Local Flow Quantities Research Articles

Neural Network-Based Hypersonic Crossflow Transition Model

A model is proposed for predicting crossflow transition in hypersonic flows. The model is based on transport equations for the crossflow amplification factor and a modified intermittency. The instability onset is estimated with a correlation function. The form of the transport equations is identical to that of typical transport equations for Reynolds-averaged Navier–Stokes (RANS) turbulence models, thus facilitating an easy integration into existing RANS codes. The crossflow amplification factor is estimated from local flow quantities using a neural network. The neural network is trained with a comprehensive database of linear stability theory data. The sensitivity of the amplification factor growth rate prediction to different dimensionless input parameters is evaluated. The input parameters that provide the most-accurate growth rate predictions are selected for the final form of the model. Based on the volume of the training data set, the model is expected to perform reliably for Mach numbers between 5 and 11 and within given upper bounds of the stagnation temperature and pressure. The model is validated for the HiFiRE-5 geometry, a circular cone at angle of attack, and a blunt swept flat plate. The amplification factor distributions obtained from the model are in good agreement with experimental data and linear stability analysis results.

A new paradigm for the role of disturbance waves on film dryout and wall heat transfer in annular two-phase flow

The entire liquid-film dryout process in vertical annular two-phase boiling flow is characterized experimentally, from inception to completion, in a newly defined flow regime between annular two-phase flow and mist flow, called ‘intermittent dryout’. Experiments are conducted using saturated R245fa flowing vertically through a heated transparent channel. Liquid-film thickness measurements are taken during dryout events. The state (wet or dry) of the heated surface is determined using a laser reflectance measurement, and the signal is used to calculate the time-averaged dry fraction, fdry. The local heat transfer coefficient (HTC) is characterized as a function of fdry. For all the investigated mass fluxes, optimum boiling conditions (OBC), corresponding to the heat flux that results in maximum HTC, consistently occur when the time-averaged dry fraction is fdry≈0.05. Subsequently, the critical boiling transition (CBT), corresponding to the heat flux that results in a significantly lower HTC, can occur for dry fractions of fdry>0.1. Further insight into the liquid film behavior within the intermittent dryout regime is obtained by combining analyses of high-speed videos, time-resolved liquid-film thickness signals, and statistics about the duration of and time between dryout events. In general, the dryout mechanism is dominated by disturbance waves.Mechanistic model frameworks are presented for the prediction of two-phase HTC and heat flux associated with dryout. The model inputs are local flow quantities (pressure, mass flux, vapor quality, and liquid-film thickness) and output a prediction for local heat transfer coefficient. The model framework accurately predicts the local two-phase HTC for a range of mass flux and vapor quality in the annular flow regime. The model framework captures the observed initial increase in the local HTC and subsequent decrease that is associated with liquid-film dryout. The mechanistic approach provides predictions for time-averaged HTC and OBC heat flux that both agree well with experimentally measured values.

Machine-learning data-driven modeling of laminar-turbulent transition in compressor cascade

Laminar-turbulent transition plays a critical role in the aerodynamics of axial-flow compressor cascades. The Reynolds-averaged Navier–Stokes method is so far the most practicable and popular means for numerical simulations of transitional flows to support aerodynamic analysis and the design of compressor cascades. However, the prediction accuracy of the conventional transport equation-based transition models has reached a plateau. In the present work, a machine-learning data-driven transition modeling method that can take full advantage of high-fidelity simulation data is proposed. The turbulence intermittency is calculated algebraically from local flow quantities through a neural network. The proposed method is then applied to construct an algebraic transition model, which is tailored for compressor cascades and coupled with the Spalart–Allmaras turbulence model. The validation results show that the constructed transition model is able to predict flows in compressor cascades with transition in both the Kelvin–Helmholtz instability-induced and bypass modes. Furthermore, the constructed transition model exhibits higher prediction accuracy for both the transition modes than the conventional intermittency factor equation-based transition model. This work demonstrates the effectiveness and promising prospect of machine-learning and data-driven methods in the modeling of complex flow physics.

