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Ship movement in the shallow seas creates a significant hydrodynamic pressure field about the ship that has effect on the environmental structures such as waterway beds, stationary or moving neighbored vessels, and can also affect marine life. Therefore, the study of this phenomenon is very important in many applications. The present study investigated the hydrodynamic pressure field caused by an oil tanker with 247 m long, 53 m wide and 17 m draft moving at different speeds of 10, 15 and 20 knots on a sea level with a depth of 80 m. The fluid flow governing equations including the continuity equation, the momentum equations, and the K-ε turbulence model are solved numerically and the SIMPLE algorithm is used to correlate the pressure and velocity fields. An accurate Trimmer's structured mesh has been utilized to discrete the studied domain around the ship. To validate the methodology, the obtained dimensionless velocity field is compared with those presented by other works a good consistency is observed. As expected, the magnitude of the hydrodynamic pressure field varied as a function of the distance to the body of the vessel, ship's traveling velocity and magnitude of the draft. In this study, the minimum effects of the pressure were for the case of 10 knots (the minimum working velocity of heavy vessels) and 80 m of depth with a maximum pressure of 980 Pa. The results show that the pressure field dissipation occurs more rapidly in close distances to the vessel, and the pressure field domain decreases with a lower slope in far away from of the body. a hydrodynamic pressure correlation is obtained based on the depth and ship's velocity. Two and three-dimensional hydrodynamic pressure contours are also presented for different depths and velocities. Moreover, he hydrodynamic pressure increments in 12 and 7 m drafts are investigated and that shows after the 3/4 height of the bulbous bow lies below the sea surface, the increase in draft has little effect on the hydrodynamic pressure field.

The hydrodynamic pressure field and wake field generated by an advancing underwater vehicle are used as key factors for its detection and identification. In this paper, a numerical method is established based on the Reynolds-averaged Navier–Stokes equation. Following grid convergence verification for seven kinds of grids, the differences between the hydrodynamic pressure field and wake field are investigated using the gridding method and turbulence model. Subsequently, on a validation basis, the characteristics and correlation of the hydrodynamic pressure field and wake field for an underwater vehicle at different water depths are studied, showing that the SST turbulence model is applicable to underwater vehicle hydrodynamic pressure fields with low grid requirements. A high grid density and the LES turbulence model are needed to accurately capture information about the wake field’s flow field. As the underwater vehicle approaches the bottom, the hydrodynamic pressure field characteristics change significantly, and the wake field characteristics do not significantly change. In the region behind the hull, the influence of the wake field on the hydrodynamic pressure field is complex, but it has a short acting distance. The correlation in the area behind the hull is crucial for identifying, detecting, and tracking targets.

A ship advancing in restricted waters may cause a change in the surrounding velocity field, which in turn results in the hydrodynamic pressure field according to the variations in the ship speed. Accurate prediction of ship hydrodynamic pressure fields in restricted waters is therefore essential and important in the military and engineering fields. Based on the potential flow theory and the thin-ship assumption, dividing complex restricted waters with varying depths into the inner and outer domains with constant depths, a mathematical modeling method is developed and carried out for analyzing and solving the partial differential equations consisting of the governing equation with a dispersion effect, initial and boundary conditions, then the analytical solution of hydrodynamic pressure field caused by a ship advancing in complex restricted waters is obtained. The continuity of the analytical solution is confirmed and the correctness of the analytical solution is validated by simplifying to a simple water and comparing with available data. Moreover, the mathematical modeling method can be extended to study the hydrodynamic problems of ships in more complex waters.

