of the fluid flow and the cavitation creation. Moreover, CFD simulations of this experiment, limited to the single phase flow are compared to the experiments. Through the experimental testing, visual observations, and computational predictions, the role of the cavitation on the fluid forces is better understood.

Introduction

The accurate evaluation of the forces acting on the stem is important for actuators sizing and stem guide selection in order to insure proper functionality and safety. It is recognized that a significant part of the total resultant force come from the flow-induced components. The flow-induced forces acting on the disc can be decomposed into the axial forces (along the stem axis, see Figure 2) and the transverse forces (perpendicular to the stem). For globe valves, the maximum axial load is evaluated analytically taking into account the fluid pressure and the additional sealing force. Additionally, the axial force is measured as part of many standard qualification tests.

In contrast, virtually no publicly available information regarding the transverse force [1 exists (to the knowledge of the authors)]. It exists no relevant data because the transverse force is erroneously judged as insignificant and its measurement requires a sophisticated instrumentation to be placed in the fluid area, thus to be exposed to high pressure and temperature.

Cavitation is a phenomena widely investigated by the scientific community. However, most works focus on noise and the erosion aspect [START_REF] Brennen | Cavitation and Bubble Dynamics[END_REF][3] and on performance loss due to choking [START_REF] Tullis | Hydraulics of Pipelines, Pumps, Valves, Cavitation, Transients[END_REF][5] [START_REF] Afnor Nf | Industrial-process control valves. Part 2-3: flow capacity. Test procedures[END_REF]. No public information is known to the authors on the influence of cavitation on flow-induced force.

An experimental test matrix was developed to evaluate the behavior and dependencies of the axial and transverse forces on factors such as flow rate, cavitation level and valve disc position. An ad-hoc scale 1 valve model was modeled based on an actual 2" Velan globe valve. It includes:

 A Plexiglas body to allow flow visualization  A heavily instrumented stem to measure forces and moment components The model was placed in a flow loop allowing for fine-tuning of the flow rate and cavitation conditions.

Additionally, one phase CFD computations were run in order to estimate their capabilities in such domains.

Experimental setup

The mock-up

The Plexiglas model body is machined to precisely match the fluid vein of the real twoinch Velan globe valve (see Figure 1).

The stem and the disc (see Figure 2) come from a real Velan valve and are welded together. The stem diameter is reduced from 26.7 mm (1.05 inch) to 14 mm (0.55 inch) over a 90 mm (3.54 inch) section of its length. The stem modifications were made to allow the deflection occurring during the experiment to be measured accurately by the gauges. Twelve strain gauges were fixed to the stem at different positions chosen to allow an accurate measurement of the moment and the resultant forces.

The loop

The model was placed on the test section of the Modulab loop at the MFEE department at EDF R&D. At this facility, the pump RPM, the temperature and the downstream pressure can easily be set. The downstream pressure can even be sub atmospheric, thus enabling a fine-tuning of the cavitation level.

The fluid media used was common demineralized water and was maintained at 23°C (73 °F) throughout the experiment.

The test procedure

The experiment was performed under the following operating conditions: The tests are conducted such that good time-averaged values at each condition set could be obtained. Based on preliminary tests, it is determined that stabilize conditions occur after 30 seconds. The data is measured at 200 Hz.

Visualization

During the experiment, visual recordings of each test was recorded using a conventional camera. Furthermore, a high-speed camera was used at 13,000 frames per second to

Transverse force

Axial force record some specific cavitation pattern (see Figure 3 and Figure 4). A more detailed description of the cavitation seen in the mock-up is given in [START_REF] Ferrari | Fluid flow force measurement under various cavitation state on a globe valve model[END_REF] or [START_REF] Ferrari | Cavitation et efforts dans un robinet à soupape de DN 50, observations sur une maquette à l'échelle 1[END_REF] and some of the movies recorded can be found on the Internet [9].

Experimental results

Spotting cavitation influence

In theory, when the flow is sufficiently turbulent, the flow-induced forces are proportional to the valve pressure drop (DP) and to the flow rate squared (Q²). Using this statement as a basis, it is considered in this paper that any deviation from such a behavior is caused by cavitation. Cavitation, via the well-known phenomena of choking, reduces the valve performance i.e. it decreases the flow rate Q expected at a certain pressure drop DP. Q² and DP are then not proportional to each other anymore.

This gives us two different points of view to study cavitation influence on flow-induced forces. Each of them may lead to different conclusions:  The DP point of view, which considers that a valve has to undergo a certain pressure drop. This is the point of view favored in this article since it is believed to be more relevant by the author.  The Q² point of view, which considers the valve at a certain flow rate.

Most of the figures included in this paper show time-average force values using either point of view. On each of these figures, four curves are plotted, one for each specified downstream pressure. Since low system pressure facilitates cavitation creation, cavitation develops at lower DPs and flow rates on low downstream pressure curves than on high downstream pressure curves. Cavitation influence is observed when a nonlinear relationship between the measured value and the independent variable occur. At 2mm, the DP point of view (Figure 5) show that the curves remain nearly linear, no significant influence due to cavitation can be observed. From the Q² point of view instead (Figure 6), since choking impedes the flow rate, low downstream pressure (i.e. high cavitation level) curves are shifted to the left. The effects of cavitation are thus seen as an increase of the transverse force. In other words, to overcome the additional pressure drop created by cavitation and to sustain the flow rate, an increase in the upstream pressure (hence DP) is required, consequently the transverse force increases.

Cavitation influence on transverse force Observations

At 16mm, the DP point of view illustrates (Figure 9) that as cavitation appears with increasing DP the curves become nonlinear and cavitation reduces the transverse force.

From the Q² point of view instead (Figure 10), cavitation has no effect on the transverse force. In other words, the additional pressure drop created by cavitation reduces the pressure drop felt by the stem and thus the transverse force.

At 6mm (Figure 7 and Figure 8), it exhibits a transitional behavior between the two previous observed openings. Other graphs at 3 and 4mm opening (not shown in this paper) confirm the transitional behavior.

Based on the experimental data, the effect of cavitation on the transverse force depends on the valve disc position.

Interpretation

Visual observations of the flow inside the mock-up under the highest level of cavitation attained during the experiment show that at small disc positions (Figure 11), the disc head is enveloped by cavitation, whereas at high opening (Figure 12), cavitation is detached and forms downstream from the disc. Figure 13 and Figure 14 represent schematically the presence of cavitation in the flow.

In Figure 13 the disc head is enveloped by caviation, the additional pressure drop created by cavitation is combined with the usual valve pressure drop. In Figure 14 cavitation is detached and the cavitation pressure drop is exerted downstream from the disc. Thus, a theoretical intermediate pressure P int is created between the disc and the cavitation cloud.

According to [START_REF] Afnor Nf | Industrial-process control valves. Part 2-3: flow capacity. Test procedures[END_REF], for a turbulent flow without cavitation, the flow rate is:

0 1 *   down up P P Cv N Q   (1)
and for a turbulent flow with full cavitation: Without cavitation, P int = P down . At full cavitation, P int replaces P down in equation ( 1):

0 1 * * *   vap f up l v P F P F C N Q   (2)
0 int 1 *   P P Cv N Q up   (3)
combined with equation ( 2), it gives:

vap f l l up P F F F P P * * ² ²) 1 ( int    (4)
Considering the thermodynamic conditions of the experiment and the F l found at 16 mm opening, it yields: Figure 15 shows the transverse force plotted twice versus P up -P int . Once in black, where P int = P down , like in the previous chapter Observations, it thus redraws exactly Figure 9.

