Stokes Model Research Articles

Incorporating Unresolved Stresses in Blood Damage Modeling: Energy Dissipation More Accurate Than Reynolds Stress Formulation.

Reynolds Averaged Navier Stokes (RANS) models are often used as the basis for modeling blood damage in turbulent flows. To predict blood damage by turbulence stresses that are not resolved in RANS, a stress formulation that represents the corresponding scales is required. Here, we compare two commonly employed stress formulations: a scalar stress representation that uses Reynolds stresses as a surrogate for unresolved fluid stresses, and an effective stress formulation based on energy dissipation. We conducted unsteady RANS simulations of the CentriMag blood pump with three different closure models and a Large Eddy Simulation (LES) for reference. We implemented both stress representations in all models and compared the resulting total stress distributions in Eulerian and Lagrangian frameworks. The Reynolds-stress-based approach overestimated the contribution of unresolved stresses in RANS, with differences between closure models of up to several orders of magnitude. With the dissipation-based approach, the total stresses predicted with RANS deviated by about 50% from the LES reference, which was more accurate than only considering resolved stresses. The Reynolds-stress-based formulation proved unreliable for estimating scalar stresses in our RANS simulations, while the dissipation-based approach provided an accuracy improvement over simply neglecting unresolved stresses. Our results suggest that dissipation-based inclusion of unresolved stresses should be the preferred choice for blood damage modeling in RANS.

Physically constrained eigenspace perturbation for turbulence model uncertainty estimation

Aerospace design is increasingly incorporating design under uncertainty-based approaches to lead to more robust and reliable optimal designs. These approaches require dependable estimates of uncertainty in simulations for their success. The key contributor of predictive uncertainty in computational fluid dynamics (CFD) simulations of turbulent flows are the structural limitations of Reynolds-averaged Navier–Stokes models, termed model-form uncertainty. Currently, the common procedure to estimate turbulence model-form uncertainty is the eigenspace perturbation framework (EPF), involving perturbations to the modeled Reynolds stress tensor within physical limits. The EPF has been applied with success in design and analysis tasks in numerous prior works from the industry and academia. Owing to its rapid success and adoption in several commercial and open-source CFD solvers, in-depth verification and validation of the EPF is critical. In this work, we show that under certain conditions, the perturbations in the EPF can lead to Reynolds stress dynamics that are not physically realizable. This analysis enables us to propose a set of necessary physics-based constraints, leading to a realizable EPF. We apply this constrained procedure to the illustrative test case of a converging-diverging channel, and we demonstrate that these constraints limit physically implausible dynamics of the Reynolds stress tensor, while enhancing the accuracy and stability of the uncertainty estimation procedure.

Flow Characterization in a Partially Liquefied Vitreous Humor

The purpose of this study is to systematically examine the basic fluid dynamics associated with a fully liquid region within a porous material. This work has come about as a result of our investigation on the ocular fluid dynamics and transport process in a partially liquefied vitreous humor. The liquid is modeled as a sphere with Stokes flow while the surrounding infinite porous region is described by Brinkman flow. The development here provides basic three-dimensional axisymmetric results on flow characterization and also serves to evaluate the limits of validity of Darcy flow analysis for the same geometry. In the Darcy flow model, the liquid region is also treated as a porous region with a much higher permeability. Therefore, both liquid and porous regions are modeled by Darcy’s law. Besides the analytical results from Brinkman–Stokes model, the simpler case of Darcy–Darcy flow for the same geometry has been provided. The results of both cases are compared and the differences between the two sets of results provide the range of validity of our computational model (Khoobyar et al. in J Heat Transf 144:031208, 2022). Some interesting fluid-dynamical aspects of the system are observed through the analysis. For the Darcy–Darcy system, the liquid region velocity is uniform throughout, as expected for potential flow. With the Brinkman–Stokes model, the liquid region has a paraboloidal profile with the maximum possible peak value of six times the far-field velocity in the porous medium. With the liquid region having a lower resistance, the flow tends to converge there for both models as it seeks the path of least resistance. As for the validation of the Darcy–Darcy model, it is a good approximation as far as the exterior flow is concerned. However, the liquid region flow profiles for the two models are different as noted. The current Brinkman–Stokes model has led to explicit analytical solutions for the flow field for both regions. This has permitted an asymptotic analysis giving deeper insight into the flow characterization.

