Reynolds-averaged Navier-Stokes Research Articles

Numerical and Experimental Analysis of Lifting Forces of Fin Stabilizers with Fin–Hull Interaction

A fin stabilizer is one of the most effective and frequently used anti-roll devices for maintaining a ship’s operational safety and passengers’ comfort. However, the discrepancy between theoretical hydrofoil-based predictions and the actual dynamic lift force of fin stabilizers due to fin–hull interactions requires more research attention. This study investigates the effect of fin stabilizers on mitigating roll motion through the performance of extensive computational fluid dynamic (CFD) simulations over a range of fin angles and ship speeds. Model tests were carried out to validate the drag and motion results obtained from numerical analyses. The results show that fin stabilizers significantly reduce the roll motion, with the lift coefficient values influenced by the Reynolds number, leading to differences between the theoretical and Reynolds-averaged Navier–Stokes (RANS) calculations. At a high Froude number (Fr), the actual fin lift is about half of the theoretical value. These findings highlight the requirement of selecting an adequate fin area at the predominant ship speeds and ensuring an effective anti-roll effect with fin–hull interaction. This study provides a significant reference for ship design, as well as stabilization system control and optimization.

The impact of the Impeller's hub design on the performance and blood damage in a microaxial mechanical circulatory support device – A numerical study

This study uses CFD methods to investigate the effects of the impeller's geometry on the hemodynamic characteristics, pump performance, and blood damage parameters, in a percutaneous microaxial Mechanical Circulatory Support (MCS) device. The numerical simulations employ the steady state Reynolds-Averaged Navier-Stokes approximation using the SST k−ω turbulent model. Three different impeller models are examined with different hub conversion angles (α = 0○, 3○ and 5○). The analysis includes 23 cases for different pressure heads (Δp = 60–80 mmHg) and angular velocities (ω = 30–52 kRPM). The obtained flow rate is compared between the cases to assess the effect of the impeller's design and working conditions on the pump performance. The comparative risk of shear-induced platelet activation is estimated using the statistical median of the stress-accumulation values calculated along streamlines. The risk of hemolysis is estimated using the average exposure time to shear stress above a threshold (τ > 425 Pa). The results reveal that the shape of the impeller's hub has a great impact on the flow patterns, performance, and risk of blood damage, as well as the angular velocity. The highest flow rate (Q = 3.7 L/min) and efficiency (η = 11.3 %) were achieved using a straight hub (α = 0○). Similarly, for the same condition of flow and pressure, the straight hub impeller has the lowest blood damage risk parameters. This study shed light on the effect of pump design on the performance and risk of blood damage, indicating the roles of the hub shape and angular velocity as dominant parameters.

Influence of Incident Shock on Fuel Mixing in Scramjet

During the operation of hypersonic vehicles, a reciprocal coupling effect is manifested between the inlet and the combustion chamber. This results in an unavoidable non-uniformity of conditions at the combustion chamber’s entrance, which, in turn, influences the fuel mixing within the chamber. The present study employed the Reynolds-averaged Navier–Stokes (RANS) equations to perform a numerical simulation of an X-51-like vehicle, with a focus on examining the impact of isolation section length and multi-injection strategies on the fuel mixing characteristics within the combustion chamber under conditions of non-uniform inflow. The findings indicated that a supersonic non-uniform inlet triggers incident shock waves, leading to a non-uniform pressure distribution across the flow section. Moreover, the position of injection was found to be pivotal in regulating penetration depth and mixing efficiency. The incident shock wave, bow shock, and boundary layer separation shock interacted with each other to increase local pressure. The coupling of high and low pressures generated an adverse pressure gradient that led to boundary layer separation, which further enhanced fuel penetration depth.

