Sanghyun Kim

This study investigates the optimal placement of a surge tank to mitigate pressure fluctuations induced by water hammer in a complex, real-world water distribution network (WDN). A transient-flow numerical model was developed using the Method of Characteristics (MOC) integrated with surge tank theory, applied to a simplified and skeletonized representation of the target network. To determine the most effective installation site, Particle Swarm Optimization (PSO) was employed across 32 candidate nodes. Transient events were simulated through valve closure and reopening operations at the terminal nodes of the network. The results indicate that Node 41 is the optimal location for minimizing head fluctuations. Specifically, the maximum head fluctuation was reduced from 64.32 m in the unprotected system to 51.79 m with the surge tank at Node 41, representing a 19.48% improvement in hydraulic stability. These findings emphasize the critical role of strategic surge tank positioning and provide a robust technical framework for the design and operation of surge protection systems in complex WDNs.

Urban water distribution systems often dissipate excess hydraulic energy through pressure-reducing valves to maintain safe operating conditions, particularly in cities with complex topography. This study investigates the potential for sustainable energy recovery using microturbines in a large-scale urban water distribution system, with a focus on the city of Busan, South Korea. A digital twin of the Busan water transmission and distribution network was developed to analyze system-wide hydraulic characteristics, including elevation, hydraulic head, pressure, and flow. Candidate locations for microturbine installation were identified based on existing pressure regulation points and quantified using hydraulic simulation results. The recoverable power and energy potential were estimated by considering flow rate, available head difference, and turbine efficiency, and the model results were validated using operational data and field investigations at selected sites. The results show that significant recoverable energy is concentrated at pressure-reducing valve locations where excess pressure coincides with high flow rates and substantial pressure differentials under representative operating conditions. The maximum recoverable energy at a single site was estimated to be approximately 16.9 MWh/month, indicating that distributed microturbine installations can provide meaningful supplementary energy recovery. The findings demonstrate that digital twin–based analysis offers a systematic and practical approach for identifying energy recovery opportunities in urban water distribution systems and can support more energy-efficient and sustainable water utility operations.

Time series of soil moisture is an important status variable for understanding hillslope hydrological processes at the mountain hillside because soil moisture plays a critical role in regulating water retention, generating runoff and controlling vegetation dynamics. In order to explore the ultimate interpretation based on past hydrologic information (e.g., precipitation and soil moisture history) for contemporary soil moisture status, several machine learning models had been applied using systematically collected soil moisture measurements along transects in a hillslope. The fitness of models was evaluated in terms of coefficient of determination, mean absolute error and root mean square error. Appropriate lag extent for parsimonious modeling of soil moisture was explored and determined through heuristic approaches which can be explained by historic gain and loss and uncertainty contribution. Modeling results indicate that the vertical infiltration to weather rock as primary hydrological process for most measurement points. Two distinct modeling performances in soil moisture modeling at top hill and streamside points indicate the degree of hydrologic process complexity can be identified through delineated AI modeling results. This study highlights the potential of machine learning based time series modeling for prediction of soil moisture and corresponding hydrologic process configuration in the mountain hillslope.

Urban water distribution systems, as integral parts of underground pipeline networks, face challenges from aging infrastructure, operational demands, and transient pressure surges that can compromise structural integrity and service reliability. This work introduces a cost-oriented multi-objective design framework that explicitly accounts for both the likelihood of fire flow failure and the risks posed by transient pressures. The approach links a probabilistic reliability model with a transient pressure evaluation module, and couples both within a non-dominated sorting genetic algorithm to generate Pareto-optimal design solutions. Design solutions are constrained to maintain transient pressures within permissible limits, ensuring enhanced pipeline safety while optimizing capital costs. Case studies show that adopting a minimum 150 mm distribution main improves fire flow capacity and reduces transient-induced failure risks. The proposed method provides a predictive, computational tool that can be integrated into digital twin environments, supporting sustainable infrastructure planning, long-term monitoring, and proactive maintenance for resilient urban water supply systems.