And once in blue, using equation ( 5) to evaluate P int . Figure 16 is just a close-up of Figure 15. From Figure 16 it can be seen that as cavitation appears with increasing P up -P int , the black curves detach one by one from the linear behavior whereas the blue curves reattach one by one. It passes from the validity range of Equation ( 1) to the validity range of Equation ( 2). In conclusion, at large disc positions ( i.e. when cavitation located far from the disc) the transverse force has a linear behavior versus P up -P int and P int can be estimated using Equation 4.

Conclusion

Cavitation does not cause any dramatic raise of transverse force, whatever is the point of view used. Its influence on transverse force depends on the opening of the valve. An interpretation based on cavitation location is proposed.

From the DP point of view, cavitation acts only as a transverse force limiter at large disc openings. From the Q² point of view, cavitation increases the transverse force at small disc positions. However, since much lower values have been observed at low opening, it seems reasonable to conclude that cavitation act as a general limiter.

Axial force

Figure 17 to Figure 20 show the average axial load versus DP at various opening positions. At 2mm opening (Figure 17), the stem is compressed linearly as DP increases, but as the valve opens, the acceleration of the fluid under the disc generates low pressures and thus a traction force. At 6mm (Figure 19), traction and compression cancel each other and at 16 (Figure 20), their addition results in a traction. From Figure 20, it is seen that cavitation reduces the final traction force. No other important effect on axial load has been recorded.

Maximum transverse force position

At low opening position, the flow around the disc is almost axisymmetric resulting in nearly no transverse force. At large disc position, the disc is retracted out of the main flow area and is thus less exposed, which reduces the transverse force values. The maximum transverse force is consequently at a mid stroke position.

To evaluate this position, the hydraulic surface is used :

(6)
It is computed using data recorded without cavitation.

Figure 21 shows the hydraulic surface found from the experiment and the simulation.

The experimental results increase until 6mm and then show a lower value at 16mm opening. Additional data between those two points would be needed to accurately determine the maximum transverse force position. Numerical results show a maximum at 8mm.

Transverse force vs. axial force

The importance of transverse force can only be estimated against the scale of axial force. In this chapter, as an example, in-plant extreme conditions are considered:  upstream pressure P up = 270bar  downstream pressure P down = 20 bar

The maximum axial force occurs when the valve is closed. The analytical formula below takes into account the fluid force and the seating force (contact force between the seat and the disc to insure sealing):

  seat down stem down up seat Axial D E P D P P D F        ² 4 ² max , (7)
D seat and D stem are the seat (45 mm) and stem (26.9 mm) diameters and E is the linear contact pressure: for a sphere-cone sealing, E = 150N/mm is stipulated. This gives:

N F Axial 46200 max ,  (8)
The maximum transverse force is estimated using the maximum hydraulic surface experimentally found (see chapter Maximum transverse force position) : 774 mm² at 6mm, cavitation is thus not taken into account. It gives:

max , max max , * % 25 11600 ) ( * Axial down up Tran F N P P S F     (9)
Therefore, during its life-time, such a globe valve is subjected to transverse force of the order of 25% the axial force for which it was sized, which could lead to wear or galling of the stem and guide.

Numerical results

The computational Fluid Dynamics (CFD) software package ANSYS CFX is used to predict the resultant flow field and flow-induced forces at 2, 6, 8, 10, 16 mm. Three different simulations are performed at each disc position. The nominal valve differential pressures used for the three simulations are 0.5, 1 and 2 bar. The computational domain includes the fluid enclosure of the body, 20 pipe-diameters of upstream pipe, and 31 pipe-diameters of downstream pipe. The flow is assumed to be fully turbulent and the turbulence is modeled using the k- RNG turbulence model. Additional runs are performed using the k- SST model and good agreement between the two turbulence models exists.

Boundary Conditions

The boundary conditions used in the CFD analyses consisted of an inlet velocity boundary, a static pressure outlet, and no-slip frictional walls for the pipe walls, internal body surfaces, and disc. The inlet velocity profile was defined using the 1/7th power-law profile given by Eq. ( 5) below. The maximum velocity was chosen to produce the desired flow rate through the valve.

  7 1 R r 1 V r V / max / ) (   (5)
The turbulence inflow condition at the inlet was defined by setting the turbulence intensity to 5%. The static pressure was defined at the outlet and was set to 4 bar (absolute).

Numerical Results

A comparison of the hydraulic surface for the axial and transverse force (shown in Figure 21 and Figure 22 provide a comparison of experimental and numerical results. In general, the experimental and numerical results are in good agreement; however, the CFD prediction of the transverse force at 6 mm is about 30% less than the experimental data.

Figure 22 The hydraulic area based on experimental data and numerical results for the axial force are compared.

In addition to the experimental observations, the CFD predictions provide additional insight into the flow field. The pressure contours on the disc and along the internal body surfaces help illustrate the relationship between the axial and transverse hydraulic surface based on disc position.

Disc and Internal Body Surface Pressure Contours

The axial and transverse forces are better understood by examining the pressure contours on the disc head. The axial load is strongly dependent on the pressure differences between the upstream and downstream disc surfaces that are perpendicular to the disc axis (e.g., the disc nose). The transverse force is dependent on the pressure contours on the sides of the disc. In Figure 23

Conclusion

A 2" Plexiglas globe valve model designed to permit visual observation and measurements of flow-induced force on the stem was built. An extensive test program was conducted with varying key parameters such as disc position and operating conditions.

No dramatic increase of force from cavitation has been observed. Instead, it appeared to act as a force limiter at high disc opening by reducing the pressure drop seen from the disc.

The maximum transverse force was observed at intermediate opening (between 6 and 10mm) and it was calculated that it could reach 25% of the maximum axial force.

Numerical predictions were in good agreement with experimental data and together with the analysis of experimental results supplied a better understanding of the flow-induced force on the stem.



  Valve opening positions: 2, 3, 4, 6 and 16 mm (full open)  Downstream pressure: 0.4, 0.8, 1.4, 4 bar (absolute)  Flow rate: 0 to maximum pump capabilities (~70m 3 /h)

Figure 1 .

 1 Figure 1. Sketch of the globe valve model.

Figure 2 .

 2 Figure 2. Sketch of the stem and the disc

Figure 3 .

 3 Figure 3. opening: 1mm, downstream pressure: 0.8bar, flow rate:15m 3 /h.

Figure 4 .

 4 Figure 4. opening:4mm, downstream pressure: 0.4bar, flow rate:23m 3 /h.

Figure 5 to

 5 Figure 5 to Figure 9 show the time-average transverse force versus the pressure drop or the squared flow rate for three disc positions: 2, 6 and 16mm disc positions.

Figure 5 .Figure 6 .Figure 7 .Figure 8 .Figure 9 .

 56789 Figure 5. Mean transverse force vs. pressure drop at 2mm

Figure 11 .

 11 Figure 11. cavitation pattern at 2mm opening under high flow rate



  with: C v : the flow coefficient  DP : the pressure drop (P up -P down )  F f : the water critical pressure ratio factor = F l : the pressure recovery factor = 0.79 at 16mm opening  N 1 : a numerical constant dependant on the units used  P c : water critical pressure = 220 bar  P down : pressure far downstream from the valve  P up : pressure upstream from the valve  P vap : the vapor pressure = 0.028 bar at 23°C  ρ : water density  ρ 0 : water density at 15°C

Figure 15 .Figure 16 .

 1516 Figure 15. Mean transverse force vs. P int . In black: P int = Pd own , in blue: P int =0.3759P up +0.017 Figure 16. Zoom from Figure 15. Mean transverse force vs. P int . In black: P int = Pd own , in blue: P int =0.3759P up +0.017.

Figure 17 .Figure 18 .Figure 19 .

 171819 Figure 17. Mean axial load vs. pressure drop at 2mm

Figure 21 .