Fluid-to-fluid similarity and CFD predictions of surrogate fluid as a replacement for R11 refrigerant used in a subcooled flow boiling analysis: Validation against reference experimental data

In this study, we present the possibility of substituting the primary fluid Freon 11 (CCl3F, R11) used in the experimental thermohydraulic test facility which offers the advantage to obtain phase change at relatively low heat fluxes and pressure if compared to water working fluid. Nevertheless, R11 is a CFC (Chlorofluorocarbures) part of the gases that contribute to the degradation of the ozone layer.For this purpose, four working fluids (non-CFC) are judiciously selected to replace the R11, namely the R123, R134a, R143a and CO2, and this through multidimensional Computational Fluid Dynamics (CFD) modeling of steady state flow boiling phenomenon. The calculation model covers a wide range of mass fluxes (435 – 1443 kg/m2s) and heat fluxes (1.2– 36 W/cm2); it predicts the heat transfer rates and transitions points for each flow regime. In this research, the experiment's data were compared to the outcomes of Ansys-CFX computations, using a steady Reynolds-Averaged Navier-Stokes (Steady-RANS) model, to investigate the best replacement refrigerant for R11. The turbulence effects on the mean flow and near wall are taken into consideration using the Shear Stress Transport (SST k-ω) turbulence model. The results from the four fluids were transposed to their water equivalent and compared to those deduced from the experimentation and correlations available in the literature to select the appropriate substitution fluid. The results qualitative and quantitative for vertical and horizontal subcooled boiling flow at the CHF location show that, the most effective surrogate fluid is R123 (HCFC), is still widely used in the refrigeration and air-conditioning industry, then R134a (HFC), then R143a (HFC).

Comparative study of nonlinear wave-induced global loads on a container ship in regular head waves predicted with numerical methods based on weakly nonlinear potential theory and Reynolds-averaged Navier-Stokes equations

ABSTRACT Wave-induced loads on the ship hull are of significant interest for many reasons. These loads, however, are highly nonlinear in realistic wave conditions and consequently difficult to predict. In this paper, we analysed and compared wave-induced loads acting on a container vessel in regular head waves computed with a newly developed weakly nonlinear potential theory-based code and a flow solver based on the Reynolds-averaged Navier-Stokes Equations. We investigated both long and short waves with moderate steepness. Initially, the vessel was completely fixed and the ship motions were suppressed, then the vessel was free to heave and pitch motion. We compared time-histories of the computed wave-induced longitudinal and vertical global forces as well as the pitch moment and we deeply analysed nonlinear effects by applying a Fourier transformation and comparing the corresponding harmonic amplitude up to the fourth order. Our results demonstrated satisfactory agreement between the two numerical methods, especially when the ship was free to heave and pitch. Additionally, the results provided detailed information about the capabilities and limitations of the weakly nonlinear potential theory-based approach.

Usage of high-fidelity large eddy simulation to improve the turbulence modeling of Reynolds averaged navier stokes simulation in film cooling applications via a neural network

In this study, high-Fidelity Large eddy simulation (LES) data was used to create a surrogate turbulence model to enhance the estimation of turbulent heat flux in a Reynolds averaged Navier Stokes (RANS) solver for film cooling applications. The LES simulation (A flat plate film cooling application with a Reynolds number of 17,382, a blowing ratio of 1 and a density ratio of 2) was validated by comparison with experimental data. A correlation analysis was performed to identify the most influential parameters, and temperature gradient, velocity gradient, turbulent dissipation rate, shear strain rate, and turbulent viscosity ratio were selected as the most effective parameters. A second model was developed to create a Galilean-invariant model that remains unaffected by changes in the coordinate system. Neural network was applied to the LES training data to propose a modified surrogate dynamic turbulent Prandtl number to be applied to the k − ε realizable RANS simulation. The first surrogate model was implemented to two different geometries, one geometry was a flat plate film cooling similar to the training data but with different flow conditions, and another one was a more complex geometry with pressure gradient. The second model was only implemented on the second geometry and produced similar results. The surrogate models predicted a more accurate thermal field near the cooling wall (up-to 58 % error reduction) with a computational time complexity similar to the base RANS solver.

