CIATEQ

Wind energy is the strongest renewable energy source developed in recent decades. Being systems that are directly connected to the grid of the electrical system, it is essential to use the maximum available power of the wind and obtain the maximum electrical power converted from the turbine. In this paper, the fundamental problem of the wind turbine is how to obtain at all times the maximum output power of the turbine in a wide range of wind speed. The randomness of the wind adds an intrinsic difficulty to be able to plan the available wind energy in advance. To solve this problem, it is not necessary to know the dynamic operation of the system; we must anticipate the control response to each one of the different probable scenarios. An expert control system can be used based on human knowledge and experience, which, through proper management of its variables and adequate control of criteria to manipulate stored data, provides a way to determine solutions. In other words, it is a model of the experience of professionals in this field. The more variables in the system are considered, the more complete the model will be, and the more information will be available for decision-making, with a more efficient system and higher results in power generation as a response. For this reason, the objective of this paper is to present expert systems developed in recent years and, thus, offer a control solution that approximates the conditions of different wind turbines.

The thermal spraying process is a surface treatment which does not adversely affect the base metal on which it is performed. The coatings obtained by HVOF thermal spray are employed in aeronautics, aerospace, and power generation industries. Alloys and coatings designed to resist oxidizing environments at high temperatures should be able to develop a surface oxide layer, which is thermodynamically stable, slowly growing, and adherent. MCrAlY type (M = Co, Ni or combination of both) coatings are used in wear and corrosion applications but also provide protection against high temperature oxidation and corrosion attack in molten salts. In this investigation, CoNiCrAlY coatings were produced employing a HVOF DJH 2700 gun. The work presented here focuses on the influences of process parameters of a gas-drive HVOF system on the microstructure, adherence, wear, and oxygen content of CoNiCrAlY. The results showed that spray distance significantly affects the properties of CoNiCrAlY coatings.

Wind power is a renewable energy source that has been developed in recent years. Large turbines are increasingly seen. The advantage of generating electrical power in this way is that it can be connected to the grid, making it an economical and easily available source of energy. The fundamental problem of a wind turbine is the randomness in a wide range of wind speeds that determine the electrical energy generated, as well as abrupt changes in wind speed that make the system unstable and unsafe. A conventional control system based on a mathematical model is effective with moderate disturbances, but slow with very large oscillations such as those produced by turbulence. To solve this problem, expert control systems (ECS) are proposed, which are based on human experience and an adequate management of stored information of each of its variables, providing a way to determine solutions. This revision of recent years, mentions the expert systems developed to obtain the point of maximum power generation in a wind turbine with permanent magnet synchronous generator (PMSG) and, therefore, offers a control solution that adapts to the specifications of any wind turbine.

Copper nanoparticles (CuNPs) functionalized with polyethyleneimine (PEI) and 4-aminobutyric acid (GABA) were used to obtain composites with isotactic polypropylene (iPP). The iPP/CuNPs composites were prepared at copper concentrations of 0.25–5.0 wt % by melt mixing, no evidence of oxidation of the CuNP was observed. Furthermore, the release of copper ions from iPP/CuNPs composites in an aqueous medium was studied. The release of cupric ions was higher in the composites with 2.5 and 5.0 wt %. These composites showed excellent antibacterial activity (AA) toward Pseudomona aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus). The incorporation of CuNP into the iPP polymeric matrix slightly decreased the thermal stability of the composite material but improved the crystallinity and the storage modulus. This evidence suggests that CuNPs could work as nucleating agents in the iPP crystallization process. The iPP/CuNPs composites presented better AA properties compared to similar composites reported previously. This behavior indicates that the new materials have great potential to be used in various applications that can be explored in the future.

The design of mechanical metamaterials often uses lattice arrangements being benefited from the increase in Additive Manufacturing technologies available. Such design freedom allows the fabrication of lattice arrangements with complex curved geometries. Here we propose a whole family of novel lattice matematerials parametrized using cubic Bézier curves. The methodology presented permits the generation of unit cells with different degrees of curvature based on the location of the Bézier control points along a spiral. The apparent stiffness of these structures was characterized using finite element analysis and compression tests on additively manufactured samples using stereolithography. The mechanical properties of spiral based cubic Bézier metamaterials were related to the location of the control points, curvature, etc. It was found that the apparent stiffness decreases proportionally to the cubic root of the distance between the control point and the predefined origin of the coordinate system. Due to symmetry conditions, the slope continuity in the curves conforming the unit cells is fulfilled and the origin of the coordinate system used coincides with the center of the unit cells. The procedure presented for the synthesis of metamaterials enables the generation of structures with customized mechanical properties by adjusting the geometry of the unit cells.