Gradient-based shape optimization for unsteady turbulent simulations using field inversion and machine learning

This paper presents a method for gradient-based shape optimization using unsteady models of turbulent flowfields, for which forward simulations are already expensive and adjoint calculations diverge or require costly regularization. The proposed method targets an objective and constraints computed from the time-averaged flowfield and does not use either an unsteady adjoint nor an input-to-output surrogate. The method relies on the field-inversion, machine-learning (FIML) approach, in which a correction field modifies the production term in a Reynolds-averaged Navier-Stokes (RANS) model of the flow. Steady, adjoint-based, field inversion yields this correction field, and a neural-network model is trained, only from readily-available unsteady primal data, to reproduce the field from local flow quantities. Gradient-based shape optimization is then performed using the corrected RANS model, which must include a linearization of the correction field calculation for accurate gradients. The complete design optimization loop consists of iterations of unsteady simulation, FIML, and steady adjoint-based optimization. Low and moderate Reynolds number airfoil optimization problems demonstrate the performance of the proposed method, and comparisons to RANS-alone designs illustrate the importance of accounting for the unsteady flow effects in the optimization.

Improved Body Force Model for Estimating Off-Design Axial Compressor Performance

Based on the COMSOL software, body forces substituted into the Reynolds-averaged Navier–Stokes (RANS) equations as the source terms instead of the actual blade rows were improved to better predict the compressor performance. Improvements in parallel body force modeling were implemented, central to which were the local flow quantities. This ensured accurate and reliable off-design performance prediction. The parallel force magnitude mainly depended on the meridional entropy gradient extracted from three-dimensional (3D) steady single-passage RANS solutions. The COMSOL software could easily and accurately translate the pitchwise-averaged entropy into the grid points of the body force domain. A NASA Rotor 37 was used to quantify the improved body force model to represent the compressor. Compared with the previous model, the improved body force model was more efficient for the numerical calculations, and it agreed well with the experimental data and computational fluid dynamics (CFD) results. The results indicate that the improved body force model could quickly and efficiently capture the flow field through a turbomachinery blade row.

Assessment of Discretization Uncertainty Estimators Based on Grid Refinement Studies

Abstract This paper presents the assessment of the performance of nine discretization uncertainty estimates based on grid refinement studies including methods that use grid triplets and others that use a largest number of data points, which in this study was set to five. The uncertainty estimates are performed for the dataset proposed for the 2017 ASME Workshop on Estimation of Discretization Errors Based on Grid Refinement Studies including functional and local (boundary and interior) flow quantities from the two-dimensional flows of an incompressible fluid over a flat plate and the NACA 0012 airfoil. The data were generated with a Reynolds-averaged Navier–Stokes (RANS) solver using three eddy-viscosity turbulence models with double precision and sufficiently tight iterative convergence criteria to ensure that the numerical error is dominated by the discretization error. The use of several geometrically similar grid sets with different near-wall cell sizes for the same flow conditions lead to a wide range of convergence properties for the selected flow quantities, which enables the assessment of the numerical uncertainty estimators in conditions that are representative of the so-called practical applications.The evaluation of uncertainty estimates is based on the ratio of the uncertainty estimate over the “exact error” that is obtained from an “exact solution” obtained from extra grid sets significantly more refined than those used to generate the Workshop data. Although none of the methods tested fulfilled the goal of bounding the exact error 95 times out of 100 that was tested, the results suggest that the methods tested are useful tools for the assessment of the numerical uncertainty of practical numerical simulations even for cases where it is not possible to generate data in the “asymptotic range.”