Computational Fluid Dynamics Analysis on the Road Bike Using Different Flow Models under Extreme Inlet Velocity

At high velocities, the aerodynamic forces acting on the road bike and rider become more pronounced, potentially affecting stability and control. Riders might experience increased resistance, requiring more effort to maintain balance and direction. This research employs Computational Fluid Dynamics (CFD) to thoroughly examine the external aerodynamics of road bikes, focusing on pre-processing techniques and their impact on overall aerodynamic performance. The research applies CFD methods for geometry preparation, meshing, and material property definition within a structured workflow using a road bike model representative of the cycling industry via SimFlow software. Through systematic variations in extreme inlet velocities (40, 70, and 100 m/s) and the utilization of diverse turbulent models, k-ω Shear-Stress Transport (SST) and Reynolds-Averaged Navier-Stokes (RANS) with k-ε and k-ω, the study reveals intricate airflow patterns around the road bike. The results explain the complicated connection between turbulent models and inlet velocities and provide new information on critical aerodynamic parameters, such as pressure and maximum velocity of the road bike model.

Experimental and numerical investigation of the effects of porous bleed systems on a model scramjet inlet under high backpressure conditions

In this study, we conducted experimental and numerical investigations to assess the effects of backpressure and configurations of porous bleeds on a scramjet inlet's performance at Mach 5. Experiments in a high-enthalpy wind tunnel, using a backpressure controller and a porous bleed with 1 mm holes and 10 mm lateral spacing, were paired with Reynolds-averaged Navier-Stokes simulations to explore the mitigation of flow separation and the prevention of inlet unstart under various backpressure conditions. The simulations provided insights into how backpressure levels and porous bleed geometries impact internal flow structures, as well as the critical backpressure ratio at which inlet unstart occurs. Additional simulations varied the bleed system's hole diameter and spacing, identifying configurations that optimize aerodynamic performance by enhancing total pressure recovery and Mach number at the isolator exit, while reducing bleed mass flow rates. The study also quantified the impact of these configurations on engine thrust, determining that certain bleed system designs significantly improve thrust by efficiently suppressing the interaction between the core flow and corner recirculation zones. This study examined the internal three-dimensional flow structure characteristics under high back pressure and provided insights into porous bleed configuration capable of efficiently suppressing inlet unstart.

Numerical Analysis into the Improvement Performance of Ducted Propeller by using Fins: Case Studies on Types B4-70 and Ka4-70

Numerical analysis was conducted to assess the impact of fins on the B4-70 and Ka4-70 propeller performance. The study explored different fin variations, specifically bare fins, Propeller Boss Cap Fins (PBCF), and propeller nozzles, using computational fluid dynamics (CFD) simulations. To obtain the best results, the researchers utilized the explicit algebraic stress model (EASM) based on Reynolds-Averaged Navier-Stokes (RANS) equations and turbulence modelling. The primary goal of this study was to improve the energy efficiency of ships by examining various propeller configurations, both open and ducted. The overall conclusions indicated that the B4-70 PBCF convergent and Ka4-70 PBCF divergent with the addition of nozzle 19A exhibited the highest efficiency based on the EASM analysis. The CFD simulation results for both B4-70 and Ka4-70 propellers, utilizing a nozzle 19A with added boss cap fins, revealed several noteworthy phenomena. Firstly, for the B4-70 propeller, efficiency (η0) at J = 0.6 to J = 0.8 showed an increase of 1% to 2%. Secondly, concerning the Ka4-70 propeller, efficiency (η0) at J = 0.6 to J = 0.8 increased by 2% to 10%. These findings clearly demonstrate that the use of an ESD, such as the nozzle 19A with added boss cap fins, enhances the propulsion performance of the ship. It is evident that the CFD approach remains suitable and reliable for overall simulations.

Optimizing flow uniformity and velocity fields in aquaculture tanks by modifying water inlets and nozzles arrangement: A computational fluid dynamics study

Ensuring optimal conditions for fish, especially in terms of uniform velocity fields, is crucial for aquaculture systems to attain self-cleaning efficiency in rearing tanks and better fish behavior, which ultimately impacts their survival and growth. The current study presented a full-scale computational fluid dynamics (CFD) model of three types of circular, square and square arc angle tanks used in aquaculture farms to choose the most optimal tank based on the flow uniformity and velocity field. At the next step, the optimal arrangement of the inlet number, inlet location, inlet angle, as well as the number of nozzles on each inlet was reconfigured to create the most optimal tank. The simulations were done using the CFD software FLUENT distributed by the ANSYS Corporation. The software calculates velocities in the tanks by solving the Reynolds-averaged Navier-Stokes equations of continuity and momentum that govern the mean turbulent flow of water. The results illustrated circular and square arc angle tanks had significantly more uniform fluid velocity compared to the square tanks, which the square arc angle tank due to better flow velocity near the wall and space utilization was selected for further rearrangements. Additionally, the implementation of two entrance pipes near the corner of the two parallel walls with an entry angle of 0° in a square arc angle tank resulted in a more consistent water velocity. As the number of inlet nozzles increased to six, the water streamlines became more uniform, suggesting a more consistent water velocity throughout the tank. However, when the number of input nozzles increased to nine, the tension on the nozzle apertures were increased probably due to the decrease of distance between them. Findings of the present study concluded that two inlets in the corner with an entry angle of 0° and six nozzles on each inlet pipe could provide the optimum water velocity in the square arc angle tanks.