This paper proposes a multi-objective optimization framework that integrates fire flow failure probability analysis and hydraulic transient considerations into a least-cost design methodology. The framework combines (1) an analytical probabilistic model for estimating fire flow failure probability and (2) a transient model of evaluating fire flow conditions, both incorporated into a non-dominated sorting genetic algorithm. Pareto-optimal solutions are constrained to ensure transient pressure heads remain below permissible limits. The trade-off between capital cost and fire flow reliability is achieved through optimized pipe diameter selection and pump configuration. Case studies demonstrate that a minimum 150 mm distribution main—a common industry standard—not only enhances fire flow capacity but also effectively mitigates transient pressures. The results highlight that transient severity is a critical driver of both economic design and system performance reliability.

This study examined the impact of sea surface temperature (SST) on urban temperature across four cities located in three different countries (United States of America, Japan, and Morocco), all at nearly the same latitude, focusing on the summer season over the period from 2003 to 2020, because previously no one attempted to analyze the impact of SST on land surface temperature (LST). Data were acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) for LST and SST to evaluate the correlation between urban temperature and SST, the trends over time, and the relationship between urban areas and LST. The novelty of this study lies in its being the first to investigate the impact of SST on urban temperature based on a city’s proximity to warm and cold ocean currents. The findings revealed a positive correlation between LST and SST across all cities analyzed in this study (San Francisco, Tangier, Tokyo, and Atlantic City), and in some instances a significant positive relationship was observed at a 95% confidence level, but still the significance is in the range of weak to moderate. Specifically, the study found that during both daytime and nighttime, Tangier exhibited a decreasing trend in LST (99% confidence level) and SST. On the contrary, San Francisco displayed an increasing trend in both LST and SST during the daytime, but at nighttime, while SST continued to rise, LST showed a decreasing trend. Further analysis differentiated cities influenced by warm ocean currents (Tokyo and Atlantic City) from those affected by cold currents (San Francisco and Tangier). In Tokyo, influenced by a warm ocean current, there was a decreasing trend in LST despite increased SST. Conversely, Atlantic City, also influenced by warm ocean currents, showed an increasing trend in both LST and SST during the daytime. At nighttime, both Tokyo and Atlantic City exhibited increasing trends in LST and SST. Additionally, this study explored the correlation between urban areas and LST, finding that cities influenced by warm ocean currents (Tokyo and Atlantic City) showed a positive correlation between urban areas and LST. In contrast, cities influenced by cold ocean currents (San Francisco and Tangier) displayed a negative correlation between urban areas and LST. Overall, this research highlights the complex interplay between SST and urban temperatures, demonstrating how ocean currents and urbanization can influence temperature trends differently in cities at similar latitudes.

The detection of small side branches within underground water pipe networks can be challenging since they are usually subject to many unknown uncertainties. A transient-based method was proposed to detect multi-branching pipes in pipeline systems having uncertainties associated with unknown abnormalities. To isolate the uncertainty signal from the original pressure signal, a polynomial transient model was developed that can address nonlinear transient introduction. Through the superimposition of different pressure signals, the uncertainty signal was delineated to obtain the processed signal for multi-branch prediction. Pipeline systems with two branching pipes were tested under various uncertainties to detect multi-branching pipes. In this study, two transient generation conditions were used: an instant valve closure and a pressure injection. Under several uncertainty conditions, hydraulic pressure damping can be obtained for the detection of two branching pipes under both transient generation conditions. It is possible to predict the locations of multi-branching pipes with a prediction error of less than 10% by using proper signal processing and manipulation for two distinct transient generation methods in a pilot-scale pipeline system with several uncertainties. This study demonstrates the potential for detecting multi-branching pipes in a pipeline system, even with unknown uncertainties for a pipeline system.