Clark-Y Hydrofoil Research Articles

A new turbulent viscosity modification method based on multiphase compressibility for cavitating flows

The alteration of physical properties in cavitating flows due to phase transition presents challenges for accurately expressing turbulent viscosity in Reynolds-Averaged Navier–Stokes models. Addressing this issue is crucial for capturing cavitating flow characteristics effectively. This study introduces a modification to turbulent viscosity by considering the compressibility of the vapor–liquid mixture, applied within the k-omega Shear-Stress Transport (SST k−ω) model framework. Simulations of cavitating flow around the Clark-Y hydrofoil, National Advisory Committee for Aeronautics 66 hydrofoil, and wedge are conducted to validate the proposed method. Results indicate that the modified model can reproduce the cavity inception, development, cutoff, and shedding processes observed in the experiment. Notably, the modification model accurately reproduces distinct cavitating flow features such as U-shaped cavities, secondary shedding, and high-pressure phenomena resulting from collapse. Moreover, the predicted time-averaged velocity, time-averaged Reynolds stress component, and dominant frequencies of pressure and phase volume fraction surpass those of the original SST k−ω model, demonstrating improved performance. These findings highlight the enhanced accuracy and reliability of the proposed SST-multiphase compressibility modification k−ω model for simulating cavitating flows, thus contributing to improved understanding and prediction capabilities in relevant engineering applications.

Drag reduction of Clark-Y hydrofoil by biomimetic fish scale structure under the condition of biomimetic jet

Hydrofoil, as the basic shape of the fluid blade, is widely used in fluid transport and energy conversion. However, friction resistance and pressure differential resistance are generated in the hydrofoil flow process, resulting in substantial energy consumption and negatively affecting the economy. On this basis, we propose two drag-reducing structures based on Clark-Y hydrofoil. In the design process of the jet structure, we considered the bionic jet velocity, jet angle and jet structure position as the design parameters and obtained the optimal jet structure by using Taguchi method. Finally, the two schemes (Clark-Yori and Clark-Yopt) are numerically simulated using large eddy simulation. Results show that when Ujet = 1.44 m/s, θ = 3° and x = 18.6 mm, the jet structure can play a significant drag reduction effect. Compared with Clark-Yori, the drag coefficient of Clark-Yopt is reduced by 26.5%, and the lift drag ratio is increased by 16.4%. Compared with Clark-Yori, Clark-Yopt can reduce the wall shear stress of the leading edge of the hydrofoil, thereby diminishing the frictional resistance. Meanwhile, the jet structure can effectively balance the area of the low-pressure region on the suction side of the hydrofoil, significantly reducing the pressure differential resistance. Clark-Yopt can accelerate the vortex collapse that decreases turbulence intensity and turbulence resistance. Moreover, it can effectively block the near-wall reflux of hydrofoil and reduce the internal friction between the reflux and the main flow.

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

This paper proposes a novel approach that combines Proper Orthogonal Decomposition (POD) reduced-order system with Long Short-Term Memory (LSTM) neural network to predict flow velocity. Large Eddy Simulation (LES) is used to simulate the cavitating flow around a NACA66 hydrofoil. POD is adopted to reduce the dimensionality of the high-dimensional data. It was found that 66.81% of the flow field energy and dominant coherent structures can be captured with first eight POD modes. The LSTM network model was further used to predict the temporal data of the POD mode coefficients, and the error of the predicted coefficients was within an acceptable range. The reconstructed flow field agrees well with the real flow field and the cavitation development has also been well illustrated. This method provides a promising and efficient alternative for flow prediction and has potential for applications in fluid dynamics, aerospace engineering, and hydrodynamics.