Ranking multi-fidelity model performances in reproducing internal and external wake impacts at neighbouring offshore wind farms

Performances of multi-fidelity numerical models in reproducing the impacts of internal and external wakes on power production are evaluated against SCADA data from two operational offshore wind farms in the North Sea. Results obtained for two engineering wake models, Jensen and Cumulative Curl (default and tuned settings), and a higher-fidelity Reynolds-averaged Navier-Stokes (RANS) setup are presented here. Tuned engineering models were configured against RANS results for an idealized wind farm layout consisting of several turbine setups. Input data for the model evaluation scenarios was obtained from a met mast located between the wind farms and binned according to atmospheric stability, wind speed and direction, and turbulence intensity. Results show that the higher fidelity of RANS is most advantageous for inflows that generate more complex internal wakes (e.g., deep array effects) and in replicating external wake impacts. The tuned setups of the engineering models, modified according to stability, outperform their default setups. Default model properties are defined in accordance with developer recommendations. One of the tuned models shows close farm-level agreements with RANS and even exceeds its performances in some of the cases of lower flow complexity.

Modeling of Blockage and Wake Effect: Comparison with Field data

We apply engineering approaches for estimating the induction zone in front of a wind turbine and escalating to the global blockage at wind farm and cluster of farms scale as well accounting for wind farm wake. The methods include the vortex cylinder model and the self-similar model for blockage, and Jensen and Eddy-viscosity models for the wake estimation. To calibrate each model, Reynold Average Navier-Stokes with Actuator disc simulations are employed. Every model’s efficacy is assessed in comparison to the validation data of an operated wind farm in Scotland with more than 100 turbines of 10 MW each. We also examine the widely used process within the wind industry for transferring the application of calibrated wake and blockage models from one wind farm. According to our simulation analysis, blockage causes sideways acceleration and upstream slowdown. This also result in a gradient in the power produced by each turbine on the farm, which, if not properly accounted for, appears to be a wake model constraint. Furthermore, the response of different turbine type in another environment is not effectively predicted by the blockage and wake model, which is based on a particular turbine type and environment.

How do wind farm blockage and axial induction control interact?

Wake losses significantly reduce wind farm output, but wind farm flow control (WFFC) can substantially reduce these losses, using wake steering (yawing upstream turbines) and/or axial induction control (reducing turbine power and thrust to weaken their wakes). Previous work shows that ignoring wind farm blockage in traditional wake models represents a prediction bias of similar order to the gains achievable with WFFC. Axial induction control works by changing turbine thrust, which is also the cause of blockage; this raises the question of how the two effects interact. Induction control can more than compensate for any loss due to blockage, but here we investigate the relationship further. Induction control reduces turbine thrust coefficients to reduce wake losses, but this should also reduce blockage, suggesting that induction control might achieve higher gains in practice than predicted with blockage effects ignored. An engineering model for blockage effects was added to the wind farm code LongSim, and steady-state gains calculated for a well-known offshore wind farm, with and without blockage. The results confirm that the power gains are indeed higher if blockage is modelled. These results are corroborated by comparisons against RANS (Reynolds-Averaged Navier Stokes) simulations, in which blockage effects are implicitly modelled.

