Reynolds-averaged Navier-Stokes Model Research Articles

Physics-enhanced data-driven turbulence model for flow around submerged bodies

Data-driven turbulence modeling is regarded as an effective approach to enhance the predictive performance of Reynolds-Averaged Navier−Stokes (RANS) models. However, the stability and accuracy of such methods still require further improvement. Therefore, we propose a novel modified model based on a combined neural network. Within this framework, we construct a fully connected neural network to predict the eddy viscosity coefficient in the RANS equations, thereby achieving an implicit solution for the linear part of the Reynolds stress. Additionally, we introduce a tensor basis neural network to predict the higher-order eddy viscosity relationships between unclosed quantities and analytical quantities. Furthermore, the model incorporates pressure gradients and turbulent kinetic energy gradients as inputs, fully considering the influence of pressure gradients and strong non-equilibrium effects in turbulence, thereby enhancing the physical foundation of the model. To evaluate the performance of the constructed model in different-dimensional flow fields with higher Reynolds numbers, we conduct validation analyzes on a two-dimensional NACA0012 model and a three-dimensional SUBOFF model without appendages. The results indicate that the modified model consistently outperforms the RANS model, particularly in predicting the velocity field near the leading edge of the NACA0012 hydrofoil. The predictions of the modified model are closer to those of large eddy simulation (LES) results. Specifically, the root mean square error (RMSE) of the interpolated and extrapolated modified models is reduced by 49.2% and 33.3%, respectively, compared to the RMSE of the RANS model. When applied to three-dimensional isolated SUBOFF flows, the modified model’s predictions for the average velocity field and pressure coefficients at both ends of the SUBOFF are in high agreement with LES results, but the prediction accuracy in the middle section is slightly insufficient. Nevertheless, the prediction errors of the interpolated and extrapolated modified models are reduced by 69.5% and 63.6%, respectively, compared to the baseline RANS model, which fully demonstrates the high accuracy of the modified model in predicting the overall pressure distribution of the SUBOFF. The methods developed in this study provide valuable insights into high-precision intelligent modeling for two-dimensional and three-dimensional flows.

Investigation of transonic flows through an idealized ORC turbine vane using Delayed Detached Eddy simulations

An idealized transonic blade configuration is studied using Delayed Detached Eddy Simulations (DDES), with focus on the loss mechanisms associated with the base pressure and turbulent wake development. The working fluid is Novec649, which is commonly used in low temperature Organic Rankine Cycle (ORC) power plants. Due to the molecular complexity of the fluid, a lower pressure ratio and exit Mach number are achieved compared to an air flow through the same geometry, a phenomenon that becomes more pronounced when considering thermodynamic operating conditions that lead to stronger non-ideal gas behavior, and which is well captured by models of standard use in ORC design and analysis, such as Reynolds-Averaged Navier–Stokes (RANS) models. Overall, as the fluid isentropic exponent and the exit Mach number decrease, the losses tend to increase. When performing a breakdown of the losses, the wake region provides the dominant contribution. Unlike RANS models, the DDES resolves the largest turbulent scales in the wake region, allowing fine-detail analysis of the unsteady wake dynamics, namely vortex shedding, and its influence on the loss coefficients. In the low supersonic Mach number range considered (M2∼1–2), the vortex shedding exhibits a flapping motion in the reattachment region behind the trailing edge. The Strouhal number, classically formed with the diameter of the trailing edge, is found to increase with increasing outlet Mach number. However, when introducing a new scaling based on the neck width of the reattachment region, directly related to the shape of the recirculation bubble and the associated reattachment shocks, the Strouhal number collapses to a relatively constant value of about 0.3 in all cases. DDES also shows that, due to the large heat capacity of Novec649, temperature fluctuations in the wake are greatly reduced, leading to the suppression of the so-called energy separation phenomenon occurring in air flows. While RANS simulations capture satisfactorily the main features and global quantities of the flow, they fail in describing the flow around the trailing edge. This results in inaccurate estimates of the back pressure, which ultimately leads to an underestimation of the losses by about 20%. Unsteady RANS simulations can predict the wake unsteadiness, but do not capture the couplings between large coherent structures, the recirculation, and the shocks, also resulting in inaccurate performance predictions. Thus, the present result showcase the importance of using advanced scale-resolving simulations for improved analysis and design of ORC turbines.