Effect of biomimetic fish scale structure on the drag reduction performance of Clark-Y hydrofoil

A hydrofoil is a basic shape of fluid machinery blades, and its drag reduction performance is an important reference index in the field of fluid transportation. When fluid flows around a hydrofoil, it generates friction drag and pressure drag, greatly reducing the hydrofoil’s hydraulic performance. This study designs a bionic drag reduction structure by arranging fish scales on a Clark-Y hydrofoil. The overlapping size, thickness, and coverage area of fish scales are taken as design parameters, and the optimal design scheme is attained by using the Taguchi method. Large eddy simulation is used to numerically simulate various schemes. Results show that when the overlapping size O is 2.00 mm, the thickness h is 0.36 mm, the initial position x/C of the fish scale covering is 0 (where C is the chord length of the hydrofoil), and the hydrofoil exhibits excellent drag reduction performance. The total drag reduction rate of the hydrofoil is up to 35.15%, and the drag reduction rate of friction drag and pressure drag is up to 39.56% and 25.64%, respectively. The lift–drag ratio of the hydrofoil increases by 18.04%. The bionic fish scale structure effectively inhibits turbulence, thereby reducing the drag caused by the transformation of laminar flow to turbulence.

Reduced Order Data-Driven Analysis of Cavitating Flow over Hydrofoil with Machine Learning

Predicting the flow situation of cavitation owing to its high-dimensional nonlinearity has posed great challenges. To address these challenges, this study presents a novel reduced order modeling (ROM) method to accurately analyze and predict cavitation flow fields under different conditions. The proposed ROM decomposes the flow field into linearized low-order modes while maintaining its accuracy and effectively reducing its dimensionality. Specifically, this study focuses on predicting cavitation on the Clark-Y hydrofoil using a combination of numerical simulation, proper orthogonal decomposition (POD), and neural networks. By analyzing different cavitation conditions, the results revealed that the POD method effectively reduces the order of the cavity flow field while achieving excellent flow field reconstruction. Notably, the zeroth- and first-order modes are associated with attachment cavitation, while the second-, third- and fourth-order modes correspond to cavitation shedding. Additionally, the fifth- and sixth-order modes along the hydrofoil surface are associated with the backward jet flow. To predict the conditions of high-energy modes, the neural network proved to be more effective, exhibiting excellent performance in stable attached cavitation. However, for cloud cavitation, the accuracy of the neural network model requires further improvement. This study not only introduces a novel approach for predicting cavitation flow fields but also highlights new challenges that will require continuous attention in future research endeavors.

Numerical Investigation of the Angle of Attack Effect on Cloud Cavitation Flow around a Clark-Y Hydrofoil

Numerical Investigation of the Angle of Attack Effect on Cloud Cavitation Flow around a Clark-Y Hydrofoil

A deep learning framework for reconstructing experimental missing flow field of hydrofoil

Hydrofoils play a crucial role in enhancing the efficiency of fluid machinery designed for ocean environments, reducing lift-induced drag and contributing to improved overall performance. To optimize hydrofoil design, a profound comprehension of the complex fluid flows around the hydrofoil structure is essential. In fluid mechanics, a precise and continuous representation of flow dynamics is essential for analysis and control purposes. Thus, obtaining a complete flow field, either through computational fluid dynamics (CFD) or experimental testing is of great significance. Nevertheless, the rigorous requirements of flow field tests render it impractical to directly measure complete flows around the airfoil using current instrumentation, especially for those with complex physical geometries. To tackle this issue, a novel deep learning framework is proposed to reconstruct the complete flow field by leveraging incomplete complementary flow data. As the representative benchmark problems, the flows around the Clark-Y hydrofoil at Re = 7 × 105 and the experimental NACA0012 2D hydrofoil at Re = 1800 have been investigated under different missing-flow scenarios of varying proportions, locations and orientations. Results demonstrate a remarkable agreement between the reconstructed flow field and the ground truth data, indicating the excellent performance of the proposed deep-learning model for missing flow reconstruction. A sensitivity analysis assesses the impact of the snapshot number and the latent space, revealing the method's robustness in selecting these parameters and simplifying its implementation in practical applications. This deep learning method offers the advantage of being implemented using paired incomplete flow fields, without the need for pre-known ground truth results as labels, holding the potential for addressing more complex full-field reconstruction problems in the future.

