R Bernhaupt

G Pottier

M Pirker

D Drouet

A Battut

AUTHENTICATING THE TV USER BY BIOMETRIC CONTINUOUS MEASUREMENTS

This paper presents results from a feasibility study investigating biometric continuous measurements to identify the user holding a remote control. The study investigated a combination of accelerometers, temperature and humidity sensors and measuring skin conductance/resistance to investigate if a simple pick-up of a remote control can be sufficient to identify users in a household. Results show that eight repetitions of the movement of pick-up are enough to identify five users in a household with the necessary recognition rate validity of close to 100%. To investigate the overall user experience and acceptance of such an approach, a user study with twelve participants was performed, with an UI simulating the authentication and the personalization of the content. The hedonic quality is improved by such a method and pragmatic quality is reasonable compared to other remote controls and overall results indicate that users in this study accept the solution. Based on this study we developed a set of guidelines that help to implement such a solution in the user interface to support the necessary user experience dimensions of identity, stimulation, perception of privacy and overall user acceptance.

INTRODUCTION

We are living in an era of recommendations, customised services and tailored content. In order to provide content that is interesting for a (single) user, the system must identify the usereither by having the user identify herself in front of the TV by entering a pin-code, or using alternative means of identification via connected devices. At this identification step, any modern remote control including an orientation sensor can help: it can provide the information who currently controls the TV, replacing tedious log-in procedures, by identifying the user based on biometric continuous measurements.

Identification of users based on biometrics characteristics is nothing new in the living room. Innovative projects like Bernhaupt et al [START_REF] Bernhaupt | Personalizing the TV Experience: Vocomedia -A Case Study from Interactive TV[END_REF] and Wilfinger et al (2) using fingerprint have been already conducted in 2010 and 2009.

The goal of this research was to investigate the feasibility of a remote control (currently already applied in the market) that identifies the user via simple biometric data within a few seconds after being picked up. Building on findings from the biometrics research field that showed that bio-feedback obtained by measuring skin conductance or resistance, heart-beat, temperature or humidity can be used to identify a set of users, this study explored the combination of a 9-axis sensor in conjunction with Galvanic Skin Response sensors (GSR) for user identification, limited to the pick-up gesture of the remote control, either with the left or the right hand.

The main goals of this feasibility study were to investigate:

• the number of different users that can be distinguished using the sensor data,

• which part of the sensor data or combination of data from the sensors allows user identification with a precision of 95% or higher, and

• the number of different users that can be identified within 3 seconds after picking up the remote control.

RELATED WORK

Physical or behavioural characteristics of humans can be used to identify or authenticate a user while carrying or holding a device. The biometric information used can be divided into soft biometrics and hard biometrics. Soft biometrics (walking gait, weight or bioimpedance) are characteristics that can change over time and are not unique to a user, but still provide some information about the user, allowing identification with a certain degree of accuracy. Zewail et al (3) stated that hard biometrics, like a fingerprint or DNA, are unique to a user, hardly vary, and can be used to classify a user without any doubt.

As stated by He (4) both soft and hard biometrics have been used extensively in the last 15 years to try to identify the user of a system, especially when it comes to mobile phones.

There is a broad variety of sensors that have been used. Thus, we limit the related work to the sensors that were applied in our study.

User's Behavioural Characteristics

According to Terada et al [START_REF] Terada | A User Recognition Method Using Accelerometer for Electric Appliances[END_REF], behavioural characteristics can be salient enough to allow differentiation of multiple users by the way they pick up an item. Identification when a device is activated only needs three degrees of freedom as shown by Wolf and Willaredt (6) to identify usage of a device. Terada and colleagues (5) investigated how to identify a user from acceleration data before usage of a device (a hair dryer) to predict usage of electric appliances in advance for the home. They used acceleration data combined with angular velocity (pitch, roll, jaw) at a 100Hz sampling rate. In a study with 10 participants, each performing 10 repetitions of a task, they achieved a recognition accuracy of about 78% for 10 users (ranging from 54% to 100% for the individual users), as well as an average identification accuracy of 94% for a sub-group with high accuracy (four persons) and 82% accuracy for a low accuracy sub-group (four persons). Additionally, the same procedure was carried out with a family of four (father, mother, daughter, son), where accuracy of identification averaged 70%. To enhance identification of users via 3-, 6-or 9-axis data, the work of Weiss and Lockart [START_REF] Weiss | Identifying user traits by mining smart phone accelerometer data[END_REF] is interesting, showing that also gender, weight and height of a user can be predicted.

Electrical Properties of the Skin

A central physiological characteristic to identify a user is the electrical property of the skin.

In 2012 Sato et al [START_REF] Sato | Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects[END_REF] presented Swept Frequency Capacitive Sensing (SFCS). They use an electrode with sinusoid sweeps to send current into a conductive object. Then they measure the returned sampled signal that they call the "capacitive profile". This signal is specific to a touch pattern, so it is compared with other records in order to recognize which pattern was realized.

In 2015, Sato et al [START_REF] Sato | Zensei: Augmenting Objects with Effortless User Recognition Capabilities through Bioimpedance Sensing[END_REF], extended their work, now measuring bio-impedance with at least a pair of electrodes, allowing the identification of users based on different touch gestures and beyond touch: enabling the identification of users sitting on chairs (measuring differences between clothing resistance). The concept they used has similarities with SFCS, but measures both amplitude and phase components among all combinations of six embedded electrodes. They used a wave generator and a RF gain-and-phase detector to capture the frequency response over a wide range of frequencies (1kHz to 1.5MHz in 150 linear steps). 12 subjects delivered 2640 samples per device over a 22-day period, which were analysed for classification accuracy (CA), false acceptance rate (FAR), and false rejection rates (FRR). For analysis, they used 21 of the 22 days of data for training, and tested the data of the remaining day for every combination of days and averaged the results for all combinations. Their results were a 96% CA for a hand-pad device, as well as a 88.5% CA on a phone device.

Harrison et al [START_REF] Harrison | Capacitive fingerprinting: exploring user differentiation by sensing electrical properties of the human body[END_REF] investigated user differentiation via capacitive fingerprinting, where they achieved a 96% accuracy for distinguishing between two users. These authors highlight that their approach is compact, low-powered and inexpensive, and in real-time with a very short initial calibration. The drawbacks of the system are that it can only differentiate among a small set of concurrent users who can only touch it sequentially, not simultaneously. These authors also use SFCS and emphasize that this capacitive fingerprinting should not be confused with galvanic skin response (GSR) which measures the conductivity of the skin. In an experiment, they tested their system, and found out that the accuracy plateaus significantly at 0.5 seconds of training data (5 samples per person), resulting in an average accuracy of 96.4%. They also note the limitations of their approach, namely the persistence of the identification, as impedance shifts over time and the personal change variability might be higher than the between-person variability; furthermore, they mention the ground connection, and the need for sequential usage.

IDENTIFYING A USER HOLDING A REMOTE CONTROL Problem Description and Approach

This feasibility study aims to investigate the degree to which a unique user can be identified, and how many users can be distinguished reliably. The results gained should then inform the development of this technology and be used in later stages of this project, where future work includes the investigation of usability and user experience implications of such an approach for authentication and personalisation, as well as user acceptance and user's experiences, perceptions and concerns with this novel kind of authentication.

Feasibility Study

The feasibility study was conducted using a prototypical remote control in conjunction with additional Arduino sensors. The sensors we used were a 9-axis orientation sensor (a BNO055 intelligent 9-axis absolute orientation sensor with a 3-axis gyroscope, 3-axis accelerometer, 3-axis magnetometer), as well as a GSR module (Grove GSR sensor) via Arduino. The Arduino sensor data from the 9-axis sensor and the GSR sensor were logged on a computer in real time, the sample frequency of these sensors was approximately 10Hz. The Arduino Uno microcontroller board was kept separated from the remote control and was hidden under a small coffee table during the user study (see Figure 1). The sensors were connected to the microcontroller board using 1.5 metre cables. The 9axis sensor was attached to the top of the remote control with insulating tape to ensure that it will only move when the remote moves with the user's hand, and the GSR module was connected to thin copper strips at the sides of the remote control.

The user interface used in the experiment is called line UI and offers standard IPTV functionalities, including TV channels, video on demand, an electronic program guide, and voice commands.

