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EGU Award Ceremony (Credit: EGU/Foto Pfluegl)

Outstanding Student and PhD candidate Presentation (OSPP) Awards 2022 Athanasios Serafeim

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European Geosciences Union

Athanasios Serafeim

Athanasios Serafeim
Athanasios Serafeim

HS Hydrological Sciences

The 2022 Outstanding Student and PhD candidate Presentation (OSPP) Award is awarded to Athanasios Serafeim for the poster/PICO entitled:

Parametric model for probabilistic estimation of water losses in water distribution networks: A large scale real world application to the city of Patras in western Greece (Serafeim, A.; Kokosalakis, G.; Deidda, R.; Karathanasi, I.; Langousis, A.)

Click here to download the poster/PICO file.

Athanasios V. Serafeim holds a 5-year Diploma degree in Civil Engineering, currently working under the supervision of Prof. Andreas Langousis toward finalizing his PhD Thesis at the University of Patras in Greece (expected graduation date October 2022). His PhD research focuses on the development of an integrated, theoretically founded, and practically applicable methodological framework for resilient reduction of leakages in water distribution networks (WDNs), which combines: a) a set of probabilistic approaches for minimum night flow (MNF) estimation and parametric modeling of water losses in WDNs, and b) a combination of statistical clustering and hydraulic modeling techniques for WDN partitioning into pressure management areas (PMAs; or districted metered areas, DMAs). Athanasios is also experienced in numerical and experimental simulation of flows, environmental data analysis, stochastic simulation of hydrosystems, and hydrologic risk modeling.
The awarded OSPP presentation discussed the development of a probabilistic framework for minimum night flow (MNF) estimation in water distribution networks that: 1) parametrizes the MNF as a function of the network’s specific characteristics, and 2) parametrically describes water losses in individual PMAs as a function of the inlet/operating pressures. The developed framework was validated through flow-pressure tests in 78 PMAs of the network, indicating that the developed framework can be effectively used to improve water loss estimation and flow-pressure management in a morphologically and operationally diverse set of PMAs.