Search results for: propagation

Authors: Ömer Aktaş, Olga A. Suvorova, Oleg Tretyakov

Abstract:

The boundary value problem on non-canonical and arbitrary shaped contour is solved with a numerically effective method called Analytical Regularization Method (ARM) to calculate propagation parameters. As a result of regularization, the equation of first kind is reduced to the infinite system of the linear algebraic equations of the second kind in the space of L₂. This equation can be solved numerically for desired accuracy by using truncation method. The parameters as cut-off wavenumber and cut-off frequency are used in waveguide evolutionary equations of electromagnetic theory in time-domain to illustrate the real-valued TM fields with lossy and lossless media.

Keywords: Arbitrary cross section waveguide, analytical regularization method, evolutionary equations of electromagnetic theory of time-domain, TM field.

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Authors: Ram N. Singh, Abraham K. George, Dawood N. Al-Namaani

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The Euler-s equation of motion is extended to include the viscosity stress tensor leading to the formulation of Navier– Stokes type equation. The latter is linearized and applied to investigate the rotational motion or vorticity in a viscous fluid. Relations for the velocity of viscous waves and attenuation parameter are obtained in terms of viscosity (μ) and the density (¤ü) of the fluid. μ and ¤ü are measured experimentally as a function of temperature for two different samples of light and heavy crude oil. These data facilitated to determine the activation energy, velocity of viscous wave and the attenuation parameter. Shear wave velocity in heavy oil is found to be much larger than the light oil, whereas the attenuation parameter in heavy oil is quite low in comparison to light one. The activation energy of heavy oil is three times larger than light oil.

Keywords: Activation Energy, Attenuation, Crude Oil, Navier- Stokes Equation, Viscosity.

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Authors: Hamdi. A. Awad, Mohamed A. Al-Zorkany

Abstract:

Developing techniques for mobile robot navigation constitutes one of the major trends in the current research on mobile robotics. This paper develops a local model network (LMN) for mobile robot navigation. The LMN represents the mobile robot by a set of locally valid submodels that are Multi-Layer Perceptrons (MLPs). Training these submodels employs Back Propagation (BP) algorithm. The paper proposes the fuzzy C-means (FCM) in this scheme to divide the input space to sub regions, and then a submodel (MLP) is identified to represent a particular region. The submodels then are combined in a unified structure. In run time phase, Radial Basis Functions (RBFs) are employed as windows for the activated submodels. This proposed structure overcomes the problem of changing operating regions of mobile robots. Read data are used in all experiments. Results for mobile robot navigation using the proposed LMN reflect the soundness of the proposed scheme.

Keywords: Mobile Robot Navigation, Neural Networks, Local Model Networks

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Authors: Irfan Ahmad, Tahir Siddiqui, Rashid Ahmad Khan, Tahir Munir Butt

Abstract:

An effort to find out the smaller size of cuttings for propagation of Morus alba was made in experimental area Department of Forestry, Range Management and Wildlife, University of Agriculture, Faisalabad, Pakistan. Different size of cuttings i.e. 2", 4", 6" and 8" were planted in polythene tubes of 3.5"x7". The effort was also made to compare the performance of cuttings in open air and in polythene low tunnel. Root length, number of root branches, root diameter and root fresh and dry weight were found maximum in two inches cuttings while minimum in four inches cuttings. Root growth was found maximum in open air as compared to under polythene sheet.

Keywords: cutting sizes Morus alba, Open air and polythene sheet, root growth

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: Artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt.

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Authors: Salvatore Marra, Francesco C. Morabito

Abstract:

In this paper we present an efficient approach for the prediction of two sunspot-related time series, namely the Yearly Sunspot Number and the IR5 Index, that are commonly used for monitoring solar activity. The method is based on exploiting partially recurrent Elman networks and it can be divided into three main steps: the first one consists in a “de-rectification" of the time series under study in order to obtain a new time series whose appearance, similar to a sum of sinusoids, can be modelled by our neural networks much better than the original dataset. After that, we normalize the derectified data so that they have zero mean and unity standard deviation and, finally, train an Elman network with only one input, a recurrent hidden layer and one output using a back-propagation algorithm with variable learning rate and momentum. The achieved results have shown the efficiency of this approach that, although very simple, can perform better than most of the existing solar activity forecasting methods.