Procedure and methodological approach

The study took place in spring 2017 with eleven participants (5 males and 6 females), aged from 23 to 49 years (mean 34 years, SD = 9.31 years) in our usability lab in Neumarkt, Austria. The usability lab is designed to resemble a living room environment, and the user interface that is controlled was running on a Full HD TV in front of the user who was seated comfortably on a couch. Observation was conducted using network cameras on the ceiling of the room, supported by portable cameras on a tripod. The experimenter and one assistant guided the participants through the study and also took notes of task details, answers and comments. Additionally, the sensor data was logged for each single task and repetition to allow subsequent analysis of the feasibility and recognition accuracy of the identification process. The start and end of each repetition for each hand were also marked in the logs to allow a more detailed analysis after the study. The IPTV command and control tasks that were carried out prior to the repetitive tasks were later used to evaluate the feasibility of identifying users during runtime in a post-study analysis.

Subsequently, a Final interview (e), investigated user's preferences of such identification compared to traditional methods like the usage of a PIN code. Additionally, participants were asked for their ideas how this technology could enhance TV usage or even create new ways of using the TV. Finally, users were asked for their impression of the system regarding privacy concerns in terms of data collection, accuracy, unauthorized usage of personal information, and whether they perceive it as more or less protected by this technology compared to traditional authentication modalities like PIN and password.

RESULTS

Analysis: Gesture Recognition and Framing

We used the pickup records to train a gesture recognition component, in order to determine the best recording duration. For that we used the TANAGRA toolkit from Rakotomalala [START_REF] Rakotomalala | TANAGRA : un logiciel gratuit pour l'enseignement et la recherche[END_REF], a data mining software that is able to classify and cluster data and that can handle unlabelled data. Recognition was reliable enough (100% recall rate with a false positive rate of 1%) with records of a length of 2 seconds (approximately 20 samples) and an overlap of 50% -meaning that every record overlaps 50% with the previous one, and 50% with the next one.

Machine Learning

In order to recognize the user, we used the machine learning suite Weka of Frank et al [START_REF] Frank | The WEKA Workbench. Online Appendix for[END_REF]. The data from the repetition task was filtered down to the pick-up gesture, where we had eight records for every hand (left and right) for each of the eleven users, resulting in 176 records in total.

The average duration of a pickup gesture record was 2.028 seconds, with a standard deviation of 0.489 seconds. Classification algorithms were scored using the cross-validation method with eight folds (due to the eight repetitions we had).

In this process, scoring was carried out using the full dataset as well as using different subsets of the user data, with data from all users as well as data from a reduced number of users.

Results User Recognition from Sensors

The classifiers retained are the following: the first ones are simple Bayesian networks, with nodes based on Bayes' rule as described by Pearl [START_REF] Pearl | Bayesian Networks : a Model of Self-Activated Memory for Evidential Reasoning[END_REF]. Then we used random forests from Breiman [START_REF] Breiman | Random Forests[END_REF], which are a combination of simple decision trees refined individually during their training phase. Finally we used random subspaces -Ho ( 17) -based on trees and forests built on randomly selected subsets of the data.

Table 1 -Lists of best classifiers ordered by dataset used for their evaluation and their true positive (TP) rate. Only classifiers and parameters with a TP rate > 80% are shown.

The parameters indicated can be set in the Weka GUI. TP# = Test Participant number # Using the data from the repetitive pickup tasks, a random subspace achieved a true positive (TP) rate of 81.8% for the full set of eleven participants using an eight folds crossvalidation method. In the same conditions, Bayesian networks classification algorithms were able to recognize the user with a TP rate of 77.3%. Using only a subset of ten users, having removed one specific user that was frequently recognized as another one, accuracy went up to 95% at most with a random subspace and 89.4% with a Bayesian network (default parameters). As expected, accuracy decreases with an increase in number of different users, as pickupgesture characteristics of certain users were not different enough. This indicates that a higher sampling rate than 10Hz can be beneficial to avoid similar profiles and that a higher sampling rate will lead also to a better recognition rate. Some classifiers performed better than others given the number of users; the highest true positive rate was achieved with a random subspace with all eleven users (highest rate: 81.8%, parameters: subspace size of 0.47; 41 iterations; with a random forest as classifier) and seven users (highest rate: 96.4%; with 50 iterations). Random forests alone gave better results with six and fewer users, with a true positive (TP) rate ranging from 97.9% to 100% as the number of users goes down. A 100% true positive rate was achieved with five or fewer users. To achieve these scores, the random forest parameters were fine-tuned using a high number of iterations (500) for six users down to the default parameter value (100) for three.

Dataset

We subsequently also compared the model that we developed from the repetitive pickup task (Task 4) from ten users with eight repetitions per hand as training data to real-usage pickup gestures from tasks 1, 2 and 3 (searching a channel, finding a VOD movie, checking weather information). Here, accuracy dropped to 40% (using a random forest), which leads to the conclusion that records gained from a realistic usage might differ from a model trained with repeated pickup gestures; this also unveiled the differences between the real usage by the user when doing everyday command and control tasks, compared with the more artificial training session. This illustrates the need to refine the user's characteristics after the training phase by registering the previously recorded data every time the user logs in formally into the system. Nevertheless, satisfying accuracy was achieved again in smaller sets of users, reaching 93% TP rate with five users (random forest, with default parameters), and 100% with three users (random subspace, default parameters).

Results Usability, User Experience & qualitative Insights

In terms of usability, the interaction with the line user interface for the three command and control tasks in the television environment was assigned excellent usability by the eleven participants, with a mean rating of 93.4 on the standardized usability scale ranging from 0 -100 (where a value higher than 68 indicates good usability and a value higher than 80 indicates excellent usability).

The system also received a very good user experience rating by the eleven participants, with above-average ratings for hedonic quality, pragmatic quality as well as attractiveness metrics (PQ mean = 2.01, SD = 0.54; HQI mean = 1.55, SD = 0.61; HQS mean = 1.62, SD = 0.59; ATT mean = 2.06, SD = 0.67; HQ mean 1.58, SD = 0.57all scales ranging from -3 to +3).

Qualitative insights:

The preferred way of logging into an account for the test participants are: password (n=5), pattern (3), PIN (2), Fingerprint (2), and Two-factor-authentication (password + token) (1).

Six of the eleven persons had, in the past, used biometric information for login (voice 1 person, fingerprint 5 persons), four of them use these daily (fingerprint) and two do not use them on a regular basis.

Participants prefer the user authentication via sensor to using a traditional PIN (mean 7.45, SD 1.51 on a scale from 0 -"would absolutely prefer traditional like PIN" to 10 -"would absolutely prefer biometrics and sensors"). Ten of the eleven participants did not have privacy concerns when using the system, only one had concerns that data should only be saved locally and not be "in the cloud". Participants did not have concerns that their movement and GSR data would be collected and used without their consent.

DISCUSSION AND GUIDELINES

Based on the feasibility study, the combination of 9-axis sensor data and GSR is a reasonable good measure when up to five members of a household have to be identified. Fine-tuning the hyper-parameters of the classifiers allowed higher recognition rates for reduced user sets. Naturally, excluding users with similar movement and GSR data enhanced the overall recognition rate. This illustrates the need for higher sampling frequency and better sensors (e.g. SFCS [START_REF] Sato | Touché: enhancing touch interaction on humans, screens, liquids, and everyday objects[END_REF] or multiple localised GSR sensors ( 9)). As a guideline for user identification, we recommend the combination of the 9-axis sensor and a high sampling rate like 100Hz with a second modality (e.g. GSR or SFCS).

In terms of learning algorithms for data representing repetitions (like pick-up) and limited time (up to 2.5 seconds), the best results were achieved with random forests, random subspaces and Bayesian networks. Given that the methods applied in this study were limited, we are extending our effort to investigate artificial neural networks with back-propagation and several hidden layers as well as time-coded artificial neural networks, as these have been shown to be very stable to slight changes over time (e.g. children's hands are growing and tremor changes over longer time-frames).

Currently the recognition is limited to one single kind of gesture, pickup, with a specific starting point: remote control lying face up on the table. We plan to extend the recognition to all pickup gestures by applying the findings of Lu et al [START_REF] Lu | Unobtrusive gait verification for mobile phones[END_REF] that will support different start and ending positions for gestures. For that, they applied a projection method based on an estimation of gravity to make the data insensitive to the orientation of the object. In addition to that, extending gesture coverage will require more data gathering sessions to be conducted.

Given the high ratings in terms of usability and user experience using a modern user interface with a high-end remote control design, the following studies have to confirm these ratings in controlled studies using simple PIN entry for identification in order to allow comparison with the sensor-based identification approach. The next step in the development of such a system is the ability of the biometrical identification to (continuously) register the data on the remote control. Furthermore, a set of tasks is needed that the user must perform in order to gain some initial data for training the algorithm. Our current efforts are going in the direction of 'gamifying' the installation and set-up tasks as well as some routine tasks to gather the necessary amount of data.