 21 Figure 21. Hydraulic surface vs. Disc position

Figure 23

 23 Figure 23 The pressure contours on the disc and internal body surface are shown at three disc positions : (a) 2 mm (b) 6 mm, and (c) 16 mm (flow is from left to right). The axial load is primarily governed by the pressure acting on the disc nose (bottom view) and the transverse force is governed by the pressure acting from left to right (top veiw).

Acknowledgements

A.Archer, D.Hersant, JF.Rit and the technical teams from EDF R&D are acknowledged for their fruitful contributions and their enthusiasm.


Cyril Buttay
email: buttay@cegely.insa-lyon.fr

Dominique Bergogne

Hervé Morel

Bruno Allard

René Ehlinger

Pascal Bevilacqua

Buttay Cyril

Bergogne Dominique

Morel Hervé

Allard Bruno

Ehlinger René

Bevilacqua Pascal

A Voltage-Measurement Based Estimator for Current and Temperature in MOSFET H-Bridge

Keywords: Device characterisation, Diagnostics, Estimation techniques, Measurements, MOS device, Sensors

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

Introduction

The increasing usage of power converters in today's world make their market more challenging. Cost cuts are of major concern to remain competitive, but reliability requires expensive security margins. Monitoring is another solution to keep converters reliable while using power components closer to their limits. However, the monitoring of critical values (i.e. Current and Temperature) is either costly or inaccurate. Shunts are widely used in low power converters to measure current, but as power increases, losses in the shunt become not negligible, reducing efficiency. Transformer-based current probes are expensive, bulky and their frequency bandwidth is often an issue. In the other hand, temperature measurements can be made affordable, by using a thermistor close to the transistors. This is however inaccurate because substrate temperature and device temperature are sometimes different, especially during large power surge.

Estimation is an other way to figure out temperature and current values by measuring some variables of the device under monitoring. Extensive work and literature relate techniques based upon an estimator to avoid the cost and pitfalls of dedicated sensors [START_REF] Bergogne | An estimation method of the channel temperature of power MOS devices[END_REF][START_REF] Allard | Application of averaged Models to Real-Time Monitoring of Power Converters[END_REF][START_REF] Ammous | Transient Temperature Measurements and Modelling of IGBT's under Short Circuit[END_REF]. The method proposed in this paper is based on the measurement of the drain-to-source voltage at the terminals of one MOSFET in an inverter.

Two temperature and current-dependent voltages are used for the estimation. When in the on-state, drain-to-source voltage of a MOSFET is equal to the intrinsic on-state resistance (RDSon) multiplied by the drain current. As RDSon is temperature-dependent, drain-to-source voltage is a function of current and temperature. When frewheeling on its internal body diode, the MOSFET drain-to-source voltage is equal to the diode forward voltage, also dependent on temperature and current.

Thanks to the proximity of the body diode and the channel (a few µm), the temperature can be supposed to be identical in both areas. Assuming both measurements are performed within a short lapse of time (in comparison to thermal constants in the device), and providing a relation between the two currents in the two states, it is possible to extract the temperature and current values. In this paper, we consider that temperature is constant across the silicon die. It is referred to as "chip temperature".

Principle of operation

In a classical inverter, a dead-time is observed between the turn-off of a pair of switches (e.g. H1 and L2, as described on fig. 1) and the turn-on of the opposite pair (i.e. H2 and L1). Given the load is inductive, current flows through body diodes of H2 and L1, which will therefore act as freewheeling devices during this period. To achieve high efficiency, the dead-time must be as short as possible to reduce the voltage drop across the body diode. Transients caused by commutation last longer than the dead-time, masking the freewheeling diodes forward voltage drop, which cannot therefore be measured at this time. Forward-voltage measurement is performed by turning-off the transistor L1 a short lapse of time after the beginning of its conduction. This will not disturb the operation of the inverter, as it doesn't involve a high voltage step (less than 1V) nor high current gradient (the only change in the current path is across the silicon die). This turning-off must be done before the current ID becomes positive in the switch (with respect to the conventions below), otherwise it would flow through the diodes of H1 and L2.

The measurement of the drain-to-source voltage (in the resistive on-state) will be performed just before the end of the conduction, to ensure a good signal-to-noise ratio (the current is maximal at the end of the conduction cycle).

Analytical approach

Drain-to-source voltage is measured in two working states to achieve temperature and current estimation. In this section, we present the analytical expressions linking the voltage and the parameters being estimated in each state

A. On-state resistance model

The effect of temperature on the on-state MOSFET resistance, is quite well taken into account by classical models such as those implemented in SPICE.

For the sake of simplicity, the equations given here are those of the SPICE level1 MOS model [START_REF] Massobrio | Semiconductor device modelling with SPICE[END_REF].

In the linear region, i.e. VGS > VTH and VDS < VGS-VTH,

I D KP W L ¡ 2X jl V G S ¡ V TH ¡ V DS 2 V DS 1 ¢ ¤£ V DS ( 1
)
Where KP is the transconductance parameter, W and L respectively the width and length of the

DC bus voltage

channel, Xjl the metallurgical junction depth, VGS the gate to source voltage, VTH the gate to source threshold voltage, and ¥ is the channel length modulation. For power MOSFETs, ¥ is considered as zero. KP can be supposed to be the only temperature dependent parameter,

KP T 2 KP T 1 T 1 T 2 3 2
(2)

The influence of the temperature on the on-state resistance is given by replacing (2) into (1), and then deriving.

¦ I D ¦ V DS § KP T 1 T 1 T 2 3 2 W L ¨2X jl V G S ¨V T H ¨V DS (3)
Notice that RDSon evolves as a 1.5 power of the temperature. On a reduced temperature range, this can therefore be approached as a 2 nd order polynomial.

R DSon © C 0 C 1 T C 2 T 2 (4)

B. Intrinsic body diode forward-voltage model

As it can be seen in the extensive literature, there are few simple but accurate formulations of the diode forward-voltage drop [START_REF] Profumo | instantaneous Junction Temperature Evaluation of High Power Diodes (Thyristors) During Current Transients[END_REF][START_REF] Maxim | Power PIN Diode Electro-Thermal SPICE Macromodel with Forward and Reverse Recovery[END_REF][START_REF] Muñoz-Yagüe | High Level Behavior of Power Rectifiers: A Quantitative Analysis of the Forward Voltage Drop[END_REF]. This is due to the great number of phenomena occurring in a classical PIN diode. Ohmic contacts are of a major concern in this voltage drop, although they are hardly taken into account in numerous models. The temperature and doping gradients across the PN junction have also an influence, but are difficult to quantify.

In [START_REF] Profumo | instantaneous Junction Temperature Evaluation of High Power Diodes (Thyristors) During Current Transients[END_REF], an analytical expression is used to determine the junction temperature of a power diode from its forward-voltage and current, but is far too complex for the targeted application.

Thought for monitoring the proposed method does not require high accuracy. The chosen model is therefore as simple as the following first-order polynomial of I:

V F V S T K 2 I (5)
where Vs(T) is the temperature dependant threshold voltage, and K1 a fixed coefficient.

V S K 0 K 1 T (6) Therefore V F K 0 K 1 T K 2 I (7)

C. Temperature and current expressions

From (4) it comes, when in the on-state:

V DS 1 R DSon I V DS 1 © C 0 C 1 T C 2 T 2 I 1 (8)
when in the "freewheeling" mode, (7) yields to

V DS2 V F K 0 K 1 T K 2 I 2 (9)
VDS1 is measured at the beginning of the conduction period, while VDS2 is the value of the drain-tosource voltage at the end of the conduction. I1 and I2 are therefore different, but T can be considered as constant, due to the high thermal time constant (the self-heating will directly be taken into account in the parameter fitting).