Performance of Reynolds Averaged Navier–Stokes and Large Eddy Simulation Models in Simulating Flows in a Crossflow Ultraviolet Reactor: An Experimental Evaluation

Performance of Reynolds Averaged Navier–Stokes and Large Eddy Simulation Models in Simulating Flows in a Crossflow Ultraviolet Reactor: An Experimental Evaluation

Linear energy-stable method with correction technique for the Ohta–Kawasaki–Navier–Stokes model of incompressible diblock copolymer melt

An efficiently linear time-marching method is proposed for the incompressible fluid flows coupled Ohta–Kawasaki model of diblock copolymer melt. Although this model satisfies the energy dissipation law under appropriate boundary conditions, the existences of nonlinear terms and coupling terms lead to difficulties of designing energy-stable numerical algorithms. To overcome the difficulties resulting from nonlinear and coupling terms, we herein construct a numerical method based on a highly efficient variant of scalar auxiliary variable approach. The stabilization technique is introduced to maintain the stability with large time steps. To improve the consistency, a simple and practical relaxation strategy is used to correct the difference between time-discretized modified energy and original energy. In each time step, all variables are totally decoupled and we only need to solve linear elliptic equations in a step-by-step manner. The unique solvability and energy stability are analytically proved. Extensive numerical experiments indicate that the proposed method not only achieves desired accuracy, energy stability, and consistency, but also has good capabilities on simulating various hydrodynamically coupled pattern formations.

Triply periodic minimal surfaces based topology optimization for the hydrodynamic and convective heat transfer

Triply periodic minimal surfaces (TPMS), possessing diverse and crucial properties for thermal convection analysis, hold significant promise for topology optimization in convective and hydrodynamic systems. This study aims to develop an innovative topology optimization method based on TPMS for channel shape design. We focus on maximizing heat dissipation and mass flux while maintaining a constant mean curvature. The channel structures undergo free evolution during the optimization process, resulting in efficient and intricate geometries based on the TPMS assumption. To incorporate TPMS properties, we modify the original energy formulation, which comprises kinetic energy, thermal energy, and Ginzburg–Landau energy. The governing system encompasses the phase field model, the Darcy–Stokes model, and the reaction–diffusion heat transfer model. The hydrodynamic response within the phase change structure is computed by solving the transient Darcy–Stokes equation augmented with a temperature-dependent diffusion term. To achieve second-order temporal and spatial accuracy, we employ the Crank–Nicolson method for the time scale and the central difference method for the spatial scale. Through the utilization of the Lagrange multiplier method, we establish the unconditional decrease of the discretized original energy. This implies that the proposed scheme can utilize a large time step. To verify the robustness of our proposed method, we conduct several numerical tests from both qualitative and quantitative perspectives. The results demonstrate the effectiveness of our method in enhancing heat and mass transfer capabilities.

Influence of tip clearance on internal energy loss characteristics of axial flow pumps under different operating conditions

Axial flow pumps possess a unique structure where there must be clearances between the impeller and the piping wall, usually not exceeding 0.1% of the impeller diameter. Despite the small size of the clearance, the internal micro-vortex structures have a non-negligible impact on the main flow field of the impeller. Under the action of the pressure difference between the suction and pressure surfaces of blades, some fluids form high-energy jets in the tip clearance area, known as tip leakage vortices (TLVs). TLV interacts with the flow of the main flow field, exerting a significant impact on the internal flow state, energy loss, and hydraulic performance of the pump. To identify the influence of TLVs on the internal flow field and energy loss of axial flow pumps, this work uses a modified partially averaged Navier–Stokes (PANS) model to perform full flow field numerical calculations for a certain axial flow pump and conducts a comparative analysis of the internal flow field energy dissipation, unsteady vortex structures, energy loss, and other characteristics under three different tip clearances: 0.2 mm (0.05%D), 0.6 mm (0.15%D), and 1.0 mm (0.25%D) based on the energy transport theory. The results indicate that at optimal operating conditions, the internal energy distribution of the fluid in each flow passage is uniform, and the energy loss is primarily caused by axial backflow in the tip area; under critical rotating stall conditions, clearance size affects the distribution state of enstrophy in the guide vane flow passage, leading to average enstrophy being highest at the rim area and the most uneven distribution of enstrophy, inducing larger energy loss in the impeller; during deep stall conditions, the unevenness of internal energy distribution is stronger than that under critical stall conditions, but the overall energy loss within the impeller flow area is lower than that under critical stall conditions, while energy unevenness is mitigated as the tip clearance size increases.