A level-set framework for the wind turbine wake analysis: from high-fidelity unsteady simulations to 1D momentum theory

In the context of yawed wind turbine wakes, the validity of the so-called 1D momentum theory is arguable. The 1D momentum theory emerges from an inviscid, steady and irrotational analysis of the streamtube surrounding a wind turbine while the wake aerodynamics under yaw is modified. In this work, the mean flow streamtube around a single DTU 10MW wind turbine, under yaw misalignement and turbulent inflow is investigated with Large-Eddy Simulation combined to the Actuator Line method. The simulations are performed on a highly resolved grid counting billions of elements. The streamtube construction from the mean flow is based on the transport of accurate conservative level set functions. Integration of local flow quantities within the streamtube presents similarities when the streamtube expansion occurs past the turbine until the velocity deficit starts to recover. Mean kinetic energy and momentum budget are then presented to explain the yawed wake recovery and deflection process. Background turbulence plays a key role in the recovery process while the deflection of the wake is impacted by pressure forces on the streamtube. From these budgets, four wake regions showing similar flow dynamics are defined and correlated to local flow structures.

Numerical model to estimate subcooled flow boiling heat flux and to indicate vapor bubble interaction

There are numerous technical applications where hot components, with uneven temperature distribution, require cooling. In such applications, it is desired to provide efficient local cooling of the hot spots, while avoiding unnecessary over-cooling of the other regions. Such an approach, known as precision cooling, has several advantages. In addition to the fact that it reduces the effort for cooling, it limits the unintended heat lost to the cooling medium. In liquid cooled systems, such as Internal Combustion Engines (ICE), subcooled flow boiling offers immense potential for precision cooling. The primary challenges in extracting this potential are understanding the complexities in the subcooled flow boiling phenomenon and estimating the risk of encountering film boiling. The present study introduces a numerical model to estimate the wall heat flux in subcooled flow boiling and the model includes a mechanistic formulation to account for vapor bubble interaction. The formulation for vapor bubble interaction serves two purposes: (a) blends two well-established models in the literature, one in the isolated bubbles regime and other in the fully developed boiling regime, to estimate the wall heat flux; and (b) provides information to limit boiling in order to not encounter film boiling. The results from the new model are validated with two different experiments in the literature and the wall heat flux estimated by the model is in agreement with experimental results and responsive to different input parameters, such as bulk velocity, operating pressure and inlet subcooling. The new model requires only input of local flow quantities and hence implementation in Computational Fluid Dynamics (CFD) is straightforward.

Computational fluid-dynamics modelling of supersonic ejectors: Screening of modelling approaches, comprehensive validation and assessment of ejector component efficiencies

The efficiency of ejector-based systems (the “system-scale”) relies on the behaviour of the ejector (the “component-scale”), related to the flow phenomena within the component itself (the “local-scale”). As a consequence of this multi-scale connection, the precise prediction of the “local-scale” is of fundamental importance to sustain the design of commercially viable ejector-based systems. Although it is widely accepted that computational fluid-dynamics can achieve the prediction of the “local-scale” (CFD) modelling approaches, a broad agreement regarding the performances of numerical methods is not reached: different authors applied different methods, and a complete validation is missing so far. This paper contributes to the current discussion and closes the knowledge gap by assessing the performances of a CFD approach for single-phase supersonic ejectors. To this end, a comprehensive validation has been conducted, encompassing a wide range of ejector designs, boundary conditions and working fluids; besides, a screening of modelling approaches is conducted, encompassing a wide range of mesh criteria, geometrical modelling (2-Dimensional and 3-Dimensional approaches), solvers (density-based and pressure-based) and turbulence models (k-ε RNG and k-ω SST). The extensive comparison with experimental data allowed assessing and determining the influence of mesh criteria, geometrical modelling, solvers and turbulence models. In particular, k-ω SST has shown the best agreement with the experimental measurements concerning both global and local flow quantities, with an average entrainment ratio error of 14% and a maximum of 20%, under on-design operating mode. Finally, the simulation outcomes have been further post-processed to derive ejector component efficiencies, to contribute to the present discussion regarding closures in lumped parameter ejector modelling approaches. In conclusion, this paper thoroughly assesses the performance of a CFD model for single-phase ejector simulations and poses precise guidelines to be applied in future research activities and to support the design of ejector-based systems.

