This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive CFD simulations. Tesla valves offer efficient passive fluid control without the need for external power, making them favorable in various applications. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. This research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. This study reveals the emergence of “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to the non-uniform propagation of pressure throughout the system. This investigation employs advanced CFD simulations as a crucial tool to unravel the governing dynamics of transient flow within Tesla valve configurations. By elucidating underlying fluid dynamics, this study lays the groundwork for future Tesla valve design optimization, holding potential implications for applications where the control of transient flow events is crucial.

Historically, water utilities have relied on tried-and-true practices in the design and operation of their infrastructure, tapping new resources and expanding networks as needed. However, as the effects of climate change and/or urbanization increasingly impact both water supply and demand, utilities need new, holistic planning and management approaches. Integrated planning approaches must account for changing policies, technological progress, and unique, setting-specific operating conditions. Based on this notion, an international web seminar with faculty, researchers, and students from nine universities across five continents was conducted. In the 3-month seminar, participants were split into groups and tasked with developing future-proof, sustainable water management solutions for fictitious settings with unique resource availability, climate change predictions, demographic, and socioeconomic constraints. The goal of the seminar was to combine participants’ unique perspectives to tackle challenges in developing future water infrastructure, while forming lasting relationships. Water management concepts became more daring or “out-of-the-box” as the seminar progressed. Most groups opted for a holistic approach, optimizing existing infrastructure, integrating decentralized water management, furthering digitization, and fostering the adoption of innovative policy and planning strategies. To gauge their impact on the evolution of ideas, group dynamics and communication were observed throughout the seminar. As a result, the findings serve not only as a compendium of ideas and concepts for holistic design in the water sector, but also facilitate international collaboration, improve communication in cross-cultural teams or guide the development of training programs in water management for researchers, professional engineers, or water utilities.

This study investigates the transient flow dynamics and pressure interactions within Tesla valve configurations through comprehensive computational fluid dynamics (CFD) simulations. Previous observations indicated that Tesla valves effectively reduce the amplitude of pressure transients, prolonging their duration and distributing energy over an extended timeframe. While suggesting a potential role for Tesla valves as pressure dampers during transient events, the specific mechanisms behind this behavior remain unexplored. The research focuses on elucidating the internal dynamics of Tesla valves during transient events, aiming to unravel the processes responsible for the observed attenuation in pressure transients. The study reveals the emergence of distinctive “pressure pockets” within Tesla valves, deviating from conventional uniform pressure fronts. These pockets manifest as discrete chambers with varying lengths and volumes, contributing to a non-uniform propagation of pressure throughout the system.

Conventional drinking water monitoring relies on laboratory-based chemical and microbiological analyses that are often time intensive. This study evaluates a rapid optical alternative by integrating excitation–emission matrix (EEM) fluorescence spectroscopy, parallel factor analysis (PARAFAC), and machine learning to predict intact cell count (ICC) within a chlorinated drinking water distribution system. A full scale network in the Okanagan Valley, Canada was monitored, and fluorescence-derived dissolved organic matter (DOM) components were used as predictors in tree-based classification models. Fluorescence alone showed moderate skill in predicting ICC, but incorporating contextual factors (chlorine residual and turbidity) markedly improved performance. The ICC models could categorically distinguish microbial levels within the distribution system, though absolute accuracy declined at sites not seen during training, indicating a need for site-specific calibration. Notably, this work is the first to assess fluorescence-based metrics for determining ICC in drinking water distribution systems. The findings demonstrate that a unified fluorescence-PARAFAC approach can provide timely alternative measures for water quality, as it shows promise for microbial risk indication when augmented with operational data. Overall, the work highlights the potential of fluorescence-informed machine learning to bridge conventional chemical and microbiological monitoring, offering a pathway toward more responsive water quality management.

This research offers valuable insights into both the historical analysis and future management of water distribution network behavior. By examining the impacts of materials and identifying temporal patterns, the study presents a comprehensive viewpoint essential for informed decision-making in water network management. The findings emphasize the relationship between pressure dynamics and burst occurrences, leading to opportunities for further investigation and the development of more precise prediction and mitigation strategies for water distribution systems. The results indicate a consistent pattern of higher real positive percentages compared to random detection rates across the years tested. This suggests a significant correlation between burst events and pressure anomalies, with the detection system performing better than random chance.