Generalized wall-modeled large eddy simulation model for industrial applications

In this work, a generalized wall-modeled large eddy simulation model (GWMLES) is presented. An extended formulation of the classical WMLES approach is proposed that also enables the modeling of the entire log-layer by using a Reynolds-averaged Navier–Stokes (RANS) model. GWMLES is validated against direct numerical simulations, large eddy simulations (LES), WMLES, hybrid RANS/LES, unsteady RANS (URANS), and experimental data of test cases featuring industrial flows. It is demonstrated that GWMLES does not share the main shortcoming of current WMLES models. When the entire log-layer is solved with a RANS model, GWMLES gives a level of accuracy similar to recent LES results, as well as computational cost savings that are proportional to the Reynolds number in wall-bounded flows. The model shows superior performance than URANS even when the resolved portion of the energy spectrum is reduced. Motivated by the different time scales of the flow and RANS variables, it requires approximately 30% lower computational costs than the detached eddy simulation family models in the turbulent flows considered. These features represent significant advancements in the simulation of wall-bounded flows at high Reynolds numbers, particularly in industrial applications.

A method for examining ensemble averaging forms during the transition to turbulence in HED systems for application to RANS models

This paper discusses a strategy to initialize a two-dimensional (2D) Reynolds-averaged Navier–Stokes model [LANL's Besnard–Harlow–Rauenzahn (BHR) model] in order to describe an unsteady transitional Richtmyer–Meshkov (RM)-induced flow observed in on-going high-energy-density ensemble experiments performed on the OMEGA-EP facility. The experiments consist of a nominal single-mode perturbation (initial amplitude a0≈10 and wavelength λ=100 μm) with target-to-target variations in the surface roughness subjected to the RM instability with delayed Rayleigh–Taylor in a heavy-to-light configuration. Our strategy leverages high-resolution three-dimensional (3D) implicit large eddy simulations (ILES) simulations to initialize BHR-relevant parameters and subsequently validate the 2D BHR results against the 3D ILES simulations. A suite of five 3D ILES simulations corresponding to five experimental target profiles is undertaken to generate an ensemble dataset. Using ensemble averages from the 3D simulations to initialize the turbulent kinetic energy in the BHR model (K0) demonstrates the ability of the model to predict the time evolution of the interface as well as the density-specific-volume covariance, b. To quantify the sensitivity of the BHR results to the choice of K0 and the initial turbulent length scale, S0, we execute a parameter sweep spanning four orders of magnitude for both S0 and K0, generating a parameter space consisting of 26 simulations. The Pearson's correlation coefficient is used as a measure of discrepancy between the 2D BHR and 3D ILES simulations and reveals that the ranges 8≲S0≲20 μm and 109≲K0≲1010 cm2/s2 produce predictions that agree best with the 3D ILES results.

Optimization of Rotor Tip Cavity Shapes for Mitigating Aerodynamic Tip Leakage Losses

Rotor blade tip leakage loss influences turbine stage efficiency. A parametric optimization method based on five design variables combined with Non-Linear Programming by Quadratic Lagrangian to optimize the turbine efficiency estimated from computational fluid dynamics analysis has been used to obtain estimates of the optimal cavity depth and split rib location within the tip cavity, which appears to improve the stage efficiency by 1.5%. Besides the design point, the influence of the optimized tip cavity shape impacts the performance of the rotor at different rotational speeds and pressure ratios. In this study, by comparing different tip cavity shapes, it is shown that a dual-cavity shape on the rotor blade tip with a stream-direction split rib can effectively control the air flow bending angle inside the tip clearance and improve the aerodynamic performance. The compressible unsteady Reynolds-averaged Navier–Stokes model is used to verify the adaptability of the optimized tip cavity shape to periodic wake disturbances, and the results demonstrate that the time-averaged estimate of the efficiency can be improved by 1.3% compared to that of the flat rotor tip.