Effects of buoyancy and thermophysical property variations on the flow of supercritical carbon dioxide

The flow and heat transfer behaviours of fluids at supercritical pressure have been studied using direct numerical simulations (DNS), in which one or more thermal properties are artificially frozen to discern the various physical mechanisms from each other so as to better understand the complex phenomena. Different from previous similar studies on this topic, this study focuses on the axial flow development resulted from the large variations of thermophysical properties. The contribution of the flow inertia has been quantified by analysing the momentum balance for each case studied, which has been found to be significant throughout the entire length of the pipe in cases when buoyancy is considered. The effect of the inertia on momentum in turn impacts on turbulence production, generally delaying flow laminarisation. Such an influence of flow development is non-trivial and cannot be omitted in flow analysis and heat transfer calculations. This suggests that the results of simplified analyses based on a spatially developed flow cannot be directly applied to such flows despite they can be very useful in developing fundamental understanding of the physics. Similarly, this also explains that in some cases, buoyancy parameters based on local flow quantities cannot describe heat transfer deterioration accurately. The effect of variable viscosity alone can cause turbulence reduction by flattening the velocity profile, but it will not turn the velocity profile to an M-shape, which can only be achieved by buoyancy.

High-order natural transition simulation method based on deep residual network

Turbulence model combined with machine learning is one of the research hotspots in fluid mechanics. The existing approaches reconstruct or modify the turbulence eddy viscosity or Reynolds stress based on the experimental/numerical data. In this paper, we reconstruct the mapping function between intermittency and the mean flow variables by deep neural network (ResNet), developing an quasi-algebraic transition model coupled with the Spallart-Allmaras (SA) model. We mainly concentrate on the natural transition flows and take the results calculated by the computational fluid dynamics solver with the SST-<i>γ-Re<sub>θ</sub></i> model as the training data. Seventeen local mean flow quantities satisfying the Galilean invariants are selected as the input features. Five-time cross validation is performed to avoid overfitting. Combining with the high-precision weighted compact nonlinear format, S&K, T3a- transition plate and S809 airfoil are used to test the performance of the model. The results are compared with those from the SST-<i>γ-Re<sub>θ</sub></i> transition model, showing that the pure data-driven ResNet model can predict the intermittent field accurately, which greatly improves the ability of SA model to simulate the natural transition flow. For the example of S&K and T3a- transition plate, the comparison of wall friction shows that the SA-ResNet model is in good agreement with the experimental result, but the BC model, which is also an algebraic model, predicts the transition position of the T3a- transition plate model prematurely. The training data do not contain any numerical solution about airfoil, but the model can still be applied to the case of S809 airfoil with different attack angles. The predicted results of lift resistance characteristics, frictional coefficient distribution and transition position are close to the results from the SST-<i>γ-Re<sub>θ</sub></i> transition model. On this basis, another advantage of the model is the solution efficiency. The efficiency is improved more significantly in the case with larger mesh quantity. With the same convergence accuracy, the CPU time required by the SA-ResNet model for the S&K plate case is 85.6% that of the SST-<i>γ-Re<sub>θ</sub></i> transition model, while the CPU time required by the S809 airfoil with a larger mesh volume is only 67.2% that of the later model. This study demonstrates the great potential of machine learning in the construction of transition models.

Verification of the Axial Momentum Theory for Propellers with a Uniform Load Distribution

