Exploring the optimal experimental setup for surface flow velocity measurements using PTV
Advances in flow monitoring are crucial to increase our knowledge on basin hydrology and to understand the interactions between flow dynamics and infrastructures. In this context, image processing offers great potential for hydraulic monitoring, allowing acquisition of a wide range of measurements with high spatial resolution at relatively low costs. In particular, the particle tracking velocimetry (PTV) algorithm can be used to describe the dynamics of surface flow velocity in both space and time using fixed cameras or unmanned aerial systems (UASs). In this study, analyses allowed exploration of the optimal particle seeding density and frame rate in different configurations. Numerical results provided useful indications for two field experiments that have been carried out with a low-cost quadrocopter equipped with an optical camera to record RGB videos of floating tracers manually distributed over the water surface. Field measurements have been carried out using different natural tracers under diverse hydraulic and morphological conditions; PTV’s processed velocities have been subsequently benchmarked with current meter measurements. The numerical results allowed rapid identification of the experimental configuration (e.g., required particle seeding density, image resolution, particle size, and frame frequency) producing flow velocity fields with high resolution in time and space with good agreement with the benchmark velocity values measured with conventional instruments.
KeywordsRiver flow monitoring Surface flow velocity UAS PTV
All authors made a substantial contribution to this paper. S.F.D. carried out all the numerical analysis, processed the data, analyzed the results, wrote the first draft, and managed the manuscript iterations among authors. S.M. designed the experiment, programmed the codes for numerical simulations, contributed to the interpretation of the results and writing. A.P. contributed to the data analysis, interpretation of the results and helped in writing the first draft. L.M. and C. S. contributed to the interpretation of the results and improvement of the document. All authors contributed to the field measurements.
This work has been funded by the COST Action CA16219 “HARMONIOUS—Harmonization of UAS techniques for agricultural and natural ecosystems monitoring” and was carried out within a scientific agreement between the Civil Protection Department of Basilicata, the Interuniversity Consortium for Hydrology (CINID), and the University of Basilicata to the start-up of the Basilicata Hydrologic Risk Center. AP thanks the support of the European Commission under the ELARCH program (Project Reference number 552129-EM-1-2014-1-IT-ERA MUNDUS-EMA21). This publication reflects only the authors’ view and the Commission is not liable for any use that may be made of the information contained herein.
- Adrian, R. J., & Westerweel, J. (2011). Particle image velocimetry. New York: Cambridge University Press.Google Scholar
- Cierpka, C., Lütke, B., & Kähler, C. J. (2013). Higher order multi-frame particle tracking velocimetry. Experiments in Fluids, 54(5).Google Scholar
- Gharahjeh S. and Aydın I.,(2015) Stream gauging by combined use of surface PTV and CFD techniques in channel flows, in:Proceedings of the 36th World Congress, The Hague Netherlands, .Google Scholar
- Kim, Y., Muste, M. V. I., Hauet, A., Krajewski, W. F., Kruger, A., & Bradley, A. A. (2008). Stream discharge using mobile large-scale particle image velocimetry: a proof of concept. Water Resources Research, 44, 1–6.Google Scholar
- Le Coz, J., Jodeau, M., Hauet, A., Marchand, B., & Le Boursicaud, R. (2014). Image-based velocity and discharge measurements in 424 field and laboratory river engineering studies using the free Fudaa-LSPIV software, Proceedings of the International 425 Conference on Fluvial Hydraulics. River Flow, 2014, 1961–1967.Google Scholar
- Leibundgut, C., & Maloszewski, P. (2011). Tracer Hydrology. In Tracers in hydrology. John Wiley & Sons.Google Scholar
- Manfreda, S., McCabe, M. F., Miller, P., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., Müllerová, J., Tauro, F., De Lima, M. I., De Lima, J. L., Frances, F., Caylor, K., Kohv, M., Maltese, A., Perks, M., Ruiz-Pérez, G., Su, Z., Vico, G., & Toth, B. (2018). On the use of unmanned aerial systems for environmental monitoring. Remote Sensing, 10(4), 641.CrossRefGoogle Scholar
- Muste, M., Fujita, I., & Hauet, A. (2008). Large-scale particle image velocimetry for measurements in riverine environments. Water Resources Research, 44(4).Google Scholar
- Patalano, A., García, C. M., & Rodríguez, A. (2017). Rectification of image velocity results (RIVeR): a simple and user-friendly toolbox for large scale water surface particle image velocimetry (PIV) and particle tracking velocimetry (PTV). Computers and Geosciences, 109, 323–330.CrossRefGoogle Scholar
- Raffel, M., Willert, C. E., Wereley, S., & Kompenhans, J. (2013). Particle image velocimetry: a practical guide. Springer.Google Scholar
- Tauro, F., & Grimaldi, S. (2017). Ice dices for monitoring stream surface velocity. Journal of Hydrology-Environment Resources, 14, 143–149.Google Scholar
- Tauro, F., Grimaldi, S., Petroselli, A., & Porfiri, M. (2012). Fluorescent particle tracers for surface flow measurements: a proof of concept in a natural stream. Water Resources Research, 48(6).Google Scholar
- Tauro, F., Petroselli A., Porfiri M., Giandomenico L., Bernardi G., Mele F., Spina D., and S. Grimaldi (2016), A novel permanent gauge-cam station for surface-flow observations on the Tiber River, Geoscientific Instrumentation Methods and Data Systems, 5(1), 828 241–251.Google Scholar
- Tauro F., Piscopia R., and S. Grimaldi (2017c), Measuring surface flow in natural streams through optical methods: large scale particle image velocimetry or particle tracking velocimetry? IAHS 2017 Scientific Assembly, 10–14 July 2017, Port Elizabeth, South Africa, IAHS2017–250.Google Scholar
- Tauro, F., Selker, J., van de Giesen, N., Abrate, T., Uijlenhoet, R., Porfiri, M., Manfreda, S., Caylor, K., Moramarco, T., Benveniste, J., Ciraolo, G., Estes, L., Domeneghetti, A., Perks, M. T., Corbari, C., Rabiei, E., Ravazzani, G., Bogena, H., Harfouche, A., Brocca, L., Maltese, A., Wickert, A., Tarpanelli, A., Good, S., Lopez Alcala, J. M., Petroselli, A., Cudennec, C., Blume, T., Hut, R., & Grimaldi, S. (2018). Measurements and observations in the XXI century (MOXXI): innovation and multidisciplinarity to sense the hydrological cycle. Hydrological Sciences Journal, 63(2), 169–196.CrossRefGoogle Scholar