A progressive data-augmented RANS model for enhanced wind-farm simulations

The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier–Stokes (RANS) models are pivotal in enhancing our comprehension of the complex flow within and around wind farms; hence, improving their capacity to accurately model turbulence within this context is a vital research goal. The enhancement is essential for a precise prediction of wake recovery and for capturing intricate flow phenomena such as secondary flows of Prandtl’s second kind behind the turbines. To reach these objectives, here, we propose a progressive data-augmentation approach. We first incorporate the turbine-induced forces in the turbulent kinetic energy equation of the widely used k−ωSST model. Afterward, we utilize data from large-eddy simulations to progressively enhance the Reynolds-stress prediction of this baseline model, accurately capturing the evolution of eddy viscosity in the wake, as well as the emergence of secondary flows. We then apply the optimized model to two unseen cases with distinct layouts and conduct a comparative analysis focusing on the obtained quantities such as normalized streamwise velocity deficit, turbulence intensity, and power output. We also evaluate the performance of the augmented model in predicting wake characteristics by comparing it with wind-tunnel measurement data. Our comparisons and validations demonstrate the superior performance of the progressive data-augmented model over the standard version in all cases considered in this study.

Non-equilibrium turbulent boundary layers in high reynolds number flow at incompressible conditions: effects of streamline curvature and three dimensionality

The physics and computational prediction of turbulent boundary layer flow over axisymmetric and three-dimensional bodies are examined. Three cases were considered for which extensive experimental results and companion Reynolds-averaged Navier Stokes (RANS) solutions were obtained and/or available in the open literature. These cases all have Reynolds numbers based upon the freestream velocity and body geometric scale on the order of 10 5 to 10 6 , which is large for laboratory scales but small compared to the maximum scales observed for full-scale vehicles. Despite significant differences in approach flow fields and geometries for these three cases, some common themes emerged in the findings. All cases involved complications due to pressure gradients combined with streamwise curvature, and all exhibited regions of turbulence reduction due to accelerated flow. These complications led to discrepancies in computed results even in attached flow regions where it is often assumed that RANS models provide reliable predictions. The authors recommend further work on modelling approaches that can capture rapid distortion effects on turbulence transport that can be incorporated into industry-useful frameworks. Two cases involving laterally symmetric, three-dimensional wall-mounted hills with aft-body separation revealed that asymmetric mean flow fields are likely to result. This finding has been observed in experiments conducted in multiple facilities and in computations using multiple solvers and turbulence models. It is concluded that non-unique and asymmetric global flow solutions are fundamental to flow cases with lateral geometric symmetry involving turbulent boundary layer separation. Further work is also needed to accurately predict low-frequency unsteadiness due to geometries that produce non-unique mean flow fields. For such flows, it remains to be definitively determined whether experimentally observed modes of the mean flow are equivalent, or nearly equivalent, to asymmetric mean flow solutions obtained using RANS approaches.

Development of a Generalizable Data-Driven Turbulence Model: Conditioned Field Inversion and Symbolic Regression

This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier–Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent efforts have focused on data-driven methods such as field inversion and machine learning (FIML) to correct this issue by adjusting the baseline equations. However, these FIML methods often reduce accuracy in attached boundary layers. To address this issue, we developed a “conditioned field inversion” technique. This method adjusts the corrective factor β (used by FIML) in the shear-stress transport (SST) model. It multiplies β with a shield function fd that is off in the boundary layer and on elsewhere. This maintains the accuracy of the baseline model for the attached flows. We applied both conditioned and classic field inversion to the NASA hump and a curved backward-facing step, creating two datasets. These datasets were used to train two models: SR-CND (symbolic regression-conditioned, from our new method) and SR-CLS (symbolic regression-classic, from the traditional method). The SR-CND model matches the SR-CLS model in predicting separated flows in various scenarios, such as periodic hills, the NLR7301 airfoil, the 3D SAE (Society of Automotive Engineers) car model, and the 3D Ahmed body, and outperforms the baseline SST model in the cases presented in the paper. Importantly, the SR-CND model maintains accuracy in the attached boundary layers, whereas the SR-CLS model does not. Therefore, the proposed method improves separated flow predictions while maintaining the accuracy of the original model for attached flows, offering a better way to create data-driven turbulence models.

