The Water Network Tool for Resilience (WNTR) is a Python package widely used for the simulation and analysis of Water Distribution Networks (WDNs), providing tools such as network modification, pressure-dependent demand simulation, and resilience evaluation. Although it produces time-varying data reflecting WDN activity, it requires the complete parameter configuration at the beginning of the simulation and it requires a full restart when modifying network parameters such as pipe leaks or demand variations. Here, we present Dyn-WNTR, an extension that enables dynamic adaptation of network parameters during simulation. By integrating novel functions for real-time network updates, Dyn-WNTR allows modifications, such as the introduction of leaks or demand variations, without requiring a simulation restart. This significantly improves the flexibility of hydraulic simulations, allowing more efficient scenario testing, real-time optimization, and adaptive control strategies. We also release a dataset with dynamic simulations under diverse operating conditions and network events. To encourage a broad use and future developments, the source code of Dyn-WNTR and the dataset are publicly available. By reducing computational costs and improving flexibility, Dyn-WNTR extends the capabilities of WNTR towards more advanced and real-time frameworks, particularly suited for reinforcement learning applications and digital twin modeling, where continuous interaction with the simulation environment is essential.
Dyn-WNTR: dynamic network adaptive extension for hydraulic simulations with WNTR / Locatelli, P., Cattai, T., Palumbo, S., Cuomo, F.. - (2025). (IFIP Networking 2025 Limassol; Ciprus ).
Dyn-WNTR: dynamic network adaptive extension for hydraulic simulations with WNTR
Pierluigi Locatelli
;Tiziana Cattai;Simone Palumbo;Francesca Cuomo
2025
Abstract
The Water Network Tool for Resilience (WNTR) is a Python package widely used for the simulation and analysis of Water Distribution Networks (WDNs), providing tools such as network modification, pressure-dependent demand simulation, and resilience evaluation. Although it produces time-varying data reflecting WDN activity, it requires the complete parameter configuration at the beginning of the simulation and it requires a full restart when modifying network parameters such as pipe leaks or demand variations. Here, we present Dyn-WNTR, an extension that enables dynamic adaptation of network parameters during simulation. By integrating novel functions for real-time network updates, Dyn-WNTR allows modifications, such as the introduction of leaks or demand variations, without requiring a simulation restart. This significantly improves the flexibility of hydraulic simulations, allowing more efficient scenario testing, real-time optimization, and adaptive control strategies. We also release a dataset with dynamic simulations under diverse operating conditions and network events. To encourage a broad use and future developments, the source code of Dyn-WNTR and the dataset are publicly available. By reducing computational costs and improving flexibility, Dyn-WNTR extends the capabilities of WNTR towards more advanced and real-time frameworks, particularly suited for reinforcement learning applications and digital twin modeling, where continuous interaction with the simulation environment is essential.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