Keywords: Elman neural networks, sunspot, solar activity, time series prediction.

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Authors: Andras Szekrenyes

Abstract:

This work presents the mixed-mode II/III prestressed split-cantilever beam specimen for the fracture testing of composite materials. In accordance with the concept of prestressed composite beams one of the two fracture modes is provided by the prestressed state of the specimen, and the other one is increased up to fracture initiation by using a testing machine. The novel beam-like specimen is able to provide any combination of the mode-II and mode-III energy release rates. A simple closed-form solution is developed using beam theory as a data reduction scheme and for the calculation of the energy release rates in the new configuration. The applicability and the limitations of the novel fracture mechanical test are demonstrated using unidirectional glass/polyester composite specimens. If only crack propagation onset is involved then the mixed-mode beam specimen can be used to obtain the fracture criterion of transparent composite materials in the GII - GIII plane in a relatively simple way.

Keywords: Composite, fracture mechanics, toughness testing, mixed-mode II/III fracture.

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Authors: Harpreet Singh Dhillon, Abhijit Mitra

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A reduced-bit multiplication algorithm based on the ancient Vedic multiplication formulae is proposed in this paper. Both the Vedic multiplication formulae, Urdhva tiryakbhyam and Nikhilam, are first discussed in detail. Urdhva tiryakbhyam, being a general multiplication formula, is equally applicable to all cases of multiplication. It is applied to the digital arithmetic and is shown to yield a multiplier architecture which is very similar to the popular array multiplier. Due to its structure, it leads to a high carry propagation delay in case of multiplication of large numbers. Nikhilam Sutra, on the other hand, is more efficient in the multiplication of large numbers as it reduces the multiplication of two large numbers to that of two smaller numbers. The framework of the proposed algorithm is taken from this Sutra and is further optimized by use of some general arithmetic operations such as expansion and bit-shifting to take advantage of bit-reduction in multiplication. We illustrate the proposed algorithm by reducing a general 4x4-bit multiplication to a single 2 x 2-bit multiplication operation.

Keywords: Multiplication, algorithm, Vedic mathematics, digital arithmetic, reduced-bit.

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Authors: Supriya Pal, Kalyan Adhikari, Somnath Mukherjee, Sudipta Ghosh

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This study presents the application of artificial neural network for modeling the phenolic compound migration through vertical soil column. A three layered feed forward neural network with back propagation training algorithm was developed using forty eight experimental data sets obtained from laboratory fixed bed vertical column tests. The input parameters used in the model were the influent concentration of phenol(mg/L) on the top end of the soil column, depth of the soil column (cm), elapsed time after phenol injection (hr), percentage of clay (%), percentage of silt (%) in soils. The output of the ANN was the effluent phenol concentration (mg/L) from the bottom end of the soil columns. The ANN predicted results were compared with the experimental results of the laboratory tests and the accuracy of the ANN model was evaluated.

Keywords: Modeling, Neural Networks, Phenol, Soil media

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Authors: Rameshwar Kawitkar

Abstract:

With the exponentially increasing demand for wireless communications the capacity of current cellular systems will soon become incapable of handling the growing traffic. Since radio frequencies are diminishing natural resources, there seems to be a fundamental barrier to further capacity increase. The solution can be found in smart antenna systems. Smart or adaptive antenna arrays consist of an array of antenna elements with signal processing capability, that optimize the radiation and reception of a desired signal, dynamically. Smart antennas can place nulls in the direction of interferers via adaptive updating of weights linked to each antenna element. They thus cancel out most of the co-channel interference resulting in better quality of reception and lower dropped calls. Smart antennas can also track the user within a cell via direction of arrival algorithms. This implies that they are more advantageous than other antenna systems. This paper focuses on few issues about the smart antennas in mobile radio networks.

Keywords: Smart/Adaptive Antenna, Multipath fading, Beamforming, Radio propagation.

