Aleixo, Rui

Antico, Federica

Ferreira, Rui M. L.

Mendes, Luís

English

Hydraulic Engineering Repository

Article, Published VersionAleixo, Rui; Mendes, Luís; Antico, Federica; Ferreira, Rui M. L.MiCas: from pressure signals to counting bedload particlesHydrolinkVerfügbar unter/Available at: https://hdl.handle.net/20.500.11970/109357Vorgeschlagene Zitierweise/Suggested citation:Aleixo, Rui; Mendes, Luís; Antico, Federica; Ferreira, Rui M. L. (2017): MiCas: from pressuresignals to counting bedload particles. In: Hydrolink 2017/1. Madrid: International Associationfor Hydro-Environment Engineering and Research (IAHR). S. 17-19. https://iahr.oss-accelerate.aliyuncs.com/library/HydroLink/HydroLink2017_01_Sensors.pdf.Standardnutzungsbedingungen/Terms of Use:Die Dokumente in HENRY stehen unter der Creative Commons Lizenz CC BY 4.0, sofern keine abweichendenNutzungsbedingungen getroffen wurden. Damit ist sowohl die kommerzielle Nutzung als auch das Teilen, dieWeiterbearbeitung und Speicherung erlaubt. Das Verwenden und das Bearbeiten stehen unter der Bedingung derNamensnennung. Im Einzelfall kann eine restriktivere Lizenz gelten; dann gelten abweichend von den obigenNutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Documents in HENRY are made available under the Creative Commons License CC BY 4.0, if no other license isapplicable. Under CC BY 4.0 commercial use and sharing, remixing, transforming, and building upon the materialof the work is permitted. In some cases a different, more restrictive license may apply; if applicable the terms ofthe restrictive license will be binding.17hydrolink number 1/2017IAHRTo characterize the morphological evolution ofloose bed channels it is necessary to develop adeep understanding of bedload transportprocesses at several scales. Despite theadvancements made in the last 60 years, drivenby the improvements in measurementtechniques, research efforts on grain-scalemechanics of bedload are still required,especially to clarify the intermittent nature ofbedload, its stochastic structure and its scaledependence.Several types of instrumentation have beenemployed to pursue this research. Existinginstrumentation can be grouped into: i) weighing methods, that measure thecumulative weight of the sediments, ii) impact methods, that count the impacts ofparticles on a sensitive plate and iii) digital image processing methods, thatrely on image acquisition and processing toidentify and count the particles. For bedload discharge purposes, most of theexisting devices, independently of the methodgroup that belong to, are not designed to workin recirculating flumes (in the application ofweighing methods), and/or do not have thecapability to resolve individual particles andparticle flow rates in a time resolved manner (inthe application of weighing methods and someimpact methods), or are too expensive in termsof data storage requirements and processingtimes (for the use of image processingmethods). The MiCas device, used in a new pressure-based impact method, is intended to overcomethose limitations. It has been initially developedby researchers of the Fluid Mechanics for theBuilt and Natural Environment (FMBNE) Groupof the Civil Engineering Research and Innovationfor Sustainability (CERIS) Center and is currentlyinstalled in the Hydraulics Laboratory of InstitutoSuperior Técnico of the University of Lisbon. Itwas designed to meet the following key require-ments: simple data output composed of timeinstant and location of impacts; no need forpost-processing – impacts determined throughMiCas: FROM PRESSURE SIGNALS TO COUNTING BEDLOADPARTICLESBY RUI ALEIXO, LUÍS MENDES, FEDERICA ANTICO & RUI M.L. FERREIRAA new device to measure bedload transport, MiCas, is presented. It analyzes pressure time seriesby recognizing the imprints of impacts of individual particles as they hit pressurized membranes. A pattern analysis algorithm is used to identify the impact events. The implementation of thisprinciple in a dedicated microprocessor allows for real-time measurements of particle hits andcumulative particle count. MiCas particle counts correlate well with the results of image analysis.MiCas provides hardware-based measurements, hence its key advantages of minimal needs ofdata storage and low processing times to retrieve bedload discharge rates.Figure 1. Schematic of the key mechanical features of the MiCas systemhardware and firmware; capable of computingsimple