This article describes the main characteristics of a cable-based parallel manipulator called Milli-Cassino Tracking System (Milli-CaTraSys), which was developed at LARM in Cassino. Operation models and formulations are proposed both for kinematics and statics. In particular, a procedure is proposed for error estimation to know error effects on the end-effector pose. Numerical simulations have been carried out to characterize both the procedure results and system behavior. Results of experimental tests and simulations are also compared to show the feasibility and practical efficiency of a Milli-CaTraSys prototype.

Graphene oxide sheets (GO) were functionalized with hydroxyapatite nanoparticles (nHAp) through a simple and effective hydrothermal treatment and a novel physicochemical process. Microstructure and crystallinity were investigated by Fourier transform infrared spectroscopy (FT‐IR), Raman spectroscopy, X‐ray diffraction (XRD), ultraviolet‐visible (UV‐Vis) absorption spectroscopy, and thermogravimetric analysis (TGA). Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) were performed to characterize the morphology of the functionalized material. The resulting novel materials combine the biocompatibility of the nHAp with the strength and physical properties of the graphene.

In this work, we report the synthesis of copper nanoparticles (Cu NPs), employing the chemical reduction method in an aqueous medium. We used copper sulfate pentahydrate (CuSO4·5H2O) as a metallic precursor; polyethylenimine (PEI), allylamine (AAM), and 4-aminobutyric acid (AABT) as stabilizing agents; and hydrated hydrazine as a reducing agent. The characterization of the obtained nanoparticles consisted of X-ray, TEM, FTIR, and TGA analyses. Through these techniques, it was possible to detect the presence of the used stabilizing agents on the surface of the NPs. Finally, a zeta potential analysis was performed to differentiate the stability of the nanoparticles with a different type of stabilizing agent, from which it was determined that the most stable nanoparticles were the Cu NPs synthesized in the presence of the PEI/AAM mixture. The antimicrobial activity of Cu/PEI/AABT toward P. aeruginosa and S. aureus bacteria was high, inhibiting both bacteria with low contact times and copper concentrations of 50–200 ppm. The synthesis method allowed us to obtain Cu NPs free of oxides, stable to oxidation, and with high yields. The newly functionalized Cu NPs are potential candidates for antimicrobial applications.

Abstract Relaxations in chitin have been investigated in the temperature range 298–523 K using impedance spectroscopy in the frequency range 10 −1 –10 8 Hz. The objective was to detect a glass‐transition temperature for this naturally occurring, semicrystalline polysaccharide. The impedance study was complemented with X‐ray diffraction, thermogravimetric, and differential scanning calorimetry measurements. Preliminary impedance data treatment includes the subtraction of the dc conductivity contribution, the exclusion of contact and interfacial polarization effects, and obtaining a condition of minimum moisture content for further analysis. When all these aspects are taken into account, two relaxations are clearly revealed in the impedance data. For the first time, evidence is presented for a relaxation process, which exhibits a non‐Arrhenius temperature dependence, in dry α‐chitin (∼0.1% moisture content), and likely represents the primary α‐relaxation. This evidence suggests a glass transition temperature for chitin of 335 ± 10 K estimated on the basis of the temperature dependence of the conductivity and of the relaxation time. A second relaxation in dry α‐chitin, not previously reported in the literature, is observed from 353 K to the onset of thermal degradation (∼483 K) and is identified as the σ‐relaxation often associated with proton mobility. It exhibits a normal Arrhenius‐type temperature dependence with activation energy of 113 ± 3 kJ/mol. The latter has not been previously reported in the literature. A high frequency secondary β‐relaxation is also observed with Arrhenius activation energy of 45 ± 1 kJ/mol. © 2009 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 47: 932–943, 2009

The population growth demands a greater generation of energy, an alternative is the use of small wind turbines, however, obtaining maximum wind power becomes the main challenge when there are drastic changes in wind speed. The angle of the blades rotates around its longitudinal axis to control the effect of the wind on the rotation of the turbine, a proportional-integral controller (PI) for this angle achieves stability and precision in a stable state but is not functional with severe alterations in wind speed, a different response time is necessary in both cases. This article proposes a novel pitch angle controller based on auto-tuning of PI gains, for which it uses a teaching–learning based optimization (TLBO) algorithm. The wind speed and the value of the magnitude of the change are used by the algorithm to determine the appropriate PI gains at different wind speeds, so it can adapt to any sudden change in wind speed. The effectiveness of the proposed method is verified by experimental results for a 14 KW permanent magnet synchronous generator (PMSG) wind turbine located at the Universidad Autónoma de Querétaro (UAQ), Mexico.

