This page has only limited features, please log in for full access.
In this study, a water reattachment length was calculated by adopting two different models. The first was based on Unsteady Reynolds-Averaged Navier–Stokes (URANS) k-omega with Shear Stress Transport (SST); the second was a Large Eddy Simulation (LES) with Wall-Adapting Local Eddy-Viscosity (WALE). Both models used the same mesh and were checked with Taylor length-scale analysis. After the analysis, the mesh had 11,040,000 hexahedral cells. The geometry was a symmetrical expansion–contraction tube with a 4.28 expansion ratio that created mechanical energy losses, which were taken into account. Moreover, the reattachment length was estimated by analyzing the speed values; the change of speed value from negative to positive was used as the criterion to recognize the reattachment point.
Daniel Teso-Fz-Betoño; Martin Juica; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta. Estimating the Reattachment Length by Realizing a Comparison between URANS k-Omega SST and LES WALE Models on a Symmetric Geometry. Symmetry 2021, 13, 1555 .
AMA StyleDaniel Teso-Fz-Betoño, Martin Juica, Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta. Estimating the Reattachment Length by Realizing a Comparison between URANS k-Omega SST and LES WALE Models on a Symmetric Geometry. Symmetry. 2021; 13 (9):1555.
Chicago/Turabian StyleDaniel Teso-Fz-Betoño; Martin Juica; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta. 2021. "Estimating the Reattachment Length by Realizing a Comparison between URANS k-Omega SST and LES WALE Models on a Symmetric Geometry." Symmetry 13, no. 9: 1555.
Flow control device modeling is an engaging research field for wind turbine optimization, since in recent years wind turbines have grown in proportions and weight. The purpose of the present work was to study the performance and effects generated by a rotating microtab (MT) implemented on the trailing edge of a DU91W250 airfoil through the novel cell-set (CS) model for the first time via CFD techniques. The CS method is based on the reutilization of an already calculated mesh for the addition of new geometries on it. To accomplish that objective, the required region is split from the main domain, and new boundaries are assigned to the mentioned construction. Three different MT lengths were considered: h = 1%, 1.5% and 2% of the airfoil chord length, as well as seven MT orientations (β): from 0° to −90° regarding the horizontal axis, for five angles of attack: 0°, 2°, 4°, 6° and 9°. The numerical results showed that the increases of the β rotating angle and the MT length (h) led to higher aerodynamic performance of the airfoil, CL/CD = 164.10 being the maximum ratio obtained. All the performance curves showed an asymptotic trend as the β angle reduced. Qualitatively, the model behaved as expected, proving the relationship between velocity and pressure. Taking into consideration resulting data, the cell-set method is appropriate for computational testing of trailing edge rotating microtab geometry.
Alejandro Ballesteros-Coll; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta; Jose Manuel Lopez-Guede. Rotating Microtab Implementation on a DU91W250 Airfoil Based on the Cell-Set Model. Sustainability 2021, 13, 9114 .
AMA StyleAlejandro Ballesteros-Coll, Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Jose Manuel Lopez-Guede. Rotating Microtab Implementation on a DU91W250 Airfoil Based on the Cell-Set Model. Sustainability. 2021; 13 (16):9114.
Chicago/Turabian StyleAlejandro Ballesteros-Coll; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta; Jose Manuel Lopez-Guede. 2021. "Rotating Microtab Implementation on a DU91W250 Airfoil Based on the Cell-Set Model." Sustainability 13, no. 16: 9114.