LES and RANS Modeling of an 84-Pin Hexagonal Rod Bundle with Spacer Grid and Experimental Validation

As part of an ongoing integrated research project (IRP), detailed investigations of the flow characteristics of an 84-pin hexagonal rod bundle representing a potential modular gas-cooled fast reactor design have been performed. The rod bundle features a pitch-to-diameter ratio of 1.5 and a large central guide tube, along with simple spacer grids and an outer enclosure. The primary focus of the current work is modeling and simulation of this geometry using multiple computational techniques. These include high-fidelity large eddy simulation (LES) and advanced Reynolds-averaged Navier-Stokes (RANS) approaches. The flow predictions are validated at a Reynolds number of 12 000 using matched index of refraction particle image velocimetry experimental data that were also generated by Texas A&M University as part of the IRP. The comparison data include velocity magnitude and turbulent kinetic energy (TKE) at different lateral locations and at multiple distances downstream of the spacer grid. Many characteristics of the experimental flow are replicated in the LES and RANS runs, and a general agreement between simulation and experiment is shown. Except for the immediate vicinity of the grid, LES is able to capture the velocity magnitude within a 10% error and the TKE to within the experimental uncertainty. The LES shows notably better agreement with the experimental data than RANS, particularly regarding the TKE decay behavior downstream of the grid. Both methods show significant departures from the experiment in their predictions close to the central guide tube. The experimental and high-fidelity data will be used to inform closures for novel multiscale methodologies. The long-term goal of the work is to use the high-fidelity data to yield improved multiscale thermal analysis techniques for solving fuel performance problems of direct relevance to industry.

Particle resolved DNS and URANS simulations of turbulent heat transfer in packed structures

An accurate prediction of turbulent heat transfer in packed-bed reactors is fundamental for the design and performance of such reactors. Various classes of Reynolds-Averaged Navier-Stokes models are available in the literature for predicting turbulent heat transfer via closing the Reynolds stress and Turbulent Heat Flux terms. In this work, we investigate the accuracy of representative unsteady RANS turbulence models in modeling heat transfer characteristics in packed-bed reactors. For this accuracy assessment, the performance of the examined unsteady RANS models is compared with particle-resolved Direct Numerical Simulations including heat transfer. The simulations are conducted in a three dimensional periodic domain consisting of randomly arranged cylindrical particles for Reynolds number Rep=1000. The extracted mean quantities (e.g. velocity and temperature), turbulent heat flux components and turbulent kinetic energy are used as a reference for the RANS models' accuracy assessment. The SST k−ω and Spalart-Allmaras models performed well in predicting mean fields. However, all models tended to mispredicted the turbulent quantities including the turbulent viscosity and heat flux vector. The thermal field calculations rely on the turbulent viscosity based Simple Gradient Diffusion Hypothesis for modeling turbulent heat flux, which introduce challenges in capturing the coupling between thermal and turbulent fields.

Turbulence model adaptability at critical Reynolds numbers and applications in wake control via fairings