CFD assessment of RANS turbulence modeling for solidification in internal flows against experiments and higher fidelity LBM-LES phase change model

Generation-IV nuclear reactors’ potential coolant candidates include molten salts and liquid metals. Owing to their high melting temperature, both the design and safety assessments of these reactors need to take into consideration potential solidification events during normal or abnormal operation. This work assesses the Finite Volume Computational Fluid Dynamics (FV-CFD) enthalpy-porosity method combined with the Reynolds Averaged Navier Stokes (RANS) model k−ω to model internal solidification under turbulent flow conditions for high and low Prandtl numbers at a moderate computational cost, which is required for full-core nuclear reactor analyses. Concerning high Prandtl numbers, the predictions of macroscopic quantities generated by the FV-CFD RANS model are contrasted against a well-known internal solidification experiment on water (Thomason et al., 1978). A key factor in the satisfactory agreement between the CFD RANS model and the experiment is the addition of an interface turbulence-damping source to the specific dissipation rate equation (ω). For the low Prandtl numbers involved in liquid metals, there is a lack of high-fidelity experiments due to the complicated measurements. Thus, to perform the FV-CFD RANS model assessment, we develop and validate a high-fidelity phase-change model based on the Lattice Boltzmann Method (LBM) with a Large Eddy Simulation (LES) turbulence model. To the author’s knowledge, this is the first attempt to integrate LES turbulence models with phase-change solidification in the LBM context. Considering the computational time advantage of the FV-CFD RANS model over higher fidelity models and the good agreement of its predictions against experiments and the LES model demonstrated, this work demonstrates that FV-CFD RANS is an attractive tool to model internal turbulent solidification.

Improved prediction of turbomachinery flows using Reynolds stress model with [formula omitted] transition model

Transition is a prominent flow phenomenon in turbomachinery, especially at low and moderate Reynolds numbers. The turbomachinery internal flow often encounters complex vortex structures, typically accompanied by strong turbulence anisotropy and non–equilibrium. Conventional Reynolds–averaged Navier–Stokes (RANS) modeling with linear eddy–viscosity models (LEVMs) may fail short in capturing certain complex flow features. The Reynolds stress model (RSM) based on a higher turbulence closure level has more potential to reasonably consider turbulence anisotropy and non–equilibrium for such complex flows within turbomachinery than the LEVMs. However, the current RSM lacks an effective representation of transition phenomena. This paper presents an improved prediction of turbomachinery flows using RSM with transition modeling. The transition effects are incorporated into the RSM (SSG/LRR–ω model) via Menter's γ transport equation. The blending of the SSG/LRR–ω model with the γ transition model (SSG/LRR–ω–γ model) is firstly validated based on the T3 series flat plate cases with zero pressure gradient (T3A, T3B, T3A–) and variable pressure gradients (T3C1–C5). Comparisons with experiments show that the SSG/LRR–ω–γ model predicts corner separation flows with transition more accurately, although there is still room for further improvement. Then, the performance of the SSG/LRR–ω–γ model is assessed in a linear highly loaded prescribed velocity distribution (PVD) cascade and a NACA65 K48 compressor cascade. Comparisons with experiments indicate that, in both compressor cascades, the SSG/LRR–ω–γ model can predict more accurately corner flows with boundary layer transition on suction surfaces than the SSG/LRR–ω and SST model. Nevertheless, it still exhibits a tendency to overestimate the size of corner separation, especially in cases characterized by strong adverse pressure gradients. Comparisons of skin friction and velocity profiles predicted by the SSG/LRR–ω model with and without transition modeling illustrate the rationale behind the improved prediction. Further improvement of the SSG/LRR–ω–γ model for predicting turbomachinery corner flows should be conducted in future.

Constrained Recalibration of Reynolds-Averaged Navier–Stokes Models

The constants and functions in Reynolds-averaged Navier–Stokes (RANS) turbulence models are coupled. Consequently, modifications of a RANS model often negatively impact its basic calibrations, which is why machine-learned augmentations are often detrimental outside the training dataset. A solution to this is to identify the degrees of freedom that do not affect the basic calibrations and only modify these identified degrees of freedom when recalibrating the baseline model to accommodate a specific application. This approach is colloquially known as the “rubber-band” approach, which we formally call “constrained model recalibration” in this paper. To illustrate the efficacy of the approach, we identify the degrees of freedom in the Spalart–Allmaras model that do not affect the log law calibration. By subsequently interfacing data-based methods with these degrees of freedom, we train models to solve historically challenging flow scenarios, including the round-jet/plane-jet anomaly, airfoil stall, secondary flow separation, and recovery after separation. In addition to good performance inside the training dataset, the trained models yield similar performance as the baseline model outside the training dataset.

How far do indirect signal production mechanisms influence regularity in the three-dimensional Keller–Segel–Navier–Stokes system?