When using a pure inductive load, I1 and I2 are linked by:

I 2 I 1 U t L load ( 10
)
where t is the time interval between the set of two data acquisitions, U is the DC-bus voltage, and Lload the load inductance. From ( 9), it comes:

I 2 V DS2 K 0 K 1 T K 2 (11)
Using ( 10) and ( 11) in ( 8) yields:

V DS 1 C 0 C 1 T C 2 T 2 U ! t L load " V DS2 K 0 K 1 T K 2 (12)
T is therefore obtained by solving the third order polynomial (12). ( 11) gives the corresponding I2 value.

Parameter estimation A. Description of the experimental setup

An inverter is built for demonstration purposes. It operates at constant frequency and duty cycle, and the load is a low-value inductance. Theses experimental conditions ensure a pure-AC triangularshaped load current, allowing for both voltage measurement (VF and VDSon) to occur within a short lapse of time. The transistors of the bridge are 75V, 100 A rated devices.

The inverter (H-bridge) is driven using burst signals, to keep transistors self-heating as low as possible, and Insulated Metal Substrate (IMS) is used to guarantee the best thermal spreading. This bridge is placed into a furnace to set the ambient temperature. With this setup, we can assume chip temperature to be equal to ambient temperature at the equilibrium. However, passive components of the inverter (input capacitor bank and inductive load) cannot withstand temperatures up to 180°C and have to be kept out of the furnace. This results in a non-negligible stray inductance along the circuit. This has a detrimental effect on the linearity of the current waveform (see fig. 4)

The drain-to-source voltage is acquired using two P6139 500MHz passive probes in a differential setup and a TDS7054 Tektronix digital oscilloscope. Current is measured with a Pearson Electronics model 101, 80MHz bandwidth current transformer. Drain-to-ground and source-to-ground voltages were clamped using zener diodes to avoid saturation of the oscilloscope input amplifiers. Temperature is acquired as the average value given by two thermocouples placed at both end of the bridge's heatsink. Temperature accuracy is estimated to be within the ±1°C range. More accuracy is not required because of the simplicity of the body diode forward voltage drop model we chose above.

B. Parameter identification

R DSon E C 0 F C 1 T F C 2 T 2 (13)
When fitting data, such a polynomial yields to: C2=16×10

-8 G .T -2 , C1=32×10 -6 G .T -1 , C0=58×10 -4 G
The body diode forward-voltage measurement set, shown on fig. 8 is more complex to identify. Linear fit is chosen instead of more powerful math functions as it is more compact to implement in hardware, while giving satisfying results in our case. Fitting is set to minimise error at high current, near SOA (Safe Operation Area) limits, where accuracy is at a premium to keep the converter reliable. As a consequence, error is not negligible at low current. Higher order modelling would be required to reduce this inaccuracy, but is beyond of the scope of the targeted application.

In our case, a very simple expression gives the value of the body diode voltage drop:

V F H V S T I K 2 I
The relation between Vs and temperature can be seen on fig. 9 , and is expressed by [START_REF] Maxim | Power PIN Diode Electro-Thermal SPICE Macromodel with Forward and Reverse Recovery[END_REF]. Body diode forward voltage can therefore be expressed as follow:

V F K 0 P K 1 T P K 2 I
From the identification of data with [START_REF] Muñoz-Yagüe | High Level Behavior of Power Rectifiers: A Quantitative Analysis of the Forward Voltage Drop[END_REF] it comes: K2 =1.9×10 -3 G , K1 = -1.5×10 -3 V.K -1 , K0 = 0.786 V This set of parameters (C0, C1, C2, K0, K1, K2), allows the calculation of temperature and current across the MOSFET by only proceeding to two distinct drain-to-source voltage measurements.

Application and discussion

A. Implementation of the method

The method described above is implemented in a microcontroller, to realise a monitoring system that computes temperature and current across one of the MOSFET in an inverter. Two subsystems are required to perform the estimation. A driver board, built around a FPGA (Field Programmable Gate The acquisition path is made of a differential amplifier, followed by a fast sample and hold amplifier (700 ns blocking delay). This allows the relatively low microcontroller's analog-to-digital converter to be used, avoiding the need for a dedicated high speed converter. Both voltage measurements (VDSon and VF) are made through the same acquisition path.

The equations described above are implemented into the microcontroller. Built for the sake of demonstration purposes, the experimental setup allows some simplifications. Bridge duty cycle is set to 50%, with a low value inductive load. This results in a pure AC, symmetric triangular-shaped load current. We therefore chose a lapse of time between beginning of the conduction of the monitored MOSFET and VF voltage acquisition equal to the lapse of time between acquisition of VDSon and the end of conduction. Due to symmetry of the current shape, the current intensity when acquiring VF and when acquiring VDSon are equals with opposite signs. As I1 = -I2 (12) becomes:

V DS1 H RQ C 0 S C 1 T S C 2 T 2 V DS2 S K 0 S K 1 T K 2 (14)
To figure out the temperature value, the microcontroller solves (14) by using a dichotomy algorithm. Temperature being known, Current comes directly from (11).

B. Results

As the current waveform presents a constant shape, the system can directly display the RMS current value. This allows for precise measurement using the oscilloscope, as it integrates current value over several periods. Peak and RMS current Temperature computation requires about 1ms, due to the intense use of floating point 32-bits numbers and the dichotomy algorithm, which requires 8 computations of (12) in the worst case (temperature is coded as an 8-bits integer). Current computation is a lot faster (few tens of µs) as it only requires a single floating point multiply and two additions.

Accuracy of the monitoring system is tested over full current and temperature rating of the MOSFETs. Measurement points are different from those of the parameters estimation (above) to ensure our method is reliable. Driving of the bridge is still made using burst square signals to avoid self-heating that would result in different chip and heatsink temperatures.

The current and temperature estimated by the monitoring system are compared to the values measured by the current probe and the thermometer. Two error maps (see fig. 10 andfig. 11) are given.

On the current estimation error card (fig. 10), one can notice that error is high for low currents. This is mainly due to insufficient resolution of the estimator, which calculates current value as an integer. Error does however never exceed 2 Amps over the whole map.

Temperature estimator error map (fig. 11) is more complex, The hypothesis of proportionality between body diode forward voltage drop and drain current is accurate at high current level (see fig. 8), but as it decreases, the error increases. Fitting of measurement curves is chosen to optimise estimation at high-current (to achieve high reliability), at the cost of reduced accuracy when switching low current.

In addition, it appears that temperature estimation is very sensitive to the forward voltage drop of the body diode. A high resolution measurement of this voltage (such as 14-bit A/D conversion) would be required to increase estimation accuracy.

To prove the system meets its requirements (accurate near SOA limits), a global factor of merit is proposed:

T I ,T U T I 2 V T T 2 (15)
where

W x X x measured Y x estimated x measured (16)
It is plotted on fig. 12. It can be seen that on the upper right half of the map, values calculated by the estimator have maximal accuracy.

C. Application to thermal impedance measurement

A laboratory application of the estimator is the measurement of thermal impedance. Assuming losses in each transistor are known, as well as internal and ambient temperature, one can calculate the equivalent thermal resistance of the heatsink-substrate-MOSFET assembly shown fig. 13. R TH `T chip a T Ambient P losses (17) Losses measurement are performed on one of the inverter legs of the bridge, using a calorimetric method [START_REF] Bruno | Design of a calorimetric system using bond graphs[END_REF]. One of the previously used leg is dipped into oil, which temperature rise is measured, while the other is kept out of the calorimeter. The oil tank is placed into a thermally-controlled bath to ensure energy exchange is constant. The overall thermal capacitance of the oil tank (including the bridge leg) is calibrated by dissipating a known power (using a resistor) and by measuring the temperature rise. The inverse process is performed when measuring losses of the bridge leg: temperature rise is monitored, thermal capacity is now known, dissipated energy can therefore be figured out. As the bridge operates for a precise amount of time, the measured energy yields to losses value (power).

