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.
Wind energy has become an important source of electricity generation, with the aim of achieving a cleaner and more sustainable energy model. However, wind turbine performance improvement is required to compete with conventional energy resources. To achieve this improvement, flow control devices are implemented on airfoils. Computational fluid dynamics (CFD) simulations are the most popular method for analyzing this kind of devices, but in recent years, with the growth of Artificial Intelligence, predicting flow characteristics using neural networks is becoming increasingly popular. In this work, 158 different CFD simulations of a DU91W(2)250 airfoil are conducted, with two different flow control devices, rotating microtabs and Gurney flaps, added on its Trailing Edge (TE). These flow control devices are implemented by using the cell-set meshing technique. These simulations are used to train and test a Convolutional Neural Network (CNN) for velocity and pressure field prediction and another CNN for aerodynamic coefficient prediction. The results show that the proposed CNN for field prediction is able to accurately predict the main characteristics of the flow around the flow control device, showing very slight errors. Regarding the aerodynamic coefficients, the proposed CNN is also capable to predict them reliably, being able to properly predict both the trend and the values. In comparison with CFD simulations, the use of the CNNs reduces the computational time in four orders of magnitude.
When structural damage appears in architecture, as in civil engineering, one of the first things to do is to analyse the level of stresses supported by the structural elements of the building. In most of the cases, the deduction of these stresses is carried out by means of one verification of the "design loads", using analytic methods based ( o r not) on standardised computer assisted applications. Nowadays, there is not one experimental method close enough to deduce the loads really supported by the structural elements. This article deals precisely about the resolution of this problem by means of the hole drilling technique applied on stone constructions as this is very common in architectural heritage. The particular use of the hole drilling technique in architectural heritage is named Donostia method by the authors, and allows both the deduction of the principal stress values and their directions. This is a very important aspect when the structural damages are generated by anomalous loads.
The main objective of this work was to determine the worth of installing an electrical battery in order to reduce peak power consumption. The importance of this question resides in the expensive terms of energy bills when using the maximum power level. If maximum power consumption decreases, it affects not only the revenues of maximum power level bills, but also results in important reductions at the source of the power. This way, the power of the transformer decreases, and other electrical elements can be removed from electrical installations. The authors studied the Spanish electrical system, and a particle swarm optimization (PSO) algorithm was used to model battery sizing in peak power smoothing applications for an electrical consumption point. This study proves that, despite not being entirely profitable at present due to current kWh prices, implanting a battery will definitely be an option to consider in the future when these prices come down.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.