Figure 1 -

 1 Figure 1 -remote control with Arduino sensors (right) and Arduino breadboard (left)

Figure 2

 2 Figure 2 -architecture and outputs of the user recognition component

Figure 3 -

 3 Figure 3 -User test with assistant logging tasks (right) and participant performing a task (centre)


Eva Bednářová

Evelina Colacino

Frédéric Lamaty
email: frederic.lamaty@umontpellier.fr

Martin Kotora
email: martin.kotora@natur.cuni.cz

Eva Bednµr

Frødøric Lamaty

3-Halopyridines

Keywords: alkynes, cyclotrimerization, h omogeneous catalysis, nitrogen heterocycles, ruthenium

A Ruthenium Complex-Catalyzed Cyclotrimerization of

Halodiynes with Nitriles. Synthesis of 2-and

Pyridines andt heir more complexd erivatives are an important class of heteroaromatic compounds.S ubstancesp ossessingt he pyridine framework are found in numerous branches of chemistry. [1] Outo fm any synthetic methods used for their preparation, [2] perhaps the simplest and the most efficient, is catalytic cyclotrimerization of alkynesw ith nitriles by using transitionmetal compounds. [START_REF] Weding | Forr ecent reviewso nt ransitionm etal-catalyzed [2+ +2+ +2] cycloadditions for pyridine synthesis,s ee:a )[END_REF] Althoughanumber of catalytic protocols have been developed, there is still ac onsiderable spacet o explore cyclotrimerization for hitherto untried combinations of alkynes and nitriles.I nt his respect, it wouldb ed esirable to develop ap rocedure allowing synthesiso fp yridines possessingareactive functional group on the pyridine scaffold that woulda llow further transformations.I tw ould be synthetically interesting if ac yclotrimerization would allow formation of halopyridinesb yac atalytic reactiono fh aloalkynes with nitriles.

As far as the cyclotrimerization of chloro-,b romoand iodoalkynesi sc oncerned, they can efficiently react with alkynes in cyclotrimerization processes to give the corresponding halobenzenes.T hese processes include:( i) Ru-or Co-catalyzed reactions of monoiododiynesa nd diiododiynes [START_REF] Yamamoto | [END_REF] with alkynes to iodobenzenes that were further functionalized by cross-coupling reactions; [5] (ii)R u-or Rh-catalyzed cyclotrimerizationo fm onobromodiynes with alkynes as ar oute to new potentially selective inhibitors of tyrosine kinase 2; [6] (iii)R u-catalyzed cyclotrimerization of ah ighly substituted chlorodiynea nd alkyne during the course of the sporolide Bs ynthesis. [7] Although, aR u-complex catalyzed cycloaddition of haloalkynes with nitrile oxides and organic azides has been recently described, [8] their cyclotrimerization with nitriles providing halopyridines,interestingly,has not been reported so far (to the best of our knowledge). Our interesti nc yclotrimerization of the halodiynes with the nitriles stemmedf or the fact that this procedure could be useda sa ni mportant step in syntheses of pyridines and derivatives thereof.T heyc an be usedi nn umerous homogeneous catalytic racemic or enantioselective processes as ancillary ligands and their derivatives (e.g., N-oxides,e tc.) as Lewis basic organocatalysts. [1b,c,9] One such an example is Bolms ligand, which has the bipyridine scaffold. [10] Therefore,i t wouldbet hus desirable to develop the cyclotrimerization of halodiynes with alkynes to substituted halopyridines,b ecause they could serve as convenient intermediates for synthesis of bipyridines and other types of pyridine based ligands.

At the outset, the cyclotrimerization of iododiyne 1a with ethyl cyanoformate 2a as model compounds was screened under different conditions,t oe xplore ap ossibility for the preparation of iodopyridines (Table 1). Ther eactionw as carried out in dichloroethane (DCE) in the presence of al argee xcesso fc yanoformate (20 equiv.) to ensure high conversion by using the previouslyr eported conditions for Ru-catalyzed cyclotrimerization of iodoalkynes with alkynes [Cp*RuCl(cod) at 20 8 8C].Gratifyingly,t he cyclotrimerizationt ookp lace and provided three products 3aa, 4aa,a nd 5aa in 23, 33, and 3% isolated yields (59% combinedy ield) (entry 1). Compounds 3aa and 4aa were regioisomers formed by different insertion pathways into the intermediate ruthenacycle,w hereas 5aa possessed chlorine insteado fi odine.T he formation of this compound wasabit puzzling, but subsequent investigation couldr eveal the origin of its formation (vide infra). Then, the effects of the nitrile/alkyne ratio,c atalyst load and solvent on the course of the reactionw eree xplored. Theo btained results (entries 2-5)indicated the 2/1 nitrile/diyne ratiotobeoptimal giving products in 76% combinedy ield.U sing 10 mol% of the catalyst seemedt ob eo ptimal for high yields of the products( entries6-8). As fara st he reactionmedium is concerned, cyclotrimerization proceeded to give high isolated yields of the products (77-89%) in dichloromethane,t etrahydrofuran, chloroform, andc yclopentyl methyle ther (entries 9-12). Attempts to induce the cyclotrimerization with Co or Rh catalysts were not successful, despite the fact that these catalysts were shown to catalyze the cyclotrimerization of iodoalkynesw ith alkynes. [4e,5] In both casest he reactiond id not take place;m oreover, slight decomposition of iodoalkyne 1a waso bserved (entries13a nd 14). Although speculative,t heir inactivity may arise from ac ompetitive oxidative addition of the reactive spC À halogen bond that might oxidatively addt ot hese compounds providing catalytically inactive species. [START_REF] Ckenzie | Foro xidative additiono fh aloalkynest oC o, Au and Pd complexes see:a )D[END_REF] With respect to the above describedr esults,t he following conclusions could be made on the optimal reactionc onditions:( i) 2/1 nitrile/alkyne ratio, (ii)1 0mol% of Cp*RuCl(cod), (iii) chlorinated solvents as the reactionm edium, (iv) reaction temperature of 20 8 8C. Thes tructureo ft he regioisomers was unequivocally confirmed by single crystalX -ray analyses of 3aa and 4aa (Figure 1a nd Figure 2).

Then the efficacy of the Ru-catalyzed cyclotrimerization of bromo-1b and chlorodiynes 1c with cyanoacetate 2a was examined for comparison under the optimized conditions (Table 2). Ther eactionw ith bromodiyne 1b proceeded with full conversion of the starting material and provided am ixture of 3ba, 4ba, and 5aa in ac ombined9 1% isolated yield (entry 2). Theu se of the chlorodiyne 1c furnished 3ca and 4ca in al ower yield of 72% (entry 3). Obviously,t he use of the bromo derivative 1b was advantageous with respect to yields of products.A ttempts to increase the reactionr ate of cyclotrimerization of 1b by using AgOTf to generate ac ationic complex [12] or to sup-Table 1. Catalytic cyclotrimerization of 1a with 2a under different conditions. Entry 2a (equiv.) Catalyst [a] (mol%)T ime [h] Solvent [b] Yield [%] [c] Ru 52 7D CE 18 [d] 19 [d] 3 [d] 40 [d] 32

Ru 52 7D CE 32 [d] 39 [d] 5 [d] 76 [d] 45

Ru 52 7D CE 26 [d] 28 [d] 2 [d] 56 [d] 51 0R u52 7D CE 16 [d] 21 [d] 2 [d] 39 [d] 5C o [e] 58 2t oluenen rn r-

- 14 2R h [f] 2.5 36 DCE traces traces -- [a] Ru = Cp*RuCl(cod); Co = CpCo[P(OEt) 3 ]diethylfumarate;R h= [Rh(cod)]BF 4 ,( R)-BINAP.
R eactions were run at 20 8 8C unless otherwisen oted. [b] DCE = 1,2-dichloroethane,DCM = dichloromethane,C PME = cyclopentylmethyl ether. [c] Isolated yields unless otherwise noted;nr= no reaction. [d] Yields determined by 1 HNMR. [e] Thereaction was run at 100 8 8C. [f] Thereaction was run at 20 8 8C( 20 h) and then at 50 8 8C( 16 h).

Adv. Synth. Catal. 2016, 358,1916 -1923 2 016 Wiley-VCH Verlag GmbH &Co. KGaA, Weinheim 1917 press formation of 5aa by addition of Bu 4 NBr did not meet with success.T he former resulted in the formation of ac omplexr eactionm ixture,i nw hich only traces of the desired products were detected (entry 4). Thel atter hado nly am arginali fa ny effect on the product distribution (entry 5).