This paper reconsiders the Keller–Segel–Navier–Stokes model with indirect signal production and weak logistic-type degradation in a three-dimensional (3D) bounded and smooth domain. One recent literature has asserted that for all reasonably regular initial data, the associated initial-boundary value problem with certain sub-quadratic degradation possesses at least one global generalized solution which is uniformly bounded and converges to the constant equilibrium in [Formula: see text] with any [Formula: see text]. Nevertheless, the knowledge on regularity properties of solution has not yet exceeded some information on fairly basic integrability features. The present study firstly elevates the regularities of some solution components under the same assumption on degradation exponent, especially establishes the global boundedness in [Formula: see text] with [Formula: see text], and secondly asserts that each of these generalized solutions becomes eventually classical and bounded in [Formula: see text] under some suitably strong sub-quadratic degradation assumption and an explicit smallness condition on the intrinsic growth rate of cells relative to some powers of the degradation coefficient. As a by-product of the latter, these solutions are shown to stabilize toward the corresponding spatially homogeneous state in [Formula: see text]. Our results indeed provide a more in-depth understanding on the global dynamics of solutions, and significantly improve previously known ones. In comparison to the related contributions in the case of direct signal production, our findings inter alia rigorously reveal that the indirect signal production mechanism results in some genuine regularizing effects in the sense that the possibly destabilizing action of chemotactic cross-diffusion in the 3D Keller–Segel–Navier–Stokes system can be greatly weakened or even completely excluded by this indirectness.

Discontinuous Galerkin Two-Grid Method for the Transient Navier–Stokes Equations

Abstract In this paper, we apply a two-grid scheme to the DG formulation of the 2D transient Navier–Stokes model. The two-grid algorithm consists of the following steps: Step 1 involves solving the nonlinear system on a coarse mesh with mesh size 𝐻, and Step 2 involves linearizing the nonlinear system by using the coarse grid solution on a fine mesh of mesh size ℎ and solving the resulting system to produce an approximate solution with desired accuracy. We establish optimal error estimates of the two-grid DG approximations for the velocity and pressure in energy and L 2 L^{2} -norms, respectively, for an appropriate choice of coarse and fine mesh parameters. We further discretize the two-grid DG model in time, using the backward Euler method, and derive the fully discrete error estimates. Finally, numerical results are presented to confirm the efficiency of the proposed scheme.

Invariance embedded physics-infused deep neural network-based sub-grid scale models for turbulent flows

In this paper, we present two novel physics-infused neural network (NN) architectures that satisfy various invariance conditions for constructing efficient and robust Deep Learning (DL)-based sub-grid scale (SGS) turbulence models for use in Large Eddy Simulation (LES) procedures widely used in various fluid engineering applications. The first architecture, called tensor basis neural networks (TBNN), recently proposed in the context of Reynolds-averaged Navier–Stokes (RANS) modeling, and introduced herein for SGS modeling of wall-bounded turbulence, embeds the analytical expansion of the SGS turbulence stresses into integrity basis tensors composed of the symmetric and anti-symmetric parts of the resolved velocity gradient tensor, thus incorporating the Galilean, rotational and reflectional invariances. In our second approach, a relatively simple yet powerful architecture, called Galilean Invariance embedded Neural Network (GINN) incorporates the Galilean invariance, and takes in as inputs the independent components of the integrity basis tensors in addition to the invariant inputs in a single input layer. Explicit filtering of data from direct simulations of the canonical channel flow at two friction Reynolds numbers Reτ≈395 and 590 provided accurate data for training and testing these models. Both sets of models are used to predict the SGS stresses for feature datasets generated with different filter widths, and at different Reynolds numbers. The GINN model yields less prediction error on test datasets (mean squared error ∼0.4) compared to the TBNN model (mean squared error ∼0.5). Upon comparison, it is revealed that GINN has better feature extraction capacity owing to its ability to establish relations between and extract information from cross-components of the integrity basis tensors and the SGS stresses. Based on their predictive performance, both models proposed herein have shown great promise for use in a posteriori actual LESs. The present work illustrates the significance of physics infusion as well as invariance embedding into NN architecture for constructing efficient and robust DL-based SGS turbulent models in achieving superior predictive efficacy.

A numerical method for MHD Stokes model with applications in blood flow

Abstract The magnetohydrodynamic (MHD) Stokes equations have several applications in the field of biofluid dynamics. In the present study, we propose the staggered finite volume method (S-FVM) for MHD Stokes equations and establish its equivalence to a nonconforming finite element approximation. We also theoretically establish the convergence of the proposed S-FVM. The error estimation is carried out in an unstructured grid framework which is known for its flexibility and robustness in dealing with complex domains. The apriori estimate shows that the L 2 {L}_{2} -norm of the error for the pressure and velocity components is of order h h , the spacial grid size. After validating the numerical performance of the scheme against benchmark test cases, we do numerical simulations for the blood flow through an injured arteriole and analyze the influence of the magnetic force on hemodynamics in the arteriole under an injured condition.