The water-drop shaped fairings with varying shape angles are attached to a circular cylinder to achieve wake control and vortex suppression at critical Reynolds numbers. To ensure the capability of Reynolds averaged Navier–Stokes (RANS), detached eddy simulation (DES) and large eddy simulation (LES) models at the critical Reynolds number region, three representative turbulence models are employed: LES with σ subgrid-scale (SGS) model, delayed DES model with improved wall-modeling capability (IDDES) and shear stress transport (SST) k−ω RANS model. These models are utilized to simulate flow around a circular cylinder at Reynolds number Re=2.5×105. The solver used in this paper is further developed based on the high-resolution algorithm platform for incompressible flow (HRAPIF). The comparative analysis of the results from the three turbulence models has been rigorously validated and investigated. An exhaustive examination of the mean flow field, Reynolds stresses, characteristic lengths, and instantaneous flow fields among the models reveals instructive insights. The IDDES and σ-LES models predict the hydrodynamic forces, the so-called ‘drag crisis’, alongside the pressure distribution and skin friction coefficient with high precision. The σ-LES model stands out for its superior accuracy, while the IDDES model is also a viable alternative, offering commendable accuracy with a reduced demand for mesh density. Subsequently, the IDDES model is selected for wake control calculations using fairings with five distinct shape angles (30∘,45∘,60∘,75∘ and 90∘) at Re=2.5×105. In-depth comparisons to the bare cylinder and subcritical results reveal that the wake control effect varies at the critical Reynolds region. The fairing with a 30° shape angle substantially suppresses hydrodynamic forces. The lift coefficient experiences a remarkable decrease of approximately 96%, while the drag coefficient diminishes by about 90%. Concurrently, fairings with angles from 45° to 90° lead to reductions in drag coefficient of 11.6%, 10%, 3% and 4%, respectively. A 75% lowering in the lift force coefficient is found even with a short fairing with shape angle α=90∘, which may be a meaningful instruction to industrial design.

Influence of Dilution and Effusion Flows in Generating Variable Inlet Profiles for a High-Pressure Turbine

Abstract The elevated flow temperatures and pressures exiting gas turbine combustors affect the efficiency and durability of the high-pressure turbine stage. Understanding the effects that result from the upstream dilution and effusion flow interaction in creating the combustor exit profiles is important to advancing gas turbines. Understanding how common combustor features, such as dilution jets and effusion cooling, interact based on computational predictions guided the design of a non-reacting profile simulator capable of producing a wide range of non-uniform temperature profiles representative of those entering high-pressure turbines. The mechanical design of a new simulator device, which will be experimentally tested in the future, is presented in this paper along with computational predictions of flow and thermal fields. The simulator was designed for modular installation into the Steady Thermal Aero Research Turbine (START), which is a continuous-duration, steady-state turbine facility that houses a single-stage test turbine. Features of the simulator included interchangeable liner panels with multiple rows of dilution jets and wall effusion cooling to study various hole diameters and patterns. The dilution jets generate elevated turbulence intensities and tailor the temperature profiles in the radial and circumferential directions. The air mass flow distribution and source flow temperatures are independently controlled. To aid in the simulator design, computational fluid dynamics (CFD) simulations using Reynolds-averaged Navier–Stokes modeling were conducted with a two-level Design of Experiments (DOE) approach to determine a number of engine-representative target profiles with temperature shapes that are mid-radius peaked, outer-diameter peaked, inner-diameter peaked, and uniform. A sensitivity analysis of the CFD DOE results determined which factors significantly affected the profile shape so that the target profiles could be produced. The analysis indicated that the main contributor to the temperature profile shape at near-wall vane radial span locations of 0–15% and 85–100% was the injection temperature of the effusion flow. The main contributor at radial span locations of 15–40% and 60–85% was the injection temperature of the third-row dilution jets, and at the mid-radius location of 40–60% vane radial span, the main contributor was the diameter of the first-row dilution holes.

Validation of numerical method of unsteady Reynolds-averaged Navier–Stokes coupled with discrete phase model and optimization for the snow-resistance performance of bogies of a high-speed train

This paper presents an investigation aimed at enhancing the snow resistance of bogies in a three-car grouped alpine high-speed train (HST) that operates in a snowy region. The study employs a combination of the unsteady Reynolds-averaged Navier–Stokes model, coupled with the discrete phase model. This modeling approach has been validated through wind tunnel tests, including flow field tests, two-phase flow tests, and snow-ice tests. The study comprehensively compares the characteristics of the wind-snow flow, spatial snow concentration, and the distribution of snow on the train's underbody between two scenarios: the original train's underbody structure and the multifactor collaborative optimization scheme (MCOS). The results indicate that the MCOS case facilitates the escape of snow particles from the bogie cavities by weakening the upward and reverse flows. Furthermore, the MCOS case significantly reduces the concentration of snow particles around the heat-producing elements and decreases the accumulation of snow on both the lower and upper surfaces of the bogies. As a result, it reduces snow accumulation on the bogies by 50.6%, 56.4%, 60.8%, and 62.4% at train speeds of 200, 250, 300, and 350 km/h, respectively. In summary, this research provides valuable insights for improving the snow resistance of HSTs operating in snowy regions, with potential applications in enhancing railway transportation safety and efficiency.