In order to elucidate the formation of the chloro derivatives,t he following experiments were carried out, Firstly, 3aa (containing approximately 12% of 5aa)w as mixed with an equimolara mount of Cp*RuCl(cod) in deuteratedd ichloromethane and stirred at 20 8 8C. According to the 1 HNMR analysis of the reactionm ixture (thankst oc haracteristic signals of 3aa and 5aa)a lready 50% of 3aa was converted to 5aa after 10 hw hile after 10 days the conversion was 80% (see Figure SI-1 andF igure SI-2 in the Supporting Information).

Ah alogen exchange reactioni sasynthetically interesting reactionp roceeding in the presence of various transitionm etal compounds,i ncluding ruthenium complexes. [START_REF] Forr | a )A.V igalok[END_REF] TheR u-catalyzed halogen exchange has been observed with triflates ([Cp*Ru(MeCN) 3 ]OTf and LiBr) [14] anda cyl halides (CpRuCl(PPh 3 ) 2 with Me(CO)X, X = Cl, Br, I). [15] As for the reactionm echanism of the Ru-catalyzed halide exchange,t wo hypotheses have been proposed. Thef irst one assumes the halogen exchange to proceed via oxidative addition forming ac ationic Ru(IV) 18 electronc omplex followed by ligande xchange (TfO À for X À )a nd subsequently undergoingr eductivee limination. The second one,b ased on experimentalr esults and DFT calculations,p roposes the course of the reaction to proceed via ar adical pathway. Since thermochemical data clearly show that in 2-chloropyridine the C À Cl bond [BDE (C À Cl) = 90.5 kcal mol À1 ] [16] is much stronger than the C À Ib ond in 2-iodopyridine[ BDE (C À I) = 63.1 kcalmol À1 ]. [17] We assume this difference in Table 2. Ru-catalyzed cyclotrimerization of diynes 1 with nitrile 2a.

Entry

1 (equiv.) Time [h] Additives Yield [%] [a] 34
5aa [b] Combined [a] Isolated yields. [b] Thecompound'ss tructure is thes ame as that of 3ca.

1 1a 37 - 3aa 29
Adv. Synth. Catal. 2016, 358,1916 -1923 2 016 Wiley-VCH Verlag GmbH &Co. KGaA, Weinheim 1918 bond strength to be the drivingf orce of the reaction. [START_REF]As for the BDE for Ru À Cl and Ru À Ib onds,o nly two sets of data are available.T he first one shows that BDE for Ru À Ii ss lightly higher than for Ru À CI[END_REF] To verify this assumption 2-iodopyridinew as stirred with Cp*RuCl(cod) in deuterated dichloromethane.T he exchangep roceeded in the same fashion, but the reactionr atew as much faster:5 0% conversion to 2-chloropyridine was observed alreadya fter 5 hours and9 0% conversion after 48 h( see Figure Having elucidated the formationo ft he chloro derivatives,w ed ecided to proceedw ith an assessment of the reactions cope andt he cyclotrimerization of various nitriles 2 with 1b was undertaken (Table 3). Although the reactionw ith cyanoformate 2a proceeded to give high yields of products (Table 2, entry 2), the reactions with other nitriles gavev ariable yields. Thus the reactionw ith cyanoacetate 2b at 20 or 50 8 8C did not yield the desired products. On the other hand, the use of highly electron-deficient malononitrile 2c at 20 8 8Cp rovided am ixture of 3bc (59%) and 4bc (13%) yield along with minor amount of 5ac (2%) (entry 1). Ap ossible product of the double cyclotrimerization was not observed. Ar eactiono f1b with benzonitrile 2d and 4-chlorobenzonitrile 2e didn ot provide the expected productsa t2 0o r5 08 8C. Interestingly,r eactionw ith 3,5-difluorobenzonitrile 2f and 2,4,6-trifluorobenzonitrile 2g gaveo pposite results. Thef ormer gaver iset o3bf and 5af in 16% and3 % isolated yields,r espectively (entry 2), whereas 4bf was formed just in traces (~1%). Gratifyingly,t he use of highly electron-deficient nitrile 2h at 20 or 50 8 8Cp ro- [a] Isolated yields. [b] nd = not detected. [c] An inseparable mixture of regioisomers.T he ratio was determined from 1 HNMR. vided 3bh in 30% yield, alongw ith am inor amount of 5ah (5%)( entry 3). Interestingly,f ormationo fr egioisomer 4bh was not detected. Theu se of electrondeficient nitriles such as 3,5-bis(trifluoromethyl)benzonitrile 2i and 4-nitrobenzonitrile 2j resulted in the formation of both regiosiomers.T he former furnished 3bi and 4bi in 31% and1 2% isolated yields along with 5ai (5%)( entry 4). Thel atter furnished 3bj and 4bj in 27% and5 %i solated yieldsa long with 5aj (6%). Carrying out the reactiona t5 0 8 8Ch ad only marginale ffect on the overall yields of productsa nd provided 3bj, 4bj,a nd 5aj in 31, 8, and5 %y ields,r espectively (entry 5). Interestingly,t he reaction with 1,4-dicyanobenzene 2k gaver ise only to traces of the expectedp roducts. Then our attention turned to acyl cyanidess uch as acetyl cyanide 2l,p ivaloylc yanide 2m and benzoyl cyanide 2n.I na ll cases the reaction proceeded at 20 8 8Ct og ivem ixtureso fb oth regioisomers in good isolated yields (entries 6, 7, and 8). Formation of chloro derivatives 5al-5an was observed in the usual extent (1-3%). Thea bove mentioned results indicate that only strongly electron-deficient nitriles enter this type of cyclotrimerization reaction. It should be also noted that somewhat better results, that is,u pt o1 5% higher yields,w ere obtained with new batcheso ft he catalyst, indicating that the course of the reactionw ith respect to conversions andy ields could be affected by the catalyst quality.

Thes cope of the reactionw ith respect to diynes was tested afterwards( Table 4). Cyclotrimerizations were carried out with 1-bromo-1d,1 -bromo-1'phenyl-1e,a nd 1-bromo-1'-trimethylsilyl-1f dipropargyl ethers giving riset ot he corresponding mixtures of regioisomers 3da-3fa and 4da-4fa in good combinedy ields of 36, 55, and8 7% (entries 1-3). It is worth noting that the reactionw ith 1d providedo nly regioisomer 3da,t he other one -4da -w as not detected in the reactionm ixture andt he reaction had to be carrieda t5 0 8 8C. Interestingly,areactionw ith 1bromo-1'-cyclopropyl dipropargyl ether 1g did not proceeda t2 0o ra t5 08 8C( entry 4). Thed iscrepancy in reactivity of 1f and 1g cannot be simply rationalized in terms of steric hindrance,b ecause according to the Charton parameters, [19] the trimethylsilyl group (n = 1.40) should exert larger steric hindrance than the cyclopropyl group (n = 1.06). In addition, ac omplexr eaction mixture was obtained in the reactionw ith 1g and, according to its 1 HNMR analysis,t he presence of other compounds possessingt he cyclopropane ring was not noticed. Hence side-reactions involving cleavage of the cyclopropane ring might hamper the overall outcome of the cyclotrimerization.T he use 1bromo-1,6-heptadiyne 1h did not provide the expected pyridine derivatives 3ha and 4ha (entry 5);p referential homocyclotrimerization to ab romobenzene SI-1 was observed instead. As imilar result waso bserved in cyclotrimerization of diethyl 2-(3-bromoprop-2ynyl)-2-(prop-2-ynyl)malonate 1i in which homocyclotrimerization to SI-2 was the major reactionp athway. In spite of that, 3ia was isolated in low yield of 12% and only traces of 4ia were detected (entry 6). On the other hand, cyclotrimerization of diethyl 2-(3-bromoprop-2-ynyl)-2-(but-2-ynyl)malonate 1j with 2a proceeded to give 3ja and 4ja in 31 and 38% yields,r espectively (entry 7). Cyclotrimerizations of 1-bromo-1,7-octadiyne 1k with 2a did not give the desired products( entry 8). Theser esults are not so much surprising since as imilar phenomenon hadb een ob- nr [b] 4ha nr [b] 5ha -- [d] -- [d] [a] Isolated yields. [b] nd = not detected, nr = no reaction. [c] An inseparable mixture of regioisomers.T he ratio was determined from 1 HNMR. [d] Homocyclotrimerization of the startingdiyne was observed.

Adv. Synth. Catal. 2016, 358,1916 -1923 2 016 Wiley-VCH Verlag GmbH &Co. KGaA, Weinheim 1920 served in Ru- [4e] as well as in Rh-catalyzed [20] cyclotrimerizations of halodiynes previously. It should be noted that in cases of cyclotrimerization of diynes possessing the terminalt ripleb ond (1h, 1i,a nd 1k) homocyclotrimerization of diynes was observed as an undesirable sider eaction( in case of 1k only traces of the benzene product were detected by 1 HNMR analysis of the reactionmixture).