statistics in real time such as cumulativeparticle counting and discrete lateral distributionof cumulative particle counts; able to run forvery large periods (days, weeks); ability todetect particle impacts of large size fractionsthat are separated by a few milliseconds;composed of robust and relatively cheapcomponents.These characteristics allow the collection of verylong time series of particle impacts since theoutput data are provided in a file with positionsand instants of particle hits, which is relativelysmall (order of kb or a few Mb), compared tothe size of the high-speed video footageneeded to extract similar information (order ofhundreds of Mb or Gb). Data can be collectedwith very high temporal resolution sincesuccessive impacts distanced about 12 ms intime can be identified. Characterization of inter-mittency (Ancey et al. 2008) and discussion ofscale effects in the definition of bedload rates(Ballio et al. 2014) can be based on the dataFigure 2. Overall view of MiCas system from downstream with membrane boxesinstalled in the flume visible in blue. Each membrane box constitutes a binSensors, data acquisition & analysis softwareThe analog Multiplexer unit (MUX) follows,connecting in round-robin fashion, one of thePS1,PS2 or PS3 sensors output signal to theinput of the Programmable Gain Amplifier (PGA)unit. The PGA output is then connected to the8th order Butterworth Low Pass Filter (LPF-8)input, unit, which contributes for the aliasingeffect reduction. Following the LPF-8 unit outputthe signal enters the Analog to Digital Converter(ADC) to be sampled by the microcontroller(µC). The digital sampled signal is thenprocessed with Digital Signal Processingtechniques to extract the relevant signal charac-teristics that define a bead impact on themembrane box surface. Those characteristicsare then fed to a pattern recognition algorithm toidentify the presence of the impact pattern. If animpact is identified, then the instant of theimpact detection is recorded and sent to theclient terminal connected through the RS-232and USB interfaces. A Liquid Crystal Display(LCD) monitor is also updated with the numberof impacts detected and displays a graphic thatshows the lateral bedload discharge distributionin real-time. SIGNAL PROCESSING AND PARTICLEIDENTIFICATIONWhen a particle impacts the membrane, itgenerates a pressure signal. This pressuresignal is converted into an electrical signal bymeans of a pressure transducer. This signal isthen filtered and analysed to be confirmed as aparticle impact. It is important to identify thesignature of a particle impact on the membrane,being able to distinguish it from other signals’signatures like for instance, the turbulencesignal. To do so, the k-Nearest Neighbour (k-NN)algorithm is used in the signal processing andanalysis. The k-NN is a pattern recognitionalgorithm that can be fitted within the machinelearning sub-area of artificial intelligence. Byfitting an unknown measurement data asbelonging to the class of its nearest alreadyknown classified measurement that is in theagent knowledge base, where for this case, theagent is MiCas. For this case three classes ofevents are considered: 1) particle impact, 2)silence (i.e. background noise) and 3) turbu-lence. For each class the system is trained usingthe MiCas only subjected to each event. Forexample, the training for particle impact classwas made with impacts produced by droppingindividual particles at random locations of themembrane in still water. After the trainingprocess, the system can identify the differentevents.provided by this system. Further details aboutthe design process and applications can beconsulted in the website of FMBNE(http://www.fluidmechenv.tk/). Preliminary resultsand detailed description of the key features ofthe system have been published in Mendes etal. (2016).MiCas – THE PRESSURE-BASEDIMPACT PARTICLE COUNTERMiCas stands for Medidor integrado deCaudais sólidos, in Portuguese, or integratedsystem for solid discharge measurement inEnglish. This device counts impacts on asensitive surface and was conceived to beinstalled at the downstream end of a mobilebed reach of a laboratory recirculation flume, assketched in Figure 1. It is divided in twosubsystems: mechanical and electronic. Themechanical system includes the sensitiveelement (blue surface in Figure 2), composed ofa hollow box filled with air at a certain pressure,P, connected to a