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Authors: Tales H. J. Moreira, Thiago M. R. Dias, Gray F. Moita

Abstract:

The adviser-advised relationship, in addition to the evident propagation of knowledge, can provide an increase in the scientific production of the advisors. Specifically, in post-graduate programs, in which the advised submit diverse papers in different means of publication, these end up boosting the production of their advisor, since in general the advisors appear as co-authors, responsible for instructing and assisting in the development of the work. Therefore, to visualize the orientation process and the scientific production resulting from this relation is another important way of analyzing the scientific collaboration in the different areas of knowledge. In this work, are used the data of orientations and postgraduate supervisions from the Lattes curricula, from the main advisors who work in the Engineering area, to obtain an overview of the process of orientation of this group, and even, to produce Academic genealogical trees, where it is possible to verify how knowledge has spread in the diverse areas of engineering.

Keywords: Academic genealogy, advice, engineering, lattes platform.

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Authors: Ahmed Abdelghany, Bernard Uguen, Christophe Moy, Dominique Lemur

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The Internet of Things (IoT) is developed to ensure monitoring and connectivity within different applications. Thus, it is critical to study the channel propagation characteristics in Low Power Wide Area Network (LPWAN), especially LoRaWAN. In this paper, an in-depth investigation of the reciprocity between the uplink and downlink Channel State Information (CSI) is done by performing an outdoor measurement campaign in the area of Campus Beaulieu in Rennes. At each different location, the CSI reciprocity is quantified using the Pearson Correlation Coefficient (PCC) which shows a very high linear correlation between the uplink and downlink CSI. This reciprocity feature could be utilized for the physical layer security between the node and the gateway. On the other hand, most of the CSI shapes from different locations are highly uncorrelated with each other. Hence, it can be anticipated that this could achieve significant localization gain by utilizing the frequency hopping in the LoRa systems to get access to a wider band.

Keywords: IoT, LPWAN, LoRa, RSSI, effective signal power, onsite measurement, smart city, channel reciprocity.

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Authors: Alexandra Leukauf, Alexander Schirrer, Emir Talic

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Numerical computation of wave propagation in a large domain usually requires significant computational effort. Hence, the considered domain must be truncated to a smaller domain of interest. In addition, special boundary conditions, which absorb the outward travelling waves, need to be implemented in order to describe the system domains correctly. In this work, the linear one dimensional wave equation is approximated by utilizing the Fourier Galerkin approach. Furthermore, the artificial boundaries are realized with absorbing boundary conditions. Within this work, a systematic work flow for setting up the wave problem, including the absorbing boundary conditions, is proposed. As a result, a convenient modal system description with an effective absorbing boundary formulation is established. Moreover, the truncated model shows high accuracy compared to the global domain.

Keywords: Absorbing boundary conditions, boundary control, Fourier Galerkin approach, modal approach, wave equation.

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Authors: Md. Fazlul Karim, Ahmad Izani Ismail, Mohammed Ashaque Meah

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This paper focuses on the development of a 2-D boundary fitted and nested grid (BFNG) model to compute the tsunami propagation of Indonesian tsunami 2004 along the coastal region of Penang in Peninsular Malaysia.

In the presence of a curvilinear coastline, boundary fitted grids are suitable to represent the model boundaries accurately. On the other hand, when large gradient of velocity within a confined area is expected, the use of a nested grid system is appropriate to improve the numerical accuracy with the least grid numbers.

This paper constructs a shallow water nested and orthogonal boundary fitted grid model and presents computational results of the tsunami impact on the Penang coast due to the Indonesian tsunami of 2004. The results of the numerical simulations are compared with available data.

Keywords: Boundary Fitted Nested Model, Tsunami, Penang Island, 2004 Indonesian Tsunami.

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Authors: Nureddin Dinler, Nuri Yucel

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Improving the performance of internal combustion engines is one of the major concerns of researchers. Experimental studies are more expensive than computational studies. Also using computational techniques allows one to obtain all the required data for the cylinder, some of which could not be measured. In this study, an axisymmetric homogeneous charged spark ignition engine was modeled. Fluid motion and combustion process were investigated numerically. Turbulent flow conditions were considered. Standard k- ε turbulence model for fluid flow and eddy break-up model for turbulent combustion were utilized. The effects of valve angle on the fluid flow and combustion are analyzed for constant air/fuel and compression ratios. It is found that, velocities and strength of tumble increases in-cylinder flow and due to increase in turbulence strength, the flame propagation is faster for small valve angles.

Keywords: CFD simulation, eddy break-up model, k-εturbulence model, reciprocating engine flow and combustion.

