This study details the design and implementation of a real-time river monitoring station established on the Sakarya River, capable of instantaneously tracking water levels and flow rates. The system comprises an ultrasonic distance sensor, a GSM module (Global System for Mobile Communications), which enables real-time wireless data transmission to a server via cellular networks, a solar panel, a battery, and a microcontroller board. The river monitoring station operates by transmitting water level data collected by the ultrasonic distance sensor to a server via a communication module developed on a microcontroller board using an Arduino program, and then sharing these data through a web interface. The developed system performs regular and continuous water level readings without the need for human intervention. During the installation and calibration of the monitoring station, laboratory and field tests were conducted, and the obtained data were validated by comparison with data from the hydropower plant located upstream. This system, mounted on a bridge, measures water levels twice per minute and sends these data to the relevant server via the GSM module. During this process, precipitation data were utilized as a critical reference point for validating measurement data for the 2023 hydrological year, with changes in precipitation directly correlated with river water levels and calculated flow values, which were analyzed accordingly. The real-time river monitoring station allows for instantaneous monitoring of the river, achieving a measurement accuracy of within 0.1%. The discharge values recorded by the system showed a high correlation (r² = 0.92) with data from the hydropower plant located upstream of the system, providing an accurate and comprehensive database for water resource management, natural disaster preparedness, and environmental sustainability. Additionally, the system incorporates early warning mechanisms that activate when critical water levels are reached, enabling rapid response to potential flood risks. By combining energy-independent operation with IoT (Internet Of Things)-based communication infrastructure, the developed system offers a sustainable solution for real-time environmental monitoring. The system demonstrates strong applicability in field conditions and contributes to advancing technologies in flood risk management and water resource monitoring.

This study presents a computational–experimental assessment of two industrial centrifugal fans used in cement production, focusing on aerodynamic optimization and energy efficiency validation. The first case concerns a Farin Kiln Filter Fan initially constrained by existing inlet duct geometry, which caused vortex formation, flow asymmetry, and a pressure loss exceeding 15%. CFD analyses identified major inlet vortices and asymmetric splitter loading, guiding a redesigned configuration with an expanded fan body (1982–2520 mm), an increased outlet width (1808–1858 mm), and a vortex breaker to stabilize inlet flow. CFD simulations indicated a flow rate of 601,241 m³/h, static pressure of 2200 Pa, and total pressure of 2580 Pa, achieving an 83% efficiency. Field validation confirmed a 34.4% reduction in shaft power, 30% decrease in torque, and 4% gain in efficiency, corresponding to 449 MWh/year energy savings and 180 t CO₂/year emission reduction, assuming 8000 operational hours. The second case involves an Induced Draft (ID) Fan designed for 441,643 m³/h flow at 990 rpm. Transient CFD simulations using the SST k–ω model captured rotor–stator interaction and confirmed the effectiveness of the design revisions in suppressing swirl and flow separation. The optimized design achieved 8653 Pa static pressure, 9203 Pa total pressure, and 83% efficiency under design conditions. Field measurements showed a 26.2% drop in shaft power and 19.6% improvement in efficiency, yielding 2527 MWh/year energy savings and an estimated 1011 t CO₂/year emission reduction. Overall, the CFD-guided redesign framework demonstrated strong alignment between simulations and field measurements, highlighting the method’s practical relevance for improving fan performance and energy sustainability in industrial systems.

This study presents a scenario-based, two-dimensional flood modeling approach to assess the potential downstream impacts of a piping-induced dam failure triggered by seismic activity. The case study focuses on the Doğantepe Dam in northwestern Türkiye, located near an active branch of the North Anatolian Fault. Critical deformation zones were previously identified through PLAXIS 2D seismic analyses, which served as the physical basis for a dam break scenario. This scenario was modeled using the HEC-RAS 2D platform, incorporating high-resolution topographic data, reservoir capacity, and spatially varying Manning’s roughness coefficients. The simulation results show that the flood wave reaches downstream settlements within the first 30 min, with water depths exceeding 3.0 m in low-lying areas and flow velocities surpassing 6.0 m/s, reaching up to 7.0 m/s in narrow sections. Inundation extents and hydraulic parameters such as water depth and duration were spatially mapped to assess flood hazards. The study demonstrates that integrating physically based seismic deformation data with hydrodynamic modeling provides a realistic and applicable framework for evaluating flood risks and informing emergency response planning.

This study presents the first regional validation of water surface elevation (WSE) measurements derived from the Surface Water and Ocean Topography (SWOT) satellite in Türkiye, focusing on three hydrologically distinct inland water bodies: Lake Burdur (a natural lake), Lake Van (a deep alkaline lake), and the Çatalan Dam Reservoir (a managed artificial reservoir). SWOT-derived WSE were assessed against State Hydraulic Works (DSİ) ground-based observations. Initial comparisons revealed substantial discrepancies, with correlation coefficients below 0.30 and root mean square errors (RMSEs) exceeding 0.77 m, highlighting the need for further refinement. To improve accuracy, data filtering based on SWOT quality flags and the exclusion of observations with high WSE standard deviations and uncertainties were applied. These steps enhanced the correlation coefficients above 0.51 and reduced RMSEs to below 1.27 m. Systematic underestimations were observed for Lake Van and Çatalan Dam, whereas both under- and overestimations occurred for Lake Burdur. Applying the median of residuals as a bias correction further decreased RMSEs to below 0.37 m across all study sites. These results underscore the potential of SWOT for monitoring inland water bodies in regions experiencing water scarcity, such as Türkiye, while emphasizing the importance of advanced data processing, integration of high-resolution geoid models, and continued cross validation with in situ measurements. The proposed methodology, both computationally efficient and straightforward, offers a transferable framework for improving SWOT-based WSE and ultimately SWOT derived river discharge estimations based on WSE values across diverse inland water systems worldwide.