Search Results (27)

The present work investigated numerically turbulent airflows over a hybrid Darrieus/Savonius vertical axis wind turbine. Firstly, the isolated turbines were validated in comparison to previous studies from the literature. Later, new recommendations were obtained for the simulation of a hybrid turbine subject to turbulent airflow. The numerical simulations consisted of the solution of time-averaged equations of mass and momentum in x and y directions using the finite volume method, available in the commercial code Ansys Fluent (version 2022 R1). For closure of turbulence, the k − ω SST (Shear Stress Transport) model was employed. For lower magnitudes of tip speed ratio (TSR), the hybrid turbine improved the power coefficient (C_P) compared to the Darrieus turbine (e.g., by 70% at TSR = 0.75), thereby demonstrating the self-starting capability of the hybrid configuration. Unexpectedly, at the optimal TSR = 1.5, the hybrid turbine performed about 6.5% better than the Darrieus turbine, indicating that the balance between the additional power generated by the Savonius rotor and losses caused by flow disturbances in the hybrid configuration was positive. As a novelty, results highlighted the role of each rotor (Darrieus and Savonius) for the performance of the hybrid turbine by comparing it with isolated Darrieus and Savonius turbines under the same conditions. Full article

In this study, a computational thermo-fluid dynamics simulation of a wide-slot jet impingement heating process is performed. The present configuration consists of a turbulent incompressible air jet impinging orthogonally on an isothermal cold plate at a Reynolds number of around 11,000. The two-dimensional mean turbulent flow field is numerically predicted by solving Reynolds-averaged Navier–Stokes (RANS) equations, where the two-equation eddy viscosity k-$\omega$ model is utilized for turbulence closure. As the commonly used shear stress transport variant overpredicts heat transfer at the plate due to excessive turbulent diffusion, the recently developed generalized k-$\omega$ (GEKO) model is considered for the present analysis, where the primary model coefficients are suitably tuned. Through a comparative analysis of the various solutions against one another, in addition to reference experimental and numerical data, the effectiveness of the generalized procedure in predicting both the jet flow characteristics and the heat transfer at the plate is thoroughly evaluated, while determining the optimal set of model parameters. By improving accuracy within the RANS framework, the importance of model adaptability and parameter tuning for this specific fluid engineering application is demonstrated. This study offers valuable insights for improving predictive capability in turbulent jet simulations with broad engineering implications, particularly for industrial heating or cooling systems relying on wide-slot jet impingement. Full article

A balanced flowmeter not only inherits the advantages of orifice plate flowmeters but also stabilizes the flow field, reduces permanent pressure loss, and effectively increases the cavitation threshold. To perform an in-depth analysis of flow characteristics through the perforated plate and achieve performance optimization for the liquid hydrogen (LH₂) measurement, a numerical calculation framework is established based on the mixture model, realizable turbulence closure, and Schnerr–Sauer cavitation model. The model is first evaluated through comparison with the liquid nitrogen (LN₂) experimental results of a self-developed balanced flowmeter as well as the measuring setup. The flow coefficient and pressure loss coefficient are especially considered, and a comparison is made with the orifice plane considering cavitation and non-cavitation conditions. The cavitation cloud and temperature contours are also presented to illustrate the difference in the upper limit of the Re between water, LN₂, and LH₂ flow. The results show that compared to LN₂ and water, LH₂ has a larger cavitation threshold, indicating a wider range of Re number measurements. Full article

The Xfinity Racing Series is an American stock car racing series organized by NASCAR. For the 2017 racing season, NASCAR introduced new regulations with the objective of creating a level playing field by reducing aerodynamic influence on vehicle performance. In this context, the primary objective of this work is to explore the differences in the aerodynamic performance between the 2016 and 2017 Toyota Camry Xfinity racecars using only open-source Computational Fluid Dynamics (CFD) and CAE tools. During the CFD validation process, it was observed that none of the standard turbulence models, with default turbulence model closure coefficients, were able to provide racecar aerodynamic characteristics predictions with acceptable accuracy compared to experiments. This necessitated a fine-tuning of the closure coefficient numeric values. This work also demonstrates that it is possible to generate CFD predictions that are highly correlated with experimental measurements by modifying the closure coefficients of the standard $k-\omega$ SST turbulence model. Full article