Finally cyclotrimerization of 2a with 1,1'-dibromodiynes was screened (Scheme 1). In the case of dibromodiynes 1l-1n cyclotrimerization proceeded uneventfully and furnishedt he desired products 6la-6na in high isolated yields of 83, 81, and 77%, respectively.I na ll cases also chloro derivatives 5la-5na were formed as minor by-products (2-6%). As expectedo n the base of previous results (entry 8, Table 4) cyclotrimerization of 1,8-dibromo-1,7-octadiyne 1o did not give the desired product either at 20 or at 50 8 8C.

Last but not least, utilization of the preparedb romopyridines 3ba and 4ba in cross-coupling reactions with 3,5-dimethylphenylboronica cid 6 and vinylboronic acid pinacol ester 7 wasb riefly screened (Scheme 2). Thus reactiono f3ba with 6 and 7 under the recentlyr eported Suzuki coupling conditions [21] [Pd(PPh 3 ) 2 Cl 2 (10 mol%), CsF,d ioxane, H 2 O, 95 8 8C] providedthe expected phenylated andvinylated products 8 and 9 in very good 92 and7 9% isolated yields, respectively.A nalogously proceededt he reactions with 4ba that gave rise to phenylated andv inylated products 10 and 11 in nice 89 and 83% isolated yields, respectively.

In summary,( i) 1-halo-and 1,7-dibromoheptadiynes could be successfully cyclotrimerized with electrondeficient nitriles to the corresponding halopyridines under catalysis of the Ru-complex (10 mol% of the catalyst is required);( ii)t he optimal nitrile/alkyne ratio is 2/1;( iii)t he optimal reaction temperature is 20 8 8C, in the case of less reactivea lkynes/nitriles, 50 8 8Cc ould be used, (iv) the reactionc an be run in different solventss uch as chloroalkaneso re thers,( v) for the reactions of 1a, 1b,and 1j with 2a aslight preference for 3-halopyridine is observed, whereasf or other cases as light preference for the formationo f2halopyridinesw as observed. Ab rief study regarding cross-coupling reactions of the regioisomeric products was undertaken:b oth regioisomers reacted almost quantitatively providing basic proofo fc oncept for further functionalization.

Experimental Section

Ethyl4 -Iodo-7-methyl-1,3-dihydrofuro [3,4-c]pyridine-6-carboxylate (3aa), Ethyl 7-Iodo-4-methyl-1,3-dihydrofuro [3,4-c]pyridine-6-carboxylate (4aa), and Ethyl 4-Chloro-7-methyl-1,3-dihydrofuro [3,4-c]pyridine-6carboxylate( 5aa)

Into ad ried flask containingC p*RuCl(cod)( 7.6 mg, 0.02 mmol) under an argon atmosphere,D CE (1 mL) and nitrile 2a (40 mg, 0.4 mmol) were slowly added. Then diyne 1a (47 mg, 0.2 mmol) dissolved in DCE (1.2 mL) was added during the courseo f1 5min and the reaction mixture was stirred at 20 8 8C. After the full consumptiono ft he starting diyne (disappearanceo ft he respective spot from TLC analysis), volatiles were evaporated under reduced pressure. Column chromatography of the residue on silica gel (gradi-Scheme 1. Cyclotrimerizations of dibromodiynes 1l-1n with 2a. Scheme 2. Cross-couplingo f3ba and 4ba with boronic acid derivatives 6 and 7 under Suzuki conditions. ent 10/1!5/1 hexanes/EtOAc) furnished 22mgo fa ninseparable mixture of 3aa and 5aa (yields:2 9% and 5% determined from 1 HNMR) and 30 mg (yield:45%)o f4aa as colourless solids.T he combined yield is 79%.

3aa: mp 168 8 8C( for am ixture containing 5aa); 1 HNMR (600 MHz, CDCl 3 ): d = 5.24 (t, J = 2.4 Hz, 2H,C H 2 ), 5.05 (t, J = 2.4 Hz, 2H,C H 2 ), 4.44 (q, J = 7.1Hz, 2H,C H 2 ), 2.36 (s, 3H,C H 3 ), 1.42( t, J = 7.1 Hz, 3H,C H 3 ); 13 CNMR (150 MHz,CDCl 3 ): d = 165.23,150.24,148.83,143.74,128.30,106.17,76.44,74.52,62.18,1 5.76,14. 21, 153.15,1 47.58, 141.41, 136.33, 128.08, 74.08, 73.18, 62.20, 15.88,1 4.38;I R( drift KBr): 1470, 1416, 1311, 1290, 1260, 1186, 1066, 1039, 17, 155.36, 151.49, 151.02, 135.65, 80.69, 78.74, 73.78, 62.53, 21.70, 14.31; IR (drift KBr): 1730, 1419, 1377, 1335, 1308, 1204, 1159, 1060, 905, 854 cm Forf urthere xperimental details,c haracterizationf or all new compounds,a nd X-ray data, [START_REF]CCDC 1442030 (3aa)and CCDC 1442031 (4aa)contain the supplementary crystallographic data[END_REF] see the Supporting Information.

n max = 1715,
n max =

Figure 1 .

 1 Figure 1. ORTEP drawing of 3aa with 30% thermal ellipsoids.

Figure 2 .

 2 Figure 2. ORTEP drawing of 4aa with 30% thermal ellipsoids.

  -3 and Figure SI-4 in the Supporting Information). However, attempts to carry out the iodine-chlorine exchangeu nder catalytic conditions [10 mol% Cp*RuCl(cod)] in the presence of LiCl (THF,M eOH, DMF) or Me 4 NCl( DCE/MeOH) in various solvents were not met with success ando nly traces (~1-2%) of the desired 2-chloropyridine were observed after prolonged reactiont ime (1 week)e veni ft he temperature was increased to 50 8 8C.

  37;I R( 3aa + + 5aa;d rift KBr): n max = 2977, 1709, 1467, 1413, 1380, 1281, 1183, 1063, 1099, 905 cm À1 ;H R-MS( EI-TOF): m/z = 332.9861, calculated for C 11 H 12 NO 3 I( M): 332.9862; R f (5/1 hexanes/EtOAc) = 0.31 (the samev alue for 3aa and 5aa). 3ca: mp 112.4 8 8C; 1 HNMR (600 MHz, CDCl 3 ): d = 5.18 (s, 4H,2 CH 2 ), 4.44 (q, J = 7.1 Hz, 2H,C H 2 ), 2.41 (s,3 H, CH 3 ), 1.42 (t, J = 7.1 Hz, 3H,C H 3 ); 13 CNMR (150 MHz, CDCl 3 ): d = 165.

  905 cm À1 ; HR-MS( EI-TOF): m/z = 241.0503, calculatedf or C 11 H 12 NO 3 Cl (M): 241.0506. 4aa: mp 113 8 8C; 1 HNMR (600 MHz, CDCl 3 ): d = 5.26 (t, J = 2.1 Hz, 2H,C H 2 ), 5.06 (t, J = 2.1Hz, 2H,C H 2 ), 4.47 (q, J = 7.1 Hz, 2H,C H 2 ), 2.44 (s,3H, CH 3 ), 1.44 (t, J = 7.1 Hz, 3H,C H 3 ); 13 CNMR (150 MHz, CDCl 3 ): d = 166.

  À1 ;H RMS( EI-TOF): m/z = 332.9864, calculated for C 11 H 12 NO 3 I( M): 332.9862; R f (5/1 hexanes/ EtOAc) = 0.16.

Table 3 .

 3 Ru-catalyzed cyclotrimerization of variousn itriles 2 with 1b.

	Entry	2	R =	Te mp.[ 8 8C]	Time [h]				Yield [%] [a]
							345	Combined
	1	2c		20	13	3bc	59	4bc	13	5ac	27 4
	2	2f		20	143	3bf	16	4bf	traces	5af	31 9
	3	2h		20	40	3bh	30	4bh	nd [b]	5ah	53 5
	4	2i		20	69	3bi	31	4bi	12	5ai	54 8
	5	2j		50	10	3bj	31	4bj	8	5aj	54 4 [c]
	6	2l		20	11	3bl	48	4bl	33	5al	18 2
	7	2m		20	15	3bm	32	4bm	11	5am	24 5
	8	2n		20	12	3bn	51	4bn	13	5an	36 7

Table 4 .

