Flash Flood Simulation Using Geomorphic Unit Hydrograph Method: Case Study Of Headwater Catchment Of Xiapu River Basin, China

The flash flood refers to flood produced by heavy local rainfalls and often occurs in mountainous areas. It is characterized by a quick rise of water level causing a great threat to the lives of those exposed. Many countries and regions face the threat of flash floods. However, some traditional hydrological models can hardly simulate the flash flood process well due to the lack of hydrological data and the insufficient understanding of complicated runoff mechanism in mountainous and hilly areas. According to this condition, a new hydrological model based on the framework of Xinanjiang model, widely used in humid and semi-humid regions in China, is presented to simulate flash flood. The highlight of new model is using the geomorphic unit hydrograph (GUH) method to simulate the overland flow process. This method has clear physical concept and can easily provide unit hydrographs of various time intervals only based on DEM data. This feature makes the method extremely valuable in ungauged catchment. The new presented hydrological model is used in the headwater catchment of Xiapu River basin and the results demonstrate that the computed data generally agrees well with the measured data and it can be treated as a useful tool for flash flood hazard assessment in mountainous catchment

Diethyl 4-meth­oxyoxalyl-3,5-diphenyl­pyrrolidine-2,2-dicarboxyl­ate

In the title compound, C25H27NO7, the pyrrolidine ring exhibits an envelope conformation and the benzene rings form a dihedral angle of 33.47 (11)°. In the crystal, pairs of N—H⋯O hydrogen bonds link the mol­ecules into centrosymmetric dimers. Weak C—H⋯O inter­actions link the dimers into layers parallel to the bc plane

Lateral and Longitudinal Coordinated Control of Intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

This study focuses on improving the trajectorytracking control for intelligent vehicles in high-speed and largecurvature limit conditions. To this end, a high-precision fivedegree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, acoordinated lateral and longitudinal control system is developed.The lateral model predictive control (MPC) involves two crucialstages: initially, a desired trajectory with associated speed datais generated based on path curvature. Subsequently, using thehigh-precision 5-DOF dynamics model, an objective functionis formulated to minimize the difference between the vehicle’scurrent state and the desired state. This process determines theoptimal front wheel steering angle, taking into account vehiclepositional constraints and steering limitations. Additionally, adouble proportional–integral–derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure basedon real-time position and speed data, ensuring integrated controlover both lateral and longitudinal movements. The effectivenessof this approach is confirmed through real vehicle testing andsimulation. Results show that the high-precision 5-DOF dynamicsmodel markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executesprecise trajectory tracking. In extreme scenarios of high-speedand large curvature, the enhanced model substantially improvestrajectory accuracy and driving stability, thus promoting safevehicle operation

Lateral and Longitudinal Coordinated Control of Intelligent Vehicle Based on High-Precision Dynamics Model under High-Speed Limit Condition

This study focuses on improving the trajectorytracking control for intelligent vehicles in high-speed and largecurvature limit conditions. To this end, a high-precision fivedegree-of-freedom (5-DOF) dynamics model (HPM) that incorporates suspension characteristics is introduced. Furthermore, acoordinated lateral and longitudinal control system is developed.The lateral model predictive control (MPC) involves two crucialstages: initially, a desired trajectory with associated speed datais generated based on path curvature. Subsequently, using thehigh-precision 5-DOF dynamics model, an objective functionis formulated to minimize the difference between the vehicle’scurrent state and the desired state. This process determines theoptimal front wheel steering angle, taking into account vehiclepositional constraints and steering limitations. Additionally, adouble proportional–integral–derivative (PID) controller for longitudinal control adjusts the throttle and brake pressure basedon real-time position and speed data, ensuring integrated controlover both lateral and longitudinal movements. The effectivenessof this approach is confirmed through real vehicle testing andsimulation. Results show that the high-precision 5-DOF dynamicsmodel markedly enhances the accuracy of vehicle response modeling, and the coordinated control system successfully executesprecise trajectory tracking. In extreme scenarios of high-speedand large curvature, the enhanced model substantially improvestrajectory accuracy and driving stability, thus promoting safevehicle operation

GWSM4C-NS: improving the performance of GWSM4C in nearshore sea areas

Predicting nearshore significant wave heights (SWHs) with high accuracy is of great importance for coastal engineering activities, marine and coastal resource studies, and related operations. In recent years, the prediction of SWHs in two-dimensional fields based on deep learning has been gradually emerging. However, predictions for nearshore areas still suffer from insufficient resolution and poor accuracy. This paper develops a NS (NearShore) model based on the GWSM4C model (Global Wave Surrogate Model for Climate simulations). In the training area, the GWSM4C -NS model achieved a correlation coefficient (CC) of 0.977, with a spatial Root Mean Square Error (RMSE), annual mean spatial relative error (MAPE), and annual mean spatial absolute error (MAE) of 0.128 m, 10.7%, and 0.103 m, respectively. Compared to the GWSM4C model’s predictions, the RMSE and MAE decreased by 59% and 60% respectively, demonstrating the model’s effectiveness in enhancing nearshore SWH predictions. Additionally, applying this model to untrained sea areas to further validate its learning capability in wave energy propagation resulted in a CC of 0.951, with RMSE, MAPE, and MAE of 0.161m, 12.9%, and 0.137m, respectively. The RMSE and MAE were 43% and 39% lower than the GWSM4C model’s interpolated predictions. The results shown above suggest that the newly proposed model can effectively improve the performance of GWSM4C in nearshore areas