Turbulence in fluids has been a popular research topic for many years due to its influence on a wide range of applications. Computational Fluid Dynamics (CFD) tools are able to provide plenty of information about this phenomenon, but their computational cost often makes the use of these tools unfeasible. For that reason, in recent years, turbulence modelling using Artificial Neural Networks (ANNs) is becoming increasingly popular. These networks typically calculate directly the desired magnitude, having input information about the computational domain. In this paper, a Convolutional Neural Network (CNN) for predicting different magnitudes of turbulent flows around different geometries by approximating the equations of the Reynolds-Averaged Navier-Stokes (RANS)-based realizable k-ε two-layer turbulence model is proposed. Using that CNN, alternative network structures are proposed to predict the velocity fields of a turbulent flow around different geometries on a rectangular channel, with a preliminary stage to predict pressure and vorticity fields before calculating the velocity fields, and the obtained results are compared with the ones obtained with the basic structure. The results demonstrate that the proposed structures clearly outperform the basic one, especially when the flow becomes uncertain. In addition, considering the results, the best network configuration is proposed. That network is tested with a domain with multiple geometries and a domain with a narrowing of the channel, which are domains with different conditions from the training ones, showing fairly accurate predictions.
Koldo Portal-Porras; Unai Fernandez-Gamiz; Ainara Ugarte-Anero; Ekaitz Zulueta; Asier Zulueta. Alternative Artificial Neural Network Structures for Turbulent Flow Velocity Field Prediction. Mathematics 2021, 9, 1939 .
AMA StyleKoldo Portal-Porras, Unai Fernandez-Gamiz, Ainara Ugarte-Anero, Ekaitz Zulueta, Asier Zulueta. Alternative Artificial Neural Network Structures for Turbulent Flow Velocity Field Prediction. Mathematics. 2021; 9 (16):1939.
Chicago/Turabian StyleKoldo Portal-Porras; Unai Fernandez-Gamiz; Ainara Ugarte-Anero; Ekaitz Zulueta; Asier Zulueta. 2021. "Alternative Artificial Neural Network Structures for Turbulent Flow Velocity Field Prediction." Mathematics 9, no. 16: 1939.
The computational cost and memory demand required by computational fluid dynamics (CFD) codes simulations can become very high. Therefore, the application of convolutional neural networks (CNN) in this field has been studied owing to its capacity to learn patterns from sets of input data, which can considerably approximate the results of the CFD simulations with relative low errors. DeepCFD code has been taken as a basis and with some slight variations in the parameters of the CNN, while the net is able to solve the Navier–Stokes equations for steady turbulent flows with variable input velocities to the domain. In order to acquire extensive input data to the CNN, a data augmentation technique, which considers the similarity principle for fluid dynamics, is implemented. As a consequence, DeepCFD is able to learn the velocities and pressure fields quite accurately, speeding up the time-consuming CFD simulations.
Alvaro Abucide-Armas; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta; Adrian Teso-Fz-Betoño. A Data Augmentation-Based Technique for Deep Learning Applied to CFD Simulations. Mathematics 2021, 9, 1843 .
AMA StyleAlvaro Abucide-Armas, Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Adrian Teso-Fz-Betoño. A Data Augmentation-Based Technique for Deep Learning Applied to CFD Simulations. Mathematics. 2021; 9 (16):1843.
Chicago/Turabian StyleAlvaro Abucide-Armas; Koldo Portal-Porras; Unai Fernandez-Gamiz; Ekaitz Zulueta; Adrian Teso-Fz-Betoño. 2021. "A Data Augmentation-Based Technique for Deep Learning Applied to CFD Simulations." Mathematics 9, no. 16: 1843.
Vortex Generators (VGs) are applied before the expected region of separation of the boundary layer in order to delay or remove the flow separation. Although their height is usually similar to that of the boundary layer, in some applications, lower VGs are used, Sub-Boundary Layer Vortex Generators (SBVGs), since this reduces the drag coefficient. Numerical simulations of sub-boundary layer vane-type vortex generators on a flat plate in a negligible pressure gradient flow were conducted using the fully resolved mesh model and the cell-set model, with the aim on assessing the accuracy of the cell-set model with Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) turbulence modelling techniques. The implementation of the cell-set model has supposed savings of the 40% in terms of computational time. The vortexes generated on the wake behind the VG; vortical structure of the primary vortex; and its path, size, strength, and produced wall shear stress have been studied. The results show good agreements between meshing models in the higher VGs, but slight discrepancies on the lower ones. These disparities are more pronounced with LES. Further study of the cell-set model is proposed, since its implementation entails great computational time and resources savings.
