Impingement jet cooling is widely used in industrial applications such as gas turbine blades and electronic equipment. Previous studies have shown that thermal performance can be further enhanced when the impingement channel is partially or fully packed with high-porosity metal foams. Although direct numerical simulations (DNS) of impinging jets and channel flows with porous foams have provided valuable insights, the flow characteristics of impinging jets with porous walls remain insufficiently understood. Therefore, this study numerically investigates the flow characteristics of turbulent slot jets with porous walls using DNS based on the lattice Boltzmann method (LBM), as a preliminary step toward evaluating heat transfer performance. Simulations are conducted at a Reynolds number of 5,000 based on the jet inlet velocity and jet slot width. The porous wall is modelled using Kelvin cell arrays with a porosity of 0.95. Three configurations are considered: a solid wall (Case S), a low-permeability porous wall (Case LP), and a high-permeability porous wall (Case HP). The jet slot width and channel height are identical in all cases. The flow structure and turbulence characteristics are first examined, followed by an analysis of the hydraulic characteristics. The results show that the porous wall significantly alters the mean flow structure. In Case LP, only a small portion of the jet penetrates the porous wall near the impingement region, while the main flow forms a wall jet along the porous wall surface. In contrast, in Case HP, most of the jet penetrates the porous wall, forming a wall jet within the porous wall before gradually re-emerging into the clear flow region. Intense vortices smaller than the Kelvin cell size were generated inside the porous wall by wake flows around the cell ligaments. Such fine-scale turbulence can only be captured by geometry-resolved DNS. Turbulence intensity becomes relatively large inside the porous wall, particularly in Case HP. The drag coefficient reaches its maximum near the impingement point regardless of permeability; however, the peak value depends on wall permeability. Overall, the presence of the porous wall increases the hydraulic penalty due to the additional flow resistance of the porous wall.

The aerodynamic characteristics of the Ahmed model at different yaw angles reveal valuable information about vortex dynamics and surface pressure behavior. This study utilizes stereo-particle-image-velocimetry measurement of flow around the Ahmed model, and clarifies detailed flow patterns at yaw angles of 10, 15 and 20 degrees, which enable us to comprehensively analyze vortex interactions and their effects on aerodynamic pressure on the top surface of the Ahmed model. An advanced vortex detection algorithm was employed, and the characteristics of the side-edge separated vortex such as size and circulation, which are vital to understanding the airflow over the models surfaces, were identified and quantified. Additionally, the relationship of vortices and optimized pressure sensors by the Bayesian D-optimality greedy (BDG) algorithm was investigated. The analyses illustrate that the BDG algorithm strategically locates sensors in areas significantly affected by dynamic pressure changes due to vortex activity and the geometry of the model, whereas this optimized placement of sensors enhances the quality of data collection and the precision of aerodynamic estimations. The outcomes of this research are particularly relevant to the development of autonomous vehicles, where precise sensor placement is crucial for vehicle stability and efficiency. The results of the present study indicate that properly aligned sensor placements beneath vortex paths can improve sensor-based monitoring and control systems. Further research is necessary to validate these initial findings, which could lead to promising improvements in aerodynamic strategies for enhancing the stability and efficiency of smart vehicles.

This study presents a high-fidelity computational fluid dynamics (CFD) analysis of the detailed flow characteristics and loss-generation mechanisms in a bidirectional impulse turbine used for oscillating water column (OWC) wave energy conversion. Bidirectional impulse turbines inherently experience efficiency degradation compared with unidirectional turbines, because the geometric symmetry required for reciprocating flow prevents optimal incidence control. Understanding the associated loss mechanisms is therefore essential for improving turbine performance. The CFD model was validated against existing experimental data, showing good agreement in torque coefficient, pressure-drop coefficient, and efficiency. Following this validation, the study examined pressure drop, swirl velocity, and total pressure loss in the upstream guide vane, rotor, and downstream guide vane. The results indicate that major losses originate from flow separation at the rotor leading edge and at the downstream guide vane leading edge. Additionally, an analysis of energy-flux evolution within the rotor passage quantitatively clarified the distribution of energy transfer and dissipation near the peak-efficiency operating condition. These findings highlight that reducing losses in the rotor and downstream guide vane is crucial for enhancing turbine performance. Overall, the insights obtained in this study contribute to establishing design guidelines for improving the efficiency of bidirectional impulse turbines for wave energy applications.

In this study, the overdetermined discrete empirical interpolation method (ODEIM), an extension of the discrete empirical interpolation method (DEIM), is reformulated, and parametric studies by varying the numbers of modes and sensors are conducted. Conventional DEIM employs the QR method to determine sensor locations, which places an upper limit on the number of sensors. Therefore, the proposed ODEIM, using the determinant-based greedy (DG) method, allows for an arbitrary number of sensor points, regardless of the number of modes. The stability of reduced-order systems is systematically examined by applying these methods to two highly distinct benchmarks: a numerically unstable, time-dependent free-surface relaxation problem, and an inherently stable, steady-state Laplace equation problem. Through extensive parametric evaluations, this study clarifies the previously unestablished relationships among the number of state modes rx, the number of nonlinear modes rf , and the number of sensors p, thereby establishing practical guidelines to minimize reconstruction errors. For the steady-state Laplace equation problem, accurate estimation results were obtained over a wide range of mode numbers, showing that DEIM is sufficiently effective. On the other hand, for the free-surface relaxation problem including time integration, DEIM often results in large errors or divergence. However, stable and accurate approximation results are found to be obtained by considering the differences in energy decay characteristics between the state and nonlinear fields, and by increasing the sensor count in ODEIM even for the free-surface relaxation problem. The proposed parameter selection criteria provide a practical guideline for ensuring computational stability in overdetermined reduced-order systems. These findings contribute to the development of robust dimensionality reduction techniques for complex fluid simulations.