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Authors: Mat Syai'in, Adi Soeprijanto

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An Optimal Power Flow based on Improved Particle Swarm Optimization (OPF-IPSO) with Generator Capability Curve Constraint is used by NN-OPF as a reference to get pattern of generator scheduling. There are three stages in Designing NN-OPF. The first stage is design of OPF-IPSO with generator capability curve constraint. The second stage is clustering load to specific range and calculating its index. The third stage is training NN-OPF using constructive back propagation method. In training process total load and load index used as input, and pattern of generator scheduling used as output. Data used in this paper is power system of Java-Bali. Software used in this simulation is MATLAB.

Keywords: Optimal Power Flow, Generator Capability Curve, Improved Particle Swarm Optimization, Neural Network

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Authors: Khenfer-Koummich Fatima, Mesbahi Larbi, Hendel Fatiha

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This paper presents a recognition system for isolated words like robot commands. It’s carried out by Time Delay Neural Networks; TDNN. To teleoperate a robot for specific tasks as turn, close, etc… In industrial environment and taking into account the noise coming from the machine. The choice of TDNN is based on its generalization in terms of accuracy, in more it acts as a filter that allows the passage of certain desirable frequency characteristics of speech; the goal is to determine the parameters of this filter for making an adaptable system to the variability of speech signal and to noise especially, for this the back propagation technique was used in learning phase. The approach was applied on commands pronounced in two languages separately: The French and Arabic. The results for two test bases of 300 spoken words for each one are 87%, 97.6% in neutral environment and 77.67%, 92.67% when the white Gaussian noisy was added with a SNR of 35 dB.

Keywords: Neural networks, Noise, Speech Recognition.

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Authors: Kong Fah Tee, Lutfor Rahman Khan, Hongshuang Li

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An advanced Monte Carlo simulation method, called Subset Simulation (SS) for the time-dependent reliability prediction for underground pipelines has been presented in this paper. The SS can provide better resolution for low failure probability level with efficient investigating of rare failure events which are commonly encountered in pipeline engineering applications. In SS method, random samples leading to progressive failure are generated efficiently and used for computing probabilistic performance by statistical variables. SS gains its efficiency as small probability event as a product of a sequence of intermediate events with larger conditional probabilities. The efficiency of SS has been demonstrated by numerical studies and attention in this work is devoted to scrutinise the robustness of the SS application in pipe reliability assessment. It is hoped that the development work can promote the use of SS tools for uncertainty propagation in the decision-making process of underground pipelines network reliability prediction.

Keywords: Underground pipelines, Probability of failure, Reliability and Subset Simulation.

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Authors: Paul O'Leary, Matthew Harker

Abstract:

This paper presents unified theory for local (Savitzky- Golay) and global polynomial smoothing. The algebraic framework can represent any polynomial approximation and is seamless from low degree local, to high degree global approximations. The representation of the smoothing operator as a projection onto orthonormal basis functions enables the computation of: the covariance matrix for noise propagation through the filter; the noise gain and; the frequency response of the polynomial filters. A virtually perfect Gram polynomial basis is synthesized, whereby polynomials of degree d = 1000 can be synthesized without significant errors. The perfect basis ensures that the filters are strictly polynomial preserving. Given n points and a support length ls = 2m + 1 then the smoothing operator is strictly linear phase for the points xi, i = m+1. . . n-m. The method is demonstrated on geometric surfaces data lying on an invariant 2D lattice.

Keywords: Gram polynomials, Savitzky-Golay Smoothing, Discrete Polynomial Moments

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Authors: Alexander Sutor, David Demetz

Abstract:

We present methods for developing wireless and traceable sensors for photobioreactors or photoreactors in general. The main focus of application are reactors which are wirelessly powered. Due to the promising properties of the propagation of magnetic fields under water we implemented an inductive link with an on/off switched hartley-oscillator as transmitter and an LC-tank as receiver. For this inductive link we used a carrier frequency of 298 kHz. With this system we performed measurements to demonstrate the independence of the magnetic field from water or salty water. In contrast we showed the strongly reduced range of RF-transmitter-receiver systems at higher frequencies (433 MHz and 2.4 GHz) in water and in salty water. For implementing the traceability of the sensors, we performed measurements to show the well defined orientation of the magnetic field of a coil. This information will be used in future work for implementing an inductive link based traceability system for our sensors.