The paper provides an evaluation of the errors embodied in the Axial Momentum Theory (AMT) as applied to a uniformly loaded actuator disk model without wake rotation. Although this model exhibits some unphysical features, such as the tip singularity and the violation of the angular momentum equation, it is still considered a touchstone in the theoretical aerodynamics of propellers. To simplify the model, a purely mathematical assumption is commonly used in the differential form of the axial momentum equation, i.e., the contribution of the pressure forces on the lateral surface of the infinitesimal streamtubes swallowed by the disk is neglected. In this paper, the errors introduced by this simplifying assumption are evaluated by comparing the results of the AMT with those of a nonlinear method modelling the free wake as the superposition of ring vortices distributed along the wake boundary. Firstly, the validity of this method is verified in terms of global performance coefficients. Then, using a CFD approach, it is also verified in terms of local flow quantities. The comparison between the ring-vortices method and the AMT shows that, for a highly loaded propeller, significant errors exist in the axial velocity at the disk, especially near the tip. Moreover, despite the uniform load, the axial velocity at the disk varies in the radial direction. Instead, the velocity magnitude remains almost uniform only for values of the thrust coefficient lower than 1.

CFD Simulations of the New MEXICO Rotor Experiment under Yawed Flow

This paper describes the conduction of CFD simulations with DLR’s flow solver TAU for comparison with the experimental data that has been used within the third phase of the MexNext-III project. More precisely computations for three different inflow velocities under yawed flow condition have been conducted and compared to the experimental results. Important aspects of the simulations such as a sufficiently long simulation time or a sufficiently small time-step are discussed. At first local flow quantities such as the flow velocity along axial and radial traverses, pressure distributions at four radial stations and normal and tangential forces at these sections are compared. Subsequently the integral rotor quantities i.e. rotor thrust and rotor torque are revised. The integration of the CFD results is performed using the complete surface dataset as well as two subsets. The comparison proves that the agreement can substantially be improved if the subset resembles the experimental dataset. Furthermore the integration of the experimental dataset is reviewed and a weak spot in the current reconstruction procedure of the trailing edge pressure is identified. The proposed alternative provides evidence that further improvements are possible. The final comparison shows an acceptable agreement for the medium and high inflow speed case yielding 0% and 10% difference for integral thrust and 9% and 14% for integral torque. The results for the low inflow speed case are divers: while the agreement for the integral thrust is excellent, it is less satisfactory for the integral torque which amounts to 41%.

A Contribution for the Assessment of Discretization Error Estimators Based on Grid Refinement Studies

This paper presents grid refinement studies for statistically steady, two-dimensional (2D) flows of an incompressible fluid: a flat plate at Reynolds numbers equal to 107, 108, and 109 and the NACA 0012 airfoil at angles of attack of 0, 4, and 10 deg with Re = 6 × 106. Results are based on the numerical solution of the Reynolds-averaged Navier–Stokes (RANS) equations supplemented by one of three eddy-viscosity turbulence models of choice: the one-equation model of Spalart and Allmaras and the two-equation models k – ω SST and k−kL. Grid refinement studies are performed in sets of geometrically similar structured grids, permitting an unambiguous definition of the typical cell size, using double precision and an iterative convergence criterion that guarantees a numerical error dominated by the discretization error. For each case, different grid sets with the same number of cells but different near-wall spacings are used to generate a data set that allows more than one estimation of the numerical uncertainty for similar grid densities. The selected flow quantities include functional (integral), surface, and local flow quantities, namely, drag/resistance and lift coefficients; skin friction and pressure coefficients at the wall; and mean velocity components and eddy viscosity at specified locations in the boundary-layer region. An extra set of grids significantly more refined than those proposed for the estimation of the numerical uncertainty is generated for each test case. Using power law extrapolations, these extra solutions are used to obtain an approximation of the exact solution that allows the assessment of the performance of the numerical uncertainty estimations performed for the basis data set. However, it must be stated that with grids up to 2.5 (plate) and 8.46 (airfoil) million cells in two dimensions, the asymptotic range is not attained for many of the selected flow quantities. All this data is available online to the community.

