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Wind turbine maximum power point tracking control based on unsupervised neural networks
Muñoz-Palomeque Eduardo,Sierra-García J Enrique,Santos Matilde 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
The main control goal of a wind turbine (WT) is to produce the maximum energy in any operating region. When the wind speed is under its rated value, the control must aim at tracking the maximum power point of the best power curve for a specific WT. This is challenging due to the non-linear characteristics of the system and the environmental disturbances it is subjected to. Direct speed control (DSC) is one of the main techniques applied to address this problem. In this strategy, it is necessary to design a speed controller to adjust the generator torque so to follow the optimum generator speed. In this work, we improve the DSC by implementing this speed controller with a radial basis function neural network (NN). An unsupervised learning algorithm is designed to tune the weights of the NN so it learns the control law that minimizes the generator speed error. With this proposed unsupervised neural control methodology, the electromagnetic torque that allows the optimal power extraction is obtained, and thus the best power coefficient (${C}_\mathrm{p}$) values. The proposal is tested on the OpenFAST non-linear model of the National Renewable Energy Laboratory 1.5 MW WT. Simulation results prove the good performance of this neuro-control approach as it maintains the WT variables into the appropriate range and tracks the rated operation values. It has been compared with the controller included in OpenFAST giving up to 7.87% more power.
J. P. Morán-Lázaro,F. López-Urías,E. Muñoz-Sandoval,M. Courel-Piedrahita,A. Carreon-Alvarez,V. M. Rodríguez-Betancourtt,I. Zamudio-Torres,E. S. Guillén-López,A. Palafox-Corona 대한금속·재료학회 2023 ELECTRONIC MATERIALS LETTERS Vol.19 No.1
The acetone contained in human breath is of great interest for the health sector as it is a marker that allows to diagnoseand control diabetes in a non-invasive way. However, its concentration is extremely low. Therefore, high-performanceacetone sensors are still a challenge. With this in mind, MgCo 2 O 4 nanoparticles were synthesized using a microwaveassistedcolloidal route with subsequent calcination. Structural and morphological characterizations were done through varioustechniques. The MgCo 2 O 4 sensor was fabricated with the sample calcined at 500 °C. The sensing results showed that thesensor could detect acetone vapors ranging from 0.5 to 50 ppm at an optimum operating temperature of 250 °C with a highresponse, repeatability, stability, and selectivity. These sensing characteristics revealed that MgCo 2 O 4 could be used as a newsensor material to detect acetone in exhaled human breath.
J.A. Carmona,P. Ramírez,L.A. Trujillo-Cayado,A. Caro,J. Muñoz 한국공업화학회 2018 Journal of Industrial and Engineering Chemistry Vol.59 No.-
The present study is focused in the rheological and microstructural characterization of aqueous sepiolite gels and the influence of the addition of ionic surfactants. Stable sepiolite gels were prepared using a high-shear homogenization process. The rheological characterization of sepiolite gels reveals shear thinning behaviour. It was observed that yield stress was very influenced by the addition of ionic surfactant. Cryo-SEM micrographs of sepiolite gels are in good agreement with the rheological behaviour. It has been shown that is feasible to obtain sepiolite gels with tailored rheological properties as function of the type and amount of surfactant added.
Synthesis and characterization of YAG : Eu spray dryed powders
J. Zárate-Medina,R. López-Juárez,E. A. Aguilar-Reyes,J. Muñoz-Saldaña 한양대학교 세라믹연구소 2008 Journal of Ceramic Processing Research Vol.9 No.1
YAG compounds doped with Eu have been successfully synthesized by spray drying a solution of precursor nitrates. Aluminum and yttrium nitrates, europium oxide, ethylene glycol and citric acid were used as reagents. The resultant solution was fed to a spray dryer to obtain precursor powders, which were in turn heat treated at 800, 850 and 900oC and characterized by X-ray diffraction. Morphology of the synthesized powders was observed by scanning and transmission electron microscopy. Results showed that the crystallization temperature of the powders is below 850oC without the presence of intermediate phases. The powders consist of spherical agglomerates constituted of primary particles of 40 nm diameter approximately. YAG compounds doped with Eu have been successfully synthesized by spray drying a solution of precursor nitrates. Aluminum and yttrium nitrates, europium oxide, ethylene glycol and citric acid were used as reagents. The resultant solution was fed to a spray dryer to obtain precursor powders, which were in turn heat treated at 800, 850 and 900oC and characterized by X-ray diffraction. Morphology of the synthesized powders was observed by scanning and transmission electron microscopy. Results showed that the crystallization temperature of the powders is below 850oC without the presence of intermediate phases. The powders consist of spherical agglomerates constituted of primary particles of 40 nm diameter approximately.