Viscous-Layer Compressibility Correction for Two-Equation Reynolds-Averaged Navier–Stokes Models

The baseline two-equation Reynolds-averaged Navier–Stokes (RANS) models include fluid density but lack calibration for compressible flows, making them inadequate for high Mach numbers. Various compressibility corrections have been proposed to integrate the van Driest transformation or the semilocal transformation, i.e., a given compressible law of the wall, into the RANS formulation. Prior work has focused mainly on the logarithmic layer, but upon evaluating these log-layer compressibility corrections, we find that they do not significantly improve skin friction estimates. To overcome this challenge, we develop viscous-layer compressibility corrections. We do that by altering the dissipation terms. These corrections conform the RANS model to the semilocal scaling, resulting in more accurate predictions of mean velocity and temperature in a posteriori tests. Although not the primary focus of this study, we also find that the baseline one-equation Spalart–Allmaras model, which was proposed before the concept of semilocal scaling, produces results consistent with the semilocal scaling in a posteriori tests without requiring any compressibility correction.

Development and evaluation of a buoyancy-driven airflow window with multioperation modes

This study introduces a novel, cost-effective window-integrated passive system (WIPS) that enhances indoor air quality and maintains comfortable temperatures throughout both summer and winter. The WIPS utilizes buoyancy-driven air flow through dual air cavities that alternately preheat or cool the incoming air depending on the season. The system’s design was optimized for Seoul, Korea, through computational fluid dynamics (CFD) simulations using Fluent 2021 R2 software, which employed a Reynolds-Averaged Navier-Stokes (RANS) model with RNG κ-ε turbulence modeling. Key design adjustments included the use of glass and unplasticized polyvinyl chloride (UPVC) to minimize unintended airflow, as well as modifications to the geometry and size of the air cavities to maximize efficient air flow and thermal effectiveness. The optimized design demonstrated significant improvements in thermal performance, achieving a preheating effect up to 35 °C in winter and reducing indoor temperatures by 10 °C in summer. These results underline the potential of WIPS to provide a sustainable solution for building ventilation and climate control.

A Comparison of Local and Global Strategies for Exploiting Field Inversion on Separated Flows at Low Reynolds Number

The prediction of separated flows at low Reynolds numbers is crucial for several applications in aerospace and energy fields. Reynolds-averaged Navier–Stokes (RANS) equations are widely used but their accuracy is limited in the presence of transition or separation. In this work, two different strategies for improving RANS simulations by means of field inversion are discussed. Both strategies require solving an optimization problem to identify a correction field by minimizing the error on some measurable data. The obtained correction field is exploited with two alternative strategies. The first strategy aims to the identification of a relation that allows to express the local correction field as a function of some local flow features. However, this regression can be difficult or even impossible because the relation between the assumed input variables and the local correction could not be a function. For this reason, an alternative is proposed: a U-Net model is trained on the original and corrected RANS results. In this way, it is possible to perform a prediction with the original RANS model and then correct it by means of the U-Net. The methodologies are evaluated and compared on the flow around the NACA0021 and the SD7003 airfoils.

Separation and reattachment fixed Reynolds-stress model for iced airfoil and wing simulations

Differential Reynolds-stress models (RSMs) are the higher level of Reynolds-averaged Navier-Stokes (RANS) modeling and generally considered to have more potential than eddy viscosity models (EVMs) when applied to separated flows. Nevertheless, some peculiarities have been observed in simulating flows over iced wings. The first is the numerical stability problem caused by non-streamlined wall surfaces. To weaken this, a wall-boundary-natural scale λ=ω−1/8 is introduced into SSG/LRR RSM in this paper. The second is to remedy the problem of the unsatisfactory non-equilibrium characteristic in separating shear layer (SSL), a correction determined by anisotropy of Reynolds normal stresses is developed. The last focuses on the unphysical backbending of the streamlines near stagnation/reattachment (SR) points. Since the SSL correction will worsen this defect, a SR correction is carried out to blended with it. Four two-dimensional iced airfoils and a three-dimensional iced wing are simulated and then the above measures are confirmed to be the positive.