Here, we assume that the only losses of the leg come from the MOSFETs, and each transistor dissipates half the total power. Symmetries in the leg layout as well as in the transistor drives (using DC/DC converters and optic fiber to achieve good isolation) allow for the latter assumption. Losses in connections are not taken into account, although the high load current (up to 150 Amps) makes them not negligible. This will have a detrimental effect on result accuracy, and one cannot expect better than 10%.

The H-bridge is then mounted on a water-cooled plate, and the estimator is used to figure out the internal temperature of one of the transistors. Water cooling is required to ensure the ambient temperature remains constant while dissipated power varies. This imposes some changes in the inverter layout (longer busbar and different MOSFET to estimator-board connection), and again has a detrimental effect on the accuracy. obviously not the only source of losses (all connections become very hot, proving in a practical way that their losses are not negligible), resulting in 25% dispersion.

Further developments and conclusion

The principle of voltage-measurement based estimator for current and temperature in MOSFET H-Bridge is presented in this paper. It exhibits several advantages over usual monitoring systems: It provides the temperature of the active area of the chip , it is integrable in the driver circuitry, and avoids the common pitfalls of high-current sensing.

The proposed method is implemented into a small outline microcontroller board with a single analog acquisition path.

Accuracy of the estimation over full temperature and current range is measured and analysed (error mapping). Confidence maps confirm the reliability of the estimator.

An application to thermal impedance measurement is briefly described, using the estimator along with a calorimetric method. Dispersion of the results is relatively high due to two major experimental difficulties: electrical response change because geometry of the circuit is altered to suit the calorimeter, and calorimetric method gives global losses without the possibility to remove the contribution of power conductors and connectors.

Aimed at industrial converter monitoring, the estimator is however applied to a laboratory experiment. Driving signals (fixed duty cycle) and load (almost pure inductance, with a low value with respect to the switching frequency) are adapted to suit a demonstration purpose only. A few adaptations, mainly software, would allow the proposed method to meet "Real World Applications" particularities.

Thanks to current and temperature properties (long time constant vs. switching frequency, periodic behaviour, isothermal areas...) some assumptions can be made, allowing the implementation of the presented technique in operational converter.

As a conclusion, it is confirmed that the proposed method, based on voltage measurements, allows for the estimation of two key variables in MOSFET-based converters : current and internal chip temperature.

fig. 2 .

 2 fig. 2. Drive and power waveforms in the bridge

fig. 3

 3 fig. 3 Burst drive signals avoid MOSFET self-heating

Fig 6

 6 Fig 6 represents the measured drain-to-source voltage when the MOSFET is in the on-state, for 6 different ambient temperatures. As can be seen on fig.7, the RDSon behaves as a 2 nd order polynomial:

fig. 5 4

 54 fig. 5 Drain-to-source voltage during the conduction period. 0,0 5,0x10 -6 1,0x10 -5 1,5x10 -5 2,0x10 -5 2,5x10 -5

fig. 7 .fig. 8 .fig. 9 .

 789 fig. 7. Evolution of the MOSFET on-state resistance as a functionof temperature

  fig. 12 Error map

  fig. 14 The monitoring system, including bridge drive (top), estimator (bottom) and power supply (right). fig. 15. Evolution of leg losses with bridge supply voltage 0 5 10 15 20 25

Table 1

 1 Calculation of thermal impedance from internal temperature, ambient temperature and losses in the transistor.

	Supply Voltage	17.6V	19.2V	22V
	Total leg losses	45.8W	58.8W	68.8W
	Losses per transistor	22.9W	29.4W	34.4W
	Estimated Temperature	33°C	39°C	47°C
	Ambient temperature	17.8°C	17.8°C	18.2°C
	Thermal impedance	0.66°C/W	0.72°C/W	0.84°C/W

As expected, results are strongly affected by the assumptions we made above. Transistors are fig.

10

Current estimation error map


F Cherqui
email: fcherqui@gmail.com

R James

P Poelsma

C Szota

M J Burns

T D Fletcher

J.-L Bertrand-Krajewski

LESAM 2022

Keywords: Low-cost, monitoring, nature-based solution, real-time, stormwater control measures, water level

Traditional underground drainage infrastructure (i.e. pipe networks) offers the benefit of conveying excess rainwater from urban centres quickly, but with negative impacts for receiving waters. Consequently, many cities are introducing stormwater control measures (SCMs) to retain and infiltrate excess stormwater, reducing the peaks that can overwhelm networks. Most common SCMs include swales, trenches, infiltration basins, green roofs and porous pavements. Until recently, research often focussed on the design of such systems, with less focus on long term performance and maintenance. Given the scarce data available, monitoring is a priority for Operation & Maintenance (O&M) activities, and to gather the necessary knowledge for asset management. Real-time low-cost monitoring provides an opportunity to meet this need. This communication presents an open-source water level monitoring system which is already deployed in the field. The paper also discusses key elements of design related to low-cost monitoring systems.

Stormwater control measures asset management

According to [START_REF] Raymond | An Impact Evaluation Framework to Support Planning and Evaluation of Nature-based Solutions Projects[END_REF] "nature-based solutions (NBS) are solutions to societal challenges that are inspired and supported by nature". They offer great potential in addressing a variety of challenges in many domains (H2020, 2015), and providing ecosystemic services [START_REF] Nesshöver | The science, policy and practice of nature-based solutions: An interdisciplinary perspective[END_REF]. Regarding urban water, "NBS can contribute to sustainable urban water management by increasing infiltration, enhancing evapotranspiration, providing storage areas for rainwater and removing pollutants" [START_REF] Raymond | An Impact Evaluation Framework to Support Planning and Evaluation of Nature-based Solutions Projects[END_REF]. Such solutions are often applied at or near to the source of runoff: swales, infiltration trenches, green roofs or porous roads, etc. They have emerged worldwide in the last 30 years under many names such as stormwater control measures (SCMs), best management practices or sustainable urban drainage systems [START_REF] Fletcher | SUDS, LID, BMPs, WSUD and more -The evolution and application of terminology surrounding urban drainage[END_REF]. SCMs are integrated within the urban landscape, most often open-air and thus have high visibility and public accessibility. Since the emergence of SCMs, operational and research questions have largely focused on their design. After several decades, there is, however, a growing concern regarding their medium and long-term performance and maintenance, putting at risk the adoption of SCMs, with local government at risk of withdrawing from SCM implementation, driven by concerns about the long-term financial and operational sustainability of such systems [START_REF] Morison | Transitioning to a waterways city: municipal context, capacity and commitment[END_REF]. Most of the present management of SCMs is based on the run-to-failure approach, partly due to resource limitations and lack of knowledge. Shifting to proactive management in the short-term is only possible by drastically increasing the monitoring (more asset monitored more frequently). This will allow anticipating failure and providing a better understanding of their behaviour.

