Abstract Accurate prediction of flood waves from reservoir failures and their propagation is critical to effective flood hazard assessment and risk management. Flood waves are sensitive to floodplain topography, channel geometry, and hydraulic structures located along flow paths in downstream areas, and thus the accuracy of flood wave modelling is dependent on the precision and accuracy of the representation of those features. This study introduces a novel approach to flood wave modelling by accurately representing 3D objects in downstream areas using the Structure-from-Motion (SfM) technique. Through the use of Unmanned Aerial Vehicle (UAV), this method captures topographic complexities, accounting for ground objects like bridges and trees that impact flood propagation. The 3D model offers enhanced representation of turbulent flow dynamics and computational efficiency, especially handling large topography datasets using the volume of fluid (VOF) method. Predictions from this new 3D approach were validated against recent reservoir failure observations and contrasted with traditional 2D models. The results revealed that the 3D model displayed a significant 84.4% reproducibility when juxtaposed with actual inundation traces. It was 35.5%p more accurate than the 2D diffusion wave equation (DWE) and 17.1%p more than the 2D shallow water equation (SWE) methods in predicting flood waves. The DWE was the least accurate among the results, whereas the SWE fared better but still struggled with intricate floodplains. In conclusion, the 3D method, combined with the structure-from-motion technique, emerges as a promising alternative to traditional modelling methods.
Recently, rapidly growing industry and abnormal climate change have generated high concentrations of fine dust. This fine dust settles on greenhouses and hinders photosynthesis of the plants within. In this study, the greenhouse environment was reproduced using fine dust devices, and the fine dust adhesion and washing efficiency were compared. The optimal coating agent was selected by performing a coating experiment to assess the changes in the light transmittance and contact angles of eight greenhouse coatings incorporating different coating agents. The most pronounced adhesion rate was observed in an ion humidification test for (NH4)2SO4. The coating agent with Teflon, which exhibited the largest contact angle in this study, had the highest washing efficiency, followed by the coating agent with the highest polydimethylsiloxane ratio using isopropyl alcohol (IPA) as a solvent, and the coating agent with the highest polydimethylsiloxane using water as a solvent. However, the ingredients added to the agents with Teflon or IPA were judged inappropriate for greenhouse use due to environmental reasons. Therefore, the coating agent with the highest polydimethylsiloxane using water as a solvent, which is the most suitable coating agent and satisfies both the washing efficiency and contact angle requirements, is expected to be used as a greenhouse coating agent to prevent light transmittance reduction in greenhouses due to fine dust accumulation.
An agrivoltaic system (AVS) offers a potential strategy for meeting global demands for renewable energy and sustainability by integrating photovoltaics and agriculture. Many empirical studies have installed facilities and cultivated actual crops, revealing that AVSs improve land use efficiency. However, it is rare for actual end-users and farmsteads to adopt AVSs owing to a lack of standardised models and design criteria. In this study, we conducted a comprehensive AVS design considering agronomic aspects and structural safety along with an analysis of design criteria to promote the dissemination of AVSs. Based on the photovoltaic module arrangement and adjusting installation conditions, various design types were considered to reflect on-site conditions and user preferences. In addition, safety standards for disaster resistance and trade-offs among shading ratio, power generation capacity, and quantity of structural members were analysed. The safety assessment results demonstrated that the column of the AVS structure was vulnerable to wind loads, and the safety standards varied according to the adjusted column spacing. The narrower the column design, the more advantageous the safety and power generation and the more disadvantageous the crop cultivation environment and installation cost. The sequentially mounted type allowed relatively less solar radiation to reach the crop and generated more solar energy. When the modules were mounted at a distance, the structural safety was slightly reduced; however, more solar radiation and economic feasibility were secured. These results will support decision-making regarding AVS designs, help in identifying the sensitivity of crops to shading, and be utilised for the establishment of a standardised AVS model to promote dissemination.
Abstract This study attempts to evaluate the influence of design factors and flow characteristics on the discharge capacity of trapezoidal piano key weirs. To evaluate the influence of the main design factors on discharge rates, 9 models were simulated, with width ratios of 1.25, 1.75, and 2.25 and sidewall angles of 2, 4, and −2°. As the auxiliary design factors, the square Sq-parapet from previous research and the newly proposed triangular Tri-parapet were integrated into the models with high discharge capacity. We used the computational fluid dynamics simulation to analyze the fluid dynamics and provide the optimal design characteristics for trapezoidal piano key weirs. Our findings reveal that certain changes in the design, namely in the sidewall angle and width ratio, can increase the discharge rates by up to 14.7 and 13.6%, respectively. Furthermore, as a result of applying Sq-parapet and Tri-parapet to the model weirs, we found that the discharge efficiency of Tri-parapet was significantly higher compared to the existing Sq-parapet models, with improvement of up to 53.8 and 49.5%. This study contributes to understanding the influence of various design factors on the discharge capacity of trapezoidal PK weirs and offers insights for optimizing their design.
Abstract Predicting flood wave propagation from reservoir failures is critical to practical flood hazard assessment and risk management. Flood waves are sensitive to topography, channel geometry, structures, and natural features along floodplain paths. Thus, the accuracy of flood wave modelling depends on how precisely those features are represented. This study introduces an enhancing approach to flood wave modelling by accurately representing three-dimensional objects in floodplains using the structure-from-motion (SfM). This method uses an unmanned aerial vehicle to capture topographic complexities and account for ground objects that impact flood propagation. Using the three-dimensional volume of fluid numerical approach significantly improves an enhanced representation of turbulent flow dynamics and computational efficiency, especially in handling large topography datasets. Reproductions from this enhanced three-dimensional approach were validated against recent reservoir failure observations and contrasted with traditional two-dimensional models. The results revealed that the suggested three-dimensional methodology achieved a significant 84.4% reproducibility when juxtaposed with actual inundation traces. It was 35.5%p more accurate than the two-dimensional diffusion wave equation (DWE) and 17.1%p more than the shallow water equation (SWE) methods in predicting flood waves. This suggests that the reproducibility of the DWE and SWE decreases compared to the three-dimensional approach when considering more complex floodplains. These results demonstrate that three-dimensional flood wave analysis with the SfM methodology is optimal for effectively minimising topographic and flood wave reproduction errors across extensive areas. This dual reduction in errors significantly enhances the reliability of flood hazard assessments and improves risk management by providing more precise and realistic predictions of flood waves.
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