RANS wake surrogate: Impact of Physics Information in Neural Networks

Artificial Neural Networks (ANNs) are being applied as a faster alternative to Computational Fluid Dynamics (CFD) for wind turbine engineering wake models. Unfortunately, ANNs can fail to generalize if the data is insufficient. Physics-Informed Neural Networks (PINNs) can improve convergence while lowering the required data amounts. This paper investigates the PINN methodology systematically by considering varying amounts of data and physics collocation points. This work considers the rotationally symmetric Reynolds Averaged Navier-Stokes (RANS) formulation. Initially, a baseline fully data-driven ANN is studied to determine a suitable network size. Then, multiple PINN-based wake surrogates are trained with continuity and momentum conservation knowledge, varying amounts of data, and physics collocation points. It was found that including physics information under the best circumstances could improve accuracy by 18% at the cost of increasing the training time by a factor of 116. The findings imply that physics information can improve neural network based wake surrogates.

A critical insight into the most effective CFD settings to model atmospheric stability in wind energy applications

An accurate reconstruction of the Atmospheric Boundary Layer (ABL) is key for the estimation of energy production and loads in modern wind turbines since, at current rotor heights, the vertical structure of the ABL is heavily influenced by thermal stratification effects. The wind power community usually accounts for these effects by semi-empirical modifications to the neutral ABL profile obtained using linear solvers such as WAsP; this approach, however, may not be suitable for sites with complex terrain, time-dependent thermal effects, or dense canopies. In these applications, methods with higher fidelity, such as Computational Fluid Dynamics (CFD) approaches based on steady or unsteady Reynolds Averaged Navier-Stokes (RANS) equations are needed. While CFD is recognized as providing more accurate results for these realistic cases, its use is still not a common best practice. In the current study, the WindModeller CFD tool by Ansys was employed to assess the effect of various inlet boundary conditions on the predicted wind speed profiles in unstable and stable atmospheric conditions. The test case is a wind farm in North Dakota, USA, characterized by a simple terrain but strong atmospheric stability effects and temperature fluctuations. The comparison of CFD results with experimental measurements and WAsP-CFD simulations reveals a high level of agreement between CFD and experimental data in stable conditions. In the case of unstable conditions, despite the good representation of the wind field, a high sensitivity to inlet boundary conditions is highlighted.

A numerical study on transport of dust particles accompanied by air gouging process and energy consumption in a circulating ventilation workshop

A modified circulating layered ventilation system is proposed to address the issue of particulate pollutants in the casting cleaning workshop, taking into account the characteristics of end treatment and production processes. By combining Reynolds Average Navier-Stokes (RANS) turbulence model and Discrete Phase Model (DPM), the particle transport law in the coupled flow process of thermal plumes generated by purified circulating airflow and heat source was explored, and ventilation energy consumption was evaluated from the perspective of particle removal energy efficiency ratio. Comparing three ventilation modes: mixed ventilation (MV), wall-based attachment ventilation (WAV), and wall-based attachment ventilation with deflector (WAV-D), it was found that the large vortex region formed by the change in airflow direction during WAV played a key role in particle transport. Placing deflector helped to reduce the impact of vortex recirculation zones generated by the intersection of airflow and plumes on particles. A higher fresh air rate will weaken the diffusion effect of particles in the horizontal air lake region. Continuously increasing the air supply volume to a certain level has little effect on the transportation of low-inertia particles, yet it still manages to effectively enhance the removal efficiency of medium-inertia particles. However, for high-inertia particles, an increase in air supply can easily lead to excessive particle concentration in local areas. When the particle removal efficiency reaches a certain level, higher supply air volume and fresh air rate may lead to a significant decrease in energy efficiency ratio.

A multi-fidelity approach for wind farm simulations and comparison with field data

The prediction of energy production and structural loads within wind farms is of high interest for wind industries to optimize the wind farm design and control under a variety of atmospheric conditions. However, it is still a key challenge to predict them accurately and efficiently due to the complex interactions between wind turbines and turbulent flows. Nowadays, high-fidelity simulations using Reynolds-Averaged Navier-Stokes (RANS) or Large-Eddy Simulation (LES) coupled with actuator disk or actuator line method are widely developed and used in wind farm analysis, but it still needs a huge computational resource, especially in the application of a commercial wind farm with large number of turbines. In this context, a mid-fidelity simulation tool based on the Dynamics Wake Meandering (DWM) model called FAST.Farm has been developed by National Renewable Energy Laboratory (NREL) in order to tackle this challenge. It allows to capture essential physics at the turbine scale as well as at the farm scale in a computational efficient manner. This study focuses on the calibration of DWM model which is the key to minimize inaccuracies between mid-fidelity and high-fidelity simulations regarding key performance indicators, e.g. thrust and power production. The calibration is made for DTU10MW wind turbine and shows a good improvement in accuracy compared to default parameters. The calibrated DWM model is finally employed to forecast the power generation for two turbines within a reference wind farm. A good improvement on the prediction is also obtained thanks to the calibrated parameters.