Deng, H.; Zhang, Z.-H.; Gu, J.-N., and Liu, J.-B., 2016. Hydrodynamic pressure field caused by a ship sailing near the coast. Based on the shallow-water wave-potential flow theory and the assumption of a slender ship, a mathematical model, whose dispersion relation is improved, has been established for the ship hydrodynamic pressure field (SHPF), which is suitable for a wider speed range. The finite difference method is used for calculating the SHPF, and the artificial viscous terms are added in the boundary conditions to ensure the stability of the nonlinear equation solution. The comparison between the calculated results and the experimental ones shows that the calculation method is feasible and the improved mathematical model is more accurate and effective. The calculation and analysis of the SHPF caused by ships deviating from the channel centre line or sailing near the coast in shallow water indicate that the narrower the distance between the ship and the coastal sidewall the greater will its effects be on the coast, the seabed, and the coastal architecture around the ship at the subcritical speed, which will cause effects on the coast, seabed, and coastal architectures of a wider water area behind the ship to a large extent at supercritical speed.

It is well known that the pressure fluctuations on both sides of a cylinder and those in its oscillating near-wake region are both sound sources at low Reynolds and Mach numbers. However, assessment of the propagating capacity and quantification of the radiating versus non-radiating components of these two sound sources are not currently available for this important benchmark aeroacoustic problem. Here, we isolate the radiating acoustic sound sources from the non-radiating hydrodynamic pseudo-sounds by applying the wavelet decomposition technique of Mancinelli et al. (J. Fluid Mech., vol. 813, 2017), previously used in subsonic jet-noise experiments, to decompose the cylinder near-field pressure fluctuations obtained from our direct numerical simulations. Rigorous independence and convergence analyses of the wavelet decomposition procedure are performed. It is found that the radiating acoustic component strongly dominates over the non-radiating hydrodynamic component at near-field locations above and upstream of the cylinder. In the oscillating near-wake region, the hydrodynamic component dominates over the acoustic component at most frequencies, except at the vortex shedding frequency where they exhibit comparable strengths. Furthermore, within the oscillating near-wake region, the overall sound pressure level associated with the hydrodynamic pressure fluctuations exceeds that associated with the acoustic pressure fluctuations. Away from the oscillating near-wake region, the hydrodynamic pressure fluctuations decrease dramatically while the acoustic counterparts decay slowly, demonstrating that the hydrodynamic pressure fluctuation does not propagate, and that the acoustic pressure fluctuation is the only component to propagate to the far field.

Vortex-induced vibrations of bluff bodies occur when the vortex shedding frequency is close to the natural frequency of the structure. Of interest is the prediction of the lift and drag forces on the structure given some limited and scattered information on the velocity field. This is an inverse problem that is not straightforward to solve using standard computational fluid dynamics methods, especially since no information is provided for the pressure. An even greater challenge is to infer the lift and drag forces given some dye or smoke visualizations of the flow field. Here we employ deep neural networks that are extended to encode the incompressible Navier–Stokes equations coupled with the structure’s dynamic motion equation. In the first case, given scattered data in space–time on the velocity field and the structure’s motion, we use four coupled deep neural networks to infer very accurately the structural parameters, the entire time-dependent pressure field (with no prior training data), and reconstruct the velocity vector field and the structure’s dynamic motion. In the second case, given scattered data in space–time on a concentration field only, we use five coupled deep neural networks to infer very accurately the vector velocity field and all other quantities of interest as before. This new paradigm of inference in fluid mechanics for coupled multi-physics problems enables velocity and pressure quantification from flow snapshots in small subdomains and can be exploited for flow control applications and also for system identification.

An experimental investigation of droplet formation induced by an external electric field in a T-shaped microfluidic device is presented. The effect of electric field is reported for scenarios where the hydrodynamics is known to be governed by the cumulative effect of hydrodynamic pressure and interfacial tension acting on the liquid–liquid interface. Experiments reveal that the electrohydrodynamic phenomena transforms the droplet formation mechanism by inducing pinning of the dispersed phase to the channel wall, leading to a significant decrease in the droplet filling time and hence a decrease in the size of droplets generated. The experimental observations are used to formulate a correlation between the droplet size, applied electric field, fluid properties and flow parameters. A mechanistic explanation of droplet formation process using a mathematical model is also presented. Simulations reveal that the droplets are formed primarily due to normal electric stress acting on the liquid–liquid interface. The electric stress results in a distinct feature of pinning and early onset of neck formation of the emerging dispersed phase, leading to a reduction in the size of the droplet formed for the same hydrodynamic conditions. The findings reported demonstrate that an applied electric field has the potential to produce relatively smaller-sized droplets than that possible through hydrodynamics alone.