Modeling Laminar Separation Bubbles by Field Inversion Method

This paper presents the application of a data-driven method for turbulence modeling. The main aim is to enhance the prediction capabilities of an existing Reynolds-averaged Navier–Stokes (RANS) model for laminar separation bubbles. The recently developed field inversion method has been chosen to infer corrections to the shear stress transport turbulence model. An innovative approach has been proposed to reduce the computational cost of the modeling procedure. The objective function makes use of the friction coefficient to compare data coming from the RANS model with reference large-eddy simulation data available in the literature. The performed test cases responded well to the optimization procedure which provided a correction field that boosts the turbulent kinetic energy within the bubble. The proposed procedure is the necessary building block to define a new turbulence model able to overcome the present limitations in simulating laminar separation bubbles.

Dynamic simulation and analysis of the influence of urethral morphological changes on urodynamics after benign prostatic hyperplasia surgery: A computational fluid dynamics study

Background and objectiveComputational fluid dynamics (CFD) technology has been widely used in medicine to simulate and analyse urine flow characteristics in urology. In previous studies, researchers have modelled the analysis with a simple circular urethra, ignoring the effect of the patient's true urethral morphology on the urinary flow rate. Moreover, the studies tended to be steady-state simulations rather than dynamic simulations. Therefore, this study is established a relatively realistic model of the posterior urethra based on MRI data combined with the urodynamic data of patients and analysed the urodynamic characteristics of the posterior urethra model after benign prostatic hyperplasia (BPH) surgery using a CFD dynamic simulation. MethodsBased on clinical MRI data, a three-dimensional real urethral model was established for two patients with BPH after surgery. The boundary conditions were set according to the patients’ real urodynamic data, and a Reynolds averaged Navier‒Stokes model was used for transient simulations. The dynamic simulation depicted the entire urination process, and the urine flow characteristics were studied under real urethral morphology after surgery. Results1. By comparing the three-dimensional trajectory of urine and the vortex identification cloud map based on the Q criterion, we intuitively observed the distribution of the vortex in the model, and a ‘gourd-shaped’ urethra was more likely to generate a vortex than a ‘funnel-shaped’ urethra. 2. After surgery for BPH, the changes in the posterior urethral pressure were mainly concentrated in the urethral membrane, and the velocity increased while the pressure decreased. The curve of the posterior urethral pressure changes during urination was simulated and calculated. The posterior urethral pressure gradients of the two patients were 6.6 cmH2O and 5.26 cmH2O. ConclusionsThe complete urinary discharge process can be dynamically simulated using CFD techniques. By comparing the simulation results, the posterior urethral morphology can have an important impact on the urinary flow characteristics. Determining the location of vortex generation can lay a foundation for personalized surgical plans for patients in the future. Furthermore, numerical simulations can provide a new method for the study of non-invasive posterior urethral pressure gradients.

Unsteady Reynolds-Averaged Navier–Stokes Simulations of a Ducted Wind Turbine

Abstract An unsteady Reynolds-averaged Navier–Stokes model on body-fitted meshes in a commercial package (SimericsMP+) with a mismatched grid interface is used to study fluid dynamics around a ducted wind turbine. The model is validated by studying turbulent flow past a marine propeller. The nondimensional thrust and torque coefficients are compared against experimental data and results from a large eddy simulation model. Both coefficients are found to be within 3% of experimental results. Following this validation, the impact of different tip speed ratios on the ducted wind turbine's fluid dynamics is assessed. The optimal tip speed ratio is found to be the design value of 3.93 with a maximum power coefficient of 0.465 based on the duct exit area. The corresponding thrust coefficient is found to be 1.02 based on the rotor area. Lower tip speed ratios experience larger flow separation on the duct interior. Higher tip speed ratios decrease the size of the low-velocity region behind the hub. The ducted wind turbine's performance at design conditions is compared to an open rotor. The ducted wind turbine increases the power coefficient by 96% over the open rotor. The impact of hub size on the ducted wind turbine is also studied by simulating a smaller hub with 77% diameter. At the design tip speed ratio, the smaller hub has a power coefficient of 0.417. The maximum power coefficient is found to be 0.446 at a higher tip speed ratio of 4.5.