Keywords: Wireless sensors, traceable sensors, photoreactor, internal illumination, wireless power.

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Authors: Sarbjot Singh Sandhu

Abstract:

This paper presents the experimental results of a single cylinder Enfield engine using an electronically controlled fuel injection system which was developed to carry out exhaustive tests using neat CNG, and mixtures of hydrogen in compressed natural gas (HCNG) as 0, 5, 10, 15 and 20% by energy. Experiments were performed at 2000 and 2400 rpm with wide open throttle and varying the equivalence ratio. Hydrogen which has fast burning rate, when added to compressed natural gas, enhances its flame propagation rate. The emissions of HC, CO, decreased with increasing percentage of hydrogen but NOx was found to increase. The results indicated a marked improvement in the brake thermal efficiency with the increase in percentage of hydrogen added. The improved thermal efficiency was clearly observed to be more in lean region as compared to rich region. This study is expected to reduce vehicular emissions along with increase in thermal efficiency and thus help in reduction of further environmental degradation.

Keywords: Hydrogen, CNG, HCNG, Emissions.

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Authors: Ε. Giovanis

Abstract:

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer after the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model in the out-of-sample period. The idea behind this approach is to propose a parametric regression with weighted variables in order to test for the statistical significance and the magnitude of the estimated autoregressive coefficients and simultaneously to estimate the forecasts.

Keywords: Autoregressive model, Error back-propagation Feed-Forward neural networks, , Gross Domestic Product

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Authors: E. Giovanis

Abstract:

In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from input-hidden layer of the training process. The forecasts are compared with those of the ordinary autoregressive model and we conclude that the proposed regression-s forecasting results outperform significant those of autoregressive model. Moreover this technique can be used in Autoregressive-Moving Average models, with and without exogenous inputs, as also the training process with genetics algorithms optimization can be replaced by the error back-propagation algorithm.

Keywords: Autoregressive model, Feed-Forward neuralnetworks, Genetic Algorithms, Gross Domestic Product

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Authors: M. F. Haroun, T. A. Gulliver

Abstract:

In this paper, an encryption algorithm is proposed for real-time image encryption. The scheme employs a dual chaotic generator based on a three dimensional (3D) discrete Lorenz attractor. Encryption is achieved using non-autonomous modulation where the data is injected into the dynamics of the master chaotic generator. The second generator is used to permute the dynamics of the master generator using the same approach. Since the data stream can be regarded as a random source, the resulting permutations of the generator dynamics greatly increase the security of the transmitted signal. In addition, a technique is proposed to mitigate the error propagation due to the finite precision arithmetic of digital hardware. In particular, truncation and rounding errors are eliminated by employing an integer representation of the data which can easily be implemented. The simple hardware architecture of the algorithm makes it suitable for secure real-time applications.

Keywords: Chaotic systems, image encryption, 3D Lorenz attractor, non-autonomous modulation, FPGA.

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Authors: T. Sanches, K. Bousson

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As part of the development of a 4D autopilot system for unmanned aerial vehicles (UAVs), i.e. a time-dependent robust trajectory generation and control algorithm, this work addresses the problem of optimal path control based on the flight sensors data output that may be unreliable due to noise on data acquisition and/or transmission under certain circumstances. Although several filtering methods, such as the Kalman-Bucy filter or the Linear Quadratic Gaussian/Loop Transfer Recover Control (LQG/LTR), are available, the utter complexity of the control system, together with the robustness and reliability required of such a system on a UAV for airworthiness certifiable autonomous flight, required the development of a proper robust filter for a nonlinear system, as a way of further mitigate errors propagation to the control system and improve its ,performance. As such, a nonlinear algorithm based upon the LQG/LTR, is validated through computational simulation testing, is proposed on this paper.

Keywords: Autonomous flight, LQG/LTR, nonlinear state estimator, robust flight control and stability.

