Reynolds-averaged Navier-Stokes Model Research Articles

Simulation Study on the Navigation Resistance and Shape Optimization of a New Type of Amphibious Vehicle

Amphibious vehicles are important equipment used by emergency rescue teams to quickly pass through water networks. A numerical model of amphibious vehicle underwater navigation in the lower shell was established in this study using computational fluid dynamics, the Reynolds-averaged Navier–Stokes model, and the volume-of-fluid method to investigate the navigation performance of a new all-terrain all-water amphibious emergency rescue vehicle. The navigation resistance was calculated at different speeds. The characteristics of the flow field around the vehicle body were analyzed, and optimization measures for drag reduction by installing a stern flap were proposed. The simulation results show that the existing vehicle body has a relatively high navigation resistance, and the flow field of the amphibious vehicle body is significantly improved after using stern flaps with larger angles and sizes. When the stern flap angle was 38°, the drag-reduction effect was 23%, which effectively improved the navigation performance of the amphibious vehicle.

Detached Eddy Simulation of Micro-Vortex Generators Mounted on NACA 4412 Airfoil

Abstract Micro Vortex Generators (MVGs) are simple vane-type devices installed on surfaces i.e. aircraft wing and tail, to control boundary layer flow separation. They are cost-effective and require an efficient numerical method to resolve the embedded longitudinal vortical structure, as the flow behind them is highly unsteady. This numerical study aims at analyzing the effect of MVGs on the NACA 4412 airfoil performance using the detached eddy simulation (DES) at 60,000 Reynolds number based on the free stream velocity of 11 m/s and the wing chord of 0.1 meter. The MVGs are installed in a row in counter-rotating configuration at 10% chord of the airfoil. The DES is utilized to resolve the high energetic scales of the vortex behind the MVGs which Reynolds Averaged Navier-Stokes (RANS) is not likely to resolve properly. A comparison of DES simulations with the RANS simulations shows that RANS modeling is not sufficient to capture the airfoil flow characteristics at higher angles of attack. The DES simulations resolve the turbulent scales downstream of the MVGs effectively and produce significant improvement in NACA 4412 wing performance. It has been observed that airfoil lift coefficient at 10 degree angle of attack with the MVGs is 5% higher than the airfoil without MVGs. MVGs along the airfoil chordwise location demonstrate that MVGs at 25% chord are more effective in managing separation over the airfoil compared to those at 10% chord location.

RANS model predictions for desalination discharges implemented with a dynamic turbulent Schmidt number approach

Desalination discharges are commonly in the form of inclined negatively buoyant jets (INBJs). Numerical predictions of INBJs remain a challenge. While accurate large eddy simulations (LES) of INBJs have become available very recently, they are substantially more resource intensive. Reynolds-averaged Navier–Stokes (RANS) modelling is potentially more efficient and can be more readily applied in practice. However, existing RANS simulations show substantial error when compared with experimental measurements. In this study, RANS simulations of 45° INBJs are performed with a dynamic turbulent Schmidt number (DTSN) approach. This new approach involves extracting turbulent Schmidt number (Sct) profiles in the INBJs from recently published LES data that have been validated by experiments. Detailed cross-sectional Sct profiles in INBJs are reported here for the first time. The relationship between Sct and a local mean flow parameter is also determined from the LES data. RANS simulations are then performed—with Sct being allowed to change dynamically during the simulation according to the pre-determined relationship. The results show that the DTSN approach improved the overall predictive capabilities of the RANS model to a limited extent. However, significant issues remain in terms of the models’ ability to predict dilutions in the descending portion of the flow. Importantly, the DTSN simulations demonstrate that the model predictions are sensitive to the determination of the relationship between Sct and the local flow parameter. Further improvements in the DTSN approach are therefore possible with refinement to the characterisation of this relationship. Based on a discussion of the present and recent literature describing RANS simulations of INBJs, the authors encourage a more cautious interpretation of the current predictive capabilities of RANS simulations in the context of INBJs.

Simulation of Flow Around a Finite Rectangular Prism: Influence of Mesh, Model, and Subgrid Length Scale.