Toward low-cost solutions

Low-cost technologies have emerged few years ago in other fields, such as agriculture [START_REF] Fisher | Automated Collection of Soil-Moisture Data with a Low-Cost Microcontroller Circuit[END_REF] and air quality [START_REF] Morawska | Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?[END_REF]. They are emerging now in our urban drainage community [START_REF] Santos | The use of low-cost RPAS application for erosion bank monitoring[END_REF][START_REF] Shi | Detecting urban illicit discharges by using an army of low-cost sensors[END_REF]. Their advent opens up the potential for entirely new approaches, where numerous sensors measure various aspects of SCM state and performance, generating alerts to those involved in their maintenance. Such sensors could control changes to system configuration to optimise performance relative to operating conditions and maintenance state of the system. Delivering on this potential will require investigating new challenges and imagining innovative ways of monitoring. Among the other challenges, it will be necessary to consider the reliability of the monitoring system, the skills related to customised hardware and programming and the management of large data sets. Low-cost solutions will, however, improve monitoring possibilities with real-time data acquisition, processing, and alert, along with the increase of spatial resolution. A key challenge is to optimise the use of new technologies, rather than simply replacing the functionality of existing monitoring systems. Regarding SCMs asset management, the review of scientific literature and operational guidelines, associated with exchanges with stakeholders, has proven that the water level is the best solution to inform on the overall operation of the system. The paper presents a low-cost and real-time water level monitoring system and discusses some of the key elements of design (Figure 1). How to build a low-cost monitoring system? Monitoring specifications and choice of the sensor. The proposed system aims to monitor the water level in SCMs to inform O&M activities. The objective is to minimize the costs of design, construction, installation, data management and monitoring system maintenance. The expected maximum water level range is below 2 m with an acceptable accuracy of 2 cm. The system provides real-time data to raise alerts (high / low water level, low-battery level, absence of new data). To reduce cleaning needs, the sensor is not in contact with the water; this constraint drastically limits the choice of sensor [START_REF] Morris | Measurement and Instrumentation[END_REF]. Power management and communication. Power management and communication are key elements of the system. To avoid frequent site visits, the system needs to be self-powered. Low power management is a frequently discussed topic and can involve a variety of hardware or software solutions. Easy-to-implement solutions include decreasing the measurement time, increasing the sleep time between measurements, and decreasing the communication consumption. LPWAN (lowpower wide-area network) communication facilitates minimum energy consumption and maximum range for communication, when compared to cellular (GSM) communication. These solutions are however often one-way communication, with a limited bandwidth [START_REF] Mekki | A comparative study of LPWAN technologies for large-scale IoT deployment[END_REF], which is acceptable for the present project (no image or video to be sent). Based on the existing coverage, the LoRaWAN technology is used where available (no subscription fees) or alternatively Sigfox (subscription fees). Optimal measurement cycle. Each measurement cycle can be broken down into two phases: an active phase and a sleeping (passive) phase. The active phase consists in the initialisation (powering of the sensors), the measurement, the communication of data, and a termination (power down of the sensors). During the sleeping phase, the system consumes as little energy as possible until the next active phase. The number of cycles per hour represents a trade-off between power management (more measures mean more energy consumption), communication constraints (LPWAN often limits the number of messages per day) and the monitoring requirement. In the present case, a measure is sent every 15 minutes. The duration of the measurement phase is very important because of its high-energy consumption. It is again a trade-off between the quality of the measurement (improved when using the median of n measurements) and the total measurement duration (n times the duration of one measurement). This trade-off is highly depending on the sensor chosen and the measurement performance expected. Testing the sensor and the measurement procedure. Following laboratory tests [START_REF] Cherqui | A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring[END_REF], the ultrasonic sensor JSN-SRT04 has been chosen because (i) it is one of the most commonly available sensors, (ii) it is waterproof, (iii) it is easy to install, and (iv) it provides an expected accuracy within a compatible range of measure. The fact that a sensor is very commonly used is very important because it implies mass production and consequently very low cost (<$7 AUD for the JSN-SRT04), but also a large community of users and potentially a high number of open-source projects and extensive knowledge to aid deployment and troubleshooting. According to the lab results, the sensor shows low variation between repeated measurements: approximately 0.01 m. It has thus been decided to calculate the median of 25 measurements (maximum measurement duration of less than 10 seconds). The total active cycle lasts less than 20 seconds: initialisation, measurements (distance, air temperature, air relative humidity and battery voltage), communication and termination. The system is thus active less than 3% of the time when using a 15-minute time step (20/920 seconds). Field installation. The system is encapsulated in a 100 mm PVC pipe in order to provide a cheap waterproof case and make it less noticeable, with the aim of reducing the risk of vandalism. The enclosure also allows easy removal/replacement without changing its location. Figure 2 presents the system deployed on site. The system runs on three AA rechargeable batteries and a 0.5 W solar panel. Performance of the system. Data can be accessed in real time using the online platform http://mind4stormwater.online. One of the first system deployed in the field has been running for more than a year and has provided almost continuous time series. Figure 3 presents the water level and battery voltage (peaks of voltage are due to the solar panel) time series. According to initial tests without the solar panel, the system can run more than two weeks without sun. Existing thinking and practices will need to be adapted to such particular assets, leading to new research and business prospects. It is already clear that more monitoring will be required to shift from a run-to-failure approach to a proactive management. At the same time, lowcost technologies are opening new potentials for the monitoring of asset with the miniaturization and falling costs, allowing better spatial and temporal resolution. Such technologies can be the start of this "out of the box thinking".

Figure 1 .

 1 Figure 1. Key elements for the design of a real-time low-cost monitoring system

Figure 2 .

 2 Figure 2. System installed on the field (on the left of the pole) and photos of the encapsulation system made for easy fixing and removal without changing the location of the sensor.

Figure 3 .

 3 Figure 3. Data gathered in real time for an Australian monitoring system installed in a wetland in Melbourne: water level (a) and battery woltage (b.) with a zoom (c) to see the daily pattern.

Acknowledgement

This research received financial support from Melbourne Water, through the Melbourne Waterway Research-Practice Partnership (http://mwrpp.org), from the European Union's Horizon 2020 research and innovation programme under Grant Agreement no. 786566 (https://mind4stormwater.org), and from the Rhone Mediterranean Corsica Water Agency under the Cheap'Eau project. This research was performed within the framework of the OTHU (Field Observatory for Urban Water Management) and the EUR H2O'Lyon (ANR-17-EURE-0018).


published or not. The documents may come

Survival mechanisms and adaptive evolution of Pseudomonas aeruginosa in the urinary tract

Objectives of the study

A previous study has already started to characterize the genomic evolution of the isolates. pH  For a total of 35 isolates Thank you !

  adaptive evolution of Pseudomonas aeruginosa in the urinary tract Caroline Duval, Univ Rouen Normandie, UNICAEN, INSERM, DYNAMICURE UMR 1311 F-76000 Rouen, France Pseudomonas aeruginosa (P. aeruginosa) Gram-negative bacillus, ubiquitous 6.3 Mb genome and ≈ 5600 genes 8% of regulatory genes → High capacity to adapt to its environment Mainly responsible for urinary tract infections and especially recurrent infections due to biofilm and multiresistance to antibiotics the mechanisms of urinary survival and adaptation in order to better understand the pathogenesis of the recurrent urinary tract infections.❑ Phenotypic characterization of sequential urinary clinical isolates of P. aeruginosa.❑ Post-genomic study of the adaptive evolution of P. aeruginosa in the urinary tract.

Acknowledgments

Pr. Martine Pestel-Caron Dr. Sandrine Dahyot Pr. Jean-Christophe Giard


Ning Ding

P Hamel

F Cherqui

J.-L Bertrand-Krajewski

Q Zhu

Water Sensitive Urban Design performance in Singapore: monitoring with lowcost sensors

Keywords: ABC waters Programme, Low-cost sensors, Monitoring system, Water sensitive urban design

The ABC Waters Programme, with its design based on Water Sensitive Urban Design (WSUD), has successfully implemented stormwater control systems across Singapore for 15 years. Yet, there is relatively little attention paid to monitoring ABC Waters features' medium to long-term performance. Compared with traditional monitoring system, lowcost sensors' technology presents economic advantages and flexibility for operation and communication. In this study, we aim to 1) analyse the potential of low-cost sensors for medium to long-term monitoring of WSUD in Singapore; 2) propose a monitoring protocol that can be promoted and used widely. The monitoring system we present is composed of low-cost sensors and Arduino platform. Several kinds of sensors have been identified from a literature review, and lab testing for sensors is in progress. After completing the testing, the monitoring system will be established in site to monitor the performance of WSUD in improving water quality and reducing runoff.