Computational fluid dynamic (CFD) models and workflows are often developed in an ad hoc manner, leading to a limited understanding of interaction effects and model behavior under various conditions. Machine learning (ML) and explainability tools can help CFD process development by providing a means to investigate the interactions in CFD models and pipelines. ML tools in CFD can facilitate the efficient development of new processes, the optimization of current models, and enhance the understanding of existing CFD methods. In this study, the turbulent closure coefficient tuning of the SST $k-\omega$ Reynolds-averaged Navier–Stokes (RANS) turbulence model was selected as a case study. The objective was to demonstrate the efficacy of ML and explainability tools in enhancing CFD applications, particularly focusing on external aerodynamic workflows. Two variants of the Ahmed body model, with 25-degree and 40-degree slant angles, were chosen due to their availability and relevance as standard geometries for aerodynamic process validation. Shapley values, a concept derived from game theory, were used to elucidate the impact of varying the values of the closure coefficients on CFD predictions, chosen for their robustness in providing clear and interpretable insights into model behavior. Various ML algorithms, along with the SHAP method, were employed to efficiently explain the relationships between the closure coefficients and the flow profiles sampled around the models. The results indicated that model coefficient ${\beta }^{*}$ had the greatest overall effect on the lift and drag predictions. The ML explainer model and the generated explanations were used to create optimized closure coefficients, achieving an optimal set that reduced the error in lift and drag predictions to less than 7% and 0.5% for the 25-degree and 40-degree models, respectively. Full article

Computational simulations of three-dimensional flow around a NACA 0018 wing with an aspect ratio of $AR=5$ were carried out by using the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations with the Shear-Stress Transport turbulence model closure. Simulations were performed to capture aerodynamic stall hysteresis by using the developed pseudo-transient continuation (PTC) method based on a dual-time step approach in CFD OpenFOAM code. The flow was characterized by incompressible Mach number $M=0.12$ and moderate Reynolds number $Re=0.67\times {10}^6$. The results obtained indicate the presence of noticeable aerodynamic hysteresis in the static dependencies of the force and moment coefficients, as well as the manifestation of bi-stable flow separation patterns, accompanied by the development of asymmetry in the stall zone. The URANS simulation results are in good agreement with the experimental data obtained for the NACA 0018 finite-aspect-ratio wing in the low-speed wind tunnel under the same test conditions. A new phenomenological bifurcation model of aerodynamic stall hysteresis under static and dynamic conditions is formulated and is proven to be able to closely match the experimental data. Full article

The generalized k-$\omega$ formulation provides a relatively new flexible eddy-viscosity Reynolds-averaged Navier–Stokes modeling approach to turbulent flow simulation, where free coefficients allow for fine-tuning and optimal adjusting of the turbulence closure procedure. The present study addressed the calibration of this versatile model for the aerodynamic design of an innovative mid-range commercial airplane by carrying out a series of simulations for varying model coefficients. Comparing the different solutions with each other, as well as with reference experimental and higher-fidelity numerical data, the performance of the generalized procedure in predicting the aerodynamic loading on the aircraft model was systematically examined. While drawing particular attention to the high-lift regime, the set of model parameters giving the best results was practically determined. Full article

To improve the reliability of the computational fluid dynamics (CFD) models of wind-driven pollutant dispersion within urban settings, a re-calibration study is conducted to optimize the standard $k-\epsilon$ model. A modified optimization framework based on the genetic algorithm is adapted to alleviate the computational expenses and to further identify ranges for each empirical coefficient to achieve the most reliable and accurate predictions. A robust objective function is defined, incorporating both the flow parameters and pollutant concentration through several linear and logarithmic measures. The coefficients are trained using high-quality and full-scale tracer experiments in a mock urban arrangement simulating a building array. The proposed ranges are $0.14\le C_{\mu }\le 0.15$, $1.30\le C_{\epsilon 1}\le 1.46$, $1.68\le C_{\epsilon 2}\le 1.80$, $1.12\le {\sigma }_{\epsilon }\le 1.20$, and $0.87\le {\sigma }_k\le 1.00$. A thorough evaluation of the predicted flow and concentration fields indicates the modified closure is effective. The fraction of predictions within the acceptable ranges from measurements has increased by 8% for pollutant concentration and 27% for turbulence kinetic energy. The generality of the calibrated model is further tested by modeling additional cases with different meteorological conditions, in which the calculated validation metrics attest to the noteworthy improvements in predictions. Full article