This study investigates the flow field around a finite rectangular prism using both experimental and computational methods, with a particular focus on the influence of the turbulence approach adopted, the mesh resolution employed, and different subgrid length scales. Ten turbulence modelling and simulation approaches, including both 'scale-modelling' Reynolds-Averaged Navier-Stokes (RANS) models and 'scale-resolving' Delayed Detached Eddy Simulation (DDES), were tested across six different mesh resolutions. A case with sharp corners allows the location of the flow separation to be fixed, which facilitates a focus on the separated flow region and, in this instance, the three-dimensional interaction of three such regions. The case, therefore, readily enables an assessment of the 'grey-area' issue, whereby some DDES methods demonstrate delayed activation of the scale-resolving model, impacting the size of flow recirculation. Experimental measurements were shown to agree well with reference data for the same geometry, after which particle image velocimetry (PIV) data were gathered to extend the reference dataset. Numerical predictions from the RANS models were generally quite reasonable but did not show improvement with further refinement, as one would expect, whereas DDES clearly demonstrated continuous improvement in predictive accuracy with progressive mesh refinement. The shear-layer-adapted (SLA) subgrid length scale (ΔSLA) displayed consistently superior performance compared to the more widely used length scale based on local cell volume, particularly for moderate mesh resolutions commonly employed in industrial settings with limited resources. In general, front-body separation and reattachment exhibited greater sensitivity to mesh refinement than wake resolution. Finally, in order to correlate the observed DDES mesh requirements with the observations from the converged RANS solutions, an approximation for the Taylor microscale was explored as a potential tool for mesh sizing.

Adaptive detached eddy simulations of incident shock-induced separation

The low-frequency unsteadiness associated with shock wave turbulent boundary layer interactions (STBLI) poses significant numerical challenges for Reynolds–averaged Navier–Stokes models as well as scale-resolving methods such as direct numerical simulations and large-eddy simulations and requires hybrid methods like detached eddy simulations (DES) from an engineering perspective. The recently developed adaptive DES (ADES) method [Yin and Durbin, “An adaptive DES model that allows wall-resolved eddy simulation,” Int. J. Heat Fluid Flow 62, 499 (2016)], which was originally introduced for low-speed flows and extended to transonic flows, is a promising approach. However, its efficacy for high-speed flows needs to be assessed. In the present study, a compressible version of the ADES method has been implemented and set to test for supersonic flows with complex STBLI features. Impinging STBLI at M∞=2.30 is investigated numerically using compressible ADES method. No inflow turbulence fluctuations are provided in the upstream (time-averaged) boundary layer profile so as to verify the ADES method's capability to predict the low-frequency “breathing” motion of the bubble and the shear layer flapping/shedding in the mid-frequency range, thus demonstrating the significance of downstream mechanism in the unsteadiness associated with STBLI. Time series analysis of surface pressure distribution and modal analysis of numerical schlieren using proper orthogonal decomposition are carried out to understand the dominant mechanisms and associated frequencies. In the interaction zone, starting from the intermittent region to the region downstream of reattachment, a transition of the dominant frequency from low-to-mid-to-high ranges is observed. The separation shock motion and the shear layer shedding are observed to have dominant Strouhal numbers [Stδ0, defined based on the boundary layer thickness (δ0) upstream of interaction] in the ranges of 0.01 and 0.1, respectively, consistent with the literature. From correlation and coherence analyses, it is also observed that the separation bubble is exercising breathing motions at lower frequencies [Stδ0∼O(0.01)] despite lacking the inflow turbulence, thus supporting the downstream influence arguments. The present study demonstrates that the ADES model is capable of predicting essential features of STBLI, including low- and mid-frequency events and a part of high-frequency phenomena.

Linear stability analysis of turbulent mean flows based on a data-consistent Reynolds-averaged Navier–Stokes model: prediction of three-dimensional stall cells around an airfoil

Stall cells are transverse cellular patterns that often appear on the suction side of airfoils near stalling conditions. Wind-tunnel experiments on a NACA4412 airfoil at Reynolds number ${Re}=3.5 \times 10^5$ show that they appear for angles of attack larger than $\alpha = 11.5^{\circ }\ (\pm 0.5^{\circ })$ . Their onset is further investigated based on global stability analyses of turbulent mean flows computed with the Reynolds-averaged Navier–Stokes (RANS) equations. Using the classical Spalart–Allmaras turbulence model and following Plante et al. (J. Fluid Mech., vol. 908, 2021, A16), we first show that a three-dimensional stationary mode becomes unstable for a critical angle of attack $\alpha = 15.5^{\circ }$ which is much larger than in the experiments. A data-consistent RANS model is then proposed to reinvestigate the onset of these stall cells. Through an adjoint-based data-assimilation approach, several corrections in the turbulence model equation are identified to minimize the differences between assimilated and reference mean-velocity fields, the latter reference field being extracted from direct numerical simulations. Linear stability analysis around the assimilated mean flow obtained with the best correction is performed first using a perturbed eddy-viscosity approach which requires the linearization of both RANS and turbulence model equations. The three-dimensional stationary mode becomes unstable for angle $\alpha = 11^{\circ }$ which is in significantly better agreement with the experimental results. The interest of this perturbed eddy-viscosity approach is demonstrated by comparing with results of two frozen eddy-viscosity approaches that neglect the perturbation of the eddy viscosity. Both approaches predict the primary destabilization of a higher-wavenumber mode which is not experimentally observed. Uncertainties in the stability results are quantified through a sensitivity analysis of the stall cell mode's eigenvalue with respect to residual mean-flow velocity errors. The impact of the correction field on the results of stability analysis is finally assessed.

