Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow field that embeds all spatial and temporal flow information is an open question, especially in the case of turbulent flows. In this paper, a deep learning (DL) method based on computational CNN layers is presented, focusing on reconstructing turbulent open channel flow fields of various resolutions. Starting from couples of images with low/high resolution, we train our DL model to efficiently reconstruct the velocity field of consecutive low-resolution data, which comes from a sparse-grid Direct Numerical Simulation (DNS), and focus on obtaining the accuracy of a respective dense-grid DNS. The reconstruction is assessed on the peak signal-to-noise ratio (PSNR), which is found to be high even in cases where the ground truth input is scaled down to 25 times.

In this study, we investigate the performance of the smoothed particle hydrodynamics (SPH) method regarding the computation of confined flows in microchannels. Modeling and numerical simulation with SPH involve the representation of flowing matter as distinct mass points, leading to particle discretization of the Navier–Stokes equations. The computational methodology exhibits similarities with other well-established particle methods, such as molecular dynamics (MD), dissipative particle dynamics (DPD), and smooth dissipative particle dynamics (SDPD). SPH has been extensively tested in the simulation of free-surface flows. However, studies on the performance of the method in internal flow computations are limited. In this work, we study flows in microchannels of variable cross-sections with a weakly compressible SPH formulation. After preliminary studies of flows in straight constant cross-section ducts, we focus on channels with sudden expansion and/or contraction. Flow models based on periodic or various inlet/outlet boundary conditions and their implementations are discussed in the context of 2D and 3D simulations. Numerical experiments are conducted to evaluate the accuracy of the method in terms of flowrate, velocity profiles, and wall shear stress. The relation between f and Re for constant cross-section channels is computed with excellent accuracy. SPH captured the flow characteristics and achieved very good accuracy. Compressibility effects due to the weakly compressible smoothed particle hydrodynamics (WCSPH) formulation are negligible for the flows considered. Several typical difficulties and pitfalls in the application of the SPH method in closed conduits are highlighted as well as some of the immediate needs for the method’s improvement.

Understanding flow dynamics around hydraulic structures is essential for optimizing water management systems and predicting flow behavior in real-world applications. In this study, we simulate a 3D flow control system featuring a sluice gate and a weir, commonly used in hydraulic engineering. The focus is on accurately incorporating modified dynamic boundary conditions (mDBCs) and viscosity treatment to improve the simulation of complex, turbulent flows. We assess the performance of the Smoothed Particle Hydrodynamics (SPH) method in handling these challenging conditions. Especially when the boundary conditions and applicability to industry are two of the SPH method’s grand challenges. Simulations were conducted on a Graphics Processing Unit (GPU) using the DualSPHysics code. The results were compared to theoretical predictions and experimental data found in the literature. Key hydraulic characteristics, including 3D flow effects, hydraulic jump formation, and turbulent behavior, are examined. The combination of mDBCs with the Laminar plus sub-particle scale turbulence model achieved the correct simulation results. The findings demonstrate agreement between simulations, theoretical predictions, and experimental results. This work provides a reliable framework for analyzing turbulent flows in hydraulic structures and can be used as reference data or a prototype for larger-scale simulations in both research and engineering design, particularly in contexts requiring robust and precise flow control and/or environmental management.