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Hiroyasu Yasuda

Publications and source records attributed to Hiroyasu Yasuda.

2 recordsLinked to original sources

Effect of Discharge on the Nonlinear Bar Growth: A Flume Experiment

Alternate bars are ubiquitous bedforms in alluvial rivers, and excessive bar-height growth can increase flood risk and disrupt ecosystems. Predicting bar-height variability requires quantifying the dependence of bar growth rate on water discharge, yet this has rarely been achieved because conventional flume measurements interrupt the flow and cannot continuously capture bar evolution. Here we conducted laboratory experiments under four discharges and four channel slopes (16 conditions) and continuously mapped bed topography using Stream Tomography, a nonintrusive, flow-through measurement technique. Bar height exhibits sigmoidal growth and is well described by the Landau equation, allowing robust estimation of the growth rate and equilibrium height. The inferred dimensionless growth rate tends to increase as discharge decreases, but its sensitivity is weaker than that predicted by stability theory for a single alternate-bar mode. This discrepancy becomes more pronounced at lower discharge, indicating limitations of linearization and the influence of interactions among multiple bar modes. Furthermore, numerical flow simulations over the measured bed topography reveal that, during bar development, lower-discharge conditions produce a relatively larger bar height-to-depth ratio, making the flow more prone to deflection. In such cases, sediment transport is restricted downstream of depositional areas, thereby limiting bar migration. Consequently, flow and sediment transport become concentrated in the scour zones, enhancing local scour and promoting the growth of bar height. These results provide benchmark constraints for bar stability theories and help improve predictions of how bar height responds to changes in discharge.

physics.flu-dyn

Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation

This work proposes convolutional-sparse-coded dynamic mode decomposition (CSC-DMD) by unifying extended dynamic mode decomposition (EDMD) and convolutional sparse coding. EDMD is a data driven analysis method for describing a nonlinear dynamical system with a linear time-evolution equation. Compared with existing EDMD methods, CSC-DMD has an advantage of reflecting spatial structure of the target. As an example, the proposed method is applied to river bed shape estimation from the water surface observation. The estimation problem is reduced to sparsity-aware restoration with a hard constraint, which is given by the CSC-DMD prediction, where the algorithm is derived by the primal-dual splitting method. A time series set of water surface and bed shape measured through an experimental river setup is used to train and test the system. From the result, the significance of the proposed method is verified.

eess.SP