Development and validation of a turbulence model based on drift velocity

This paper proposes a novel turbulence model based on drift velocity. By employing a partial-averaging method, the model decomposes turbulent fluctuations into two symmetric groups and derived transport equations for the drift velocity, successfully preserving the first-order statistical information of turbulence. The drift velocity model addresses the limitations of traditional Reynolds-Averaged Navier–Stokes models in near-wall turbulence simulations by reconstructing the drift velocity momentum equation and incorporating a wall destruction term for accurate resolution. Implemented on the OpenFOAM platform, the model's performance is evaluated against several standard benchmarks, including the flat plate boundary layer, 0012 airfoil bypass, and backward-facing step flow. Results demonstrate improved accuracy of the drift velocity model in simulating complex turbulent flows.

Implicit near-wall domain decomposition approach for large eddy simulation of turbulent flow separation

To mitigate the high computational cost associated with resolving small near-wall eddies in large eddy simulation (LES) while achieving acceptable accuracy, this work extends the implicit near-wall domain decomposition (INDD) method to a hybrid Reynolds-averaged Navier–Stokes (RANS)/LES zonal decomposition approach. In this framework, the LES solution is first computed using a Robin-type (slip) wall boundary condition on a coarse mesh. This solution is then iteratively corrected in the near-wall region based on an updated solution derived from simplified one-dimensional (1D) RANS equations. It is important that the composite solution is smooth thanks to the specially designed slip boundary conditions. The performance of the proposed approach is assessed for flow over a plain channel at different Reynolds numbers and for a periodic hill at ReH=10,595, using very coarse meshes. Applying the INDD technique to both flows results in a significant accuracy improvement compared to unresolved LES, demonstrating good generalization capability across different turbulent viscosity models, such as algebraic or RANS turbulence models. Meanwhile, when applying the k – l RANS model to channel flow cases, the quality of predictions by INDD remains relatively independent of interface locations. Compared to reference wall-resolved LES, the INDD technique significantly reduces the overall computational cost by using simplified RANS equations and more importantly, by requiring far fewer grid points.

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 study of gust wind speed using a novel unsteady Reynolds-Averaged Navier-Stokes model

In this study, a novel unsteady Reynolds-Averaged Navier-Stokes (URANS) model is proposed in conjunction with a prespecified averaging time and turbulent inflow, and a new peak factor that considers the effect of averaging time used in URANS is derived to calculate gust wind speed. Firstly, the URANS incorporated with a prespecified averaging time and turbulent inflow is proposed to predict the time series of wind speed over flat terrain and investigate the variation of higher-order moments and zero-crossing rate at which the fluctuating wind speed changes algebraic sign with the averaging time. The predicted higher-order moments are less sensitive to the averaging time, but the predicted zero-crossing rate slightly decrease with increasing the averaging time due to the moving average effect. Secondly, a new peak factor is proposed to consider the averaging time. Finally, the gust wind speeds over flat terrain and around a single building predicted using the proposed URANS and the new peak factor based on the Hermite model are found to be in good agreement with the LES results, while those predicted by the conventional models deviate more from the LES results.

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.

A simple steady-state inflow model of the neutral and stable atmospheric boundary layer applied to wind turbine wake simulations

Abstract. Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a steady-state Reynolds-averaged Navier–Stokes (RANS) model of the neutral and stable atmospheric boundary layer (ABL) is introduced. The model incorporates buoyancy in the turbulence closure equations using a prescribed Brunt–Väisälä frequency, does not require a global turbulence length-scale limiter, and is only dependent on two non-dimensional numbers. Assuming a constant temperature gradient over the entire ABL, although a strong assumption, leads to a simple and well-behaved inflow model. RANS wake simulations are performed for shallow and tall ABLs, and the results show good agreement with large-eddy simulations in terms of velocity deficit from a single wind turbine. However, the proposed RANS model underpredicts the magnitude of the velocity deficit of a wind turbine row for the shallow ABL case. In addition, RANS ABL models can suffer from numerical problems when they are applied as a shallow-ABL inflow model to large wind farms due to the low-eddy-viscosity layer above the ABL. The proposed RANS model inherits this issue, and further research is required to solve it.

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.