The DNS of film boiling requires strong computational resources that are difficult to obtain for daily CFD use by expert practitioners of industrial R&D. On the other hand, film boiling experiments are associated with the usage of expensive and highly sophisticated apparatus, and research to this end is relatively difficult due to high heat flow rates that are present in the process itself. When combined with transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid is proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, turbulent flow was determined as an important parameter in obtaining an accurate solution to temperature field evolution in a solid specimen, via the proper modeling of the turbulent kinetic energy (TKE) value, that was imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin–Helmholtz instability theory in conjunction with boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10% of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient matched the error bands prescribed by the authors of this paper, which presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30% higher. Further inspection revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method shows full capability for further pursuing industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach. Full article

The present work aims to study the aerodynamic characteristics of a newly designed three-bladed horizontal-axis wind turbine (HAWT) using the Computational Fluid Dynamic (CFD) method. The blade geometry is designed using an improved Blade Element Momentum (BEM) method to be similar in size to the Ampair300 wind turbine. The shear stress transport (SST) transition turbulence model closure is utilized to solve the steady state three-dimensional Reynolds Averaged Navier-Stokes (RANS) equations. The Ansys Fluent CFD solver is used to solve the problem. Then, a comparison between the two turbines’ operating conditions is conducted by monitoring the pressure coefficient, pressure contours and velocity vectors at five different radial positions. The analysis of the Tip Speed Ratio (TSR) effects on the turbine efficiency and on the flow behavior on the blade and in the near wake is carried out. For 8 m/s wind speed, the optimum pitch angle is also investigated, and the results are prepared against each TSR. Full article

We present a modeling tool capable of computing carbon dioxide (CO₂) fluxes over a non-uniform boreal peatland. The three-dimensional (3D) hydrodynamic model is based on the “one-and-a-half” closure scheme of the system of the Reynolds-Averaged Navier–Stokes and continuity equations. Despite simplifications used in the turbulence description, the model allowed obtaining the spatial steady-state distribution of the averaged wind velocities and coefficients of turbulent exchange within the atmospheric surface layer, taking into account the surface heterogeneity. The spatial pattern of CO₂ fluxes within and above a plant canopy is derived using the “diffusion–reaction–advection” equation. The model was applied to estimate the spatial heterogeneity of CO₂ fluxes over a non-uniform boreal ombrotrophic peatland, Staroselsky Moch, in the Tver region of European Russia. The modeling results showed a significant effect of vegetation heterogeneity on the spatial pattern of vertical and horizontal wind components and on vertical and horizontal CO₂ flux distributions. Maximal airflow disturbances were detected in the near-surface layer at the windward and leeward forest edges. The forest edges were also characterized by maximum rates of horizontal CO₂ fluxes. Modeled turbulent CO₂ fluxes were compared with the mid-day eddy covariance flux measurements in the southern part of the peatland. A very good agreement of modeled and measured fluxes (R² = 0.86, p < 0.05) was found. Comparisons of the vertical profiles of CO₂ fluxes over the entire peatland area and at the flux tower location showed significant differences between these fluxes, depending on the prevailing wind direction and the height above the ground. Full article