 4 Ru-catalyzed cyclotrimerization of various diynes 1 with 2a.

	Entry	1	XR =	Te mp.[ 8 8C]	Time [h]				Yield [%] [a]
								345		Combined
	1	1d	OH	50	24	3da	32	4da	nd [b]	5da	43 6
	2	1e	OP h2 02 2	3ea	32	4ea	17	5ea	65 5
	3	1f	OT MS	20	13	3fa	44	4fa	36	5fa	78 7 [c]
	4	1g	O	c-Pr	50	47	3ga	traces	4ga	traces	5ga	traces	-
	5	1h	CH 2	H2 06 6	3ha

asc.wiley-vch.de

Adv.Synth. Catal. 2016, 358,1916[START_REF]CCDC 1442030 (3aa)and CCDC 1442031 (4aa)contain the supplementary crystallographic data[END_REF] 2 016 Wiley-VCH Verlag GmbH &Co. KGaA, Weinheim 1921 COMMUNICATIONS asc.wiley-vch.de

Acknowledgements

This work was supportedb yagrant from the CzechS cience Foundation (P207/11/0587). The authors would like to thank Dr.I vana Císar ˇovµ( Department of Inorganic Chemistry, Charles University in Prague) for the X-ray structure determinations. Financial support from the FrenchE mbassy in Prague is acknowledged.


M A Mejía-Morales

A Paquier

S Proust

E Mignot

Influence of a porous urban block on urban flood flow patterns

Urban flood laboratory experiments usually consider that the flow is mostly concentrated in streets and does not enter the building areas. However, urban blocks might modify the flow patterns at local scale due to the inflows or/and outflows through openings such as gates, windows, doors, etc. To investigate the influence of urban block porosity on flow distribution, flow depth and velocity in the surrounding streets, experiments were performed on a physical model. The results show that the urban block porosity does not substantially affect the discharge distribution in the streets located downstream of the urban block. However, flow depth, velocity and flow rate in the surrounding streets are more strongly modified.

INTRODUCTION

In the recent decades, urban flooding events have become more frequent [START_REF] Wahlstrom | The human cost of weather-related disasters 1995-2015[END_REF]. Understanding these extreme events is particularly important because of the huge monetary and human costs involved.

Numerical simulations of urban floods have thus increased in recent years, based on different scenarios, the first objective being the estimate of the flood hazard for the population. These studies were mainly performed using 2D numerical models (e.g. [START_REF] Mignot | Modeling floods in a dense urban area using 2D shallow water equations[END_REF][START_REF] Hunter | Benchmarking 2D hydraulic models for urban flooding[END_REF][START_REF] Abderrezzak | Modelling flash flood propagation in urban areas using a two-dimensional numerical model[END_REF][START_REF] Cea | Experimental validation of two-dimensional depth-averaged models for forecasting rainfallrunoff from precipitation data in urban areas[END_REF][START_REF] Paquier | Sensitivity of 2-D hydrodynamic modelling of urban floods to the forcing inputs: lesson from two field cases[END_REF]). However, any numerical model needs to be validated, and often there are no reliable data available to do so. Therefore, physical models offer the opportunity to study these events, as well as to validate the quality of numerical models.

The vast majority of the previous studies using physical models considered buildings or urban blocks as non-porous structures. For instance, they are represented as aligned and staggered nonporous square obstacles (e.g. [START_REF] Testa | Flash flood flow experiment in a simplified urban district[END_REF][START_REF] Yoon | Experimental study on flood inundation considering urban characteristics(ffc06-05)[END_REF][START_REF] Soares-Frazão | Dam-break flow through an idealised city[END_REF][START_REF] Huang | The impact of building coverage in the metropolitan area on the flow calculation[END_REF][START_REF] Tomiczek | Physical modelling of tsunami onshore propagation, peak pressures, and shielding effects in an urban building array[END_REF]. Others represent them as non-porous structures with geometries closer to reality (e.g. [START_REF] Ishigaki | Hydraulic model tests of inundation in urban area with underground space[END_REF][START_REF] Smith | A comprehensive urban floodplain dataset for model benchmarking[END_REF]. Only a few studies consider these structures as porous buildings (e.g. [START_REF] Zhou | Experimental assessment of building blockage effects in a simplified urban district[END_REF][START_REF] Sturm | Experimental analyses of impact forces on buildings exposed to fluvial hazards[END_REF][START_REF] Wüthrich | Experimental study on forces exerted on buildings with openings due to extreme hydrodynamic events[END_REF].

In that context, the present study is conducted using a physical model termed MURI, located in the hydraulics and hydro-morphology laboratory of INRAE Lyon-Villeurbanne, France. It has been adapted to provide an adjustable porosity to one of the urban blocks. The purpose of this work is to quantify the impact of urban block porosity on the flow rate distribution, flow depth and velocity patterns in a small-scale flooded urban area. Three cases with the same boundary conditions but different urban block porosity values and a reference case (with non-porous urban block) are compared herein.

EXPERIMENTAL SET UP

The experimental facility consists of a 1.56 m long and 0.96 m wide rectangular urban block made of transparent plastic and 4 adjacent streets of width b = 0.15 m, see Figure 1. The urban block facades are porous or non-porous as required (due to openings in the walls). The total inflow is 6.5 l/s, distributed among 2 streets: 4.5 l/s on the right street and 2.0 l/s on the left street. The flow finally leaves the facility through 4 outflows, one on each street.

A honeycomb mesh has been placed in both inlet streets to straighten out the flow. At each outlet a weir ensures a subcritical flow. The weir is 3 cm high at the outlets 2, 3 and 4, and 4.0 cm high at the outlet 1. The bottom is made of PVC and the slope of the model in the longitudinal direction (x-axis) is 0.12%.

Flow configurations

The experiments were conducted under steady flow conditions and the boundary conditions were kept constant for all cases. Four cases were studied, a case (Case 0-0) consists of a non-porous urban block configuration (the openings in the facades are closed), and the 3 following cases consist of a porous urban block configuration: Case 0-1 with 1 opening on the right and left facades, Case 0-3 with 3 openings on the right and left facades, and Case 3-3 with 3 openings on the right and left facades and 3 openings on the upstream and downstream facades, see Figure 2.

The porosity in the urban block has been defined for each facade as follows:

ψ = opening length along the facade total length of the facade (1)

Table 1 shows the porosity values in the facades for each case studied. velocity distribution obtained in different cross sections by using an ADV (Acoustic Doppler Velocimeter) with a side-looking probe, with intervals between 2 points in the vertical direction of 0.6 cm and in the spanwise direction of 1.0 cm (velocity measured for 100 s at 100 Hz at each measuring point). Flow depths are measured along the streets using an US (Ultrasonic Distance Sensor), and the streamlines within the whole measuring domain are obtained using the LSPIV technique (Large Scale Particle Image Velocimetry).

EXPERIMENTAL RESULTS

All three cases with a porous urban block are compared with Case 0-0, which is considered as a reference case (non-porous urban block configuration). In the following, the different results are normalized by the reference case data.

Flow distribution

The flow rate values measured for the reference case at the four outlets and at the upstream end of each street around the urban block are summarized in Table 2. The outflow (Q out ) distribution for the different cases studied herein does not significantly vary. The values measured at the different outlets for the four cases are very similar. When noting the largest relative difference (denoted as ∆Q out ) of outlet discharge for a given outlet between a porosity case and the reference case is about 4%, and is observed at Outlet 1, for Case 0-1 and Case 3-3, see Figure 3. In contrast, the distribution of the flow rate in the streets (Qs) surrounding the urban block for the porosity cases significantly differs from the reference case distribution. Note that the variation in flow rate distribution increases with the urban block porosity. To observe these variations, the flow rates at the upstream end of each of these reaches are compared with the reference case. Figure 4 shows the relative difference (denoted as ∆Q s ) in these flow rates for each street and for each case.

Q out Δ (%) ∆Q out = Q outi -Q out r ef Q out r ef
When comparing cases 0-1 and 0-3, the variation in flow rate with respect to the reference case is greater in the case 0-3, at least in the right, left and upstream streets. These variations are also linked to what happens inside the block, which directly affects the direction of flow through the openings in the facades (Fig. 5 depicts the streamlines for the different cases). For instance, from Case 0-3 to Case 3-3, with an increasing porosity in the upstream and downstream facades, the flow rate of the downstream street strongly decreases. The porosity impacts the flow entering the street because the flow is directed from the urban block towards the street in this facade, see Figure 5, Case 3-3.

Q s Δ (%) ∆Q s = Q si -Q s r ef Q s r ef

Flow depth and bulk velocity

As observed for the outflow discharges, the flow depths (denoted as d) in the outlet regions of the experimental facility show no significant variation compared to the reference case. The same is observed along the upstream reaches on the right and left streets, see the upper charts in Figure 6. In contrast, flow depth change along the street reaches that surround the porous urban block with a change in porosity. The most notable variations can be found in the right and downstream streets, up to 8% and 10% respectively. Similarly to the flow rates, the flow depth variations increase with an increasing urban block porosity.