Large scale coherent structures play an important role in the behavior of the combustion regime inside any type ofcombustor stabilized by swirl, with special impact on factors such as flame stability, blow off, emissions and theoccurrence of thermo-acoustic oscillations. Lean premixed combustion is widely used and is known to impact many ofthese factors, causing complex interrelationships with any coherent structure formed. Despite the extensiveexperimentation in this matter, the above phenomena are poorly understood. Numerical simulations have been usedto try to explain the development of different regimes, but their extremely complex nature and lack of time dependentvalidation show varied and debatable results. The precessing vortex core (PVC) is a well-known coherent structurewhose development, intensity and occurrence has not been well documented. This paper thus adopts an experimentalapproach to characterize the PVC in a simple swirl burner under combustion conditions so as to reveal the effects ofswirl and other variables on the latter. Aided by a high speed photography (HSP) system, the recognition and extentof several different types of PVCs were observed and discussed.

Path planning is a fundamental task for autonomous mobile robots (AMRs). Classic approaches provide an analytical solution by searching for the trajectory with the shortest distance; however, reinforcement learning (RL) techniques have been proven to be effective in solving these problems with the experiences gained by agents in real time. This study proposes a reward function that motivates an agent to select the shortest path with fewer turns. The solution to the RL technique is obtained via dynamic programming and Deep Q-Learning methods. In addition, a path-tracking control design is proposed based on the Lyapunov candidate function. The results indicate that RL algorithms show superior performance compared to classic A* algorithms. The number of turns is reduced by 50%, resulting in a decrease in the total distance ranging from 3.2% to 36%.

This paper presents and discusses a new static solver that implements the pseudotransient continuation method for the quasi-steady state analysis, or extended-period simulation of water distribution systems. The implementation is based on the concept of virtual tanks and has a clear physical meaning. The steady state solver described in this paper can analyze a pipe network under pressure deficient conditions and is free from some convergence problems that occur in the Newton-Raphson method-based solvers when analyzing a pipe network with control devices. The numerical examples considered in the paper demonstrate the convergence of the proposed method in cases where existing static solvers (e.g., that of the EPANET 2 hydraulic simulator) fail.

The research reported in this paper is related to the creation of self-adapting robots that are capable of learning manipulative skills online. The investigation includes the design of a novel neural network controller (NNC), which is based on the adaptive resonance theory (ART) and a dynamic knowledge base, whose knowledge is regulated by specific assembly operations. A force/torque (F/T) sensor was attached to the robot's wrist and this was the only information available to the NNC during the assembly operations, since the precise location of the components was unknown. The knowledge is enhanced online, based on the success in predicting the motion that reduces the constraint forces. The results demonstrate the generalisation capability of the NNC by learning the assembly of different part geometries using the same initial knowledge base. The learning time for a complete new operation was achieved in approximately 1 minute.

Bringing electricity to areas of difficult terrain is a complicated task, so it is convenient to generate power using local natural resources, such as wind, through a small horizontal-axis wind turbine (S-HAWT). However, at the rotor height of these wind turbines, the wind is often turbulent due to obstacles such as trees and buildings. For a turbine to function properly in these conditions, the action of the wind force on the rotor must be smoothed out by controlling the pitch angle. A commercial derivative-integral-proportional (PID)-type pitch controller works well when system dynamics are stable, but not when there are disturbances in the system. This paper proposes a hierarchical fuzzy logic controller (HFLC) to solve the nonlinear system effects produced by atypical winds. The methodology includes a statistical analysis of wind variability at the installation site, which determines the functions of belonging and its hierarchy. In addition, installing an anemometer in front of the turbine allows an advanced positioning of the blades in the presence of wind gusts. The algorithm was implemented in an S-HAWT, and a comparison was made to quantify the performance difference between the proposed control strategy and a conventional PID controller.

The defocus blur concept adds an artistic effect and enables an enhancement in the visualization of image scenery. Moreover, some specialized computer vision fields, such as object recognition or scene restoration enhancement, might need to perform segmentation to separate the blurred and non-blurred regions in partially blurred images. This study proposes a sharpness measure comprised of a Local Binary Pattern (LBP) descriptor and Pulse Coupled Neural Network (PCNN) component used to implement a robust approach for segmenting in-focus regions from out of focus sections in the scene. The proposed approach is very robust in the sense that the parameters of the model can be modified to accommodate different settings. The presented metric exploits the fact that, in general, local patches of the image in blurry regions have less prominent LBP descriptors than non-blurry regions. The proposed approach combines this sharpness measure with the PCNN algorithm; the images are segmented along with clear regions and edges of segmented objects. The proposed approach has been tested on a dataset comprised of 1000 defocused images with eight state-of-the-art methods. Based on a set of evaluation metrics, i.e., precision, recall, and F1-Measure, the results show that the proposed algorithm outperforms previous works in terms of prominent accuracy and efficiency improvement. The proposed approach also uses other evaluation parameters, i.e., Accuracy, Matthews Correlation Coefficient (MCC), Dice Similarity Coefficient (DSC), and Specificity, to assess better the results obtained by our proposal. Moreover, we adopted a fuzzy logic ranking scheme inspired by the Evaluation Based on Distance from Average Solution (EDAS) technique to interpret the defocus segmentation integrity. The experimental outputs illustrate that the proposed approach outperforms the referenced methods by optimizing the segmentation quality and reducing the computational complexity.