Suppression of hydrofoil unsteady cavitation by periodic jets based on fish gill respiration

Cavitation is a phase change phenomenon that occurs between the liquid and vapour phases inside a homogeneous liquid or at the interface between a solid and a liquid, and the generation, collapse and shedding of cavitation are the sources of undesirable characteristics of fluid machinery. A periodic jet scheme based on the principle of bionics is designed and applied to a Clark-Y hydrofoil with a chord length of 70 mm to suppress the cavitation of fluid machinery under high-speed flow conditions. Different schemes are numerically simulated using large eddy simulation combined with the Zwart–Gerber–Belamri cavitation model to numerically simulate the different schemes. The results show that adding periodic jet to the hydrofoil can reduce the cavitation of the hydrofoil without losing the lift resistance of the hydrofoil, and the cavitation volume is 47%–52% of the original hydrofoil.

On the effects of hybrid surface wettability on the structure of cavitating flow using implicit large eddy simulation

BackgroundSupercavitation flow is characterized by the presence of a supercavity, which can be unstable and cause unwanted motion or even loss of control of an object. To address this issue, flow control techniques are often employed. These techniques aim to manipulate the flow of liquid around the object and improve stability and control. Some of these methods include using control surfaces, injecting fluids, or more advanced techniques such as active flow control or smart materials. MethodThe aim of this study is to use computational methods to investigate the behaviour of supercavitation flow over a Clark-Y hydrofoil with different contact angle patterns (hybrid wettability) inspired by the Namib desert beetle. The hybrid wettability consists of six distinct patterns with a 160-degree contact angle for superhydrophobic surfaces and a five-degree contact angle for superhydrophilic surfaces. To predict the dynamic and unsteady behaviours of the supercavitation flow with a cavity number of 0.4, the implicit large eddy simulation (ILES) technique and Kunz mass transfer model are used. The compressive volume of fluid technique is used in conjunction with dynamic adaptive mesh refinement to track the interface between the vapour and liquid phases. The simulations are conducted using the interPhaseChangeFoam two-phase flow solver within the OpenFOAM framework. This study analyses the time-averaged and instantaneous fluid dynamic properties of the supercavitation flow of the hydrofoil with a hybrid surface, including pressure, velocity, vorticity fields, wake flow, recirculation zone, and liquid volume fraction. In addition, it examines the instantaneous cavity leading edge and flow separation, the vortical flow structure, vorticity stretching and dilatation, spanwise flow details, the creation of a low-pressure zone behind the hybrid hydrofoil, streamwise velocity fluctuations, and the cavity dynamics over an entire cycle. FindingsThe results show that the contact angle has a significant effect on the structure of the supercavitation flow. Specifically, the use of a superhydrophobic surface on the pressure side and trailing edge of a hydrofoil, along with a superhydrophilic surface on the leading edge, can reduce the thickness of the wake zone, the length of the cloud cavity, and flow instability. Additionally, this approach can delay the onset of cavitation.

Data-driven turbulence model for unsteady cavitating flow

Unsteady Reynolds-averaged Navier–Stokes (URANS) equations have been widely used in engineering fields to investigate cavitating flow owing to their low computational cost and excellent robustness. However, it is challenging to accurately obtain the unsteady characteristics of flow owing to cavitation-induced phase transitions. In this study, we propose an implicit data-driven URANS (DD-URANS) framework to analyze the unsteady characteristics of cavitating flow. In the DD-URANS framework, a basic computational model is developed by introducing a cavitation-induced phase transition into the equations of Reynolds stress. To improve the computational accuracy and generalization performance of the basic model, the linear and nonlinear parts of the anisotropic Reynolds stress are predicted through implicit and explicit methods, respectively. A data fusion approach, allowing the input and output of characterized parameters at multiple time points, is presented to obtain the unsteady characteristics of the cavitating flow. The DD-URANS model is trained using the numerical results obtained via large-eddy simulation. The training data consist of two parts: (i) the results obtained at cavitation numbers of 2.0, 2.2, and 2.7 for a Venturi flow, and (ii) those obtained at cavitation numbers of 0.8 and 1.5 for a National Advisory Committee for Aeronautics (NACA) 66 hydrofoil. The DD-URANS model is used to predict the cavitating flow at cavitation numbers of 2.5 for a Venturi flow and 0.8 for a Clark-Y hydrofoil. It is found that the DD-URANS model is superior to the baseline URANS model in predicting the instantaneous periodic shedding of a cavity and the mean flow fields.