As flow rate and flow depth vary along the streets that surround the porous urban block, flow velocity also varies. The bottom graphs in each chart in Figure 6 show the variations in bulk velocity (denoted as U b ) computed from the measured flow depths and estimated flow rates. The greatest variations with respect to the reference case are observed in the left and upstream streets, and as in these streets the variation in flow depth is not very significant, the variation in bulk velocity is mainly related to the variation in flow rates.

CONCLUSIONS

The experiments conducted during this study showed that the urban block porosity has a strong impact on the flow characteristics of the surrounding streets (up to 70% difference with respect to non-porous urban block for velocity and flow rate, with the conditions tested here), while the areas downstream and upstream of the urban block do not show a great difference with the case of a non-porous urban block (5% maximum for velocity).

The influence of the urban block porosity on the flow in the streets proved to be greater when the porosity increases. This flow is also influenced by the flow pattern within the urban block, especially whether flow exits or enters the block through the porous wall.

Flow velocity and flow depth within streets are hydraulic parameters that are taken into account by urban flood studies to assess the level of flood hazard. Therefore, the proper estimate of the level of flood hazard requires to take into account the urban block porosity and its impact on the local flow characteristics.

Figure 1 .

 1 Figure 1. Top view of the experimental set-up comprising two longitudinal streets, two transverse streets, and a porous urban block.

UpstreamFigure 2 .

 2 Figure 2. Sketch of the 4 studied cases with different urban block porosities.

Figure 3 .

 3 Figure 3. Relative difference in flow rate at each outlet (∆Q out ) with respect to Case 0-0.

Figure 4 .Figure 5 .

 45 Figure 4. Relative difference in flow rate at the upstream end of each street surrounding the urban block (∆Q s ) with respect to the reference case.

Figure 6 .

 6 Figure 6. Relative difference in flow depth (∆d) and bulk velocity (∆U b ) with respect to the reference case. Measurements along the centerline of each street. Vertical dashed lines show the location of the openings along the block facades, and vertical dash dotted lines indicate the location of the corresponding streets.

Table 1 .

 1 Porosity values for each case.

Table 2 .

 2 Flow rates (l/s) for the reference case (Case 0-0).

			Outlet
					Right	Left	Upstream Downstream
	1	2	3	4	Street Street	Street	Street
	1.95 1.60 1.54 1.44	3.95	1.11	0.55	1.99

ACKNOWLEDGEMENTS

The authors acknowledge the financial support offered by the French National Research Agency (ANR) for the project DEUFI (under grant ANR-18-CE01-0020).


S Proust

C Berni

V I Nikora

Emergence of Kelvin-Helmholtz instabilities in shallow mixing layers: An experimental study

Keywords: Shallow shear flows, coherent structures, shear layer turbulence

Free-surface shallow mixing layers behind a splitter plate were studied in a flume of rectangular cross-section consisting of a bed and sidewalls made of glass. The focus is on the effects on the emergence of horizontal Kelvin-Helmholtz-type coherent structures (KHCSs) of vertical flow confinement (i.e., flow depth) and dimensionless velocity shear λ = (U x2 -U x1 )/(U x2 +U x1 ), where U x1 and U x2 are two flow velocities characterizing the low-speed stream and high-speed stream, respectively. Two flow depths were tested, with the λ-value at the flume entrance varying between 0.1 to 0.8, with a threshold value λ t = 0.3 needed for the emergence of KHCSs in compound open-channel flows being inside this range. This paper reports new data that provide a better insight in the interplay between the constraining effect of flow depth, damping effect of three-dimensional bed-induced turbulence, and triggering effect of dimensionless shear on the formation of KHCSs.

INTRODUCTION

It has been recently shown that the emergence and development of Kelvin-Helmholtz-type coherent structures (KHCSs) in streamwise-depth-uniform and non-uniform flows in compound open channels [START_REF] Proust | Mixing layer and coherent structures in compound channel flows: effects of transverse flow, velocity ratio and vertical confinement[END_REF][START_REF] Proust | Compound open-channel flows: effects of transverse currents on the flow structure[END_REF] are driven by the dimensionless velocity shear λ = (U x2 -U x1 )/(U x2 + U x1 ), where U x2 and U x1 are the time-averaged streamwise velocities outside the mixing layers within the high-speed stream and low-speed stream, respectively. KHCSs can emerge once λ exceeds a critical value (λ t = 0.3) above which KHCSs length-scales increase with the shear [START_REF] Proust | Compound open-channel flows: effects of transverse currents on the flow structure[END_REF], irrespective of the vertical flow confinement. It is important to note that λ ≥ 0.3 is a necessary condition but not a sufficient one, as observed by [START_REF] Proust | Mixing layer and coherent structures in compound channel flows: effects of transverse flow, velocity ratio and vertical confinement[END_REF]. KHCS emergence also requires the presence of an inflection point in the time-averaged streamwise velocity profile so that Rayleigh's criterion is fulfilled (e.g., [START_REF] Huerre | Hydrodynamic instabilities in open flows[END_REF].

On the other hand, in the case of shallow mixing layers in non-compound open-channels with a rectangular cross-section, the experiments of e.g., [START_REF] Chu | Confinement and bed-friction effects in shallow turbulent mixing layers[END_REF] and [START_REF] Uijttewaal | Effects of shallowness on the development of free-surface mixing layers[END_REF] have evidenced the constraining effects of vertical flow confinement and bed-friction on the development of large coherent structures that may populate shallow mixing layers. The present study, conducted in a laboratory flume with a rectangular cross-section, is an attempt to get a better insight in the interplay between the constraining effect of the flow depth, the damping effect of three-dimensional bed-induced turbulence, and the triggering effect of dimensionless shear on the formation of KHCSs. As an initial step, a simpler case of hydraulically smooth bed and sidewalls is considered.

Inflow discharges

Flow parameters at x = 2.4 m

Cases D x,y Q Q 1 Q 2 U x1 U x2 U x,m λ Fr 1 Fr 2 [mm] [l s -1 ] [l s -1 ] [l s -1 ] [cm s -1 ] [cm s -1 ] [cm s -

EXPERIMENTS

The experiments were conducted in an 18 m long and 2 m wide open channel flume with a rectangular cross-section composed of a bed and sidewalls, both of which are made of glass (Figure 1). The flume is equipped with two independent inlet tanks (1.7 m long and 1 m wide each). Each tank is supplied with water through a tower with a constant water level reservoir. The flow rate in the left-hand tank (Q 2 ) and the flow rate in the right-hand tank (Q 1 ) are monitored with two independent flow meters. A Cartesian right-handed coordinate system is used in which x-axis is in the longitudinal direction parallel to the flume bottom, y-axis is in the spanwise direction, and z-axis is in the vertical direction (perpendicular to the flume bed). The system origin is defined as: x = 0 at the outlet of the inlet tanks; y = 0 at the right-hand sidewall; z = 0 at the channel bottom. The flow partition is maintained until x = 0.75 m by a vertical splitter plate.

The flow conditions of the test cases are presented in Table 1. Each test case is identified by its values of Q 1 and Q 2 , e.g. case 15-45 corresponds to Q 1 = 15 l s -1 and Q 2 = 45 l s -1 . Two total flow rates Q were used (60 and 30 l s -1 ) to vary the vertical flow confinement. Given a Q-value, the flow partition between the two inlet tanks was varied: five cases were investigated for Q = 60 l s -1 and three cases for Q = 30 l s -1 . The two flow cases with equal discharges Q 1 and Q 2 (15 and 15 l s -1 or 30 and 30 l s -1 ) will serve as reference flow conditions.

Water surface elevation Z and bed elevation Z b were measured using an ultrasonic probe (Baumer UNDK) for 180 s at 50 Hz at each measuring point, from x = 1 m to 17.3 m at lateral positions y = 0.5 m, 1 m, and 1.5 m (three longitudinal transects). A spatially averaged flow depth D x,y is then computed from the data of local flow depth D = Z -Z b . For the two uniform flow cases 30-30 and 15-15, the mean flow depth (averaged across and along the flow) was found to be D x,y = 59.0 and 40.1 mm, respectively. Figure 2 shows a longitudinal profile of flow depth D for these flow cases at y = 1 m.

Velocity was measured using an Acoustic Doppler Velocimeter (3-D Nortek Vectrino+) with a side looking probe (sampling volume 5 cm away from the probe). At each measuring point, the three instantaneous velocity components u x , u y , u z were recorded at 100 Hz for 300 s. Transverse profiles of velocity were measured at downstream positions x = 2.4, 4.4, 6.4 (for case 15-45 only), 8.4, 12.4, and 16.4 m, with a spatial interval of 5 cm in the spanwise direction, and at elevation z = 30 mm for Q = 60 l s -1 and z = 19 mm for Q = 30 l s -1 (i.e. at mid water depth of the reference uniform cases 15-15 and 30-30).