The developments of new manufacturing processes have impacted modern machinery. Nowadays, mechanical parts are produced with tighter tolerances that allow very high precise assemblies. On the other hand, new materials and design techniques have developed lighter elements. Thus, modern machinery operates at very high speeds and accelerations, which, in many cases, shows nonlinear dynamic behaviors. Intelligent manufacturing systems require on line monitoring equipment coordinated by the control systems. Traditionally, monitoring systems are based on the Fast Fourier Transform (FFT), which, due to its basis is unable to identify transient responses, and nonlinear behaviors. On the other hand, the FFT requires a considerable processing time that limits its application to early fault detections. The development of new sensors, signal processing techniques and faster microprocessors are key elements for modern monitoring systems. These systems require a better understanding of the machine response and the nature of the output signals. The evolution of the phase space, or phase diagram, represents how the dynamic system evolves in time. Nonlinearities and transient responses can be determined from the smoothness of the tangent vector of the phase diagram accordingly to Liouville’s theorem. Therefore, the phase diagram is a useful technique for predicting transient and nonlinear behavior in mechanical systems. Even more, the phase diagram can be implemented electronically for on line monitoring, and it can identify faults in real time. Mathematical modeling refers to the use of mathematical language to simulate the behavior of a system. Its role is to provide a better understanding and characterization of the system. In the theory of mechanical vibrations, mathematical models are helpful for the analysis of dynamic behavior of the structure being modeled (Kerschen et al. 2006). Even with advanced computers, experimental testing and system identification help designers to evaluate numerical predictions with experimental data. The term “system identification” is sometimes used in a broader context and may also refer to the extraction of information about the structural behavior directly from experimental data. In this case, it is referred as any systematic way of deriving models from experimental data. This is the main objective of any machinery monitoring systems (Masri 1994). For linear systems, modal analysis is the most popular approach for system identification. It can describe the behavior of a system for any input. Examples of this are: Ibrahim time domain method, eigensystem realization algorithm, stochastic subspace identification method, polyreference least-square complex frequency domain method among others.

The present research deals with the comparative wear behavior of a mechanically milled Al-6061 alloy and the same alloy reinforced with 5 wt.% of Al2O3 nanoparticles (Al-6061-Al2O3) under different dry sliding conditions. For this purpose, an aluminum-silicon-based material was synthesized by high-energy mechanical alloying, cold consolidated, and sintered under pressureless and vacuum conditions. The mechanical behavior was evaluated by sliding wear and microhardness tests. The structural characterization was carried out by X-ray diffraction and scanning electron microscopy. Results showed a clear wear resistance improvement in the aluminum matrix composite (Al-6061-Al2O3) in comparison with the Al-6061 alloy since nanoparticles act as a third hard body against wear. This behavior is attributed to the significant increment in hardness on the reinforced material, whose strengthening mechanisms mainly lie in a nanometric size and homogeneous dispersion of particles offering an effective load transfer from the matrix to the reinforcement. Discussion of the wear performance was in terms of a protective thin film oxide formation, where protective behavior decreases as a function of the sliding speed.

The Taguchi method, based on an orthogonal arrangement (L9, 33), the vari-ance analysis, the signal-to-noise ratios and the response surface methodol-ogy have been used to optimize maximum flank wear (VBmax) and surface roughness (Ra) of the cutting tool when turning a hardened steel AISI D2 (65 HRC) with PVD—TiAlN coated WC insert upon dry environment. By em-ploying regression models; cutting speed, cutting depth and feed rate, which optimize maximum flank wear and surface roughness were validated. Results of relation signal-to-noise ratios, showed that with cutting speed of 200 m/min, cutting depth of 0.2 mm and feed rate of 0.20 mm/rev, Ra is opti-mized. With cutting speed of 150 m/min, cutting depth of 0.4 mm and feed rate of 0.3 mm/rev, VBmax is optimized. Through the variance analysis it was concluded that the depth of cut was the main parameter that affected on the surface roughness; whereas, the feed rate was the most influential parameter on the flank wear. Confirmation test results showed that the Taguchi method was very successful in the optimization of machining parameters for mini-mum surface roughness and flank wear in the turning of the D2 steel.

results indicate that nanoclays are highly biocompatible, supporting their possible safe use for future biomedical and general-purpose applications.