pressure transducer throughsilicon tubes (the tubes passing near the flumelateral glass walls, as depicted in Figure 2). Theupward face of this prism is made of a rubbermembrane. Each pair of boxes is separated bya plastic surface that extends upward anddownstream in relation to the actual box dimen-sions. It is placed in such a way as to avoidpropagation of pressure waves to adjacentboxes, thus reducing sources of possible falseimpact detections (white vertical surfaces inFigure 2). For the experimental installation atFMBNE 10 membrane boxes were made andidentified from 1 to 10. Each membrane box isconsidered a bin. This allows characterizing thelateral distribution of bedload with a spatialresolution equal to the membrane box width.Sediment particles hitting the rubber surfacecause a pressure variation that is measured bythe pressure transducers. A sequence ofparticles impacting the membrane boxes isshown in Figure 3.The pressure transducers are part of theelectronic subsystem that convert the pressuresignal into an electrical signal, which is thensampled and analysed with Digital SignalProcessing and Patten Recognition (DSP)techniques to be validated as a particle impact.A simplified block diagram of the MiCas systemis depicted in Figure 4. The Pressure Sensors(PS1,PS2,PS3) convert the correspondingpressure signal variations into a voltage signal,which is then filtered by the respective 1st orderLow Pass Filters units (LPF-1) for noise removal.18 hydrolink number 1/2017Figure 3. Sequence of 5 mm glass beads hitting the membrane boxes. Hydraulic test with Fr = 0.756, θ = 0.014 and Φ = 0.001 (where Fr, θ and Φ stand for Froude number, Shields parameter and non-dimensional bedload rate). All frames are 0.25 seconds apart except frames 2 and 3 that are 0.10seconds apartFigure 4. Simplified block diagram of the MICAS system. PS1 to PS3 are the pressure transducers. LPFrepresents the low pass filter unit. MUX is the MiCas Multiplexer and PGA is the programmable gainamplifier. LPF 8 is an 8th order anti-aliasing Butterworth filter block. The ADC is the analog-to-digitalconverter that converts the analog signal into a digital form to be then processed by a microcontroller.This microcontroller has outputs for an LCD screen and a PCFigure 5. Spectrogram obtained from pressuresensor sampled signal of an operating flume withoccasional bead impacts on the membrane box.The red colour in the spectrogram correspond tohigher intensity signals. Vertical red lines denote a possible impact while the horizontal blue linesdenote the 150Hz-400Hz range. Scale is in dBSensors, data acquisition & analysis software19hydrolink number 1/2017IAHRExperiments were carried out in the 12.5 m longand 40.5 cm wide glass-sided flume of theLaboratory of Hydraulics and Environment ofInstituto Superior Técnico, University of Lisbon.This flume has two independent circuits forwater and sediment recirculation. A cohesion-less granular bed, composed of 4 layers of 5mm glass beads, subjected to a steady-uniformturbulent open-channel flow, was analysed. Alltests featured a period of 90 s data collection.For a detailed description of the laboratory facil-ities and test conditions see Mendes et al, 2016. The cumulative particle count results aredepicted in Figure 6.Rui Aleixo has a PhD in Engineering Sciences fromUniversité catholique deLouvain, Belgium. After apost-doc in the NationalCenter for ComputationalHydroscience and Engineering (USA) hejoined the Fluid Mechanics for the Built andNatural Environment Group of CERIS, InstitutoSuperior Técnico. He is currently working inthe University of Bologna. He is the IAHRExperimental Methods and Instrumentationcommittee (EMI) chair for the period 2015-2017. As EMI chair, he co-organized theW.A.T.E.R. Summer School and HydroSenSoft2017 conference.Luís Mendes startedworking as a softwareengineer back in 2007following his masters ininformatics and computerengineering at InstitutoSuperior Técnico. Heworked for several companies including NokiaSiemens Networks. In early 2015 he completeda B.Sc. for the electronics and computerengineering field at Instituto Superior deEngenharia de Lisboa, complementing theprevious expertise on software. SinceFebruary 2014 he joined the CERISmultidisciplinary research team at Instituto Superior Técnico.Federica Antico is a CivilEngineer graduated fromUniversity of Padova,Italy, in 2013, with specialization in Hydraulic Engineering. InMarch 