Stabilization of SIMPLE-like RANS solvers for computing accurate gradients using the complex-step derivative method

The Reynolds-averaged Navier-Stokes (RANS) approach in Computational Fluid Dynamics (CFD) is increasingly vital for aerodynamic design across wind turbines, gas turbines, aircraft, and rotorcraft. Enhancing the process through CFD-based design optimization requires numerous design variables, favoring gradient-based methods for better scalability than gradient-free methods. This research uses numerical differentiation techniques, particularly the complex-step derivative method, to compute gradients. Challenges arise during aerodynamic shape optimization when unconventional shapes disrupt RANS solver assumptions, causing convergence failures. These failures undermine optimization, prompting the need to enhance solver convergence for robust optimization. The modified-Boostconv method is a residual recombination method bolstering unstable eigenvalues to stabilize convergence of iterative solvers for nonlinear systems of equations. This study extends the modified-Boostconv method to combine it with the complex-step derivative technique, creating robust optimizations via RANS-CFD with accurate gradients, even for these numerically unstable cases. The main issue encountered during the complexification of the modified-Boostconv method is how to correctly ensure that the model reduction leading to the least-squares problem satisfies the complex-step derivative method. We test whether the dot product operation involved in the model reduction should be a Hermitian or non-Hermitian inner product. The problem is first tested for a simple analytical case using the logistic equation in combination with the modified-Boostconv method. It shows that the non-Hermitian inner product should be used. This is also confirmed with a similar study using the RANS solver.

An improved wind farm parametrization for inhomogeneous inflow

Energy losses due to wind farm clustering and wind farm interaction are rarely well represented in the wind farm design process because of the lack of fast models that can accurately account for neighboring wind farm wakes. A recently developed solution is the actuator wind farm (AWF) model, which is a Reynolds-averaged Navier-stokes (RANS) based wind farm parametrization that models a wind farm as a distributed thrust force and applies a global wind farm thrust coefficient controller. We propose an improved version of the AWF model, where each turbine employs a local thrust force controller and uses turbine thrust and power coefficients as input to better handle inhomogeneous inflow conditions. The proposed AWF model shows improved performance compared to the original AWF model in terms of predicted wind turbine power of a downstream wind farm that operates in a partial wake of an upstream wind farm, without significantly increasing the computational effort. However, the annual energy production (AEP) wake losses of a large wind farm cluster are nearly unaffected by using local or global control and input because the largest impact is found near the cut-in wind speed, which does not contribute much to the AEP wake losses.

A three-dimensional inverse method based on moving no-slip boundary for profiled end wall design

How to utilize existing flow control mechanisms to make profiled end wall design more flexible, efficient, and physical is a meaningful challenge. This study presents a three-dimensional inverse method for profiled end wall design to achieve the application of flow control mechanisms. The predetermined pressure distribution on the end wall is reached by modifying the end wall geometry during flow field calculation. A motion velocity model is derived from the normal momentum equation of the moving no-slip boundary to modify the end wall geometry. A Reynolds-Averaged Navier-Stokes (RANS) solver based on the Semi-Implicit Method for Pressure Linked Equations (SIMPLE) algorithm is adopted to simulate the flow field. Based on the mechanism understanding obtained through numerical optimization results, this study adopts the inverse method to redesign an optimized end wall in a compressor cascade. The results indicate that the redesigned end wall exhibits better loss reduction, reducing the overall total pressure loss by 5.5%, whereas the optimized end wall reduces it by 3%. The inverse method allows the imposition of desired influences on the end wall flow without constructing a database, making it highly flexible, efficient, and physical.