As early as at the end of the 19th century, shortly after mathematical rules describing fluid flow—such as the Navier–Stokes equations—were developed, the idea of using them for flow simulations emerged. However, it was soon discovered that the computational requirements of problems such as atmospheric phenomena and engineering calculations made hand computation impractical. The dawn of the computer age also marked the beginning of computational fluid mechanics and their subsequent popularization made computational fluid dynamics one of the common tools used in science and engineering. From the beginning, however, the method has faced a trade-off between accuracy and computational requirements. The purpose of this work is to examine how the results of recent advances in machine learning can be applied to further develop the seemingly plateaued method. Examples of applying this method to improve various types of computational flow simulations, both by increasing the accuracy of the results obtained and reducing calculation times, have been reviewed in the paper as well as the effectiveness of the methods presented, the chances of their acceptance by industry, including possible obstacles, and potential directions for their development. One can observe an evolution of solutions from simple determination of closure coefficients through to more advanced attempts to use machine learning as an alternative to the classical methods of solving differential equations on which computational fluid dynamics is based up to turbulence models built solely from neural networks. A continuation of these three trends may lead to at least a partial replacement of Navier–Stokes-based computational fluid dynamics by machine-learning-based solutions. Full article

The paper considers the slope flow simulation and the problem of finding the optimal parameter values of this mathematical model. The slope flow is modeled using the finite volume method applied to the Reynolds-averaged Navier–Stokes equations with closure in the form of the $k-\omega SST$ turbulence model. The optimal values of the turbulence model coefficients for free surface gravity multiphase flows were found using the global search algorithm. Calibration was performed to increase the similarity of the experimental and calculated velocity profiles. The Root Mean Square Error (RMSE) of derivation between the calculated flow velocity profile and the experimental one is considered as the objective function in the optimization problem. The calibration of the turbulence model coefficients for calculating the free surface flows on test slopes using the multiphase model for interphase tracking has not been performed previously. To solve the multi-extremal optimization problem arising from the search for the minimum of the loss function for the flow velocity profile, we apply a new optimization approach using a Peano curve to reduce the dimensionality of the problem. To speed up the optimization procedure, the objective function was approximated using an artificial neural network. Thus, an interdisciplinary approach was applied which allowed the optimal values of six turbulence model parameters to be found using OpenFOAM and Globalizer software. Full article

In this paper, we explore the improvement of the aerodynamic characteristics of wind turbine blades under stall conditions using passive flow control with slots. The National Renewable Energy Laboratory (NREL) Phase II rotor, for which detailed simulations and experimental data are available, served as a baseline for assessing the flow control system effects. The position and configuration of the slot used as a flow control system were determined using CFD analysis. The 3D-RANS equations are solved with ANSYS FLUENT using the k-ω SST turbulence closure model. The pressure coefficient for different wind speeds for the baseline configuration is compared to the available experimental data. The comparison shows that CFD results were better for the attached flow. The current work consists of a 3-D CFD modeling of a rotating blade equipped with different flow control systems: single-slot (S-S) and two-slots (T-S). The computation provides a better understanding of the influence of these flow control devices on the performance of wind turbine blades, the control of boundary layer separation, and the rotation effect. These control systems increase the power output by over 60% at high wind speeds with large separated boundary layer regions. For the configuration with the control system, the slot has shown its ability to delay the boundary layer separation. However, the improved aerodynamic performance has been proven for medium and high angles of attack where the flow is generally in the stall condition. The addition of the second slot changed the flow behavior, and an improvement was observed compared to the single slot configuration. The results are helpful for the design and development of a new generation of wind turbine blades. Full article

The present work aims to develop a computational model investigating turbulent flows in a problem that simulates an oscillating water column device (OWC) considering a Savonius turbine in the air duct region. Incompressible, two-dimensional, unsteady, and turbulent flows were considered for three different configurations: (1) free turbine inserted in a long and large channel for verification/validation of the model, (2) an enclosure domain that mimics an OWC device with a constant velocity at its inlet, and (3) the same domain as that in Case 2 with sinusoidal velocity imposed at the inlet. A dynamic rotational mesh in the turbine region was imposed. Time-averaged equations of the conservation of mass and balance of momentum with the k–ω Shear Stress Transport (SST) model for turbulence closure were solved with the finite volume method. The developed model led to promising results, predicting similar time–spatial-averaged power coefficients ($\overline{C_P}$) as those obtained in the literature for different magnitudes of the tip speed ratio (0.75 ≤ λ ≤ 2.00). The simulation of the enclosure domain increased $\overline{C_P}$ for all studied values of λ in comparison with a free turbine (Case 1). The imposition of sinusoidal velocity (Case 3) led to a similar performance as that obtained for constant velocity (Case 2). Full article