Study on the influence of active jet parameters on the cavitation performance of Clark-Y hydrofoil

Cavitation is a phase change that severely endangers the safe and stable operation of hydraulic machinery. At present, one of the effective methods to control cavitation is the active jet. However, limited literature reports on multi-parameter optimal control of cavitation flow consider jet direction, flow, and position at the same time. This study attempts to fill this gap by determining the transient interaction mechanism of active jet and hydrofoil cavitation flow and to reveal the relationship between active jet and cavitation development. Clark-Y hydrofoil is taken as the research object and the grey Taguchi algorithm is used to obtain the optimal scheme of lift-drag ratio and hydrofoil ratio. The large eddy simulation combined with the Zwart-Gerber-Belamri cavitation model was used to numerically simulate the orthogonal test program and optimize its performance. Results are as follows: The closer the jet position to the leading edge of the hydrofoil, the easier it is for the active jet to block the back jet, reducing the cavity volume and hydrofoil drag but reducing the lift coefficient; The hydrofoil shows the best hydraulic performance when the jet position and the leading edge of the hydrofoil has a distance x of 0.3L (L is the chord length), the jet ratio u/u0 is 0.3, and the angle θ between the jet and the horizontal plane is 22°; Using the optimal jet scheme increases the lift-drag ratio k of the hydrofoil by 11.43% while the cavitation volume fraction is reduced and only 30% of the original hydrofoil.

Evaluation of modified turbulent viscosity on shedding dynamic of three-phase cloud cavitation around hydrofoil – numerical/experimental analysis

PurposeThis paper aims to focus on the cavitating flow around the Clark-Y hydrofoil when the dissolved air is taken into account as the third phase. As the RNG k-epsilon model yields poor prediction due to overestimation of viscosity, the modification approaches including density corrected method, filter-based model and filter-based density correction model are used, and the turbulence model is modified. Also, the numerical results are compared with the experimental data.Design/methodology/approachThe cavitating flow is known as a complex multi-phase flow and appeared in the regions where the local pressure drops under saturation vapor pressure. Many researches have been conducted to analyze this phenomenon because of its significant impact on the erosion, vibration, noise, efficiency of turbomachines, etc.FindingsThe experiments are conducted in a rectangular test section equipped with Clark-Y hydrofoil providing cavity visualization, instantaneous pressure and vibration fluctuations. The simulations are carried out for different cavitation numbers with and without dissolved air. The Fast Fourier Transform, continues wavelet transform and temporal-spatial distribution of gray level are implemented to extract and compare the shedding frequency of experiments and numerical predictions and cavitation evolution. It is concluded that the flow structure, shedding frequency and time-averaged characteristics are highly influenced by the dissolved air. Also, the numerical prediction will be more satisfactory when the modified turbulence models are applied.Originality/valueTo the best of the authors’ knowledge, the originality of this study is the modification of the turbulence model for better prediction of cavitating flow, and the validation of numerical results with corresponding experimental data.