Turbulence strength in ultimate Taylor–Couette turbulence

We provide experimental measurements for the effective scaling of the Taylor–Reynolds number within the bulk $\mathit{Re}_{\unicode[STIX]{x1D706},\mathit{bulk}}$, based on local flow quantities as a function of the driving strength (expressed as the Taylor number $\mathit{Ta}$), in the ultimate regime of Taylor–Couette flow. We define $Re_{\unicode[STIX]{x1D706},bulk}=(\unicode[STIX]{x1D70E}_{bulk}(u_{\unicode[STIX]{x1D703}}))^{2}(15/(\unicode[STIX]{x1D708}\unicode[STIX]{x1D716}_{bulk}))^{1/2}$, where $\unicode[STIX]{x1D70E}_{bulk}(u_{\unicode[STIX]{x1D703}})$ is the bulk-averaged standard deviation of the azimuthal velocity, $\unicode[STIX]{x1D716}_{bulk}$ is the bulk-averaged local dissipation rate and $\unicode[STIX]{x1D708}$ is the liquid kinematic viscosity. The data are obtained through flow velocity field measurements using particle image velocimetry. We estimate the value of the local dissipation rate $\unicode[STIX]{x1D716}(r)$ using the scaling of the second-order velocity structure functions in the longitudinal and transverse directions within the inertial range – without invoking Taylor’s hypothesis. We find an effective scaling of $\unicode[STIX]{x1D716}_{\mathit{bulk}}/(\unicode[STIX]{x1D708}^{3}d^{-4})\sim \mathit{Ta}^{1.40}$, (corresponding to $\mathit{Nu}_{\unicode[STIX]{x1D714},\mathit{bulk}}\sim \mathit{Ta}^{0.40}$ for the dimensionless local angular velocity transfer), which is nearly the same as for the global energy dissipation rate obtained from both torque measurements ($\mathit{Nu}_{\unicode[STIX]{x1D714}}\sim \mathit{Ta}^{0.40}$) and direct numerical simulations ($\mathit{Nu}_{\unicode[STIX]{x1D714}}\sim \mathit{Ta}^{0.38}$). The resulting Kolmogorov length scale is then found to scale as $\unicode[STIX]{x1D702}_{\mathit{bulk}}/d\sim \mathit{Ta}^{-0.35}$ and the turbulence intensity as $I_{\unicode[STIX]{x1D703},\mathit{bulk}}\sim \mathit{Ta}^{-0.061}$. With both the local dissipation rate and the local fluctuations available we finally find that the Taylor–Reynolds number effectively scales as $\mathit{Re}_{\unicode[STIX]{x1D706},\mathit{bulk}}\sim \mathit{Ta}^{0.18}$ in the present parameter regime of $4.0\times 10^{8}<\mathit{Ta}<9.0\times 10^{10}$.

Variability of intra-aneurysmal hemodynamics caused by stent-induced vessel deformation

Abstract Clinical observation revealed deformations of the local vasculature due to implanted devices related to treatment of intracranial aneurysms. Pre- and post-interventional image data is reviewed and segmented. Based on Computational Fluid Dynamics (CFD) the effect of the vessel deformation on the intra-aneurysmal hemodynamics is investigated. The simulations incorporate the vessel trees with aneurysm excluding the device itself in order to consider the isolated effect of geometric modifications. As a result, the aneurysm inflow jet is redirected, changing the local flow quantities. Finally, a neck inflow rate reduction of 52.5 % is archived. Thus, a targeted vessel straightening as a novel tool for treatment planning appear to be an idea worth to discuss in the community.

A model for turbulent dissipation rate in a constant pressure boundary layer

Estimation of the turbulent dissipation rate in a boundary layer is a very involved process. Experimental determination of either the dissipation rate or the Taylor microscale, even in isotropic turbulence, which may occur in a portion of the turbulent boundary layer, is known to be a difficult task. For constant pressure boundary layers, a model for the turbulent dissipation rate is proposed here in terms of the local mean flow quantities. Comparable agreement between the estimated Taylor microscale and Kolmogorov length scale with other data in the logarithmic region suggests usefulness of this model in obtaining these quantities experimentally.