Microstructure and Mechanical Properties of AA6082‑T6 by ECAP Under Warm Processing
T. Khelfa,J. A. Muñoz‑Bolaños,F. Li,J. M. Cabrera‑Marrero,M. Khitouni 대한금속·재료학회 2020 METALS AND MATERIALS International Vol.26 No.8
An AA6082 alloy deformed by equal channel angular pressing (ECAP) was studied. Microstructural evolution of the alloyprocessed by ECAP with different passes were evaluated by using optical microscope, scanning electron microscopy coupledwith an electron backscattered diffraction (EBSD) detector and X-ray diffraction. Texture analysis showed the apparitionof two types of textures, one associated with shearing deformation and the second due to the recrystallization phenomena. Mechanical strength properties measured by tensile tests increased in the first ECAP pass, and then progressively diminisheddue to the presence of concurrent softening phenomena. Calorimetric analysis indicated a slightly increase in the recrystallizationtemperature of the deformed specimens. Also, the stored energy increased with increasing ECAP passes due to theproduction of new dislocations. The average geometrically necessary dislocation density, measured by EBSD, increased withincreasing ECAP passes. However, the rate of increase slows down with the progress of ECAP passes.
Numerical investigation of truck aerodynamics on several classes of infrastructures
Alejandro Alonso-Estébanez,Juan J. del Coz Díaz,Felipe P. Álvarez Rabanal,Pablo Pascual-Muñoz,Paulino J. García Nieto 한국풍공학회 2018 Wind and Structures, An International Journal (WAS Vol.26 No.1
This paper describes the effect of different testing parameters (configuration of infrastructure and truck position on road) on truck aerodynamic coefficients under cross wind conditions, by means of a numerical approach known as Large Eddy Simulation (LES). In order to estimate the air flow behaviour around both the infrastructure and the truck, the filtered continuity and momentum equations along with the Smagorinsky–Lilly model were solved. A solution for these non-linear equations was approached through the finite volume method (FVM) and using temporal and spatial discretization schemes. As for the results, the aerodynamic coefficients acting on the truck model exhibited nearly constant values regardless of the Reynolds number. The flat ground is the infrastructure where the rollover coefficient acting on the truck model showed lowest values under cross wind conditions (yaw angle of 90), while the worst infrastructure studied for vehicle stability was an embankment with downward-slope on the leeward side. The position of the truck on the road and the value of embankment slope angle that minimizes the rollover coefficient were determined by successfully applying the Response Surface Methodology.
Viscosity of ionic liquids using the concept of mass connectivity and artificial neural networks
José Omar Valderrama,Jéssica Makarena Muñoz,Roberto Erasmo Rojas 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.6
Artificial neural networks (ANN) and the concept of mass connectivity index are used to correlate and predict the viscosity of ionic liquids. Different topologies of a multilayer feed forward artificial neural network were studied and the optimum architecture was determined. Viscosity data at several temperatures taken from the literature for 58 ionic liquids with 327 data points were used for training the network. To discriminate among the different substances,the molecular mass of the anion and of the cation, the mass connectivity index and the density at 298 K were considered as the independent variables. The capabilities of the designed network were tested by predicting viscosities for situations not considered during the training process (31 viscosity data for 26 ionic liquids). The results demonstrate that the chosen network and the variables considered allow estimating the viscosity of ionic liquids with acceptable accuracy for engineering calculations. The program codes and the necessary input files to calculate the viscosity for other ionic liquids are provided.
A. Díaz Lantada,P. Lafont Morgado,J.M. Munoz-Guijosa,J.L. Muñoz Sanz,J. Echávarri Otero,E. Chacón Tanarro,E. De la Guerra Ochoa 국제구조공학회 2014 Smart Structures and Systems, An International Jou Vol.14 No.4
The combined use of smart materials, complementing each others' characteristics and resultingin devices with optimised features, is providing new solutions in many industries. The use of ingeniouscombinations of smart materials has led to improvements in actuation speed and force, signal-to-noise ratio,sensor precision and unique capabilities such as self-sensing self-healing systems and energy autonomy. Thismay all give rise to a revival for numerous families of smart materials, for which application proposals hadalready reached a stationary situation. It may also provide the boost needed for the definitive industrialsuccess of many others. This study focuses on reviewing the proposals, preliminary studies and successcases related to combining smart materials to obtain multifunctional, improved systems. It also examines themost outstanding applications and fields for the combined use of these smart materials. We will also discussrelated study areas which warrant further research for the development of novel approaches for demandingapplications.
Bahareh Reisi,Ali Reza Najafi Chermahini,Daily Rodríguez-Padrón,Mario J. Muñoz-Batista,Rafael Luque 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.102 No.-
Bimetallic Pd-Ni catalysts were prepared via impregnation method, with effects of Pd/Ni ratio (wt%) ontheir catalytic performance for the oxidation of cyclohexane with molecular oxygen being investigated. Synthesized catalysts were characterized by a series of techniques including XRD, Nitrogen adsorption–desorption, H2-TPR, XPS, ICP-AES, TEM, SEM, and EDX. Compared to monometallic Pd, the additionof Ni to Pd was found to be effective in enhancing the selective oxidation of cyclohexane. Parameters suchas the temperature of the reaction, reaction times, catalyst amount, initial oxygen pressure and differentsolvents were investigated. Optimum conditions to improve cyclohexane conversion and selectivitytowards KA-oil (a mixture of cyclohexanone and cyclohexanol) over 4.0%Pd-4.0%Ni/KIT-6 catalystincluded 140 C, 1.0 MPa O2 and acetonitrile as solvent. Under these reaction conditions, 10.87 % conversionand 95.45 % selectivity for KA-oil were observed after 8 h of reaction. Reusability studies of theselected catalyst revealed an outstanding stability along four reaction cycles.