Investigation of hysteresis effect of cavitating flow over a pitching Clark-Y hydrofoil

Investigation of hysteresis effect of cavitating flow over a pitching Clark-Y hydrofoil

Study of unsteady cavitating flow around Clark-Y hydrofoil using nonlinear PANS model with near-wall correction

In this paper, we describe the use of a new nonlinear partially-averaged Navier–Stokes (PANS) model with near-wall correction for simulating the cavitating flow around a Clark-Y hydrofoil. For comparison, the standard [Formula: see text]–[Formula: see text] PANS model is also used. The results demonstrate that compared to [Formula: see text]–[Formula: see text] PANS and experiment, the new PANS model shows better performance for cavitation flow, including time-averaged velocity, root mean square (rms) velocity and cavity shedding processing. Through the calculation of the lift and drag coefficient at [Formula: see text] and [Formula: see text], it can be concluded that the cavitation will decrease the lift and increase the drag of the hydrofoil, resulting in a decrease of the lift-to-drag ratio. From the analysis of different terms in both the turbulent kinetic energy (TKE) and dissipation rate transport equations of the cloud cavitation, it is found that the production term and the dissipation term are dominant in the turbulent transport, and they are mainly distributed in the vapor–liquid interface and the trailing edge of the hydrofoil.

Data-driven modal decomposition of transient cavitating flow

The objective of this paper is to identify the dominant coherent structures within cavitating flow around a Clark-Y hydrofoil using two data-driven modal decomposition methods, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). A snapshot data sequence is obtained using a large eddy simulation and the interaction between cavitation and the vortex during cloud cavity shedding evolution is investigated. Modal decomposition via POD and DMD indicates that the dominant coherent structures include the large-scale cavity–vortex, re-entrant jet, shear layer, and small-scale vortex in the wake. In addition, the flow field can be reconstructed from the most energetic POD or DMD modes. The errors in the flow reconstructions produced using the first four POD modes, first eight POD modes, and first eight DMD modes are 3.884%, 3.240%, and 3.889%, respectively. Furthermore, transient cavitating flow can be predicted via the DMD method with an error of 8.081%. The largest errors in the reconstructed and predicted results occur mostly in the shear layer, trailing edge, and near wake. POD and DMD provide accurate and practically beneficial techniques for understanding cavitating flow, although substantial challenges remain with regard to predicting this intense nonlinear system.

Euler–Lagrange study of cavitating turbulent flow around a hydrofoil

This study presented a multi-scale Eulerian–Lagrangian approach to simulate cavitating turbulent flow around a Clark-Y hydrofoil to study the bubble dynamics. A large eddy simulation was coupled with the volume of fluid method to capture the large vapor volumes in an Eulerian analysis. Micro-scale Lagrangian bubbles were then tracked by solving compressible the Rayleigh–Plesset equation and a bubble motion equation. A Gaussian kernel function was used to model the interactions between the flow field and the vapor bubbles in a coupled two-way algorithm. The predictions give satisfactory agreement with experimental data for the bubble size oscillations, bubble motion, and cavity shedding characteristics. Further investigations analyzed the influence of various parameters on the transformation between the Euler and Lagrange models. The numerical results provide detailed information about the influence of the cavitating turbulent flow on the bubble behavior, especially how the reentrant jet significantly affects the bubble generation and motion. The calculations also capture the bubble size oscillations caused by the surrounding liquid pressure variations and how these generate very high local pressures near the surface. The results show that the pressure wave released as a bubble is compressed reaches 107 Pa, which may cause cavitation erosion of the hydrofoil surface. This research provides a promising method to better investigate the bubble motion characteristics in macroscopic flows and demonstrates that the cavitation erosion caused by bubble size oscillations is significant and deserves attention.