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Authors: Sulata Maharana, B Biswas, Adity Ganguly, Ashok Kumar

Abstract:

In this paper, the requirement for Coke quality prediction, its role in Blast furnaces, and the model output is explained. By applying method of Artificial Neural Networking (ANN) using back propagation (BP) algorithm, prediction model has been developed to predict CSR. Important blast furnace functions such as permeability, heat exchanging, melting, and reducing capacity are mostly connected to coke quality. Coke quality is further dependent upon coal characterization and coke making process parameters. The ANN model developed is a useful tool for process experts to adjust the control parameters in case of coke quality deviations. The model also makes it possible to predict CSR for new coal blends which are yet to be used in Coke Plant. Input data to the model was structured into 3 modules, for tenure of past 2 years and the incremental models thus developed assists in identifying the group causing the deviation of CSR.

Keywords: Artificial Neural Networks, backpropagation, CokeStrength after Reaction, Multilayer Perceptron.

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Authors: P. Kumsawat, K. Pasitwilitham, K. Attakitmongcol, A. Srikaew

Abstract:

In this paper, an artificial intelligent technique for robust digital image watermarking in multiwavelet domain is proposed. The embedding technique is based on the quantization index modulation technique and the watermark extraction process does not require the original image. We have developed an optimization technique using the genetic algorithms to search for optimal quantization steps to improve the quality of watermarked image and robustness of the watermark. In addition, we construct a prediction model based on image moments and back propagation neural network to correct an attacked image geometrically before the watermark extraction process begins. The experimental results show that the proposed watermarking algorithm yields watermarked image with good imperceptibility and very robust watermark against various image processing attacks.

Keywords: Watermarking, Multiwavelet, Quantization index modulation, Genetic algorithms, Neural networks.

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Authors: A. L. Qureshi, A. A. Mahessar, A. Baloch

Abstract:

Flood wave propagation in river channel flow can be enunciated by nonlinear equations of motion for unsteady flow. It is difficult to find analytical solution of these non-linear equations. Hence, in this paper verification of the finite element model has been carried out against available numerical predictions and field data. The results of the model indicate a good matching with both Preissmann scheme and HEC-RAS model for a river reach of 29km at both sites (15km from upstream and at downstream end) for discharge hydrographs. It also has an agreeable comparison with the Preissemann scheme for the flow depth (stage) hydrographs. The proposed model has also been applying to forecast daily discharges at 400km downstream in the Indus River from Sukkur barrage of Sindh, Pakistan, which demonstrates accurate model predictions with observed the daily discharges. Hence, this model may be utilized for flood warnings in advance.

Keywords: Finite Element Method, Flood Forecasting, HEC-RAS, Indus river.

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Authors: Han-wool Lee, Chin-suk Hong, Weui-bong Jung

Abstract:

In this study, noise characteristics of structure were analyzed in an effort to reduce noise passing through an opening of an enclosure surrounding the structure that generates noise. Enclosures are essential measure to protect noise propagation from operating machinery. Access openings of the enclosures are important path of noise leakage. First, noise characteristics of structure were analyzed and feed-forward noise control was performed using simulation in order to reduce noise passing through the opening of enclosure, which surrounds a structure generating noise. We then implemented a feed-forward controller to actively control the acoustic power through the opening. Finally, we conducted optimization of placement of the reference sensors for several cases of the number of sensors. Good control performances were achieved using the minimum number of microphones arranged an optimal placement.

Keywords: Active Noise Control, Feed-forward Control, Noise Attenuation, Position Optimization.

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Authors: M. F. V. Montezuma, E. P. Deus, M. C. Carvalho

Abstract:

Wind turbines are equipment of great importance for generating clean energy in countries and regions with abundant winds. However, complex loadings fluctuations to which they are subject can cause premature failure of these equipment due to the material fatigue process. This work evaluates fatigue failures in small AISI 304 stainless steel turbine shafts. Fractographic analysis techniques, chemical analyzes using energy dispersive spectrometry (EDS), and hardness tests were used to verify the origin of the failures, characterize the properties of the components and the material. The nucleation of cracks on the shafts' surface was observed due to a combined effect of variable stresses, geometric stress concentrating details, and surface wear, leading to the crack's propagation until the catastrophic failure. Beach marks were identified in the macrographic examination, characterizing the probable failure due to fatigue. The sensitization phenomenon was also observed.

Keywords: Fatigue, sensitization phenomenon, stainless steel shafts, wind turbine failure.

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