Trailing-edge serrations are add ons retrofitted to wind-turbine blades to mitigate turbulent boundary-layer trailing-edge noise. This manuscript studies the physical mechanisms behind the noise reduction by investigating the far-field noise and the hydrodynamic flow field. A conventional sawtooth and a combed-sawtooth trailing-edge serration are studied. Combed-sawtooth serrations are obtained by filling the empty space between the teeth with combs (i.e. solid filaments). Both serration geometries are retrofitted to a NACA 0018 aerofoil at zero degree angle of attack. Computations are carried out by solving the explicit, transient, compressible lattice Boltzmann equation, while the acoustic far field is obtained by means of the Ffowcs Williams and Hawkings analogy. The numerical results are validated against experiments. It is confirmed that the combed-sawtooth serrations reduce noise more than the conventional sawtooth ones for the low- and mid-frequency range. It is found that the presence of combs affects the intensity of the scattered noise but not the frequency range of noise reduction. For both configurations, the intensity of the surface pressure fluctuations decreases from the root to the tip, and noise sources are mainly located at the serrations root for the low- and mid-frequency range. The presence of the filaments generates a more uniform distribution of the noise sources along the edges with respect to the conventional serration. The installation of combs mitigates the interaction between the two sides of the aerofoil at the trailing edge and the generation of a turbulent wake in the empty space between teeth. As a result, the inward (i.e. from the serration edge to the centreline) and outward (i.e. from the serration centreline to the edge) flow motions, due to the presence of the teeth, are mitigated. It is found that the installation of serrations affects the surface pressure fluctuations integral parameters. Both the spanwise correlation length and convective velocity of the surface pressure fluctuations increase with respect to the baseline straight configuration. When both quantities are similar to the one obtained for the straight trailing edge, the effect of the slanted edge is negligible, thus corresponding to no noise reduction. It is concluded that the changes in sound radiation are mainly caused by destructive interference of the radiated sound waves for which a larger spanwise correlation length is beneficial. Finally, the difference between measurements and the literature is caused by an incorrect modelling of the spanwise correlation length, which shows a different decay rate with respect to the one obtained for a straight trailing edge.

Tomographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV experiment to validate the PINN inference for the unsteady velocity field at a centre plane. To explain the observed flow physics, we also perform systematic PINN simulations at different Reynolds and Richardson numbers and quantify the variations in velocity and pressure fields. The results in this paper indicate that the proposed deep learning technique can become a promising direction in experimental fluid mechanics.

Drainage channels with step-pool system are widely used to control debris flow. The blocking of debris flow often gives rise to local damage at the steps and baffles. Hence, the estimation of impact force of debris flow is crucial for designing step-pool channel. Existing empirical models for impact pressure prediction cannot consider the influence of baffle shape. In this work, a three-dimensional smoothed particle hydrodynamics (SPH) study on the impact behavior of debris flows in step-pool systems is presented, where debris material is modeled using the regularized Bingham model. The SPH method is first checked using the results from two laboratory tests. Then, it is used to investigate the influence of baffle shape and flow density. Numerical results show that the impact pressure at the first baffle highly depends on the baffle shape; however, the largest impact pressure usually occurs at subsequent baffles due to the violent impact induced by jet flows. The peak impact pressure at the first baffle initially grows with increasing flow density; however, it starts to drop as density is beyond a threshold. Based on the numerical results, an empirical relation considering the influence of baffle shape is proposed for better prediction of debris impact pressure.