A numerical study on the turbulence characteristics in an air–water upward bubbly pipe flow

A high-resolution numerical simulation of an air–water turbulent upward bubbly flow in a pipe is performed to investigate the turbulence characteristics and bubble interaction with the wall. We consider three bubble equivalent diameters and three total bubble volume fractions. The bulk and bubble Reynolds numbers are $Re_{bulk}= u_{bulk} D/\nu _w = 5300$ and $Re_{bub}= (\langle u_{bub}\rangle - u_{bulk}) d_{eq}/\nu _w = 533\unicode{x2013}1000$ , respectively, where $u_{bulk}$ is the water bulk velocity, $\langle u_{bub}\rangle$ is the overall bubble mean velocity, $D$ is the pipe diameter and $\nu _w$ is the water kinematic viscosity. The mean water velocity near the wall significantly increases due to bubble interaction with the wall, and the root-mean-square water velocity fluctuations are proportional to $\bar {\psi }(r)^{0.4}$ , where $\bar {\psi } (r)$ is the mean bubble volume fraction. For the cases considered, the bubble-induced turbulence suppresses the shear-induced turbulence and becomes the dominant flow characteristic at all radial locations including near the wall. Rising bubbles near the wall mostly bounce against the wall rather than slide along the wall or hang around the wall without collision. Low-speed streaks observed in the near-wall region in the absence of bubbles nearly disappear due to the bouncing bubbles. These bouncing bubbles generate counter-rotating vortices in their wake, and increase the skin friction by sweeping high-speed water towards the wall. We also suggest an algebraic Reynolds-averaged Navier–Stokes model considering the interaction between shear-induced and bubble-induced turbulence. This model provides accurate predictions for a wide range of liquid bulk Reynolds numbers.

Entrainment rate predictions of axis-symmetric non-swirling jets using free-jet-theory, Reynolds-averaged Navier-Stokes modelling and large-eddy-simulations resolved up to Kolmogorov scale

Jet entrainment - relevant to mixers, sprays and combustion technologies - has been the subject of this work. We limit our considerations to air jets issued from convergent nozzles of diameter smaller than 25.4 mm, the nozzle exit Reynolds number in the range 30,000 to 100,000 and Mach numbers not exceeding 0.4. The emphasis is on jet self-similarity region (60 < x/d0 < 210) and the key question is with which accuracy can Computational Fluid Dynamics (RANS and LES) and free-jet theory predict jet entrainment. Seven jets have been considered.The realizable k-є model has outperformed the other models and provides the entrainment predictions within ±6 % margin from the measured data. The standard k-є and the Shear-Stress Transport (SST) k-ω models deliver entrainment figures which are larger than the measured data by 22 % – 24 % whilst predictions of either the Reynolds Stress Model (RSM) or Re-Normalization Group (RNG) k-є models can be off (too large) by as much as 34 % and 40 %, respectively. Such a clarity in classification of turbulence models has been obtained after minimization of numerical related errors to a degree which was not achievable in the past. The Panchapakesan&Lumly's jet has been computed using the Large Eddy Simulations with the filter size of the order of Kolmogorov scale throughout the jet e.g. at the inlet, potential core and the far field. Excellent predictions of the jet spread rate, velocity profiles and the entrainment have been obtained at the expense of huge computational resources.The well-known engineering correlation m˙e/m0˙=0.32(x/d0) provides entrainment figures that are by 10 % or less larger than the measured values.