INTRODUCTION

Cities worldwide are currently facing numerous water-related challenges, such as water scarcity, poor water quality, and floods, which have enormous human and economic impacts [START_REF] O'donnell | International Perceptions of Urban Blue-Green Infrastructure: A Comparison across Four Cities[END_REF]. Low Impact Development (LID) and Water Sensitive Urban Design (WSUD) are widely used urban water management approaches around the world, providing a unified method for integrating the interactions between the water cycle and the water utilities systems to create multi-functional landscapes and infrastructures in cities [START_REF] Gleason | Challenges of Water Sensitive Cities in Mexico: The Case of the Metropolitan Area of Guadalajara[END_REF]. The effectiveness of WSUD has been extensively studied, including stormwater runoff reduction [START_REF] Li | Evaluating the runoff storage supply-demand structure of green infrastructure for urban flood management[END_REF], water infiltration [START_REF] Gunnell | Evaluating natural infrastructure for flood management within the watersheds of selected global cities[END_REF], and water purification [START_REF] Tang | Robustness analysis of storm water quality modelling with LID infrastructures from natural event-based field monitoring[END_REF]. Many studies are conducted based on hydrological and ecological models, such as SWMM, MUSIC, MOUSE, SUSTAIN, HEC-RAS, CADDIES [START_REF] Elliott | A review of models for low impact urban stormwater drainage[END_REF][START_REF] Guidolin | A weighted cellular automata 2D inundation model for rapid flood analysis[END_REF], which need to be calibrated by using observation data obtained from field investigation [START_REF] Yin | Sponge city practice in China: A review of construction, assessment, operational and maintenance[END_REF]. However, studies often lack empirical validation, especially in the Global South and Southeast Asia [START_REF] Hamel | Blue-Green Infrastructure for Flood and Water Quality Management in Southeast Asia: Evidence and Knowledge Gaps[END_REF]. Relatively few studies have attempted to measure and quantify the medium to long-term performance of WSUD in runoff reduction and pollution mitigation, which can be explained by limited resources as traditional monitoring systems can be costly and time-consuming [START_REF] Bertrand-Krajewski | Metrology in Urban Drainage and Stormwater Management: Plug & Pray[END_REF]. Thus, it is urgent to develop low-cost, real-time, and autonomous monitoring network for existing WSUD systems. Compared with traditional monitoring equipment, lowcost sensors are affordable, typically have similar operation and maintenance requirements, and, by allowing a large decentralised spatial monitoring, have a great potential in environmental monitoring and emerging in many fields such as air quality, air temperature, and agriculture [START_REF] Cherqui | A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring[END_REF][START_REF] Sun | Application of Low-Cost Sensors for Urban Heat Island Assessment: A Case Study in Taiwan[END_REF]. Such sensors have benefitted from recent technological advances regarding falling costs, miniaturisation, ease-of-access, modularity, and open-source programming. Singapore has launched the Active Beautiful Clean (ABC) Waters Programme in 2006, promoting WSUD features in the city-state [START_REF] Lim | Sustainable urban stormwater management in the tropics: An evaluation of Singapore's ABC Waters Program[END_REF]. The relatively limited monitoring efforts restrict the ability of public agencies and private developers to provide adequate maintenance for existing systems and to optimize the design of new systems [START_REF] Hamel | Blue-Green Infrastructure for Flood and Water Quality Management in Southeast Asia: Evidence and Knowledge Gaps[END_REF][START_REF] Lim | Sustainable urban stormwater management in the tropics: An evaluation of Singapore's ABC Waters Program[END_REF]. In this study, we aim to 1) establish a continuous and autonomous water monitoring system by adopting low-cost sensors and assessing their performance, 2) monitor the medium to long-term performance of WSUD. The specific objectives are to: 1) collect information of low-cost sensors, and identify traditional and equivalent low-cost sensors for water quantity and quality monitoring, 2) test (in laboratory and in situ) and compare the multicriteria performance of traditional and low-cost sensors, and 3) develop an experimental protocol to assess the performance (water quantity and quality) of WSUD systems.

METHODS

Selection of low-cost and traditional sensors

Some of the co-authors (Zhu et al., Review of Low-cost Sensors for Monitoring Stormwater Control Measures, under review, 2021) have reviewed the existing literature on low-cost sensors and compared the performance (range, resolution, response time, price range, previous uses and experiences) of different low-cost sensors in monitoring air humidity, conductivity, flow or velocity, nitrogen, pH, phosphorus, rainfall, soil moisture, solar radiation, turbidity, water level, and wind speed. Our candidate low-cost sensors are selected based on the findings from this review.

Lab testing of low-cost and traditional sensors

First, we tested low-cost water level sensors in the laboratory and compared them with the baseline (provided by a sensor that is widely used, with good accuracy and stability). Three widely used low-cost water level sensors (laserranging, ultrasonic-ranging, and pressure) and one baseline sensor (PLS, pressure transducer) were tested. The performance of these sensors was assessed in terms of range, accuracy, precision, and sensitivity to the environment. In addition, a low-cost meteorological monitoring station is also currently tested and compared with traditional sensors. In the next step, more types of low-cost sensors and traditional sensors will be systematically tested in the laboratory for final selection before being implemented on site.

Field experiment 2.3.1 Experimental site

The first pilot site is the cleansing biotope 2 in Yunnan Garden of Nanyang Technological University, located in the southwest of Yunnan Garden (Figure 2.1). The cleansing biotope 2 has a typical design including plants, cleansing layer (filter media inside), transition layer (for preventing filtration media from entering the drainage layer), drainage layer (for assisting in the drainage of the effluent), and geotextile layer. Through water recirculation, the cleansing biotope purifies water pumped through pipes from Yunnan Garden Wetland, and discharges the treated water back to the wetland and then to the nearby canal along the Expressway. In addition, part of the runoff from North-East of South Spine Catchment Area will also be purified by cleansing biotope 2 (Figure 2.1b).

Experimental protocol

The planned location of monitoring systems is shown in Figure 2.2. For the water quality, monitoring systems will be established in locations A/C and B in November 2021 (when the lab testing has been completed). For the water level and flow velocity, monitoring system will be established in locations D and C, respectively. Water quantity indicators include water level and flow velocity. Water quality indicators include nitrogen, phosphorus, turbidity and total suspended solids (TSS), pH, and conductivity. For the data transfer, we use the Arduino Nano which sends sensor data to a nearby computer every minute through a USB cable. These data are recorded on the computer in a csv file. To ensure that the system is running continuously, a measure is sent online every 15 minutes and a remote control is installed on the computer. The lack of new data triggers an alert (email) and it is then possible to remotely control the computer and the setup.

RESULTS AND DISCUSSION

Review of low-cost sensors

The review of low-cost sensors provided the following recommendations: 1) For water level: The JSN-SR04T (ultrasonic sensor) offers the best performance to price ratio with an accuracy of ±7 mm in the range 0.225 ~ 1.9 m, and other pressure transducers, sold under different names (including KIT0139 from Gravity), provide similar levels of performance. 2) For flow or velocity: There are two main methods to monitor water flow or velocity at a low cost. One is image-based method, another method uses Doppler velocity radar. For both methods, cameras and radars all showed a certain degree of accuracy in the field but all lack long-term experimental results. Preliminary research suggests that the ER sensor and flow sensor perform best. 3) For pH: Three brands of pH sensors were found in the literature: Global Water WQ201, SensoreX S8000 and Atlas Scientific. In general, pH sensors from Atlas Scientific and SensoreX have the most potential. 4) For conductivity: There are three commonly-used sensors for measuring conductivity: Atlas Scientific, DFRobot Gravity, and YL-69 (soil moisture sensor). In general, all of them deserve further testing. 5) For turbidity and TSS: SEN0189 has good performance but its enclosure is not satisfactory for in situ usage, so it is important to find a way to protect it in water for a long-term. Solitax is a good sensor to work as a reference. 6) For nitrogen and phosphate: In general, the off-the-shelf nitrogen and phosphate sensors found in the literature were not low-cost, and they are more than 100 EUR or even 10,000 EUR. There are still some interesting low-cost sensors under development. As nitrogen and phosphate low-cost sensors are still under development, they will not be implemented in the first phase of the project, but will be considered again later when progress will have been reported about their applicability and availability.