2014, she was enrolled in the project“Sediment transport in fluvial, estuarine andcoastal environment” (SEDITRANS), as PhDCandidate at Instituto Superior Técnico(IST), Lisbon, Portugal. Her PhD researchfocuses on the experimental study ofbedload mechanics at grain-scale in agranular bed subjected to a steady-uniformturbulent open-channel flow.Rui M.L. Ferreira isAssociate Professor ofInstituto SuperiorTécnico, Universidade deLisboa and a CERISsenior researcher. For thepast 20 years, he hasbeen involved in experimental research influvial, estuarine and coastal processes,development of laboratory instrumentationand mathematical modelling of free-surfaceflows. He is a member of the leadershipteams of the Fluvial Hydraulics Committeeand of the Experimental Methods and Instrumentation Committee of IAHR.The results obtained with the two techniques arein good agreement. The measurements carriedout allowed to demonstrate that the MiCassystem is able to track particle impacts in real-time within an error margin of 2.0%. Differenttests performed under the same conditionsproved the repeatability of the MiCas systemmeasurements.The main advantages of MiCas relatively todigital image processing methods are: a) independence from optical access, thusavoiding problems with light intensity varia-tions and oscillating free surfaces; b) small volume of data associated to particlecounting, which allows the acquisition of verylong data series (hours, days) of particleimpacts. In the cases that were tested, itwould take more than two hours to generate1 MB of data with MiCAS. For the currentvalidation tests, 90 s acquisition timegenerated 25 Gb of data using the digitalmethod, but only 11 kB of MiCas data tomonitor the same experiment. On the otherhand, the time necessary to process thedigital images may be days, effectivelylimiting its usage to small time series.c) MiCas offers the possibility of real-timemeasurements, allowing for detection ofproblems during the experiments andminimizing some post-processing steps.On the downside, the MiCas system does notachieve sub-particle resolution nor highlyresolved lateral particle distribution. However,the MiCas bins can provide a coarse but reliablelateral bedload distribution. nReferences Mendes L., Antico F., Sanches P., Alegria F., Aleixo R., and FerreiraRML. (2016). A particle counting system for calculation of bedloadfluxes. Measurement Science and Technology.Ancey, C; Davison, A. C.; Böhm, T. B.; Jodeau M.; and Frey, Ph.Entrainment and motion of coarse particles in a shallow waterstream down a steep slope, in Journal of Fluid Mechanics, vol.595, p. 83-114, 2008.Ballio F., Nikora V., and Coleman S. E. (2014) On the definition of soliddischarge in hydro-environment research and applications. J.Hydraul. Res. 52, 173–84. FAA key feature of this system is the possibility ofdoing real-time measurements. In fact, thecomputations are made at the hardware levelthus allowing fast time processing. The imple-mented MiCas system consisted of 10independent membranes. The micro-controlleravailable analog to digital converter (ADC) unitswere multiplexed to scan every membrane box,thus saving costs, by reducing the number ofADC units to the ones available on-chip.Figure 5 depicts the spectrogram obtained fromone of the pressure transducers of the MiCassystem. The darker spots aligned along thevertical indicate possible impacts. Each of theseevents is analysed through the k-NN algorithmto check if it corresponds to a particle impact ornot.VALIDATION OF THE MiCas SYSTEMTo validate the results obtained by the MiCassystem, its particle cumulative count wascompared with the count obtained from theanalysis of a high-speed video footage. Eachparticle impact was tagged with membrane boxidentification (from bin 1 to bin 10) and therespective instant.Figure 6. Cumulative bead counting for two tests performed under conditions of weak bedload transport.The main flow characteristics are: flow rate, Q=0.0167m3/s; steady-state flow depth, h=0.067m; slope ofthe flume, i=0.0022; Froude number, Fr=0.756; Reynolds number, Re=46057; particle Reynolds number,Re*=182.9, Shields parameter, θ=0.014; non-dimensional bedload discharge, Φ= 0.001

MiCas: from pressure signals to counting bedload particles

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MiCas: from pressure signals to counting bedload particles

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