Combined Reynolds-averaged Navier-Stokes/Large-Eddy Simulations for an aircraft wake until dissipation regime

A new methodology has been devised to simulate the aerodynamic wake of an airliner during cruise flight using Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulations (LES). The wake evolution is simulated considering the full geometry of the aircraft and spans from the jet regime to the dissipation regime. First, the jet regime is computed using RANS modeling and state-of-the-art anisotropic mesh adaptation techniques. Second, the vortex and dissipation regimes are solved using temporal LES with flow field initialization from the previous RANS calculation. RANS information on turbulent quantities is transferred to the LES domain using synthetic turbulence methods. The methodology is first tested on a NACA-0012 wing. It is then applied to the NASA Common Research Model (CRM) geometry on cruise flight conditions. Jet/vortex interaction is studied during the jet regime up to twenty wingspans behind the aircraft. The influences of atmospheric turbulence, jet turbulence, and stable atmospheric stratification are investigated for the vortex and dissipation regimes. It is observed that atmospheric turbulence intensity and stratification both accelerate the vortex decay. Moreover, the secondary wake structure is found to be much more turbulent for numerical RANS initialization than for classical analytic initialization, showcasing the importance of early aerodynamics on wake evolution.

Drag reduction of lift-type Vertical axis wind turbine with slit modified Gurney flap

This study examines aerodynamic performance enhancement of Vertical Axis Wind Turbine (VAWT) blades with Gurney flap (GF) modified by slits. A quasi-3D computational fluid dynamics solution based on Reynolds-averaged Navier-Stokes model is employed to evaluate the effectiveness of slit GF blades, in particular the lift-to-drag ratio. The computational domain includes three pairs of GFs and slits combination along the blade span-wise direction to allow quasi-3D flow development while applying translational periodic boundary condition on the two end-wall boundaries. The inlet velocity is 9 m/s for a VAWT configuration with Tip Speed Ratios (TSRs) of 1.44, 2.64, and 3.3, respectively and each of these TSRs represents low, medium, and high regimes of TSRs. Simulation results have shown a significant 8% drag reduction for blades with slit GFs at medium range of TSRs, albeit with a 2% decrease in lift compared to blades with clean GFs. This improves the lift-to-drag ratio and enhances moment production. The power generation also shows increases of 1.5%, 6.5%, and 11.3% at low, medium, and high TSR regimes, respectively, for the analysed slit GF blades. The drag reduction is primarily attributed to the generation of small-scale vortices by the slit, dissipating large coherent flow structures more rapidly in the near wake-field.

Roughness constant selection for atmospheric boundary layer simulations using a k-ω SST turbulence model within a commercial CFD solver

Transit of an atmospheric boundary layer (ABL) profile within computational fluid dynamics (CFD) simulations is often hindered through unintended streamwise gradients in the flow resulting in ABL inhomogeneity. Within Reynolds-Averaged Navier–Stokes (RANS) turbulence modeling in Ansys Fluent, user-defined wall functions are not available for the k–ω class of RANS models as a remedy to this problem. Instead, the practitioner is required to use the sand–grain roughness property for ground surface roughness calibration, and specify a roughness height and roughness constant, Cs, accordingly. To-date, no clear guidance on their selection is available that can accommodate both high and low roughness terrains. To overcome this, a straightforward and practical calculation is presented for the roughness constant based on the standard wall function implementation. When this calculation is synthesized with other best-practice guidelines, it is possible to reliably model transiting ABL profiles based on wind-tunnel data whilst minimizing effects due to ABL inhomogeneity.

Numerical simulation of deep-water wave breaking using RANS: Comparison with experiments

Wave breaking is a multifaceted physical phenomenon that is not fully understood and remains challenging to model. An effective method for investigating wave breaking involves utilising the two-phase Reynolds-averaged Navier–Stokes (RANS) equations to directly simulate breaking waves. In this study, we apply a RANS model with an adaptively refined mesh to simulate breaking waves in deep water using the stabilised RANS model proposed by Larsen and Fuhrman. This approach enables a more efficient simulation of the physics of breaking waves compared to Direct Numerical Simulations, as it places less stringent demands on grid resolution. Our findings demonstrate that the RANS model compares well with deep water wave breaking experiments in terms of surface elevation. We also give estimates of the breaking strength parameter of our RANS simulations and compared them with the literature.