Laboratory testing of low-cost sensors

Laboratory tests are in progress at the time of writing the abstract. The following table illustrates the water level low-cost sensors and the reference sensor that have been assessed by some of the authors (Table 3.1) [START_REF] Cherqui | A platform and protocol to standardise the test and selection low-cost sensors for water level monitoring[END_REF]. These results will be updated with the Singapore based laboratory testing. In addition, some of the authors [START_REF] Zhu | Station météorologique à bas coût pour le suivi de toitures végétalisées[END_REF] are currently testing a low-cost meteorological monitoring station (rainfall intensity, air temperature and humidity, wind speed and direction, solar radiation) with positive results after 5 months of operation.

Table 3.1 Characteristics of the reference sensor and the low-cost sensors assessed according to the manufacturer. Testing results showed that the laser-ranging sensor VL53L0X is not suited for application with a required accuracy of ±20 mm and the ultrasonic-ranging sensor JSN-SR04 T offers the best performance to price ratio.

Figure 2 . 1

 21 Figure 2.1 The location (a) and water circulation (b) of cleansing biotope 2 in Yunnan Garden

Figure 2 . 2

 22 Figure 2.2 The planned locations of monitoring systems in cleansing biotope 2

Preliminary field results

The systems will be established in November 2021 after completion of the laboratory tests to select the best sensors.

If accepted, the paper will share preliminary results from the experiment.

CONCLUSIONS

Though low-cost sensors have great potential in environmental monitoring, there is still little information on their real performance and use in urban water systems. In this study, we have conducted a literature review of different types of low-cost sensors, and we are currently testing the performance of three low-cost water level and meteorological sensors in the laboratory. Based on these works, we plan to test more types of low-cost sensors in lab and in situ in the next two months, and establish the low-cost sensors monitoring system at the cleansing biotope 2 in Yunnan Garden. If accepted, the paper will share preliminary results from the lab testing and on-site monitoring.


domain: phys spi

text:
D Schanz

R Geisler

B Leclaire

J Pruvost

J Agocs

S Ewald

A Schröder

3D Lagrangian Particle Tracking within transitional flows

The development and production of laminar flow wings is one important goal in Europe's challenges to significantly reduce the fuel burn and the emission of future transport aircraft. For the aerodynamic design of laminar flow wings (NLF or HLFC), the understanding of the boundary layer transition process is essential and the further improvement of numerical prediction tools is necessary. The understanding of the effect of surface imperfections on the laminar boundary layer is still vague. Both the effect of roughness (e.g. due to small rivet heads or insects) and the effect of surface steps (e.g. between two adjacent wing panels) are important. Two different types of imperfections were studied by 3D Lagrangian Particle Tracking (LPT, [START_REF] Schröder | 3D Lagrangian Particle Tracking in Fluid Mechanics[END_REF] experiments. First, the transitional flow downstream of a backward facing step (BFS) of height ℎ ൌ 5݉݉ at several Reynolds numbers (ܴ݁ ൎ 850 െ 4150) was investigated. On the other hand, the BFS was replaced by of a small cube (1.5 mm sides). In both cases, the incoming boundary layer was kept laminar, such that a transition occurs in the wake of the step and the cube. It is known that the flow around these imperfections depends on various parameters such as the dimension ratios ሺ/ܮℎሻ, ሺߜ/ℎሻ and ሺߠ/ℎሻ, the Reynolds number, the free stream turbulence and its specific spectral distribution, etc. The transition of the boundary layer can be caused downstream due to the growth of Tollmien-Schlichting-waves in two-dimensional boundary layers or, in addition, by crossflow instabilities in three-dimensional boundary layers. The investigation of the development of disturbances due to instabilities requires the measurement of highly resolved velocity fields both in space and in time and, preferably, the skin friction distribution on the surface. For a related publication, see [START_REF] Schröder | The flow around a surface mounted cube: a characterization by time-resolved PIV, 3D Shake-The-Box and LBM simulation[END_REF]. The experiments were performed in the low speed water tunnel (THBV) at ONERA Lille, which is a gravity-driven, closed-loop water tunnel. The measurement volume was illuminated using a Photonics Industries DM200 2*150 W high-speed laser, operated at around 25 mJ per pulse at a frequency of 2.25 kHz. The light sheet was and cut to a rectangular shape of 80 × 10 mm 2 (see Fig. 1). The water was seeded using 30 μm polyamide particles (Orgasol) and the particle images were observed using a system of seven high-speed cameras (6 * Photron V2640, 1* Photron V640L). 12,597 time-steps were recorded per run; for the relevant cases up to 15 runs were performed. Here we only show results for the BFS-case at free-stream-velocity of 0.45 m/s (ܴ݁ ൎ 2250). Variable-timestep-Shake-The-Box (VT-STB) was applied using the DLR implementation [START_REF] Schanz | Shake-The-Box: Lagr. particle tracking at high particle image densities[END_REF][START_REF] Schanz | Shake-The-Box particle tracking with variable time-steps in flows with high velocity range (VT-STB)[END_REF]; in the final pass, around 80,000 to 100,000 particles were tracked instantaneously. See Fig. 2 for various cuts and a result of the application of FlowFit data assimilation [START_REF] Gesemann | From Noisy Particle Tracks to Velocity, Acceleration and Pressure Fields using Bsplines and Penalties[END_REF]. It can be seen how the laminar flow convects over the step and transitional structures develop due to instabilities in the shear layer between the quiet recirculation zone and the fast incoming flow. Vortical structures, which are mostly oriented in streamwise (stw) direction, can already be identified in the particle representation. The FlowFit result clearly shows these as systems of hairpin-shaped vortices that are created by large spanwise (spw) instabilites, stemming from the shear layer, which can be identified as extended tubular regions of negative pressure.

Fig. 1 :

 1 Fig. 1: (Left, Middle): Six cameras viewing through a six-faced prism, a seventh camera from behind. Flow from top to bottom. (Right): The BFS-model being illuminated by the laser sheet (80 × 10 mm 2 ).

Fig. 2 :

 2 Fig. 2: STB and FlowFit results for a BFS-flow, laminar boundary layer at ܴ݁ ൎ 2.250: (a) Top view on nearwall tracks (up to 2.5 mm above the wall), cc. by spanwise acceleration. (b) 10 mm spanwise slice, cc. by streamwise velocity. (c) 10 mm streamwise slice (x = 50 -60 mm), cc. by wall-normal velocity. (d) Instantaneous FlowFit result, showing isosurfaces of Q (5000/s 2 , cc. by wall-normal velocity) and of negative pressure (grey).

Fig. 3

 3 Fig.3displays statistical results from a binning process. It can be seen that the recirculation zone extends until approx. 45 mm (9 h) behind the step, where the turbulent flow reattaches to the wall. A second, counter-rotating circulation bubble is resolved close to the foot of the step, extending approx 4 mm in streamwise direction.

Fig. 3 :

 3 Fig. 3: Average of the particle properties in small bins (500 ߤ݉ streamwise, 50 ߤ݉ wall-normal, full span-wise extent) at three scalings with relative vector length of 1 (a), 10 (b) and 40 (c).