EXPERIMENTAL RESULTS

Time-averaged velocity and turbulence statistics

Spanwise distributions of time-averaged streamwise velocity U x , transverse Reynolds shear stress -ρu x u y , and transverse turbulence intensity ρu y 2 are shown in Figure 3 for cases 15-45 and 25-35. For these two cases, vertical flow confinement is similar (D x,y = 56.8 and 56.9 mm, respectively, Table 1) and inflection points in U x -distributions are observed for both cases at all x-positions.

The velocity measurements indicate that turbulence statistics in the upstream half-flume are noticeably higher for 15-45 than for 25-45 (see the data from x = 2.4 to 8.4 m). This is due to the higher values of destabilizing shear U x2 -U x1 [START_REF] Huerre | Hydrodynamic instabilities in open flows[END_REF] for the former case, where U x2 and U x1 are the time-averaged streamwise velocities outside the mixing layers within the high-speed stream and low-speed stream, respectively. Note that, here, U x1 is the velocity averaged across the plateau of U x (y) on the low speed stream side when a plateau can be observed or is the minimum value of U x on the same side; and U x2 is the velocity averaged across the plateau of U x (y) on the high speed stream side when a plateau can be observed or is the maximum value of U x on the same side.

Effect of the dimensionless velocity shear λ

Similarly to [START_REF] Proust | Mixing layer and coherent structures in compound channel flows: effects of transverse flow, velocity ratio and vertical confinement[END_REF] and [START_REF] Proust | Compound open-channel flows: effects of transverse currents on the flow structure[END_REF], we have studied the effect of the dimensionless velocity shear λ = (U x2 -U x1 )/(U x2 + U x1 ) [START_REF] Brown | On density effects and large structure in turbulent mixing layers[END_REF] on the planform shear layer turbulence. The longitudinal profiles of λ for all cases are shown in Figure 4. In the present paper, we have focused on the effect of λ on the peak values of turbulence statistics The abrupt change in shear stress is related to a change in the turbulence structure. This is shown in Figure 6 where power density P yy of transverse velocity fluctuations u y is plotted as a function of signal frequency f . The high values of Reynolds stress are always associated with the presence of large coherent structures (KHCSs), whose spectral signature is a sharp bump in the medium range of frequency f with a -3 slope on the right-hand side of the bump. For instance, for case 7-23, the two high values of -ρu x u y in Figure 5e are related to the two sharp bumps in Figure 6 (top left plot, data with λ = 0.44 and 0.39). Similar results are obtained for case 15-45 (see the data in Figure 5f with λ > 0.3, and the data in Figure 6 with λ = 0.44, 0.43, and 0.35).

When the flow is weakly sheared (case 25-35 with λ = 0.14 at x = 2.4 m, Table 1, Figure 4) or non-sheared (uniform flow case 15-15), the absence of bump in the power density spectra reflects the absence of KHCSs (Figure 6), resulting in very low shear stress values (Figures 5a,b,e,f).

Lastly, case 5-55 that is highly sheared (Table 1, Figure 5), and whose Froude number F r 2 reaches 1.7, appears to have a specific behavior when focusing on Figure 5f. The sharp change in Reynolds stress occurs at λ = 0.55 (instead of 0.3 for the moderately flow cases). This may be due to the highest values of velocity U x,m = (U x2 + U x1 )/2 (usually considered as an approximation of the convection velocity of eddies) and also to a Froude number effect on the KHCSs in the upstream half-flume with a lateral juxtaposition of a marked supercritical flow (F r 2 = 1.7) and a marked subcritical flow (F r 1 = 0.2).

Effect of flow confinement and bed-induced turbulence

Figure 6 demonstrates that shallowness has no effect on the emergence of large planform coherent structures. For instance, the flow depth D x,y equals to 38.4 mm for case 07-23 and to 40.1 mm for case 15-15 (Table 1), while the signature of KHCSs is present for the shallower flow (07-23) and absent for the deeper flow . A similar result is obtained when comparing case 15-45 and case 25-35 (values of D x,y in Table 1, Figure 6). Figures 3 and6 additionally show that turbulence statistics significantly vary from one case to another for a fixed z-elevation and flow confinement, reflecting that flow confinement is not the driving parameter in forming the turbulence structure.

In contrast, flow confinement appears to accelerate the decay in the destabilizing shear U x2 -U x1 (not shown here) and in dimensionless velocity shear λ along the longitudinal direction Regarding the potential damping effect of bed-induced turbulence on planform transverse motions [START_REF] Chu | Confinement and bed-friction effects in shallow turbulent mixing layers[END_REF][START_REF] Uijttewaal | Effects of shallowness on the development of free-surface mixing layers[END_REF], some present data do not support this previous result reported in the literature. For instance, let us focus on the distributions of ρu x2 for both cases 25-35 and 15-45 at x = 8.4 m (for which λ = 0.12 and 0.35, respectively) outside the mixing layer (y < 0.3 m). We can see that ρu

CONCLUSIONS

Free-surface shallow mixing layers behind a splitter plate were studied in 18 m long and 2 m wide laboratory channel with a glass bed. The focus was on assessing the effects of vertical flow confinement (i.e., flow depth) and dimensionless velocity shear λ = (U x2 -U x1 )/(U x2 + U x1 ) on the emergence of horizontal Kelvin-Helmholtz-type coherent structures (KHCSs). Two flow depths were tested, with the λ-value at the flume entrance varying between 0.1 to 0.8. It was found that the emergence and development of KHCSs was primarily driven by velocity shear λ, irrespective of flow depth. Same result holds for turbulence statistics. In addition, the threshold value λ t = 0.3 needed for the emergence of KHCSs in compound open-channel flows, appears to be also valid in non-compound channels with a rectangular cross-section. The results also showed that three-dimensional bed-induced turbulence had very little impact on the large transverse turbulent motions. Some two-point velocity measurements (to estimate KHCSs length-scales) will be conducted to confirm this preliminary results.

Figure 1 .

 1 Figure 1. Open-channel flume located in the Hydraulics and Hydro-morphology Laboratory (HHLab) at INRAE, Lyon-Villeurbanne. Experiments with a working length = 18 m and a working width = 2 m (right-hand 2/3 of the total flume width with bed and sidewalls made of glass).Table 1. Flow conditions of the test cases: mean flow depth (averaged across and along the flow) D x,y ; total inflow discharge Q and left-hand and right-hand inflow discharges Q 2 and Q 1 ; timeaveraged streamwise velocities U x2 and U x1 , velocity U x,m = (U x2 + U x1 )/2, dimensionless velocity shear λ, Froude numbers F r 2 = U x2 / gD x,y and F r 1 = U x1 / gD x,y at x = 2.4 m.

Figure 2 .

 2 Figure 2. Streamwise profiles of local flow depth D at transverse position y = 1 m for the two uniform flow cases 15-15 and 30-30.

Figure 3 .

 3 Figure 3. Spanwise distributions of time-averaged streamwise velocity U x , transverse Reynolds shear stress -ρu x u y , transverse normal turbulent stress ρu y 2 , and streamwise normal turbulent stress ρu x 2 for two cases with total flow rate Q = 60 l s -1

Figure 4 .

 4 Figure 4. Streamwise profiles of the dimensionless velocity shear λ = (U x2 -U x1 )/(U x2 + U x1 ) for all cases, at elevation z = 30 mm for Q = 60 l s -1 and z = 19 mm for Q = 30 l s -1 .

Figure 5 .

 5 Figure 5. Streamwise profiles of the maximum values across the channel of the transverse Reynolds shear stress -ρu x u y (a-b) and transverse normal turbulent stress ρu y 2 (c-d), and maximum Reynolds shear stress as a function of local dimensionless velocity shear λ (e-f). Measurements are taken at z = 30 mm for 60 l s -1 and at z = 19 mm for 30 l s -1 .

Figure 6 .

 6 Figure 6. Power density P yy of transverse velocity fluctuations u y as a function of signal frequency f . Measurements are taken at y = 0.9 m and z = 19 mm for cases 15-15 and 7-23, and at z = 30 mm for cases 15-45 and 25-35.

in this flow region only reflects bed-induced-turbulence intensity, we can state that this intensity is the same outside the mixing layer on the right-hand side of the flow. However, the distributions of u y 2 and Reynolds shear stress at x = 8.4 m (Figure5) and the spectra (Figure6, λ = 0.35 for case 15-45 and λ = 0.12 for case 25-35) both highlight the presence of KHCSs in case 15-45 but not in case25-35.