Koldo Portal-Porras; Unai Fernandez-Gamiz; Iñigo Aramendia; Daniel Teso-Fz-Betoño; Ekaitz Zulueta. Testing the Accuracy of the Cell-Set Model Applied on Vane-Type Sub-Boundary Layer Vortex Generators. Processes 2021, 9, 503 .
AMA StyleKoldo Portal-Porras, Unai Fernandez-Gamiz, Iñigo Aramendia, Daniel Teso-Fz-Betoño, Ekaitz Zulueta. Testing the Accuracy of the Cell-Set Model Applied on Vane-Type Sub-Boundary Layer Vortex Generators. Processes. 2021; 9 (3):503.
Chicago/Turabian StyleKoldo Portal-Porras; Unai Fernandez-Gamiz; Iñigo Aramendia; Daniel Teso-Fz-Betoño; Ekaitz Zulueta. 2021. "Testing the Accuracy of the Cell-Set Model Applied on Vane-Type Sub-Boundary Layer Vortex Generators." Processes 9, no. 3: 503.
Passive flow control devices are included in the design of wind turbine blades in order to obtain better performance and reduce loads without consuming any external energy. Vortex Generators are one of the most popular flow control devices, whose main objective is to delay the flow separation and increase the maximum lift coefficient. Computational Fluid Dynamics (CFD) simulations of a Vortex Generator (VG) on a flat plate in negligible streamwise pressure gradient conditions with the fully-resolved mesh model and the cell-set model using Large Eddy Simulation (LES) and Reynolds-Averaged Navier–Stokes (RANS) were carried out, with the objective of evaluating the accuracy of the cell-set model taking the fully-resolved mesh model as benchmark. The implementation of the cell-set model entailed a considerable reduction of the number of cells, which entailed saving simulation time and resources. The coherent structures, vortex path, wall shear stress and size, strength and velocity profiles of the primary vortex have been analyzed. The results show good agreements between the fully-resolved mesh model and the cell-set mode with RANS in all the analyzed parameters. With LES, acceptable results were obtained in terms of coherent structures, vortex path and wall shear stress, but slight differences between models are visible in the size, strength and velocity profiles of the primary vortex. As this is considered the first application of the cell-set model on VGs, further research is proposed, since the implementation of the cell-set model can represent an advantage over the fully-resolved mesh model.
Iosu Ibarra-Udaeta; Koldo Portal-Porras; Alejandro Ballesteros-Coll; Unai Fernandez-Gamiz; Javier Sancho. Accuracy of the Cell-Set Model on a Single Vane-Type Vortex Generator in Negligible Streamwise Pressure Gradient Flow with RANS and LES. Journal of Marine Science and Engineering 2020, 8, 982 .
AMA StyleIosu Ibarra-Udaeta, Koldo Portal-Porras, Alejandro Ballesteros-Coll, Unai Fernandez-Gamiz, Javier Sancho. Accuracy of the Cell-Set Model on a Single Vane-Type Vortex Generator in Negligible Streamwise Pressure Gradient Flow with RANS and LES. Journal of Marine Science and Engineering. 2020; 8 (12):982.
Chicago/Turabian StyleIosu Ibarra-Udaeta; Koldo Portal-Porras; Alejandro Ballesteros-Coll; Unai Fernandez-Gamiz; Javier Sancho. 2020. "Accuracy of the Cell-Set Model on a Single Vane-Type Vortex Generator in Negligible Streamwise Pressure Gradient Flow with RANS and LES." Journal of Marine Science and Engineering 8, no. 12: 982.