Characterisation of sheet cavity noise of a hydrofoil using the Ffowcs Williams–Hawkings acoustic analogy

Concerns about pollution of the marine environment with ship-induced noise and the scarcity of available numerical methods have recently stimulated significant amounts of research in hydroacoustic modelling. In this work, Large Eddy Simulation (LES) is used with Schnerr–Sauer mass-transfer cavitation model and a porous Ffowcs Williams–Hawkings (FW-H) acoustic analogy in order to simulate sheet cavitation on a NACA0009 hydrofoil. The aim is to investigate how well the proposed method captures the dominant noise sources associated with periodic sheet cavitation. The study further focuses on practical aspects, such as the importance of the non-linear FW-H term and convergence of the acoustic solution depending on the choice of the integration surface. This is done by correlating the radiated noise with integral and local flow quantities, such as cavity volume, lift coefficient and local vapour content. A key finding of the study is that the simulation framework is capable of correctly capturing the monopole nature of the sound generated by an oscillating cavity sheet. Results indicate that the numerical method is incapable of accurately resolving the flow during the final collapse stages of smaller cavity clouds, mainly due to mesh density limitations and the use of an incompressible flow assumption. Lack of small-scale bubble structures also causes the high-frequency range of the noise spectra to be under-predicted. Despite certain limitations the presented method offers a significant insight into the nature of cavitation-dominated noise and allows for some of the dominant sound generating mechanisms to be categorised.

An Integrated Lumped Parameter-CFD approach for off-design ejector performance evaluation

This paper presents an Integrated Lumped Parameter Model-Computational Fluid-Dynamics approach for off-design ejector performance evaluation. The purpose of this approach is to evaluate the entrainment ratio, for a fixed geometry, in both on-design and off-design operating conditions. The proposed model is based on a Lumped Parameter Model (LPM) with variable ejector component efficiencies provided by CFD simulations. The CFD results are used for developing maps for ejector component efficiencies in a broad range of operating conditions. The ejector component efficiency maps couple the CFD and the LPM techniques for building an Integrated LPM-CFD approach. The proposed approach is demonstrated for a convergent nozzle ejector and the paper is structured in four parts. At first, the CFD approach is validated by global and local data and seven Reynolds Averaged Navier Stokes (RANS) turbulence models are compared: the k–ω SST showed good performance and was selected for the rest of the analysis. At second, a Lumped Parameter Model (LPM) for subsonic ejector is developed and the ejector component efficiencies have been defined. At third, the CFD approach is used to investigate the flow field, to analyze its influence on ejector component efficiencies and to propose efficiency correlations and maps linking ejector component efficiencies and local flow quantities. In the last part, the efficiency maps are embedded into the lumped parameter model, thus creating the Integrated LPM-CFD (ILPM-CFD) model. The ILPM-CFD model is validated over experimental and CFD data and is compared with LPM Constant Efficiency models showing better performance and a wider range of applicability. The average deviation between the ILPM-CFD results and the experimental and CFD data is less than 0.75%, whereas constant efficiency models showed a deviation up to 30%. The ILPM-CFD model is able to vary the value of the ejector component efficiencies depending on the operating condition, being able to evaluate both the on-design and off-design performance of the ejector.

Enhancement of a Correlation-Based Transition Turbulence Model for Simulating Crossflow Instability

A new laminar–turbulence transition model for capturing three-dimensional boundary layers involving crossflow-induced transition has been developed by extending the existing transition model. In the present transition model, to resolve transition due to crossflow instability, the C1-criterion was evaluated locally using the Falkner–Skan–Cooke velocity profiles. Thus, only local flow quantities are used in determining the onset of three-dimensional transition. As a result, the extended transition model is compatible and works well with modern computational fluid dyanmics flow solvers, particularly for those based on unstructured meshes, under massive parallel environment. The new transition model was implemented in a three-dimensional incompressible unstructured mesh flow solver. Validations of the new transition model were made for an infinite swept-wing configuration and an inclined prolate spheroid by comparing the results with those of the baseline model and experiment. It was found that the new transition model works well by capturing the crossflow-induced transition inside three-dimensional boundary layers more accurately than the baseline model.