Numerical investigation of unsteady cavitating turbulent flows around a three-dimensional hydrofoil using stress-blended eddy simulation

The mechanism of flow instability, which involves complex gas–liquid interactions and multiscale vortical structures, is one of the hot research areas in cavitating flow. The role of turbulence modeling is crucial in the numerical investigation of unsteady flow characteristics. Although large-eddy simulation (LES) has been used as a reliable numerical method, it is computationally costly. In this work, we used a hybrid Reynolds-averaged Navier–Stokes (RANS) and LES model, that is, stress-blended eddy simulation (SBES), to improve the prediction capability for the cloud cavitating flow. Our hybrid approach introduces a shielding function to integrate the RANS model with the LES applied only regionally, such as to large-scale separated flow regions. The results showed that the periodic shedding of cavity growth, break off, and collapse around a three-dimensional Clark-Y hydrofoil was reproduced in accordance with experimental observations. The lift/drag coefficients, streamwise velocity profiles, and cavity patterns obtained by the SBES model were in better agreement with the experimental data than those obtained by the modified RANS model. The re-entrant jet dynamics responsible for the break off of the attached cavity were discussed. Further analysis of vorticity transportation indicated that the stretching and dilatation terms dominated the development of vorticity around the hydrofoil. In conclusion, the SBES model can be used to predict cavitating turbulent flows in practical engineering applications.

RANS simulation of unsteady cavitation around a Clark-Y hydrofoil with the assistance of machine learning

In the present paper, a novel machine-learning-assisted RANS method is proposed and utilized to investigate the unsteady cavitating flow around a Clark-Y hydrofoil. A data-driven machine learning model for turbulent eddy viscosity (TEV) is constructed based on high-fidelity LES results first and then makes a prediction for TEV in RANS simulation. The machine-learning-predicted TEV successfully imitates the TEV distribution characteristics of LES that there is a concentration around cavities and negative TEV in the wake. Based on the more accurate TEV field, the machine-learning-assisted RANS method makes more accurate predictions about velocity profile and basic performance parameters of the hydrofoil and gives a better representation of shedding process of sheet cavitation when compared with original RANS results. Finally, generalization performance of the machine learning model is assessed by simulating cavitating flows under different inlet velocity conditions. The results indicate that cavitation exhibits more periodicity with the decrease of inlet velocity in the range of investigated conditions, which may be associated with different TEV distribution characteristics.

Lift-drag characteristics and unsteady cavitating flow of bionic hydrofoil

The hydrofoil, a basic blade cross-section shape, is widely used in various hydraulic machinery. We establish two bionic hydrofoil models (Sside and Stop) through 3D reverse engineering and numerically simulate these models with large eddy simulation to reveal the transient evolution mechanism of the cavitating flow of a bionic hydrofoil. The simulation results of the classic Clark-Y hydrofoil are highly consistent with the experimental results. In a single cycle, the lift–drag coefficient is related to the leading edge amount and cavity range. The cavitation number affects the lift–drag coefficient and cavity length L, and large cavitation numbers are associated with small values of lift–drag coefficient and cavity length. In contrast to the Sside scheme, the Stop scheme forms a relatively stable attachment vortex near the leading edge, the influence range of the vortex stretching is weak, and the baroclinic torque term that determines the cavity vortex is enhanced. The pressure fluctuation shown by the trailing edge of the bionic hydrofoil is significantly greater than that shown by the leading edge, and the main frequency of pressure fluctuation is proportional to the cavitation number.

A new method of LES verification and validation for attached turbulent cavitating flow

The large eddy simulation (LES) is used to resolve the flow structure in the cavitating turbulent flow around the Clark-Y hydrofoil coupled with a homogeneous cavitation model. A new method is proposed in this paper to calculate the LES error of the time-averaged streamwise velocity for the LES verification and validation (V&V). From the instantaneous cavity patterns, it is demonstrated that the predicted results agree fairly well with the experimental data. With this new proposed method, the LES errors can be easily and effectively calculated with a limited mesh number, and the method might be used in the other applications of the LES V&V. Results of the LES errors obtained by the new method show that the relatively steady flow can be simulated with small errors, while the complex flow structures at the cavity shedding region might lead to an increase of errors in the LES modeling. In addition, the distributions of the resolved Reynolds stresses are used to estimate the influences of the cavitation on the turbulent fluctuations. Results indicate that the turbulent fluctuations for the cavitating flow are much larger in magnitude as compared to the cases without cavitation.